Reviewerly/exHarmony huggingface dataset:
Meta-Causal Feature Learning for Out-of-Distribution Generalization

Causal inference has become a powerful tool to handle the out-of-distribution (OOD) generalization problem, which aims to extract the invariant features. However, conventional methods apply causal learners from multiple data splits, which may incur biased representation learning from imbalanced data distributions and difficulty in invariant feature learning from heterogeneous sources. To address these issues, this paper presents a balanced meta-causal learner (BMCL), which includes a balanced task generation module (BTG) and a meta-causal feature learning module (MCFL). Specifically, the BTG module learns to generate balanced subsets by a self-learned partitioning algorithm with constraints on the proportions of sample classes and contexts. The MCFL module trains a meta-learner adapted to different distributions. Experiments conducted on NICO++ dataset verified that BMCL effectively identifies the class-invariant visual regions for classification and may serve as a general framework to improve the performance of the state-of-the-art methods.
Self-adaptive teaching-learning-based optimizer with improved RBF and sparse autoencoder for high-dimensional problems

Evolutionary algorithms and swarm intelligence ones are commonly used to solve many complex optimization problems in different fields. Yet, some of them have limited performance when dealing with high-dimensional complex problems because they often require enormous computational resources to yield desired solutions, and some of them may easily trap into local optima. To solve this problem, this work proposes a Self-adaptive Teaching-learning-based Optimizer with an improved Radial basis function model and a sparse Autoencoder (STORA). In STORA, a Self-adaptive Teaching-Learning-Based Optimizer (STLBO) is designed to dynamically adjust parameters for balancing exploration and exploitation abilities. Then, a sparse autoencoder (SAE) is adopted as a dimension reduction method to compress a search space into a lower-dimensional one for more efficiently guiding a population to converge towards global optima. Besides, an Improved Radial Basis Function model (IRBF) is designed as a surrogate one to balance training time and prediction accuracy. It is adopted to save computational resources for improving overall performance. In addition, a dynamic population allocation strategy is adopted to well integrate SAE and IRBF in STORA. We evaluate STORA by comparing it with several state-of-the-art algorithms through eight benchmark functions. We further test its actual performance by applying it to solve a real-world computation offloading problem.
A Pedestrian Detection Network Model Based on Improved YOLOv5

Advanced object detection methods always face high algorithmic complexity or low accuracy when used in pedestrian target detection for the autonomous driving system. This paper proposes a lightweight pedestrian detection approach called the YOLOv5s-G2 network to address these issues. We apply Ghost and GhostC3 modules in the YOLOv5s-G2 network to minimize computational cost during feature extraction while keeping the network's capability of extracting features intact. The YOLOv5s-G2 network improves feature extraction accuracy by incorporating the Global Attention Mechanism (GAM) module. This application can extract relevant information for pedestrian target identification tasks and suppress irrelevant information, improving the unidentified problem of occluded and small targets by replacing the GIoU loss function used in the bounding box regression with the α-CIoU loss function. The YOLOv5s-G2 network is evaluated on the WiderPerson dataset to ensure its efficacy. Our proposed YOLOv5s-G2 network offers a 1.0% increase in detection accuracy and a 13.2% decrease in Floating Point Operations (FLOPs) compared to the existing YOLOv5s network. As a result, the YOLOv5s-G2 network is preferable for pedestrian identification as it is both more lightweight and more accurate.
Coupling and Coordinated Development of Digital Economy and Rural Revitalisation and Analysis of Influencing Factors

Under the strategy of digital technology development and rural revitalisation, the digital economy has gradually become a powerful engine for rural revitalisation and development. Based on the coupling coordination mechanism of digital economy and rural revitalisation, this paper constructs the index evaluation system of digital economy and rural revitalisation and empirically studies the development characteristics of digital economy and rural revitalisation by using the coupling coordination degree and grey relation model. The results show that during 2016–2020, the overall development level of digital economy and rural revitalisation in China is high in the East and low in the West. The coupling coordination between digital economy and rural revitalisation has significant regional differences and a downward trend, and the coupling coordination degree is positively correlated. The empirical results of the grey correlation model show that the number of postal employees and per capital grain production are the most important potential factors affecting digital economy and rural revitalisation, respectively. Finally, it is suggested that the government should continue to increase investment in science, technology, and agriculture, accelerate the project of cultivating high-level scientific and technological innovation and rural management talents, and strengthen regional cooperation and collaboration.
Deep neural network for forecasting of photovoltaic power based on wavelet packet decomposition with similar day analysis

Photovoltaic (PV) power forecasting (PVPF) plays an important role in the scheduling and operation of modern power systems. Considering the highly varying and complex features of PVPF, this paper constitutes a novel hybrid deep learning forecasting method. A similar day selection method based on the levy-flight beetle antennae search algorithm is proposed to select historical days similar to the forecast day from the real-time massive data. The Pearson correlation coefficient method is utilized to select the main meteorological factors while wavelet packet decomposition is used to decompose and reconstruct the original PV power into a series of sub-signals. A deep learning model taking the sub-signals of similar days as network inputs is established with a group of gated recurrent units (GRU), where the hyperparameters of each GRU network are effectively optimized. The forecasting result of each sub-signal is integrated to obtain the final forecasting PV power value. The simulation regarding the 5-min ahead PVPF is carried out based on a real-world dataset from Alice Spring, Australia. The simulation comparisons indicate that the proposed hybrid deep learning method outperforms other competitive PVPF methods in terms of both forecasting accuracy and computational efficiency.
Modeling Analysis of Heavy Metal Evaluation in Complex Geological Soil Based on Nemerow Index Method

To accurately understand soil environmental quality and improve the problem of the traditional method, which is that it has a single evaluation factor and cannot reflect the overall condition of the soil, a complex geological soil heavy metal evaluation modeling method based on the Nemerow index method is proposed. Index evaluation methods, ArcGIS technology, and a human health risk assessment were carried out to obtain the spatial distribution of heavy metals in the soil and the current status of pollution accumulation. The comprehensive pollution index (CPI) method, geo-accumulation index (GAI) method, and potential ecological hazard (PEH) index were adopted to analyze the pollution degree of soil heavy metals. On this basis, the Nemerow index method was used to establish a complex geological soil heavy metal evaluation model, and the standard Nemerow index was calculated to complete the evaluation of heavy metal pollution in complex geological soils. The research results showed that this method could make the evaluation factors obtain reasonable scores and obtain more reasonable soil evaluation results.
Fortified-Chain 2.0: Intelligent Blockchain for Decentralized Smart Healthcare System

The Internet of Medical Things (IoMT) technology’s fast advancements aided smart healthcare systems to a larger extent. IoMT devices, on the other hand, rely on centralized processing and storage systems because of their limited computational and storage capacity. The reliance is susceptible to a single point of failure (SPoF) and erodes the user control over their medical data. In addition, Cloud models result in communication delays, which slow down the system’s overall reaction time. To overcome these issues a decentralized distributed smart healthcare system is proposed that eliminates the SPoF and third-party control over healthcare data. Additionally, the proposed Fortified-Chain 2.0 uses a blockchain-based selective sharing mechanism with a mutual authentication technique to solve the issues, such as data privacy, security, and trust management in decentralized peer-to-peer healthcare systems. Also, we suggested a hybrid computing paradigm to deal with latency, computational, and storage constraints. A novel distributed machine learning (ML) module named random forest support vector machine (RFSVM) also embedded into the Fortified-Chain 2.0 system to automate patient health monitoring. In the RFSVM module, a random forest (RF) is used to select an optimal set of features from patients data in real-time environment and also support vector machine (SVM) is used to perform the decision making tasks. The proposed Fortified-Chain 2.0 works on a private blockchain-based distributed decentralised storage system (DDSS) that improves the system-level transparency, integrity, and traceability. Fortified-Chain 2.0 outperformed the existing Fortified-Chain in terms of low latency, high throughput, and availability with the help of a mutual authentication method.
Cognitive ergonomics-based Augmented Reality application for construction performance

There is a growing interest in exploring the use of wearable Augmented Reality (AR) devices to improve the task performance of construction workers. However, user interaction with AR has not been well understood in the current literature, which may result in poor usability, occupational hazards, and low acceptance. To bridge this gap, this study introduced cognitive ergonomics theory to design and develop an AR application for improving the kinaesthetic performance of construction workers conducting onsite assembly tasks. The methodology of this study is three-fold. First, articles in relation to cognitive ergonomics were reviewed to propose a unique cognitive model that reveals the cognitive mechanisms of construction workers, including human information processing, selective attention, and attention resources. Second, the characteristics of existing AR application functions were synthesised to develop a customised and user-friendly wearable AR application that aligns with the identified cognitive mechanisms. Third, a rebar-tying experiment was conducted to validate the developed AR application. The results indicate that the experimenters instructed by the application can complete the task independently without the need to seek after expert assistance; the application has a potential to foster the skill development of construction workers and enhance their kinaesthetic performance; and the proposed cognitive model and the AR development principles are well aligned from the perspective of cognitive ergonomics, which could promote the uptake of wearable AR in the construction industry.
Information Fusion over Network Dynamics with Unknown Correlations: An Overview

Survey/review study Information Fusion over Network Dynamics with Unknown Correlations: An Overview Wangyan Li 1, and Fuwen Yang 2,* 1 College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China 2 Griffth School of Engineering, Griffth University, Gold Coast Campus, QLD 4222, Australia * Correspondence: fuwen.yang@griffth.edu.au Received: 24 October 2022 Accepted: 22 November 2022 Published: 23 June 2023 Abstract: Unknown correlations (UCs) generally exist in a wide spectrum of practical multi-source information fusion problems, and thereby, their corresponding fusion problems have become one of the most important topics in information fusion domain. During the past three decades, the research on this topic has been growing rapidly and extensively, and, as a result, various important advances have been reported. In this overview, we intend to summarize the culmination of years of development in the field of information fusion under UCs as a roadmap. First, the potential reasons leading to UCs are investigated. According to the unknown nature of correlations, we further divide UCs into two categories, i.e., fully UCs, and partially UCs. For each category, the corresponding fusion methods are reviewed. Next, this roadmap witnesses the recent development of information fusion under UCs in a distributed way thanks to the popularity of distributed sensing technology. In particular, the distributed fusion techniques based on consensus, diffusion, and multi-object tracking strategies for UCs are examined. Finally, some future perspectives on information fusion under UCs are pointed out.
Deep learning for encrypted traffic classification in the face of data drift: An empirical study

Deep learning models have shown to achieve high performance in encrypted traffic classification. However, when it comes to production use, multiple factors challenge the performance of these models. The emergence of new protocols, especially at the application layer, as well as updates to previous protocols affect the patterns in input data, making the model’s previously learned patterns obsolete. Furthermore, proposed model architectures for encrypted traffic classification are usually tested on datasets collected in controlled settings, which makes the reported performances unreliable for production use. In this paper, we study how the performances of two high-performing state-of-the-art encrypted traffic classifiers change on multiple real-world datasets collected over the course of two years from a major ISP’s network. We investigate the changes in traffic data patterns highlighting the extent to which these changes, also known as data drift, impact the performance of the two models in service-level as well as application-level classification. We propose best practices for architecture adaptations to improve the accuracy of the model in the face of data drift. We show that our best practices are generalizable to other encryption protocols and different levels of labeling granularity.
A survey: When moving target defense meets game theory

Moving target defense (MTD) can break through asymmetry between attackers and defenders. To improve the effectiveness of cybersecurity defense techniques, defense requires not only advanced and practical defense technologies but effective, scientific decision-making methods. Due to complex attacker–defender interaction, autonomous, automatic, accurate, and effective selection of the optimal strategy is a challenging topic in the field of MTD. The essence of cybersecurity lies in the interaction between the attacker and defender. Game theory is a useful mathematical tool for strategy selection in a competitive environment. It provides strong theoretical support for the analysis of cyberattack and defense behaviors and subsequent decision-making, and can significantly improve the decision-making ability of MTD. This study presents the basic concepts of MTD and game theory, followed by a literature review, to study MTD decision-making methods based on game theory from the dimensions of space, time, space–time, and bounded rationality. Limitations of MTD game decision-making studies are discussed, as well as research directions, to provide references for future research.
HealthBlock: A Framework for a Collaborative Sharing of Electronic Health Records Based on Blockchain

Electronic health records (EHRs) play an important role in our life. However, most of the time, they are scattered and saved on different databases belonging to distinct institutions (hospitals, laboratories, clinics, etc.) geographically distributed across one or many countries. Due to this decentralization and the heterogeneity of the different involved systems, medical staff are facing difficulties in correctly collaborating by sharing, protecting, and tracking their patient’s electronic health-record history to provide them with the best care. Additionally, patients have no control over their private EHRs. Blockchain has many promising future uses for the healthcare domain because it provides a better solution for sharing data while preserving the integrity, the interoperability, the availability of the classical client–server architectures used to manage EHRS. This paper proposes a framework called HealthBlock for collaboratively sharing EHRs and their privacy preservation. Different technologies have been combined to achieve this goal. The InterPlanetary File System (IPFS) technology stores and shares patients’ EHRs in distributed off-chain storage and ensures the record’s immutability; Hyperledger Indy gives patients full control over their EHRs, and Hyperledger Fabric stores the patient-access control policy and delegations.
A blockchain-based data-driven trustworthy approval process system

Approval processes are intra-organizational business processes designed to sanction the execution of specific administrative tasks. Purchasing essential stationery products, filing bills for acquired items, approving attendance at conferences, processing insurance claims, approving loans, and similar tasks are examples for which employees require approvals from multiple authorities within an organization. Common concerns mentioned by both the approving authority and the proposal submitter are the genuineness of the proposer and the time required for approving the proposal. This paper presents a novel Blockchain-based Approval Process System (BAPS) to establish mutual trust between the submitter and the approving authorities. The proposed system’s design, implementation, and evaluation are included in this paper. The suggested approach can shorten the time needed to obtain the permissions and increase transparency between the users and the authority. In addition, it eliminates issues such as the misplacement of papers. It stores the information in a secure and tamper-proof platform which is some of the most significant drawbacks of traditional paper-based systems.
Efficient Human Pose Estimation via 3D Event Point Cloud

Human Pose Estimation (HPE) based on RGB images has experienced a rapid development benefiting from deep learning. However, event-based HPE has not been fully studied, which remains great potential for applications in extreme scenes and efficiency-critical conditions. In this paper, we are the first to estimate 2D human pose directly from 3D event point cloud. We propose a novel representation of events, the rasterized event point cloud, aggregating events on the same position of a small time slice. It maintains the 3D features from multiple statistical cues and significantly reduces memory consumption and computation complexity, proved to be efficient in our work. We then leverage the rasterized event point cloud as input to three different back-bones, PointNet, DGCNN, and Point Transformer, with two linear layer decoders to predict the location of human key-points. We find that based on our method, PointNet achieves promising results with much faster speed, whereas Point Transfomer reaches much higher accuracy, even close to previous event-frame-based methods. A comprehensive set of results demonstrates that our proposed method is consistently effective for these 3D backbone models in event-driven human pose estimation. Our method based on PointNet with 2048 points input achieves 82.46mm in MPJPE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3D</inf> on the DHP19 dataset, while only has a latency of 12.29ms on an NVIDIA Jetson Xavier NX edge computing platform, which is ideally suitable for real-time detection with event cameras. Code is available at https: //github.com/ MasterHow/EventPointPose.
Safe deep learning-based global path planning using a fast collision-free path generator

In this research, a global path planning method based on recurrent neural networks by means of a new Loss function is presented, which regardless of the complexity of the configuration space, generates the path in a relatively constant time. The new Loss function is defined in such a way that in addition to learning the input data of the network, it creates an adjustable safety margin around the obstacles and ultimately creates a safe path. Moreover, a new global path planning method is also introduced, which is used to create the dataset required to train the proposed neural network. The convergence of this method is mathematically proven and it is shown that this method can also produce a suboptimal path in a much shorter time than the common methods of global path planning reported in the literature. In short, the main purpose of this research consists in providing a method which can create a suboptimal, fast and safe path for a mobile robot from any random starting point to any random destination in a known environment. First, the proposed methods will be implemented for different two-dimensional environments consisting of convex and non-convex obstacles, considering the robot as a point-mass, and then it will be implemented in a simulation environment, AI2THOR. Compared to classical global path planning algorithms, such as RRT and A*, the proposed approach demonstrates better performance in complex and challenging environments.
Examining the effect of regulatory factors on avoiding online blackmail threats on social media: A structural equation modeling approach

Individuals and organizations rely on social media to share files and knowledge. On the other hand, criminals find social media applications a rich place to attack through techniques like phishing, fraud, and blackmail. While the rate of blackmail is growing, there is a lack of literature on the subject. This study aims to examine the impact of information security regulation factors such as information security awareness, policies, and culture towards avoiding online blackmail threats. The study proposes a model constructed from technology threat avoidance theory (TTAT), the theory of planned behavior (TPB), and organizational control (OC). A quantitative approach based on an online survey was conducted to collect data from 547 employees and students from different industrial backgrounds. The results show that information security culture and information security awareness have an impact on users’ motivation to avoid online blackmail threats through attitude and subjective norms. Furthermore, perceiving a mandated policy significantly impacts avoidance behavior. In addition, apathy and anticipated regret play a significant role in avoidance motivation. These results provide evidence on the importance of social factors on impacting avoidance motivation using the TTAT model. Further, the study encourages organizations to focus more on specifying policies and building an information security culture in order to ensure users avoid these threats through compliance and motivation.