Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based On-Chain and Off-Chain Dual-Trusted Carbon Emission Trading System with Reputation Mechanism

Traditional centralized carbon emission trading systems suffer from data falsification, which is not conducive to the goal of carbon reduction. This paper proposes a blockchain-based on-chain and off-chain dual-trusted carbon emission trading system with reputation mechanism (BoodCET). We employ the Internet of Things technology, BigchainDB storage, and supervision by the Ministry of Ecology Environment for the “collection - storage - calculation - supervision” of carbon data to ensure the authenticity and credibility of carbon emission data off the chain. The Hyperledger Fabric is used to build a consortium blockchain network formed by enterprises and government regulated exchanges. BoodCET provides two trading modes, one-way auction, and agreement transfer. Enterprises get reputations by participating in trading and reducing carbon emissions, and enterprises with high reputation value have a high priority in carbon emission trading. To ensure that BoodCET does not disrupt carbon markets, we introduce a trading rationality review mechanism to review the rationality of carbon emission trading on the consortium chain. In this work, we implement a prototype of BoodCET and conduct a case study to evaluate the performance of the proposed system.
Market Derivative Risks and Regulation of Cryptocurrency Trading Using “PaoFen” Platforms

With the accelerated pace of digital transformation and the popularity of third-party payments, the financial industry has been facing escalating threats of blackmail attacks. Currently, the use of the “Paofen” platform for money laundering is an emerging form of cryptocurrency laundering, providing payment and settlement assistance for fraud, gambling and other illegal crimes, contributing to the spread of cybercrime and greatly undermining the order and security of cyberspace. This paper analyzes the operation process of “Paofen” money laundering from the concept of “Paofen”, proposes the characteristics of identifying “Paofen” money laundering, and proposes to strengthen cross-sectoral cooperation and guide third-party payment institutions to strengthen their control of “Paofen” money laundering. It also proposes to strengthen cross-sectoral cooperation, guide third-party payment institutions to strengthen monitoring and analysis of “Paofen” money laundering, and block the spread of the Internet. For the governance of network blackmail, we should achieve a chain-wide crackdown on related crimes, construct a modeled abnormal data monitoring model, and strengthen the publicity of data security governance related to network payment accounts.
Intelligent target detection of underwater optical images based on image enhancement

Underwater optical images are affected by light attenuation, scattering, noise, etc., resulting in low image quality. Therefore, it is extremely significant to improve image quality and optimize network detection performance for underwater target detection. In this work, a YOLOv8-based underwater target detection method via image enhancement is provided, making full utilization of its advantages in large-scale feature learning. To address the issue of insufficient detection accuracy of the YOLOv8 algorithm on the original optical underwater target dataset, we employ an image fusion-based image enhancement method to improve underwater optical image quality. Combining different weights and scales, this method fuses multiple enhanced images to eliminate scattering and color bias, which enhances target features significantly. The results illustrate that the designed YOLOv8-based algorithm via image enhancement improves the precision by 20.5% and exhibits better performance in small, multiple and overlapping targets scenarios, where this algorithm gives a promising way for underwater optics target detection.
The Best of Both Worlds: Integrating Semantic Features with Expert Features for Smart Contract Vulnerability Detection

Over the past few years, smart contract suffers from serious security threats of vulnerabilities, resulting in enormous economic losses. What’s worse, due to the immutable and irreversible features, vulnerable smart contracts which have been deployed in the the blockchain can only be detected rather than fixed. Conventional approaches heavily rely on hand-crafted vulnerability rules, which is time-consuming and difficult to cover all the cases. Recent deep learning approaches alleviate this issue but fail to explore the integration of them together to boost the smart contract vulnerability detection yet. Therefore, we propose to build a novel model, SmartFuSE, for the smart contract vulnerability detection by leveraging the best of semantic features and expert features. SmartFuSE performs static analysis to respectively extract vulnerability-specific expert patterns and joint graph structures at the function-level to frame the rich program semantics of vulnerable code, and leverages a novel graph neural network with the hybrid attention pooling layer to focus on critical vulnerability features. To evaluate the effectiveness of our proposed SmartFuSE, we conducted extensive experiments on 40k contracts in two benchmarks. The experimental results demonstrate that SmartFuSE can significantly outperform state-of-the-art analysis-based and DL-based detectors.
Research on Comprehensive Blockchain Regulation and Anti-fraud System

The blockchain technology has attracted attention due to its characteristics of anonymity, openness, decentralization, traceability, and tamper-resistance. However, with the development of the blockchain industry, various financial fraud activities have also emerged on the blockchain. Effective regulation is a necessary condition for the healthy development of the blockchain ecosystem. A comprehensive blockchain anti-fraud prototype system is a system that combines multiple technologies to achieve fraud detection and anti-fraud techniques, aimed at addressing the existing fraud issues in the blockchain field. In this paper, we design and implement an anti-fraud system prototype based on a graph database platform and neural network model. The prototype system has the characteristics of fast, accurate, and reliable, and can effectively combat blockchain fraud activities by identifying blockchain transaction patterns and detecting illegal transaction behavior using new heuristic learning methods and graph neural network methods to detect frequent frauds. The system monitors and detects fraudulent activities such as phishing, ICO, price manipulation, money laundering, and illegal fundraising. The research results of this paper can provide an effective solution for anti-fraud work in the blockchain field and provide technical support for future wider applications.
Blockchain-Based Federated Learning for IoT Sharing: Incentive Scheme with Reputation Mechanism

Huge amounts of data produced by millions of IoT devices are expected to be shared and leveraged as the cornerstone of real-world IoT applications, such as industrial IoT, smart grid, and intelligent transportation system. However, there still exist bottlenecks when implementing IoT sharing, such as the privacy leakage of user data, IoT data quality, and incentives for sharing these data. In this paper, we propose an online incentive framework for model sharing based on blockchain and federated learning to improve the privacy protection of IoT data. To ensure the quality of submitted data, a reputation mechanism is further designed to punish the users who do not complete the model-sharing task. Based on these settings, the model sharing problem based on federated learning is formulated as an online incentive mechanism, then we use deep reinforcement learning to obtain optimal sets of sharing users with the goal of maximizing long-term social welfare. Numerical results indicate the effectiveness of the proposed framework and incentive mechanism in IoT sharing.
A Blockchain-Enabled Decentralized Federated Learning System with Transparent and Open Incentive and Audit Contracts

Federated learning is an innovative and secure artificial intelligence model that ensures distributed privacy protection. However, FL faces serious challenges such as potential single point failures, lack of transparency in audits, and insufficient incentives for participants. In this paper, to address the above challenges, we propose a Blockchain-enabled Decentralized Federated Learning (BC-DFL) system with transparent and open incentive and audit mechanisms. We design a two-stage model training contract that enables a transparent and decentralized FL process. In the first stage, local models are collected, and after reaching the predetermined conditions, the winning nodes of blockchain mining perform model aggregation in the second stage. In addition, to reduce the block size, we leverage the Inter-Planetary File System to convert local models into hashes and record them on the blockchain. Moreover, to incentivize desirable behavior and deter malicious actions, we further propose a rewarding formula and corresponding incentive contract. Our smart contracts incorporate a mutual evaluation mechanism for nodes, which enables fair rewards for benign nodes while punishing those with nefarious intent. The experimental results reveal that the proposed system achieves a high anti-single point of failure value and high incentive goal while maintaining similar accuracy to the original FL system. The source code is available on GitHub.
Hybrid MPI-CUDA accelerated multi-frame blind deconvolution algorithm

Multi-Frame Blind Deconvolution (MFBD) algorithm is the mainstream post-processing method for atmospheric turbulence degraded images. The restoration of a single object from multiple images requires several minutes or even tens of minutes, which severely limits MFBD’s practical application. To achieve the goal of fast restoration, this paper proposes hybrid MPI-CUDA (Message Passing Interface-Compute Unified Device Architecture) accelerated MFBD algorithm. In this hybrid programming model, MPI is responsible for assigning computing tasks, and CUDA is responsible for parallel computing. Hybrid MPI-CUDA accelerated MFBD algorithm has been tested on simulated images and achieved satisfactory results. The execution speed for restoring a single high-resolution image from multiple blurred images has been increased by over 50-fold.
Mitigation of the impact of Doppler shift on ranging accuracy

Time-frequency synchronization is a basic element in some fields such as deep space exploration and satellite navigation. Its accuracy and precision are closely related to the measurement results of these systems. In high signal-to-noise ratio situation, the accuracy of time-frequency synchronization is affected by the Doppler frequency shift. In this study, we propose an interpolation model that can mitigate the impact of the Doppler frequency shift, which is constructed from the prior information of the system and related to phase difference of the pseudo-random code. A coherent heterodyne ranging system is built, which is phase-modulated by pseudo-random binary code. And interpolation model is experimentally verified. The experimental results show that the ranging deviation is not more than 0.55 mm for static target. For the moving target, the ranging deviation does not exceed 0.40 mm, and the deviation of the velocity measurement is within 0.45 mm/s.
Electronic music composition thinking using visual learning and visual sensing technology

Abstract This exploration aims to provide a kind of interactive electronic music composition thinking based on visual learning and visual sensing technology to enhance the thinking process of electronic music composition. Initially, it summarizes the functionalities of the physics components in Unity and analyzes the principles of visual interaction implementation using Virtual Reality (VR) devices and Leap Motion in Unity. Subsequently, the composition of interactive electronic music works is designed and implemented based on ultrasonic sensor technology. Lastly, this exploration focuses on the complete visual design of the audio‐visual integration of Musical Instrument Digital Interface (MIDI) music to represent Cymatics' images of the emotional content of the music. MIDI data can be assigned to provide various mapping possibilities between images and content in music visualization composition. This exploration also designs experiments based on sensory aftereffects and audio‐visual synesthesia to further determine the counterpoint law between the shape of dynamic Cymatics' images and the timbre's audio‐visual synesthetic results. The results indicate that Bass and Kick are distributed in the mid‐low frequency and sub‐low frequency range (20–160 Hz), Vocal and Lead are distributed in the mid‐high frequency range (1280–2560 Hz), and Hihat is distributed in the high‐frequency range (2560–5120 Hz). This exploration utilizes computer technology to create a music visualization method that conforms to the visual expression and aesthetic style of a multi‐sensory experience.
A deep neural network-based distortion registration technique for astronomical images

When observing astronomical targets from ground-based telescopes, sequential short-exposure astronomical image acquisition is usually performed, where the astronomical images exhibit constant distortion and quality changes, and atmospheric turbulence makes the captured astronomical images seriously affected by noise and severely reduces the resolution. In this paper, we use a deep neural network-based image distortion registration technique to eliminate atmospheric blurring by registering the sequential short-exposure astronomical images and superimposing the registered images to improve image quality. This paper introduces an attention technique based on the Cyclemorph network, which identifies three attention units on the feature extraction network and uses a self-gating soft attention technique to generate a trainable gating signal that allows the network to associate useful local information and then combines the feature vectors obtained from it to make the final prediction. Additionally, this soft attention method can help with accurate target localisation and boost the network's overall performance. To show how well the method works, it is applied to a collection of image datasets of really photographed astronomical targets as well as a set of image datasets of simulated astronomical targets built by a telescope atmospheric imaging model. The built networks go through independent training and testing. According to the experimental findings, the proposed method can successfully register consecutive astronomical photographs, neutralize the impact of atmospheric turbulence, and raise the resolution of astronomical images.
Demystifying the black box: an overview of explainability methods in machine learning

Deep learning algorithms have achieved remarkable accuracy in various domains such as image classification, face recognition, sentiment analysis, text classification, healthcare, and self-driving vehicles. However, their complex and opaque structures often hinder their adoption in mission-critical applications. The lack of interpretability in these models raises concerns about their reliability and trustworthiness. To address this challenge, Explainable Artificial Intelligence (XAI) methods have emerged to provide human-comprehensible interpretations of AI outcomes. In this paper, we delve into the latest advancements in XAI techniques, focusing on their methodologies, algorithms, and the scope of interpretations they offer. Our study revolves around evaluating these algorithms based on the quality of explanations they generate, their limitations, and their practical applications. By critically assessing their strengths and weaknesses, we aim to shed light on the potential of XAI methods to bridge the gap between high-performance accuracy and interpretability in deep learning models. Through this comprehensive analysis, we aim to provide a deeper understanding of the state-of-the-art XAI techniques, empowering researchers and practitioners to make informed decisions regarding the integration of explainability into their AI systems.
High precision 3D measurement with few images based on deep learning

Fringe projection profiling (FPP) is a technique to obtain the three-dimensional shape of an object by projecting periodic fringes onto its surface and analyzing the modulated fringes.The goal of this technique is to quickly and accurately obtain the three-dimensional shape of an object with as few fringe patterns as possible. This paper combines the fringe analysis steps of fringe projection profiling and deep learning, the proposed DARUNet network (Dense and Residual U-Net) introduces Dense Block and Residual Block on the basis of U-Net. Only three modulated fringe patterns with different frequencies need to be captured as the input of the DARUNet network, the network outputs the numerator and denominator of the wrapped phase corresponding to each frequency. After some post-processing, the three-dimensional shape of the object can be obtained. Deep learning relies on high-quality datasets, so this paper compares two methods for temporal phase unwrapping: Multi-frequency (hierarchical) and Multi-wavelength (heterodyne).The Multi-frequency method, which demonstrated superior performance, was chosen to create a high-precision 3D measurement dataset. Experiments show that the proposed network has higher precision in predicting the wrapped phase than U-Net and its series networks, and predicting the numerator and denominator of wrapped phase by fringes is also the optimal route for 3D reconstruction technology based on deep learning, this method achieves a high level of precision with a phase error of less than 0.1 radians and a depth error of less than 0.3 mm. Therefore, the method employed in this paper enables high-precision 3D measurements using only three frames of fringe patterns.
Consensusless Blockchain: A Promising High-Performance Blockchain Without Consensus

Consensus is unnecessary when the truth is available. In this paper, we present a new perspective of rebuilding the blockchain without consensus. When the consensus phase is eliminated from a blockchain, transactions could be canonized quickly using a well-defined universal rule without consuming hashing power. Thus, the transactions per second (TPS) metric of such the consensusless blockchain can be largely boosted. Although consensus blockchain is promising, several technical challenges are also crucial. For example, double-spending attacks and frequent forking events must be prevented, the credit of block’s minting must be carefully defined, and etc. To address those technical challenges, we propose several solutions for our consensusless blockchain (CB), including a naive monotonic scoring mechanism to calculate the ranking of each block in the chain, and a two-stage witness mechanism to add new blocks. The proposed CB chain is promising to offer a simplified and equipment-cheap infrastructure for rich real-world decentralized applications.
Optical image super-resolution in dark underwater background via SwinIR

The optical images quality in dark underwater background is usually degraded, influencing the results of accurate identification, terrain mapping and seabed exploration. Thus, the super-resolution methods for dark underwater optical images attract extensive research interest. In this work, the SwinIR model is provided for dark underwater optical images super-resolution. Here, the EUVP DARK dataset with dark underwater background is employed, which clusters 5500 paired images. The SwinIR networks has high speed of process with large-size image capability and plentiful image detail. Compared with the traditional SRGAN method, the super-resolution reconstruction speed increases about 18.3%, and the peak pignal-to-noise ratio (PSNR) and structural similarity (SSIM) of SwinIR test results on the EUVP DARK dataset increase by 14.4% and 15.8% respectively, the results illustrate that our method improving the accuracy and quality of reconstruction. In conclusion, the hierarchical structure of SwinIR and self-attention mechanism adaptive attention weights in generated to the image, enabling precise adjustment and control of detail and texture. This method provides an efficient approach to dark underwater image quality enhancement.
Surviving or thriving? Exploring school leaders’ perception of initiated digital transformation

The global pandemic, COVID-19 shattered everyday life as we know it and disrupted education globally. In this study, we explore an initiated digital transformation accelerated by the pandemic from school leaders’ perspectives in the context of Swedish high schools. The data consists of a questionnaire with 105 school leaders containing both fixed and open-ended response types. The results reveal significant differences in school leaders’ views on how the transition has worked, whether they and their staff have developed their digital competence, and, how it will affect the school onwards. Contributions include theorising about school leaders’ experiences of navigating an accelerated digital transformation prompted by the pandemic. We introduce four school leader profiles: (a) the enthusiast, (b) the affirmative (c) the skeptical, and (d) the hesitant. These profiles serve to encapsulate the nuanced perspectives of school leaders, thereby enriching our comprehension of their diverse requirements and priorities.
End-to-end deep learning method for absolute phase retrieval

Absolute phase plays a crucial role in various applications, including camera or projector calibration, stereo matching, structured light measurement, and fringe projection profilometry (FPP). Recently, significant progress has been made in the development of deep learning-based approaches for absolute phase recovery. Many deep neural networks have been created, improved, or directly integrated into the phase retrieval procedure. Analyzing these methods, a common trend is observed in the sequential calculation of wrapped phase, fringe order, and absolute phase. The accuracy of previous results has a direct impact on the subsequent steps, leading to potential error accumulation and reduced recovery speed. To address these challenges, we propose an end-to-end deep learning method based on Res-UNet that directly predicts the absolute phase from a single fringe image without any additional fringe patterns. The presented approach simplifies the procedure of phase unwrapping and overcomes limitations of existing techniques. To note that, to save cost and labor for training the Res-UNet, a novel and virtual digital fringe project system with 3D Studio Max is also established for generating data close to reality. Experiments have been carried out to validate the performances of the proposed method.