Reviewerly/exHarmony huggingface dataset:
Online Research Topic Modeling and Recommendation Utilizing Multiview Autoencoder-Based Approach

Recent years have witnessed tremendous growth in the publication of research articles as well as in the rise of new research topics. Articles get published in a streaming manner and therefore retrieving and recommending trending topics continuously by updating the trend of topics with time will be beneficial for young researchers. The proposed topic recommendation system is a clustering-based approach that utilizes an autoencoder framework for the generation of clusters. The autoencoder framework considers articles as input in a multiview framework and produces latent data in lower-dimensional space using graph attention-based encoder and decoder networks. The latent data is then partitioned with respect to its different views and finally, a single consensus overlapping partitioning is produced by satisfying all the views. Since the publication of articles is a continuous process, to update the trending topics continuously, the clustering-based approach is kept on and applied iteratively in a sliding window manner. The generated clusters are analyzed to extract the trending topics and recommendations for future scope. An article can belong to multiple topics and the proposed method is developed by considering such criteria and therefore tested with the modified version of the multilabel scientific article data ArXiv, named CSML.ArXiv. The superiority of the proposed method can be observed from its comparisons with existing methods, and a few baseline methods with respect to cluster formation, topic extraction, and trending topic evaluation.
Efficient Pairing-Free Certificateless Signcryption Scheme for Secure Data Transmission in IoMT

The Internet of Medical Things (IoMT) builds a bridge between patients and doctors, facilitating patients’ being diagnosed and monitored by uploading physiological indicators without visiting the hospital. However, physiological indicators are sensitive data of patients, making it a challenge to achieve verifiability of data sources while ensuring data privacy during data transmission of IoMT. Due to its ease of deployment and the ability to provide both encryption and signature, CertificateLess SignCryption (CLSC) is suitable for designing secure data-transfer protocol in IoMT. Nevertheless, internal adversaries “malicious users" and “malicious KGC", capable of launching Type I and Type II attacks, threaten the security of present CLSC schemes, making most of them insecure. In this work, after giving an example of a recent CLCS scheme suffering Type I attack, we propose an efficient pairing-free CLCS scheme suitable for secure data transmission in IoMT based on the idea of zero-knowledge proof. It not only provides confidentiality and unforgeability of transmitted data under the Type I and Type II attacks, but also achieves lower computational and communication overhead, and public verifiability. Finally, compared with the five recent CLSC schemes, theoretical analysis and experimental testing results show that the proposed scheme outperforms the other five schemes in terms of computation and communication costs as well as security. Therefore, our scheme is better suited for constructing secure data transmission in IoMT scenarios.
Federated Domain Generalization: A Secure and Robust Framework for Intelligent Fault Diagnosis

The maturation of sensor network technologies has promoted the emergence of the Industrial Internet of Things, which has been collecting an increasing volume of monitoring data. Transforming these data into actionable intelligence for equipment fault diagnosis can reduce unscheduled downtime and performance degradation. In conventional artificial intelligence paradigms, abundant individual data distributed across clients’ devices needs to be delivered to a central storage for data analysis and knowledge extraction, which may violate data privacy requirements and neglect distribution discrepancy across different clients. To tackle the issue of privacy disclosure, an edge-cloud integrated federated learning framework is developed. Then, a two-stage training mechanism is designed to establish a domain-agnostic fault diagnosis model that can achieve satisfactory diagnostic performance on unseen target domains. Comprehensive simulated experiments on two rotating machines indicate that the proposed method possesses good generalization ability and can meet the requirement of privacy protection.
An In-Depth Look at Forking-Based Attacks in Ethereum With PoW Consensus

In this paper, we analyze the performance of Ethereum data distribution network using a probabilistic model which allows accurate modeling of data propagation but also of forking, which happens when the blockchain maintained by the network temporarily splits into multiple versions due to a disagreement over the validity of a particular block. We also investigate the duration of inconsistent states of the ledger, which refers to the amount of time that the network remains split or partitioned. Finally, we model the block withholding attack and block slowdown attack, and analyze their impact on network performance of the network in terms of quality indicators such as block delivery time, the duration of ledger inconsistency, and forking probability. We also propose countermeasures for the block withholding attack.
Switching-Event-Based Interval Type-2 Fuzzy Control for a Class of Uncertain Nonlinear Systems

In this paper, a switching-event-based interval type-2 variable universe fuzzy tracking control strategy is proposed for a class of uncertain nonlinear systems. Remarkably, the nonlinearities and the large unknown uncertainties (including parametric and structural) can be allowed. Therefore, a novel switching-event-based mechanism (SEBM) is proposed. It not only determines when to update the control law, but also when to update the design parameters. At the same time, generous computing resources are saved. In addition, the interval type-2 fuzzy control (IT2FC) technology is introduced to resist unknown uncertainties by identifying the controlled model online. Moreover, the Lyapunov function is designed to prove that the Zeno phenomenon does not occur and the closed-loop system is asymptotically stable. Finally, two practical simulations are given to demonstrate the effectiveness of the proposed method.
Differentiable Rendering for Synthetic Aperture Radar Imagery

There is rising interest in differentiable rendering, which allows explicitly modeling geometric priors and constraints in optimization pipelines using first-order methods such as backpropagation. Incorporating such domain knowledge can lead to deep neural networks that are trained more robustly and with limited data, as well as the capability to solve ill-posed inverse problems. Existing efforts in differentiable rendering have focused on imagery from electro-optical sensors, particularly conventional RGB-imagery. In this work, we propose an approach for differentiable rendering of Synthetic Aperture Radar (SAR) imagery, which combines methods from 3D computer graphics with neural rendering. We demonstrate the approach on the inverse graphics problem of 3D Object Reconstruction from limited SAR imagery using high-fidelity simulated SAR data.
Federated Learning for Crowd Counting in Smart Surveillance Systems

Crowd counting in smart surveillance systems plays a crucial role in Internet of Things (IoT) and smart cities, and can affect various aspects such as public safety, crowd management, and urban planning. Using surveillance data to centrally train a crowd counting model raises significant privacy concerns. Traditional methods try to alleviate the concern by reducing the focus on individuals, but the concern still needs to be thoroughly resolved. In this work, we develop a horizontal federated learning (HFL) framework to train the crowd counting models which can preserve privacy simultaneously. This framework enables the smart surveillance system to learn from model aggregation without accessing the private data stored on local devices. Therefore, it eliminates the need for video data transmission, reduces communication costs, and avoids raw data leakage. Due to the lack of FL crowd counting datasets, we design four non-IID (non-Independent and Identically Distributed) partitioning strategies, including feature-skew, quantity-skew, scene-skew, and time-skew, to simulate real-world FL scenarios. In addition, we present an efficient fully convolutional network (e-FCN) for each client to demonstrate the practical applicability of the proposed framework. The e-FCN adopts an encoder-decoder architecture with fewer parameters, making it communication-friendly and easier to train. This design can achieve competitive performance compared to more complex models in surveillance crowd counting in literature. Finally, we evaluate the proposed HFL framework with e-FCN under our skew strategies on multiple real-world datasets, including Crowd Surveillance, ShanghaiTech PartB, WorldExpo’10, FDST, CityUHK-X, UCSD, and MALL. Extensive experiments allow us to present our developed Federated Crowd Counting benchmark as a reference for future research and provide guidance for FL algorithm selection in smart surveillance system deployment.
Feature interaction and two-stage cross-modal fusion for RGB-D salient object detection

Most existing RGB-D salient object detection (SOD) methods extract features of both modalities in parallel or adopt depth features as supplementary information for unidirectional interaction from depth modality to RGB modality in the encoder stage. These methods ignore the influence of low-quality depth maps, and there is still room for improvement in effectively fusing RGB features and depth features. To address the above problems, this paper proposes a Feature Interaction Network (FINet), which performs bi-directional interaction through feature interaction module (FIM) in the encoder stage. The feature interaction module is divided into two parts: depth enhancement module (DEM) filters the noise in the depth features through the attention mechanism; and cross enhancement module (CEM) effectively interacts RGB features and depth features. In addition, this paper proposes a two-stage cross-modal fusion strategy: high-level fusion adopts the semantic information of high level for coarse localization of salient regions, and low-level fusion makes full use of the detailed information of low level through boundary fusion, and then we progressively refine high-level and low-level cross-modal features to obtain the final saliency prediction map. Extensive experiments show that the proposed model achieves better performance than eight state-of-the-art models on five standard datasets.
An LDP-Based Privacy-Preserving Longitudinal and Multidimensional Range Query Scheme in IoT

Range queries are extensively used in various Internet of Things (IoT) applications as an essential functional requirement to provide intelligent and personalized services to users. In IoT environments, diverse types of data are generated, necessitating the design of range query schemes for multidimensional data. Privacy preservation is a key concern for range queries, leading to the proposal of several privacy-preserving solutions. However, most of these solutions are either inefficient or impractical. Moreover, existing approaches often suffer from the problem of longitudinal data privacy leakage, posing a serious threat to user privacy. Although some efforts have addressed the privacy issues of longitudinal data, practical implementations have been hesitant. To tackle these challenges, we propose a Local Differential Privacy-based (LDP) privacy-preserving scheme called the Privacy-Preserving Longitudinal and Multidimensional Range Query (PLMRQ) for IoT. Our scheme focuses on lightweight privacy preservation and eliminates the need for a trusted third party (TTP). Firstly, it is designed based on a double randomizer, ensuring effective privacy preservation of longitudinal data over time. Secondly, to mitigate excessive noise injection, PLMRQ dynamically constructs a binary tree structure by hierarchically decomposing the entire domain. Finally, through the utilization of a post-processing technique, the mean square error is efficiently reduced. Theoretical and experimental results demonstrate that the proposed PLMRQ maintains competitive utility while rigorously satisfying lneϵ1+tϵ2+1/eϵ1+etϵ2-LDP with an upper bound of ϵ1 and a lower bound of ϵ2.
The Application of Artificial Intelligence in Alzheimer's Research

Alzheimer's disease (AD) is an irreversible and neurodegenerative disease that slowly impairs memory and neurocognitive function, but the etiology of AD is still unclear. With the explosive growth of electronic health data, the application of artificial intelligence (Al) in the healthcare setting provides excellent potential for exploring etiology and personalized treatment approaches, and improving the disease's diagnostic and prognostic outcome. This paper first briefly introduces Al technologies and applications in medicine, and then presents a comprehensive review of Al in AD. In simple, it includes etiology discovery based on genetic data, computer-aided diagnosis (CAD), computer-aided prognosis (CAP) of AD using multi-modality data (genetic, neuroimaging and linguistic data), and pharmacological or non-pharmacological approaches for treating AD. Later, some popular publicly available AD datasets are introduced, which are important for advancing Al technologies in AD analysis. Finally, core research challenges and future research directions are discussed.
Joint Sample Position Based Noise Filtering and Mean Shift Clustering for Imbalanced Classification Learning

The problem of imbalanced data classification learning has received much attention. Conventional classification algorithms are susceptible to data skew to favor majority samples and ignore minority samples. Majority weighted minority oversampling technique (MWMOTE) is an effective approach to solve this problem, however, it may suffer from the shortcomings of inadequate noise filtering and synthesizing the same samples as the original minority data. To this end, we propose an improved MWMOTE method named joint sample position based noise filtering and mean shift clustering (SPMSC) to solve these problems. Firstly, in order to effectively eliminate the effect of noisy samples, SPMSC uses a new noise filtering mechanism to determine whether a minority sample is noisy or not based on its position and distribution relative to the majority sample. Note that MWMOTE may generate duplicate samples, we then employ the mean shift algorithm to cluster minority samples to reduce synthetic replicate samples. Finally, data cleaning is performed on the processed data to further eliminate class overlap. Experiments on extensive benchmark datasets demonstrate the effectiveness of SPMSC compared with other sampling methods.
Multilingual personalized hashtag recommendation for low resource Indic languages using graph-based deep neural network

Users from different cultures and backgrounds often feel comfortable expressing their thoughts on trending topics by generating content in their regional languages. Recently, there has been an explosion in multilingual information, and a massive amount of multilingual textual data is added daily on the Internet. Using hashtags for multilingual low-resource content can be an effective way to overcome language barriers because it allows content to be discovered by a wider audience and makes it easier for people interested in the topic to find relevant content, regardless of the language in which it was written. To account for linguistic diversity and universal access to information, hashtag recommendation for multilingual low-resource content is essential. Several approaches have been put forth to recommend content-based and personalized hashtags for multimodal content in high-resource languages. Data availability and linguistic differences often limit the development of hashtag recommendation methods for low-resource Indic languages. Hashtag recommendation for tweets disseminated in low-resource Indic languages has seldom been addressed. Moreover, personalization and language usage aspects to recommend hashtags for tweets posted in low-resource Indic languages have yet to be explored. In view of the foregoing, we propose an automated hashtag recommendation system for tweets posted in low-resource Indic languages dubbed as TAGALOG, capable of recommending personalized and language-specific hashtags. We employ user-guided and language-guided attention mechanisms to distill indicative features from low-resource tweets according to the user's topical and linguistic preferences. We propose a graph-based neural network to mine users' posting behavior by connecting historical tweets of a particular user and language relatedness by linking tweets according to language families, i.e., Indo-Aryan and Dravidian. Experimental results on the curated dataset from Twitter demonstrate that the proposed model outperformed recognized pre-trained language models and extant research, showing an average improvement of 12.3% and 12.8% in the F1-score, respectively. TAGALOG recommends hashtags that align with the user's interests and linguistic predilections, leading to a heightened level of tailored and engaging user experience. Personalized and multilingual hashtag recommendation systems for low-resource Indic languages can help to improve the discoverability and relevance of content in these languages.
A hybrid search mode-based differential evolution algorithm for auto design of the interval type-2 fuzzy logic system

This article proposed a hybrid search mode-based differential evolution (HSM-DE) algorithm to overcome the complex design of an interval type-2 fuzzy logic system (IT2FLS). First, transform complex design processes of the IT2FLS into optimization problems for our proposed evolutionary algorithm by decoding the whole system into design parameters regarded as search objectives. Then, the DE algorithm with a rapid convergence characteristic is adopted as a global search method for exploration according to a memetic algorithm (MA) structure. Considering the conventional DE algorithm is lack of escaping ability for the local optimal solution, a local search method combined with a simulated binary crossover (SBX) and polynomial mutation (PM) is utilized to enhance the exploitation behavior of our proposed method. In addition, simple constraints on the design parameters of the membership function are added to balance the searching freedom of the HSM-DE algorithm and the readability of the optimal IT2FLS. Finally, a simulation of a mobile two-wheel vehicle (MTWV) under a goal search task is performed to verify the effectiveness of the optimal IT2FLS auto designed by HSM-DE. All results demonstrate that the HSM-DE realizes auto design for the IT2FLS with high-performance control and outperforms the comparative methods. Moreover, four comparison methods are performed to validate the high-performance of the proposed HSM-DE on the search ability of the optimal solution.
Accelerating Federated Learning With Data and Model Parallelism in Edge Computing

Recently, edge AI has been launched to mine and discover valuable knowledge at network edge. Federated Learning, as an emerging technique for edge AI, has been widely deployed to collaboratively train models on many end devices in data-parallel fashion. To alleviate the computation/communication burden on the resource-constrained workers (e.g., end devices) and protect user privacy, Spilt Federated Learning (SFL), which integrates both data parallelism and model parallelism in Edge Computing (EC), is becoming a practical and popular approach for model training over distributed data. However, apart from the resource limitation, SFL still faces two other critical challenges in EC, i.e., system heterogeneity and context dynamics. To overcome these challenges, we present an efficient SFL method, named, which controls both local updating frequency and batch size to better accelerate model training. We theoretically analyze the model convergence rate and obtain a convergence upper bound regarding local updating frequency given a fixed batch size. Upon this, we develop a control algorithm to determine adaptive local updating frequency and diverse batch sizes for heterogeneous workers to enhance the training efficiency. The experimental results show that can reduce the completion time by about 43% and the network traffic consumption by about 31% for achieving the similar test accuracy, compared to the baselines.
IoMT: A Medical Resource Management System Using Edge Empowered Blockchain Federated Learning

As data sharing on the Internet of Medical Things (IoMT) become more complicated, the problems of divergent interests, unregulated policies, privacy and security, and the resource constraints of data owners have drawn the attention of researchers. To address the problems, this paper provides resource management in the IoMT using a proposed edge-empowered blockchain federated learning system. Also, an improved linear regressor model is proposed as the global learning model for the federated learning system. Gradient parameters are encrypted using Paillier encryption on the federated server side before they are shared by the federated clients. Blockchain is deployed to provide new security features for IoMT and edge computing. Moreover, all transactions of IoMT and edge devices are stored on the blockchain for secure cataloguing and auditing. Edge computing is employed to handle complex computing tasks on behalf of IoMT devices. Extensive simulations are conducted to validate the efficacy of the proposed system model. The results show that computing costs are minimized while still achieving the benefits of security and privacy in the proposed system. Furthermore, security analysis shows that the proposed system is protected from security attacks.
Secure and optimized satellite image sharing based on chaotic eπ map and Racah moments

The objective of this paper is to contribute to the sharing, transfer, and secured indexing of large color images. Watermarking and digital encryption have emerged as alternative and complementary solutions to ensure authorized access, facilitate authentication of content, or prevent illegal reproduction. For this reason, we suggest a new system based on four techniques derived from various transformation and decomposition methods, such as the discrete orthogonal Racah moment transformations (DORMT), which allows the information capacity to be reduced to a number of coefficients, as well as the discrete wavelet transform (DWT), which makes it possible to decompose into a certain number of multilevel sub-bands, to have the sub-band that represents most of the image information. In addition, Schur decomposition (SD) and singular value decomposition (SVD) have been used to increase security. The proposed system consists of two main phases. The first phase is the integration of a color watermark, first applying DORMT-SD-SVD to a color host image and then applying DWT-SD-SVD to achieve a compromise between capacity, invisibility and robustness in terms of quality and privacy. The second phase is the encryption of the watermarked image with an eπ map to increase security for sharing, transfer and indexing. This system uses an Aquila Optimization Algorithm (AOA) as an optimization procedure to find the best set of DORMT parameters and provide a dynamic and adaptive scale factor. The experimental results, in which the proposed system's properties are tested and compared to those of other systems, make it clear that the proposed system is useful in terms of its level of security, capacity, invisibility, and resistance to attacks that use signal processing.