Reviewerly/exHarmony huggingface dataset:
Fed-DR-Filter: Using global data representation to reduce the impact of noisy labels on the performance of federated learning

The label noise is a serious problem limiting the performance of federated learning. According to the performance evaluation for the trained federated models, data selection strategies or client selection strategies are used to solve this problem in previous studies. However, these methods require additional clean data to strengthen the election results, and they rely heavily on an initial model that is robust enough to not accumulate errors. To address these problems, we propose a novel data filtering method to deal with label noise in federated learning, which is called Fed-DR-Filter. Unlike previous methods, Fed-DR-filter focuses on identifying clean data by taking advantage of the correlation of the global data representations. The proposed solution transforms the private data into privacy-preserving data representations in each client, and identifies clean data based on the centralized data representations on the server. To evaluate the performance of Fed-DR-Filter, we conduct extensive experiments on three real-world datasets. The evaluation results show that our method outperforms the state-of-the-art approaches and is robust to various data distributions and noise levels.
Local-to-Global Support Vector Machines (LGSVMs)

For supervised classification tasks that involve a large number of instances, we propose and study a new efficient tool, namely the Local-to-Global Support Vector Machine (LGSVM) method. Its background somehow lies in the framework of approximation theory and of local kernel-based models, such as the Partition of Unity (PU) method. Indeed, even if the latter needs to be accurately tailored for classification tasks, such as allowing the use of the cosine semi-metric for defining the patches, the LGSVM is a global method constructed by gluing together the local SVM contributions via compactly supported weights. When the number of instances grows, such a construction of a global classifier enables us to significantly reduce the usually high complexity cost of SVMs. This claim is supported by a theoretical analysis of the LGSVM and of its complexity as well as by extensive numerical experiments carried out by considering benchmark datasets.
Forecasting returns of major cryptocurrencies: Evidence from regime-switching factor models

The returns of cryptocurrencies tend to co-move, with their degree of co-movement being contingent on the (bullish- or bearish-) states. Given this, we use standard factor models and regime-switching factor loadings to forecast the returns of a specific cryptocurrency based on its lagged information and informational contents of 14 other cryptocurrencies, with these 15 together constituting 65% of the market capitalization. Considering top five cryptocurrencies namely, Bitcoin, Ethereum, Ripple, Dogecoin, and Litecoin, we find significant forecastability and evidence that factor models, in general, outperform the benchmark random-walk model, with the regime-switching versions standing out in the majority of the cases.
Addressing Human Factors in Cybersecurity Leadership

This article identifies human factors in workplaces that contribute to the challenges faced by cybersecurity leadership within organizations and discusses strategic communication, human–computer interaction, organizational factors, social environments, and security awareness training. Cybersecurity does not simply focus on information technology systems; it also considers how humans use information systems and susceptible actions leading to vulnerabilities. As cyber leaders begin to identify human behavior and processes and collaborate with individuals of the same mindset, an organization’s strategy can improve substantially. Cybersecurity has been an expanding focal point from the viewpoint of human factors. Human inaccuracy can be unintentional due to an inaccurate strategic implementation or accurate unsatisfactory plan implementation. A systematic literature review was conducted to realize unintentional human factors in cybersecurity leadership. The results indicate that humans were the weakest link during the transmission of secure data. Furthermore, specific complacent and unintentional behaviors were observed, enabled by the ignorance of leaders and employees. Therefore, the enforcement of cybersecurity focuses on education, awareness, and communication. A research agenda is outlined, highlighting a further need for interdisciplinary research. This study adopts an original approach by viewing security from a human perspective and assessing how people can reduce cybersecurity incidents.
Dynamic coupling real-time energy consumption modeling for data centers

Data center energy consumption modeling is the basis for realizing efficient power management and participating in the integration of power systems. However, the existing static modeling methods are difficult to analyze the problem of real-time energy consumption and heat-transfer characteristics of the data center. To solve the aforementioned problems, a dynamic coupled energy consumption model (DCEC) including IT device and cooling device based on Matlab/Simulink is proposed in this paper. Taking a real data center composed of 1000 servers as an example, the simulation is carried out to validate the effectiveness of the DCEC model.
Two-Stage Solar Flare Forecasting Based on Convolutional Neural Networks

Solar flares are solar storm events driven by the magnetic field in the solar activity area. Solar flare, often associated with solar proton event or CME, has a negative impact on ratio communication, aviation, and aerospace. Therefore, its forecasting has attracted much attention from the academic community. Due to the limitation of the unbalanced distribution of the observation data, most techniques failed to effectively learn complex magnetic field characteristics, leading to poor forecasting performance. Through the statistical analysis of solar flare magnetic map data observed by SDO/HMI from 2010 to 2019, we find that unsupervised clustering algorithms have high accuracy in identifying the sunspot group in which the positive samples account for the majority. Furthermore, for these identified sunspot groups, the ensemble model that integrates the capability of boosting and convolutional neural network (CNN) achieves high-precision prediction of whether the solar flares will occur in the next 48 hours. Based on the above findings, a two-stage solar flare early warning system is established in this paper. The F1 score of our method is 0.5639, which shows that it is superior to the traditional methods such as logistic regression and support vector machine (SVM).
ZeroCross: A sidechain-based privacy-preserving Cross-chain solution for Monero

Sidechain-based Cross-chain exchange protocols enable payers to exchange cryptocurrencies among different blockchains via a sidechain. Many efforts, such as P2DEX (ACNS' 21), have been proposed to enhance cross-chain exchange privacy protection. However, existing sidechain-based cross-chain solutions for Monero on privacy concerns have limitations: requiring multiple pairs of parties paying simultaneously or fixed transaction amounts. This paper proposes ZeroCross, a novel privacy-preserving sidechain-based scheme that guarantees transaction unlinkability, exchanging fairness, and value confidentiality. ZeroCross designs: (i) a key exchange mechanism that guarantees exchanging fairness and (ii) a verification mechanism that utilizes CP-SNARK to ensure the transaction is confirmed without revealing the details of transactions. In addition, we discuss the influence of the remote side-channel attack in cross-chain exchange and the defence strategy. Finally, we prove the privacy and security of ZeroCross under the Universal Composability (UC) framework and evaluate the practical performance on computation and communication costs. • We propose ZeroCross, a privacy-preserving cross-chain cryptocurrency exchange solution for Monero. • We find out the influence of the remote side-channel attack in the cross-chain cryptocurrency exchange. • We formally prove the security of the ZeroCross in the Universal Composability 90 (UC) framework. • We design a series of experiments to illustrate the functionality and feasibility of ZeroCross.
Design of stereo imaging system with a panoramic annular lens and a convex mirror

Panoramic stereo imaging system can not only provide panoramic images to obtain more information, but also obtain the depth information of the target object, and it has broad application prospects in robot navigation, road planning, and obstacle avoidance. A new panoramic stereo imaging system combined with a panoramic annular lens (PAL) and a convex mirror is proposed. It provides a large panoramic vision up to 360°× (30°∼150°) and, to the best of our knowledge, the entire FOV is the largest in the existing literature. Meanwhile, the stereo field of view is 35°.
Efficient Cross-Chain Transaction Processing on Blockchains

Blockchain has received great attention in academia and industry due to its decentralization and immutability. From the perspective of transaction processing, blockchain is a distributed shared ledger and database with the characteristics of decentralization, traceability, and transparency. These features ensure the security of blockchain’s reliability. However, because a blockchain network requires complex consensus verification between users, it causes problems such as a high cost of data exchange and a low system throughput. Such problems are aggravated when executing a cross-chain transaction, as it is particularly important to ensure the atomicity and isolation of transactions across the blockchain. Considering this, in this paper, we propose the cross-chain transaction processing flow of EOVPC and efficient transaction processing based on version control. Different from the existing cross-chain transaction approaches based on locking, we propose optimistic approaches in which the updated data can be used immediately, with a rolling back procedure that guarantees atomicity. We conducted extensive experiments, which show that our approaches can improve the throughput and success rate significantly.
Methodology for Extended Reality–Enabled Experimental Research in Construction Engineering and Management

Extended reality (XR) technologies are increasingly being used as a novel research instrument to facilitate scientific inquires in the construction engineering and management (CEM) domain. By allowing humans to interact with immersive environments in controlled and monitored experimental settings, XR technologies have opened new opportunities for researchers to conduct CEM research involving human participants or concerning human behavior. Yet, XR-enabled research, as an independent, rigorous methodology for the CEM domain, is still underexplored. This paper serves as an effort to build an organized knowledge base and workflow for using XR technologies in various CEM research areas and methodological contexts. The paper first investigates the status quo of XR-enabled CEM research, by identifying current research areas in the CEM domain where XR technologies are considered the preferred or recommended methodological solutions. A process model for XR-enabled research is then proposed, with actionable recommendations about how XR-enabled research should be planned, designed, implemented, analyzed, verified, and validated. This process model is demonstrated with two illustrative case studies. Last, the paper discusses the philosophical, methodological, and technological roots of the evolution of XR-enabled CEM research and describes our vision of more enabling, adoptable, and value-adding XR-enabled research in CEM in the near future.
How do people experience the temporality of everyday life changes? Towards the exploration of existential time in HCI

The idea of time as an existential concern has found scarce attention in the Human-Computer Interaction (HCI) community. Still, time in our life is mostly experienced in connection with important existential matters. Life changes are emblematic examples of aspects of our existence that are fundamentally temporal. In this article, I explore how people experience the temporalities of “change”, by asking 16 participants to report on life changes they consider relevant and their temporal aspects. I found that temporal features like velocity, direction, and synchronization deeply impact the process of change, by affecting the participants’ opportunities to make sense of and act upon their own transformations. The study findings point to a conception of existential time that contributes to both HCI research on time and life changing events, by highlighting the relativity of temporal slowness and speediness, the malleability of time, and the fundamental openness of our time experiences.
Learning pseudo labels for semi-and-weakly supervised semantic segmentation

In this paper, we aim to tackle semi-and-weakly supervised semantic segmentation (SWSSS), where many image-level classification labels and a few pixel-level annotations are available. We believe the most crucial point for solving SWSSS is to produce high-quality pseudo labels, and our method deals with it from two perspectives. Firstly, we introduce a class-aware cross entropy (CCE) loss for network training. Compared to conventional cross entropy loss, CCE loss encourages the model to distinguish concurrent classes only and simplifies the learning target of pseudo label generation. Secondly, we propose a progressive cross training (PCT) method to build cross supervision between two networks with a dynamic evaluation mechanism, which progressively introduces high-quality predictions as additional supervision for network training. Our method significantly improves the quality of generated pseudo labels in the regime with extremely limited annotations. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods significantly. The code is released for public access1.
ID-based sanitizable signature data integrity auditing scheme with privacy-preserving

More and more companies, institutions and organizations are choosing to put vast amounts of data on the cloud. However, applications such as multimedia office, e-government and e-health systems need to withhold some data in order to hide highly confidential information when uploading to the cloud. A new data integrity auditing scheme is proposed to protect the privacy information and share the data. The idea of sanitizable signature is used to sanitize the private data to protect the privacy and generate effective signatures to verify the integrity of the data. At the same time, the identity-based encryption auditing mechanism simplifies the complex certificate management and improves the audit efficiency. The stability of the scheme is verified by calculating the Computational Diffie-Hellman Problem (CDHP) and Discrete Logarithm Problem (DLP) in the stochastic prediction model. The performance of the proposed scheme is evaluated by simulation experiments, which proves that the proposed scheme is safe and effective.
CKDF: Cascaded Knowledge Distillation Framework for Robust Incremental Learning

Recently, owing to the superior performances, knowledge distillation-based (kd-based) methods with the exemplar rehearsal have been widely applied in class incremental learning (CIL). However, we discover that they suffer from the feature uncalibration problem, which is caused by directly transferring knowledge from the old model immediately to the new model when learning a new task. As the old model confuses the feature representations between the learned and new classes, the kd loss and the classification loss used in kd-based methods are heterogeneous. This is detrimental if we learn the existing knowledge from the old model directly in the way as in typical kd-based methods. To tackle this problem, the feature calibration network (FCN) is proposed, which is used to calibrate the existing knowledge to alleviate the feature representation confusion of the old model. In addition, to relieve the task-recency bias of FCN caused by the limited storage memory in CIL, we propose a novel image-feature hybrid sample rehearsal strategy to train FCN by splitting the memory budget to store the image-and-feature exemplars of the previous tasks. As feature embeddings of images have much lower-dimensions, this allows us to store more samples to train FCN. Based on these two improvements, we propose the Cascaded Knowledge Distillation Framework (CKDF) including three main stages. The first stage is used to train FCN to calibrate the existing knowledge of the old model. Then, the new model is trained simultaneously by transferring knowledge from the calibrated teacher model through the knowledge distillation strategy and learning new classes. Finally, after completing the new task learning, the feature exemplars of previous tasks are updated. Importantly, we demonstrate that the proposed CKDF is a general framework that can be applied to various kd-based methods. Experimental results show that our method achieves state-of-the-art performances on several CIL benchmarks.
Hierarchical message-passing graph neural networks

Abstract Graph Neural Networks (GNNs) have become a prominent approach to machine learning with graphs and have been increasingly applied in a multitude of domains. Nevertheless, since most existing GNN models are based on flat message-passing mechanisms, two limitations need to be tackled: (i) they are costly in encoding long-range information spanning the graph structure; (ii) they are failing to encode features in the high-order neighbourhood in the graphs as they only perform information aggregation across the observed edges in the original graph. To deal with these two issues, we propose a novel Hierarchical Message-passing Graph Neural Networks framework. The key idea is generating a hierarchical structure that re-organises all nodes in a flat graph into multi-level super graphs, along with innovative intra- and inter-level propagation manners. The derived hierarchy creates shortcuts connecting far-away nodes so that informative long-range interactions can be efficiently accessed via message passing and incorporates meso- and macro-level semantics into the learned node representations. We present the first model to implement this framework, termed Hierarchical Community-aware Graph Neural Network (HC-GNN), with the assistance of a hierarchical community detection algorithm. The theoretical analysis illustrates HC-GNN’s remarkable capacity in capturing long-range information without introducing heavy additional computation complexity. Empirical experiments conducted on 9 datasets under transductive, inductive, and few-shot settings exhibit that HC-GNN can outperform state-of-the-art GNN models in network analysis tasks, including node classification, link prediction, and community detection. Moreover, the model analysis further demonstrates HC-GNN’s robustness facing graph sparsity and the flexibility in incorporating different GNN encoders.
Poverty, ICT and Economic Growth in SADC Region: A Panel Cointegration Evaluation

Although there is a wealth of evidence on the relationship between economic growth and poverty or poverty and information and communication technology (ICT), there are few studies on the interaction between the three factors. The triangle relationship between poverty, information and communication technology, and economic growth in SADC were investigated in this study from 2005 to 2019. The research looked at how ICT and economic growth impact poverty in SADC countries, using the instruments of the Mean-Group FMOLS, Mean-Group DOLS, and Robustness Mean-Group Estimators in achieving its major objective. The principal component analysis was employed in generating a single index value for ICT and the data were subjected to relevant econometric tests to achieve robust results. Findings showed that all the variables exhibited poverty-reducing effects in SADC except inflation. Results confirm the existence of the “leapfrogging” hypothesis for the region. It is necessary to strengthen existing bilateral links among member nations of the area to maintain the benefits of ICT’s poverty-reducing impacts, economic growth, financial development, and trade openness. As applicable in other advanced and some emerging economies, the digital competence of the region needs to be synchronized for effective ICT service delivery.
Intelligent Blockchain-Based Edge Computing via Deep Reinforcement Learning: Solutions and Challenges

The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in wireless Internet-of-Things (IoT) networks, enabling task offloading with security enhancement based on blockchain mining. Yet the existing approaches for these enabling technologies are isolated, providing only tailored solutions for specific services and scenarios. To fill this gap, we propose a novel cooperative task offloading and blockchain mining (TOBM) scheme for a blockchain-based MEC system, where each edge device not only handles computation tasks but also conducts block mining for improving system utility. To address the latency issues caused by the blockchain operation in MEC, we develop a new Proof-of-Reputation consensus mechanism based on a lightweight block verification strategy. To accommodate the highly dynamic environment and high-dimensional system state space, we apply a novel distributed deep reinforcement learning-based approach by using a multi-agent deep deterministic policy gradient algorithm. Experimental results demonstrate the superior performance of the proposed TOBM scheme in terms of enhanced system reward, improved offloading utility with lower blockchain mining latency, and better system utility, compared to the existing cooperative and non-cooperative schemes. The article concludes with key technical challenges and possible directions for future blockchain-based MEC research.
Digital Media and VR Art Creation for Metaverse

The booming of 5G, VR and XR technologies is driving the arrival of metaverse era. Metaverse development heavily relies on the adoption of the emerging technologies. Only software and hardware technology can not achieve excellent immediacy and immersion in the virtual world. The visual works applied in the metaverse are the essential content core. Visual artists play a very important role in creation fields of NFT artwork, smart cities, cultural museum interaction, games, film and televisions. Exploring the art rules and creative methods of the metaverse can improve the current digital media presentation effect and the creative level of VR art, this will attract more artists into the creation of the metaverse.
Literature review of <scp>vision‐based</scp> dynamic gesture recognition using deep learning techniques

Summary Gesture recognition is the foremost need in building intelligent human‐computer interaction systems to solve many day‐to‐day problems and simplify human life in this digital world. The traditional machine learning (ML) algorithm tried to capture specific handcrafted features, failed miserably in some real‐world environments. Deep learning (DL) techniques have become a sensation among researchers in recent years, making the traditional ML approaches quite obsolete. However, existing reviews consider only a few datasets on which DL algorithm has been applied, and the categorization of the DL algorithms is vague in their review. This study provides the precise categorization of DL algorithms and considers around 15 gesture datasets on which these techniques have been applied. This study also provides a brief overview of the numerous challenging dataset available among the research community and insight into various challenges and limitations of a DL algorithm in vision‐based dynamic gesture recognition.