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26th China Conference on Information Retrieval, CCIR 2020

作者： Huang, Liming Yan, Hongfei Ying, Siping Li, Yansong Miao, Rui Chen, Chong Su, Qi Peking University Beijing China Beijing Normal University Beijing China National Engineering Laboratory for Big Data Analysis and Application Technology Center for Big Data Research Peking University Beijing China

ISBN: (纸本)9783030567248

To solve the stock prediction problem, we propose a deep learning model base on a hierarchical attention network. Our model is divided into two models. The first model is the article selection attention network that transfers the news into a low dimension vector. This model could identify the important factors in the news that affect the stock price. The second model is a time series attention network which combines the output of the first model and the transaction data as input. In this model, we could figure out the potential impact between different dates and summarize the historical data to predict whether the stock price will rise or fall. The most innovative concept in this paper is stock encoding. The model learns the difference between each stock and make predictions more accurate by using the stock encoding. The experimental result shows that the model fully utilize text features and make better predictions than related research papers. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Empirical research on futures trading strategy based on time series algorithm 26th

Empirical research on futures trading strategy based on time...

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26th China Conference on Information Retrieval, CCIR 2020

作者： Yao, Shi Hongfei, Yan Siping, Ying Chong, Chen Qi, Su Peking University Beijing China Beijing Normal University Beijing China National Engineering Laboratory for Big Data Analysis and Application Technology Center for Big Data Research Peking University Beijing China

ISBN: (纸本)9783030567248

This article attempts to establish a trading strategy framework based on deep neural networks for the futures market, which consists of two parts: time series forecasting and trading strategies based on trading signals. In the time series forecasting task, we experimented with three types of methods with different entry points, namely recurrent neural networks with gate structure, networks combining time and frequency domain information, and network structures using attention mechanism. In the trading strategy part, the buying and selling signals and the corresponding trading volume are established according to the prediction results, and trading is conducted with the frequency of hours. In the empirical exploration part, we tested the prediction effect and strategic rate of return of various models on the copper contract. The data shows that in general, the best strategy can obtain a relatively stable income growth that has nothing to do with market fluctuations, but lacks countermeasures for rare external events with greater impact. © Springer Nature Switzerland AG 2020.

关键词： Deep neural networks

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An integrated machine learning framework for stock price prediction 26th

An integrated machine learning framework for stock price pre...

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26th China Conference on Information Retrieval, CCIR 2020

作者： Bi, Quanzhi Yan, Hongfei Chen, Chong Su, Qi Peking University Beijing China Beijing Normal University Beijing China National Engineering Laboratory for Big Data Analysis and Application Technology Center for Big Data Research Peking University Beijing China

ISBN: (纸本)9783030567248

Predicting the future price of financial assets has always been an important research topic in the field of quantitative finance. This paper attempts to use the latest artificial intelligence technologies to design and implement a framework for financial asset price prediction. The framework we use is divided into three modules: Feature engineering, Regressor, and Hyper Optimizer. The Feature engineering module extract multiple features using technical indicators, FinBERT, FFT, ARIMA, stacked auto-encoder, PCA and XGBoost. The Regressor module consists of a generative adversarial network, where the generator is Seq2Seq and the discriminator is GRU. The HyperOptimizer module will tune the parameter in GAN using the Bayesian optimization algorithm. Finally, we conducted numerical experiments on our framework, which shows that the framework implemented in this paper performs better than the benchmark method. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Semi-supervised sentiment analysis for chinese stock texts in scarce labeled data scenario and price prediction 26th

Semi-supervised sentiment analysis for chinese stock texts i...

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26th China Conference on Information Retrieval, CCIR 2020

作者： Zhaoyan, Ji Hongfei, Yan Siping, Ying Chong, Chen Qi, Su Peking University Beijing China Beijing Normal University Beijing China National Engineering Laboratory for Big Data Analysis and Application Technology Center for Big Data Research Peking University Beijing China

ISBN: (纸本)9783030567248

The application of neural network in stock prediction is developing rapidly these years because of its excellency in series data processing. However, as most of research are conducted in English, data sources and labeled data are inadequate in Chinese. Especially for natural language processing tasks in specific domain where specialized labeled data are required to train models to adapt to terminology processing, specialized labeled Chinese in text data are very scarce, such as financial text data. To tackle this challenge, we proposed a semi-supervised learning method to generate well-labeled data and train BERT, a leading natural language processing model, to obtain a trained sentiment machine. Then we got stock-related text data sentiment score based on this machine and further combine the sentiment score and other transaction data as inputs for different neural networks to predict stock price. The experimental results on a large scale of Chinese stock data and texts showed that our proposed method successfully improved prediction accuracy compared to other established methods. Besides, we also examined our method’s applicability combined with different neural networks when predicting different types of stock. © Springer Nature Switzerland AG 2020.

关键词： Deep neural networks

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TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction

arXiv

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arXiv 2023年

作者： Yang, Xinquan Xie, Jinheng Li, Xuguang Li, Xuechen Li, Xin Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China National University of Singapore Singapore

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available. As a result, literature works do not work well when there are multiple missing teeth and easily generate false predictions when the teeth are sparsely distributed. In this paper, we are trying to integrate a weak supervision text, the target region, to the implant position regression network, to address above issues. We propose a text condition embedded implant position regression network (TCEIP), to embed the text condition into the encoder-decoder framework for improvement of the regression performance. A cross-modal interaction that consists of cross-modal attention (CMA) and knowledge alignment module (KAM) is proposed to facilitate the interaction between features of images and texts. The CMA module performs a cross-attention between the image feature and the text condition, and the KAM mitigates the knowledge gap between the image feature and the image encoder of the CLIP. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TCEIP achieves superior performance than existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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Flexible WMMSE Beamforming for MU-MIMO Movable Antenna Communications

arXiv

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arXiv 2025年

作者： Yang, Songjie Wan, Zihang Xiu, Yue Ning, Boyu Li, Yong Liu, Yuenwei Yuen, Chau National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory of Wireless Optical Communication Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Hefei230026 China Henan Agricultural Environment Monitoring and Big Data Analysis Engineering Research Center Henan Agricultural University Zhengzhou450046 China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong School of Electrical and Electronics Engineering Nanyang Technological University Singapore

Movable antennas offer new potential for wireless communication by introducing degrees of freedom in antenna positioning, which has recently been explored for improving sum rates. In this paper, we aim to fully leverage the capabilities of movable antennas (MAs) by assuming that both the transmitter and receiver can optimize their antenna positions in multi-user multiple-input multiple-output (MU-MIMO) communications. Recognizing that WMMSE beamforming is a highly effective method for maximizing the MU-MIMO sum rate, we modify it to integrate antenna position optimization for MA systems, which we refer to as flexible WMMSE (F-WMMSE) beamforming. Importantly, we reformulate the subproblems within WMMSE to develop regularized sparse optimization frameworks to achieve joint beamforming (antenna coefficient optimization) and element movement (antenna position optimization). We then propose a regularized least squares-based simultaneous orthogonal matching pursuit (RLS-SOMP) algorithm to address the resulting sparse optimization problem. To enhance practical applications, the low-complexity implementation of the proposed framework is developed based on the pre-calculations and matrix inverse lemma. The overall F-WMMSE algorithm converges similarly to WMMSE, and our findings indicate that F-WMMSE achieves a significant sum rate improvement compared to traditional WMMSE, exceeding 20% under appropriate simulation conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Least squares approximations

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoke Zhu Danyang Li Xiaopan Chen Fumin Qi Fan Zhang Xiao-Yuan Jing School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Department of System Operation National Supercomuting Center in Shenzhen Shenzhen China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Facial image based kinship verification aims to decide whether there exists kinship between the given facial images. In practice, the cross-generation differences will cause adverse effects on kinship verification, which limits the performance. Therefore, how to mine the implied similarity from facial images with large cross-generation divergence is an important problem in kinship verification, which has not yet been well studied. In view of this, we propose a Similarity Mining via Implicit matching pattern LEarning (SMILE) approach for kinship verification. Specifically, SMILE mainly consists of two modules, including a Semi-coupled Multi-pattern Similarity Learning (SMSL) module and a Cross-Generation Feature Normalization (CGFN) module. The SMSL module is designed to learn multiple semi-coupled matching patterns for mining the implicit facial similarity information from different perspectives. The CGFN module aims to reduce the divergence between facial images of parent and child. Extensive experiments demonstrate that the proposed approach outperforms the existing state-of-the-art methods.

关键词： Pattern matching Similarity learning

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Real-Time Chaotic Video Encryption Based on Multi-Threaded Parallel Confusion and Diffusion

SSRN

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SSRN 2023年

作者： Jiang, Dong Yuan, Zhen Li, Wen-Xin Wang, Hai-Tao Lu, Liang-Liang School of Internet Anhui University Hefei230039 China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui University Hefei230601 China Key Laboratory of Optoelectronic Technology of Jiangsu Province Nanjing Normal University Nanjing210023 China National Laboratory of Solid State Microstructures Nanjing University Nanjing210093 China Hefei National Laboratory Hefei230088 China

Due to the strong correlation among adjacent pixels, image encryption schemes typically perform multiple rounds of confusion and diffusion to protect images against various attacks, which is time-consuming and cannot meet the real-time requirements for video encryption. Existing works, therefore, realize video encryption by simplifying encryption process or selectively encrypting specific pixels, resulting in lower security compared to image encryption. This paper proposes a real-time chaotic video encryption strategy based on parallel computing. It splits video frame into sub-frames, creates a set of threads to concurrently perform confusion and diffusion operations on corresponding sub-frames, and efficiently outputs encrypted frames. To assess its performance, two cryptosystems are implemented using different chaotic systems. Encryption speed evaluation demonstrates a significant acceleration in byte generation, confusion, and diffusion phases, enabling real-time encryption on different X86 platforms. The average encryption time is less than 42ms, despite performing five rounds of confusion and diffusion operations on each frame. Statistical and security analysis prove that the deployed cryptosystems exhibit exceptional statistical properties and provide resistance to different attacks. Moreover, our method is adopted to implement a multi-user, real-time mobile video communication system using embedded systems, validating the feasibility of the proposed strategy for various application scenarios. © 2023, The Authors. All rights reserved.

关键词： Cryptography

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Research of Improved TD3 Robotic Arm Path Planning using Evolutionary algorithm

Research of Improved TD3 Robotic Arm Path Planning using Evo...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Feng Zhang Jianqiang Xiong Shuai Yuan Ke Wen School of Electrical and Control Engineering Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China

Nowadays, application of automated intelligent robot arm devices to improve industrial production efficiency has become a popular research field in the world. The previous off-line path planning method of robotic arm has the inadequacies of low efficiency and slow speed. Although the deep reinforcement learning has accomplished many achievements in the path planning of control manipulator, there are still some problems such as long training time and low planning accuracy. To solve the abovementioned issues, we propose an improved Twin Delayed Deep Deterministic policy gradient (TD3) algorithm (Improved Cross-Entropy Method-TD3: ICEM-TD3) for the path planning of the robotic arm. First, this paper combines evolutionary strategies with TD3 to generate action networks. Then the exploration of TD3 algorithm in the action space is replaced by the exploration in the parameter space. In addition, this paper designs a new reward function to weaken the redundancy of planning and accelerate the convergence speed of training. Finally, the Gazebo simulator is adopted to verify the proposed algorithm, and the results illustrate that the proposed algorithm can greatly improve the accuracy of the path planning of the manipulator using deep reinforcement learning.

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Counterfactual data Augmentation with Denoising Diffusion for Graph Anomaly Detection

arXiv

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arXiv 2024年

作者： Xiao, Chunjing Pang, Shikang Xu, Xovee Li, Xuan Trajcevski, Goce Zhou, Fan The School of Computer and Information Engineering Henan University China The Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China The University of Electronic Science and Technology of China Sichuan Chengdu610054 China The National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu610065 China The Department of Electrical and Computer Engineering Iowa State University AmesIA50011 United States

A critical aspect of Graph Neural Networks (GNNs) is to enhance the node representations by aggregating node neighborhood information. However, when detecting anomalies, the representations of abnormal nodes are prone to be averaged by normal neighbors, making the learned anomaly representations less distinguishable. To tackle this issue, we propose CAGAD - an unsupervised Counterfactual data Augmentation method for Graph Anomaly Detection - which introduces a graph pointer neural network as the heterophilic node detector to identify potential anomalies whose neighborhoods are normal-node-dominant. For each identified potential anomaly, we design a graph-specific diffusion model to translate a part of its neighbors, which are probably normal, into anomalous ones. At last, we involve these translated neighbors in GNN neighborhood aggregation to produce counterfactual representations of anomalies. Through aggregating the translated anomalous neighbors, counterfactual representations become more distinguishable and further advocate detection performance. The experimental results on four datasets demonstrate that CAGAD significantly outperforms strong baselines, with an average improvement of 2.35% on F1, 2.53% on AUC-ROC, and 2.79% on AUC-PR. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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