【Abstract】With the rapid development of big data, assessment of data quality or model performance has become a hot scientific question. However, most existing lots of metrics focus on specific aspects of the assessment, and comprehensive assessment is rare. Therefore, it is very necessary to develop new assessment system. To address this problem, a new assessment system is constructed which is named after Chen, Chen, Hu, and Zhou (CCHZ)-distance between indices of simulation and observation (DISO) according to the contributions of Xi Chen, Deliang Chen, Zengyun Hu, and Qiming Zhou. CCHZ-DISO system builds on the Euclidean Distance and flexible determination of statistical metrics and their numbers. Due to its simplicity and flexibility, CCHZ-DISO can be readily and widely applied to any subject of science. Therefore, it follows the principle of the Chinese philosopher Lao Zi's Da Dao Zhi Jian which means that the most basic truth is very simple.
【Abstract】Given the growing increase in the number of blockchain (BC) platforms, cryptocurrencies, and tokens, non-technical individuals face a complex question when selecting a BC that meets their requirements (e.g., performance or security). In addition, current approaches that aid such a selection process present drawbacks (e.g., require specific BC knowledge or are not automated and scalable), which hinders the decision process even further. Fortunately, techniques such as Machine Learning (ML) allow the creation of selection models without human interaction by identifying the BC features that match the requirements provided by the user in an automated and flexible manner. Thus, this work presents the design and implementation of an ML-based BC selection approach that employs five ML models to select the most suitable BC given user requirements (e.g., BC popularity, fast block inclusion, or Smart Contract - SC support). The approach follows an ML-specific data flow and defines a novel equation to quantify the popularity of a BC. Furthermore, it details the models' accuracy and functionality in two distinct use cases, which shows their good accuracy (> 85%). Finally, discussions on (a) the ML usefulness, (b) advantages over rule-based systems, and (c) the most relevant features for the BC selection are presented.
【Abstract】Currently, the conventional mode of power data transaction is mediated by Web pages. Nevertheless, there are challenging issues such as privacy protection, transaction security and data reliability in power data trading. In this paper, we present a novel secure power data trading scheme (SPDTS). Firstly, the zero-knowledge proof is employed to achieve data availability and consistency without revealing the data. Then, SPDTS takes full advantage of the dispersibility and immutability of blockchain to ensure the reliability of data transactions. To keep the transaction process efficient, the processing tasks for power data are performed under smart contract. Meanwhile, a trusted execution environment (TEE) is adopted to guarantee the security of power data. Finally, we present a differential privacy scheme to safeguard the privacy information in the power data. Our study indicates that the proposed scheme can achieve privacy protection, transaction security and data reliability. Also, we conduct security analysis and verify the privacy protection property of the scheme in real cases.
【Abstract】Today, Raft's distributed cluster scale is growing rapidly and cluster throughput is declining. The Raft consensus algorithm needs to be continuously optimized to adapt to a complex and changeable application environment. To solve the above problems, we propose a federal reconstruction Committee Raft consensus algorithm FRCR. Based on the Federation reconstruction technology, the algorithm trains, updates and evaluates the model of the characteristic data set of the Raft node, runs the model to get the nodes with better performance, constructs the committee mechanism, and improves the quality and speed of the election. We also design a semi asynchronous buffer mechanism and a strategy to resist malicious node attacks to solve the inconsistency and security problems of federation aggregation. Finally, seven aspects of FRCR are tested and analyzed to verify the effectiveness of FRCR in the consensus cluster.
【Abstract】Solvent deasphalting (SDA) is a complex multiscale continuous process. The operation mode of the SDA process is not considered in the related data-driven model. Therefore, this paper proposes a time lag process prediction model with multiple operation modes to solve the above problem. First, based on random forests, the relative importance of initial input variables in the SDA process on DAO yield and Conradson carbon residual are studied and features are selected according to the results. Then, the stack denoising autoencoder (SDAE) is used to reconstruct the data and obtain the nonlinear mapping information of hidden layers of SDAE and achieve feature dimension reduction. SDAE can improve clustering accuracy of fuzzy c-means, and the operation mode of SDA process is accurately divided. Long short-term memory (LSTM) is used to establish a multicondition LSTM model. Compared with the traditional LSTM model, the multicondition LSTM model has a higher prediction accuracy with R2 > 0.95. The sensitivity analyses of the properties of feed and operating conditions on DAO yield are consistent with the principle of two-phase countercurrent extraction in the SDA process. In addition, the benchmark test of the Tennessee Eastman process shows that the proposed method is also effective in the fault detection of other processes. Because the multicondition LSTM can predict the future process measurement data according to operating mode, it can better avoid the false alarm problem and predict the fault earlier.
【Abstract】Consensus algorithms that function in permissionless blockchain systems must randomly select new block proposers in a decentralised environment. Our contribution is a new blockchain consensus algorithm called Proof-of-Publicly Verifiable Randomness (PoPVR). It may be used in blockchain design to make permissionless blockchain systems function as pseudo-random number generators and to use the results for decentralised consensus. The method employs verifiable random functions to embed pseudo-random number seeds in the blockchain that are confidential, tamper-resistant, unpredictable, collision-resistant, and publicly verifiable. PoPVR does not require large-scale computation, as is the case with Proof-of-Work and is not vulnerable to the exclusion of less wealthy stakeholders from the consensus process inherent in stake-based alternatives. It aims to promote fairness of participation in the consensus process by all participants and functions transparently using only open-source algorithms. PoPVR may also be useful in blockchain systems where asset values cannot be directly compared, for example, logistical systems, intellectual property records and the direct trading of commodities and services. PoPVR scales well with complexity linear in the number of transactions per block.
【Abstract】This article investigates the dynamical complexity and fractal characteristics changes of the Bitcoin/US dollar (BTC/USD) and Euro/US dollar (EUR/USD) returns in the period before and after the outbreak of the COVID-19 pandemic. More specifically, we applied the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) method to investigate the temporal evolution of the asymmetric multifractal spectrum parameters. In addition, we examined the temporal evolution of Fuzzy entropy, non-extensive Tsallis entropy, Shannon entropy, and Fisher information. Our research was motivated to contribute to the comprehension of the pandemic's impact and the possible changes it caused in two currencies that play a key role in the modern financial system. Our results revealed that for the overall trend both before and after the outbreak of the pandemic, the BTC/USD returns exhibited persistent behavior while the EUR/USD returns exhibited anti-persistent behavior. Additionally, after the outbreak of COVID-19, there was an increase in the degree of multifractality, a dominance of large fluctuations, as well as a sharp decrease of the complexity (i.e., increase of the order and information content and decrease of randomness) of both BTC/USD and EUR/USD returns. The World Health Organization (WHO) announcement, in which COVID-19 was declared a global pandemic, appears to have had a significant impact on the sudden change in complexity. Our findings can help both investors and risk managers, as well as policymakers, to formulate a comprehensive response to the occurrence of such external events.
【Abstract】This paper investigates the issue of robust relative orbit synchronization control for spacecraft cluster subjects to space perturbation. First, the relative orbit dynamics of the leader-follower spacecraft cluster is established. Combined with P-type learning algorithm and sliding mode control (SMC) strategy, a novel distributed learning SMC (LSMC) strategy with the consensus algorithm is explored such that all the follower spacecrafts converge to the target relative position and their velocities tend to the leader spacecraft. The distributed P-type learning algorithm is used to update and compensate space perturbation for all the follower spacecrafts. The proposed LSMC approach has the potential to achieve higher control accuracy than the traditional SMC one while it has the lower computation burden than the distributed adaptive SMC one. The stability analysis of the proposed distributed LSMC strategy is provided in detail. Finally, a numerical example is simulated to illustrate the effectiveness and superiority of the proposed distributed LSMC-based relative orbit synchronization control for spacecraft cluster.
【Abstract】This paper focuses on the potential value and future prospects of using virtual reality (VR) technology in online education. In detailing online education and the latest VR technology, we focus on meta -verse construction and artificial intelligence (AI) for educational VR use. In particular, we describe a virtual university campus in which on-demand VR lectures are conducted in virtual lecture halls, automated evaluations of student learning and training using machine learning, and the linking of multiple digital campuses.
