A5034644271- Stock Market Forecasting Methods
- Blockchain Technology Applications and Security
- Complex Systems and Time Series Analysis
- Neural Networks and Applications
- Financial Risk and Volatility Modeling
- Chaos-based Image/Signal Encryption
- EEG and Brain-Computer Interfaces
- Generative Adversarial Networks and Image Synthesis
- Market Dynamics and Volatility
- Ethics and Social Impacts of AI
- Cloud Data Security Solutions
- Blind Source Separation Techniques
- Video Surveillance and Tracking Methods
- Categorization, perception, and language
- Cryptographic Implementations and Security
- Adversarial Robustness in Machine Learning
- Security and Verification in Computing
- Language and cultural evolution
- Privacy-Preserving Technologies in Data
- Metaheuristic Optimization Algorithms Research
- Energy Efficient Wireless Sensor Networks
- Advanced Image and Video Retrieval Techniques
- Image Processing and 3D Reconstruction
- Cognitive Science and Education Research
- Data Mining Algorithms and Applications
Wuhan University
2013-2024
Objective: Lowering the savings rate is a reflection of China's economic structure moving further toward equilibrium. The aim this study to explore how unleash residents' consumption potential and lower rate, crucial challenge for high-quality development economy. pension insurance system may be positive factor in addressing issue. Methods: selects panel data from 31 provinces, autonomous regions, municipalities directly under central government mainland China 2014 2022. A two-way...
With the convergence of fixed and mobile networks, heterogeneous networks are becoming ubiquitous. Internet giants seeing plight identity authentication. To address this issue, unified access management (UAM) was conceived. This paper provides a novel scheme, named SGX-UAM, with one-time passwords (OTPs) based on Intel software guard extensions (SGX). SGX-UAM outperforms generic UAM for providing resistance to most client attacks, man-in-the-middle (MITM) phishing replay attacks denial...
Predicting criminal recidivism effectively is of major interest in criminology. In this paper, we study the ability support vector machines (SVM) to predict probability reincarceration. As a semi parametric approach, SVM minimizes structural risk whereas nonparametric models, such as neural networks, minimize empirical risk. Furthermore, differs significantly from existing logistic regression, prediction recidivism. Due relatively new application predicting field criminology, general...
We propose a token-based blockchain system that streamlines abstractions into universal token structure. In the proposed system, each has an identity enables implementation of specific rollbacks and governance make 51% attacks unprofitable. Because bookkeeping method only verifies updates ownership within transaction, supports parallel expansion cross-chain transactions without limit. The flexible authority management mechanism is regulatory-friendly, as intensity supervision can be adapted...
Abstract Cloud-edge data security is a key issue in the internet of vehicles (IoV), as potential for breaches increases more are connected. As become smarter and connected, risk unauthorized access to generated by also increases. Data encryption highly effective measure that widely used protect IoV from malicious actors. By encrypting data, it becomes virtually impossible individuals information. This ensures only intended parties can allowing secure communication between cloud edge....
Wireless sensor networks are appealing, largely because they do not need wired infrastructure, but it is precisely this feature that renders them energy-constrained. The duty cycle scheduling perceived as a contributor to the energy efficiency of sensing. This paper developed novel paradigm for modeling wireless networks; in context, an adaptive sensing strategy proposed depending on event occurrence behavior, and problem framed optimization problem. objectives include reducing depletion...
Internet of Vehicles(IoV) enables vehicles to generate and share messages improve transportation safety efficiency, especially in a smart city scenario where modern communication technology is utilized. The current IoV, however, faces three main issues: (1) existing frameworks fail build complete data management system, (2) received an untrusted environment are challenging assess for credibility, (3) the centralized ways store causing severe security efficiency problems. Blockchain-enabled...
For high-frequency statistical arbitrage, setting the proper trading threshold for each period is extremely important. We find that optimal short-run series in Chinese metal futures market demonstrates chaotic characteristics. Therefore, we propose a new arbitrage model which TGARCH applied to capture short-term price cointegration and asymmetric spread standard deviations between contracts wavelet neural network utilized predict thresholds. Backtesting results demonstrate provides more...
Large integer factorization is one of the basic issues in number theory and subject this paper. Our research shows that Pisano period product two prime numbers (or an multiple it) can be derived from themselves their product, we therefore decompose by means product. We reduce computational complexity modulo operation through "fast Fibonacci algorithm" design a stochastic algorithm for finding periods large integers. The method, which proved to slightly better than quadratic sieve method...
As an effective way in finding the underlying parameters of a high-dimension space, manifold learning is popular nonlinear dimensionality reduction which makes high-dimensional data easily to be observed and analyzed. In this paper, Isomap, one most famous algorithms, applied process closing prices stocks CSI 300 index from September 2009 October 2011. Results indicate that Isomap algorithm not only reduces stock successfully, but also classifies according their trends efficiently.
In order to learn transformation-invariant features, several effective deep architectures like hierarchical feature learning and variant Deep Belief Networks (DBN) have been proposed. Considering the complexity of those variants, people are interested in whether DBN itself has transformation-invariances. First all, we use original test data. Almost same error rates will be achieved, if change weights bottom interlayer according transformations occurred testing It implies that can store...
Big data is a term used for very large sets. Digital equipment produces vast amounts of images every day; the need image encryption increasingly pronounced, example, to safeguard privacy patients’ medical imaging in cloud disk. There an obvious contradiction between security and widespread use big data. Nowadays, most important engine provide confidentiality encryption. However, block ciphering not suitable huge real‐time environment because strong correlation among pixels high redundancy;...
Various trading strategies are applied in intraday high-frequency market to provide investors with reference signals be on the right side of at time. In this paper, we apply a strategy based combination ACD rules and pivot points system, which is first proposed by Mark B. Fisher, into Chinese market. This has been used millions traders achieve substantial profits last two decades, however, discussions concerning methods calculating specific entry point rare, crucial strategy. We suggest an...
To analyze the behavior of investors in Shanghai stock market, we mine frequent itemsets and association rules from a real securities clearing dataset. The mining results indicate that, most do not diversify their capital to avert risks according expected stock. Further analysis reveals that stocks only cover few state-owned big-cap (SB) stocks, right side rules, both global constrained, are mostly SB stocks. All these phenomenamake clear behavioral mode, market pursuit universally. On other...