Reviewerly/exHarmony huggingface dataset:
Blockchain Technology and its Disruptive Potential in the Digital Economy

Blockchain technology's disruptive potential in the digital economy has spurred a surge in scholarly interest, leading to a complex landscape of research endeavors. This study employs a comprehensive bibliometric analysis to illuminate the multifaceted dimensions of blockchain's impact. Through analysis techniques and visualization tools, including VOSviewer, we uncover publication trends, influential authors, research themes, collaboration networks, and the interdisciplinary nature of blockchain research. The results reveal a steady increase in publications over the past decade, indicating growing recognition of blockchain's significance. Influential authors emerge as key contributors, shaping the research discourse and driving innovation. Diverse research themes, ranging from fintech and digital transformation to cryptocurrencies and smart contracts, underscore blockchain's applications across various sectors. Collaborative networks highlight the emergence of focused communities, fostering interdisciplinary insights. The integration of VOSviewer facilitates an exploration of these insights, offering a nuanced perspective on blockchain's disruptive potential. Our analysis contributes to a deeper understanding of the evolving landscape, informing researchers, practitioners, policymakers, and stakeholders navigating the complexities of blockchain technology in the digital economy.
The fixed points of belief and knowledge

Abstract Self-referential sentences have troubled our understanding of language for centuries. The most famous self-referential sentence is probably the Liar, a sentence that says of itself that it is false. The Liar Paradox has encouraged many philosophers to establish theories of truth that manage to give a proper account of the truth predicate in a formal language. Kripke’s Fixed Point Theory from 1975 is one famous example of such a formal theory of truth that aims at giving a plausible notion of truth by allowing truth value gaps. However, not only the concept of truth gives rise to paradoxes. A syntactical treatment of epistemic notions like belief and knowledge leads to contradictions that very much resemble the Liar Paradox. Therefore, it seems to be fruitful to apply the established theories of truth to epistemic concepts. In this paper, I will present one such attempt of solving the epistemic paradoxes: I adapt Kripke’s Fixed Point Theory and interpret truth, knowledge and belief within the framework of a partial logic. Thereby I find not only the fixed point of truth but also the fixed points of knowledge and belief. In this fixed point, the predicates of truth, belief and knowledge find their definite interpretation and the paradoxes are avoided.
Multi-level federated learning based on cloud-edge-client collaboration and outlier-tolerance for fault diagnosis

Abstract The existing federated structure protects data privacy with only a certain level of confidentiality, and it is difficult to resist the reconstruction of other clients’ data by malicious participants inside the federated and the illegal manipulation by external attackers or interceptors on the shared information. Besides, the average fusion algorithm used in the cloud center is difficult to eliminate the negative impact of outliers on model updates, and it cannot handle and fuse the time delay or even packet loss that occurs in the information obtained from each local client promptly. Therefore, to make the federated learning (FL) mechanism with stronger privacy protection ability and security, while effectively avoiding the negative impact of outliers on the aggregation of model parameters. We innovatively establish multi-Level FL based on cloud-edge-client collaboration and outlier-tolerance for fault diagnosis. At first, we build a multi-level FL network framework based on the cloud-edge-client collaborative approach for restricted sharing of network parameters level by level without data communication. Then, the edge-side performs Euclidean metrics on the restricted shared model parameters uploaded to the primary edge by each client, and uses them to identify outliers to evaluate and weight them for outlier-tolerance; Then, an outlier-tolerance mechanism is designed based on a centralized Kalman filtering algorithm that is to adjust the modeling error weights adaptively; Lastly, the cloud center performs asynchronous aggregation on the model parameters uploaded asynchronously by the highest-level edge based on a sequential Kalman filtering algorithm and transmitted the optimal model parameters back along the original path. Finally, the effectiveness of the proposed method is verified on the collected dataset.
Workload-Aware Performance Tuning for Multimodel Databases Based on Deep Reinforcement Learning

Currently, multimodel databases are widely used in modern applications, but the default configuration often fails to achieve the best performance. How to efficiently manage and tune the performance of multimodel databases is still a problem. Therefore, in this study, we present a configuration parameter tuning tool MMDTune+ for ArangoDB. First, the selection of configuration parameters is based on the random forest algorithm for feature selection. Second, a workload-aware mechanism is based on k-means++ and the Pearson correlation coefficient to detect workload changes and match the empirical knowledge of historically similar workloads. Finally, the ArangoDB configuration parameters are optimized based on the improved TD3 algorithm. The experimental results show that MMDTune+ can recommend higher-quality configuration parameters for ArangoDB compared to OtterTune and CDBTune in different scenarios.
Quantum approximate optimization algorithm in non-Markovian quantum systems

Abstract Although quantum approximate optimization algorithm (QAOA) has demonstrated its quantum supremacy, its performance on Noisy Intermediate-Scale Quantum (NISQ) devices would be influenced by complicated noises, e.g. quantum colored noises. To evaluate the performance of QAOA under these noises, this paper presents a framework for running QAOA on non-Markovian quantum systems which are represented by an augmented system model. In this model, a non-Markovian environment carrying quantum colored noises is modelled as an ancillary system driven by quantum white noises which is directly coupled to the corresponding principal system; i.e. the computational unit for the algorithm. With this model, we mathematically formulate QAOA as piecewise Hamiltonian control of the augmented system, where we also optimize the control depth to fit into the circuit depth of current quantum devices. For efficient simulation of QAOA in non-Markovian quantum systems, a boosted algorithm using quantum trajectory is further presented. Finally, we show that non-Markovianity can be utilized as a quantum resource to achieve a relatively good performance of QAOA, which is characterized by our proposed exploration rate.
Corporate governance innovation framework to reduce credit risk in MSMEs using blockchain technology

This study aims to design a framework of corporate governance innovation to reduce the credit risks of Micro, Small and Medium Enterprises (MSMEs) by using blockchain technology. The research design uses a qualitative approach with Grounded theory analysis for data analysis with open, axial, and selective coding. This framework consists of two stages, namely development and validation. In-depth interviews are used for data collection to develop the framework while focus group discussions are used for validation. The in-depth interviews are carried out with various stakeholders, such as the government, association, MSMEs, suppliers, and banking. Findings show that blockchain technology as corporate governance innovation framework has the potential to reduce asymmetric information and credit risk. The reason is that blockchain technology can facilitate recording, immutable, partial decentralisation, and storage of business and financial transactions on a digital network and all stakeholders can access information in a transparent and valid manner that can increase transparency, accountability, responsibility, and fairness. Results of the focus group discussion indicate the validity of the proposed framework.
Random Location Poisoning Backdoor Attack Against Automatic Modulation Classification in Wireless Networks

The integration of Automatic Modulation Classification (AMC) technology and deep learning has made the technology widely used in applications such as home smart wireless systems, mobile devices, etc. If it is maliciously attacked, it will pose a serious risk to the security of users. Most of the current researches on backdoor attacks are aimed at applications related to computer vision. It has been shown that AMC systems are also prone to backdoor attacks, but their methods do not apply to a signal domain due to the difference between modulated signals and images. In this paper, we propose a backdoor attack method for deep learning-based AMC models, in which the adversary implants a backdoor to the AMC model by poisoning the amplitude at random locations of very little training data and will change its label. In the inference stage, the poisoned model will be misled to a wrong output by poisoned samples containing triggers, while ensuring the normal classification of benign samples. We demonstrate that the method can achieve a 96.7% attack success rate by infecting only 1% of the training set samples without degrading the benign accuracy rate. Since the trigger locations are randomly selected for each sample, the attack concealment is further improved, while the degree of discrepancy between the temporal waveforms before and after the attack is quantitatively evaluated.
Design and Implementation of a Data Stream Anonymization Core on FPGA

Data privacy has become the center of attention to many researchers and engineers. With high speed data transmission, data privacy can be at risk. Data stream anonymization is a fairly new and effective technique that is being currently investigated. It aims to protect data from third-party attackers. A user must keep in mind that when applying anonymization on a dataset, there will be a tradeoff between data utility and the risk of data identification. I n this paper, w e propose various anonymization cores that can be used to hide the sensitive parts of the data. The hardware implementation on FPGA of these cores is also discussed. Each implementation takes into consideration the trade-off between the throughput and the power consumption in addition to the application type and specifications. The first architecture treats a simple application where two anonymization techniques are used (i.e. Perturbation and character masking). The second implementation requires more complex anonymization techniques and extends K-anonymity criteria and L-diversity for more sensitive applications where data identification is crucial. Results are compared with existing work implementations and many improvements are applied in terms of resource utilization and throughput.
RATSR: A Region Adaptive Transformer for Image Super-Resolution Based on Heat Feature Map

In an endeavor to develop a state-of-the-art model in the realm of image acceleration super-resolution (SR), we devised a sophisticated transformer-based classifier that accounts for regional features. Our innovative transformer, RATSR, seamlessly integrates features derived from the classifier into the SR process, while introducing a groundbreaking heatmap concept. This novel approach enables the image features to substantially enhance the attentional span of the super-resolution model, better utilizing regional information. Moreover, we conducted an in-depth exploration of the similarities and disparities between feature matching and feature fusion, optimizing components that are less perceptible to global information. Through rigorous theoretical analysis, we assessed the efficacy of our approach and proposed a comprehensive model architecture accompanied by a robust training strategy. Concurrently, we substantiated its effectiveness by employing extensive experimentation, and implemented ablation studies to fine-tune the model's modules. The culmination of our research effort is evidenced in the experimental results, which reveal that our cutting-edge image super-resolution model attains exceptional performance without necessitating a substantial increase in the number of parameters.
Blockchain-assisted Cross-Domain Authentication and Access Control for Low-altitude UAV

With the development of unmanned aerial vehicles (UAVs) technology and the expansion of UAV application scenarios, the demand for communication and airspace resources is increasing explosively. Traditional UAV authentication and access control schemes cannot meet the requirements of seamless access, and face security and efficiency challenges. Considering the mobility of UAVs, cross-domain authentication operations are repeatedly performed, resulting in low efficiency. Centralized authentication scheme faces the risk of single point of failure. In addition, relying solely on cryptography methods cannot meet regulatory requirements. To solve the above challenges, we present a blockchain-assisted UAV authentication and access control scheme, where regulatory authority (RA) and service provider (SP) collaborate on-chain to conduct UAV management efficiently and securely. Specifically, Automatic Dependent Surveillance Broadcast (ADS-B) is introduced to help regulation in our scheme. Access point (AP) completes the UAV access based on ADS-B signals and on-chain information, which makes the access process reliable and secure. We demonstrate the feasibility and efficiency of our scheme through extensive experiments.
Post-Mortem of Mega Hacks - Signifying the Need for a Systemic Enterprise View on Information Security

Once, system thinking was about singular systems. Today we exist in a far more complex world, with systems interacting with systems, directly or indirectly. Information security, therefore, must involve all systems in the chain. New legal European regulations such as Guidelines for Data Protection Regulation demand that the ICT/IT world must include systems outside the organizational border to be involved and accounted for under enterprise information security umbrella. Recent mega hacks analyzed in this article point to the fact that a systems thinking perspective is needed to create modern governance, risk, and compliance security model framework. This research work puts forth a conceptual model based on Viable System Model appropriate for a major global information security restructuring. A motive for VSM is grounded in that it works fine with securing modern laws like GDPR and CCPA in supporting a needed enterprise perspective.
Application Research on Dynamic Obstacle Avoidance Path Planning of Unmanned Vehicle in Uncertain Environment

To solve the problem of uncertain environment, the dynamic collision avoidance path planning of unmanned vehicles was studied based on D* Lite algorithm. Firstly, the grid method was used to build environment model for the implementation process of D* Lite algorithm. Through the comparison with the simulation results of A* algorithm, the correctness and efficiency of D* Lite algorithm were verified in the maze environment, and the dynamic collision avoidance function of D* Lite algorithm was verified in the random obstacle environment. Finally, D* Lite algorithm was applied to simulate and analyze the dynamic collision avoidance path planning of unmanned vehicle in uncertain environment. The simulation example analysis of D* Lite algorithm based on MATLAB 2020b platform shows that in an uncertain environment, unmanned vehicles can use D* Lite algorithm for accurate and efficient dynamic collision avoidance path planning. Whether it is a simple campus park road or a complex and tortuous park road, whether it is single obstacle or multiple obstacles, the algorithm can always successfully complete the autonomous collision avoidance path planning under the dynamic map.
CycleGAN-Based Visible-Infrared Image Enhancement Method for Infrared Power Equipment Object Detection

In the realm of electrical engineering, object detection in infrared images is paramount for the maintenance of power equipment. However, the scarcity of infrared image datasets severely impedes the improvement of object detection technology for infrared images. To address this issue, we propose a data enhancement method based on Cycle-consistent Generative Adversarial Networks (CycleGAN), which can translate easily available visible images of power equipment into less accessible infrared images and achieve to expand the pool of infrared images of power equipment. Because of the cycle-consistent loss function, CycleGAN can realize image-to-image translation in the absence of paired data, therefore breaking away from the traditional requirement of needing difficult-to-obtain paired input datasets. In addition, we utilize the LSGAN loss function to prevent mode collapse. The experimental results show that our method can generate high-quality infrared images which are nearly indistinguishable from the real captured images. And the performance of the object detection algorithm is significantly improved by the enhanced dataset, proving our method is effective in infrared dataset enhancement. As an unsupervised data enhancement approach, it can also provide an idea that can be employed in other fields with limited data.
Research on Data Storage Security in Cloud Computing Environment

This paper conducts in-depth research and discussion on data storage security in cloud computing environment. This study collected 1000 dummy data from different organizations as the basic data set for the study. This data covers various types of sensitive information, including personally identifiable information, financial data, and medical records. We generate these data using appropriate data generation methods to ensure realistic characteristics and distributions. Based on existing backup technologies and algorithms, we evaluate the impact of different backup strategies on data security. Experimental results show that our data backup and recovery strategy and the application of encryption technology effectively improve the security of data in cloud storage. By selecting backup strategies and recovery mechanisms reasonably, we can ensure data availability and integrity. At the same time, employing a proper encryption scheme can protect data from the risk of unauthorized access and disclosure.
Research on Course Recommendation System Based on Portrait Technology

With the continuous integration of information technology and education, the course resource sharing platform has rapidly developed and accumulated tens of thousands of learning data, which includes a large amount of behavior information, interaction information, learning effect evaluation information, etc. How to mine the intrinsic value of data and meet the diverse learning needs of different users has become an urgent problem to be solved. Therefore, we propose a course recommendation system based on portrait technology. The system models user portraits and course portraits through basic data and historical learning data, and deeply mines educational data based on course recommendation algorithms, so as to recommend courses that match their interests and abilities for users. The course recommendation system based on portrait technology can not only be used to evaluate students' learning behavior and interest preferences, but also can formulate personalized learning strategies for different users to meet the learning needs of different users.
Channel Boosting, Cross-Layer Feature Integration, and Multi-Scale Classification for Fine-Grained Visual Classification

Fine-grained visual classification (FGVC) is extremely challenging compared to basic-level classification. Although some attention-based methods have achieved promising results, they commonly fail to explore the benefits of multi-scale visual information and neglect the importance of information from intermediate layers in the classification network. In this work, we propose a unified framework for FGVC that integrates channel attention, spatial attention, and cross-layer feature interaction. We also demonstrate the significance of information from intermediate layers. Firstly, we introduce a channel boosting module that enhances the most salient parts in the feature maps. Secondly, we enhance and classify the outputs of intermediate layers, emphasizing their importance in the classification task. Furthermore, we introduce a cross-layer feature interaction module capable of integrating information from different feature layers. Lastly, the suggested framework does not require any bounding boxes or part annotations and can be trained seamlessly end-to-end. Extensive experiments show that our proposed method achieves competitive performance on three fine-grained classification benchmarks.
Performance Evaluation of Wireless PoW Blockchain Networks: A Complex Network Perspective

The impact of complex wireless networks on the blockchain has not been well considered since the blockchain system is primarily designed in wired communication environments with enough communication resources. However, deploying blockchain systems in IoT sensors is often accompanied by complex network structures. Therefore, this paper aims to analyze the impact of complex wireless networks on blockchain performance under three classic models: nearest neighbor coupled network, Watts–Strogatz model, and Erdos-Renyi model. We set different transmission rates and network sizes to simulate complex wireless environments and demonstrate the relationship between these parameters and throughput, stale block rate, and average block propagation delay. To better assist the deployment of wireless blockchains, we utilize a Graph Neural Network (GNN) to evaluate the proof-of-work (PoW) blockchain performance under various network topologies quickly.
A Study on Category Classification Based on LightGBM for Signal Feature Extraction and K-Means Clustering

LightGBM is a framework for implementing the GBDT algorithm, which supports efficient parallel training and has the advantages of faster training speed, lower memory consumption, better accuracy, support for distributed. It has the advantages of faster training speed, lower memory consumption, better accuracy, distributed support, etc. It can handle large amount of data quickly. Dimensionality reduction is a method of pre-processing high-dimensional feature data. Dimensionality reduction is to keep the most important features of the high-dimensional data and remove the noise and unimportant features, so as to achieve the purpose of improving the speed of data processing. The K-Means algorithm is an algorithm for cluster analysis, which is mainly used to calculate data aggregation, mainly by continuously taking the nearest mean value to the seed point.