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DLIFT: A deep-learning-based intelligent fund transaction system for financial Internet of Things

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第22期33卷 e5982-e5982页

作者： Hu, Gang Xiang, Zhuoyuan Zhang, Yin Hossain, M. Shamim Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China King Saud Univ Comp Informat Sci Riyadh Saudi Arabia

Analyzing the correlation between two funds can help investors control investment risks and optimize investment portfolios, which has a strong guiding significance for fund investment in reality. Constructing an intelligent investment system with fund correlation analysis capabilities can help investors automatically make profits from financial markets. In previous research, many researchers have built intelligent investment systems using Bayesian networks, support vector machines (SVM), and LSTM models. However, the strong historical dependence between fund data and the high-dimensional and high-noise characteristics of fund data prevent traditional methods from obtaining excellent performance in fund analysis. This paper designs a deep learning-based fund intelligent trading system-DLIFT which has functions such as investment push, income prediction, and risk control. The systems data analysis module is implemented using the Improved RNN model. This model employed encoder-decoder architecture. The encoder is responsible for analyzing the fund's feature, and the decoder is responsible for analyzing the dependency relationship between the historical correlation and the current correlation. LSTM and an attention mechanism are simultaneously applied to the encoder and decoder, which enabled the discovery of the implicit dependence of time series data. This article places the designed system on a historical dataset containing multiple public funds for verification. In specific experiments, the experimental results of the comparative experiments show the superiority of our model. At the same time, the results of the ablation experiment results show that LSTM and attention mechanism play critical role in the proposed system.

关键词： attention mechanism deep learning encoder-decoder fund intelligent transaction system

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Absolute Phase Unwrapping with Deep Neural Network for Structured Light 3D Reconstruction

Absolute Phase Unwrapping with Deep Neural Network for Struc...

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第33届中国控制与决策会议

作者： Wan Chang Sen Xiang Huiping Deng Jin Wu School of Information Science and Engineering Wuhan University of Science and Technology Engin.Research Center of Metallurgical Auto.and Measurement Tech. Ministry of Education

Phase-coding structured light is an important technique in 3 D ***,a great challenge is the wrapped phase that causes geometry *** unwrapping methods such as spatial and temporal approaches face the problem of error propagation and low *** this paper,we propose to solve the phase unwrapping problem with a deep neural *** be specific,the phase unwrapping problem is cast to a semantic segmentation task,where the wrapped phase is the input and the fringe index for every pixel is the *** encoderdecoder architecture,which is like U-net,is adopted as the *** further propose a combined loss function by considering cross entropy loss,phase consistency loss and edge consistency *** 10000 artificially synthesized samples,the proposed method converges *** results demonstrate that the trained model well predicts fringe orders on both simulation data and real captured *** addition,it unwrapps every pixel independently and avoids phase error propagation,and further achieves accurate 3 D reconstruction.

关键词： 3D Reconstruction Phase Unwrapping Deep Neural Network encoder-decoder

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A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2019年 151卷 223-236页

作者： Mohammadimanesh, Fariba Salehi, Bahram Mandianpari, Masoud Gill, Eric Molinier, Matthieu C CORE Capt Robert A Bartlett Bldg1 Morrissey Rd St John NF A1B 3X5 Canada Mem Univ Newfoundland Dept Elect & Comp Engn St John NF A1C 5S7 Canada SUNY Coll Environm Sci & Forestry Environm Resources Engn Syracuse NY 13210 USA VTT Tech Res Ctr Finland Ltd Oulu Finland

Despite the application of state-of-the-art fully Convolutional Neural Networks (CNNs) for semantic segmentation of very high-resolution optical imagery, their capacity has not yet been thoroughly examined for the classification of Synthetic Aperture Radar (SAR) images. The presence of speckle noise, the absence of efficient feature expression, and the limited availability of labelled SAR samples have hindered the application of the state-of-the-art CNNs for the classification of SAR imagery. This is of great concern for mapping complex land cover ecosystems, such as wetlands, where backscattering/spectrally similar signatures of land cover units further complicate the matter. Accordingly, we propose a new Fully Convolutional Network (FCN) architecture that can be trained in an end-to-end scheme and is specifically designed for the classification of wetland complexes using polarimetric SAR (PoISAR) imagery. The proposed architecture follows an encoder-decoder paradigm, wherein the input data are fed into a stack of convolutional filters (encoder) to extract high-level abstract features and a stack of transposed convolutional filters (decoder) to gradually up-sample the low resolution output to the spatial resolution of the original input image. The proposed network also benefits from recent advances in CNN designs, namely the addition of inception modules and skip connections with residual units. The former component improves multi-scale inference and enriches contextual information, while the latter contributes to the recovery of more detailed information and simplifies optimization. Moreover, an in-depth investigation of the learned features via opening the black box demonstrates that convolutional filters extract discriminative polarimetric features, thus mitigating the limitation of the feature engineering design in PoISAR image processing. Experimental results from full polarimetric RADARSAT-2 imagery illustrate that the proposed network outperforms the convent

关键词： Deep learning Land cover Wetland Convolutional Neural Network (CNN) Fully Convolutional Network (FCN) encoder-decoder Polarimetric Synthetic Aperture Radar (PolSAR)

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Neural Image Caption Generation with Weighted Training and Reference

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COGNITIVE COMPUTATION 2019年第6期11卷 763-777页

作者： Ding, Guiguang Chen, Minghai Zhao, Sicheng Chen, Hui Han, Jungong Liu, Qiang Tsinghua Univ Sch Software Beijing 100084 Peoples R China Univ Lancaster Comp & Commun Lancaster England

Image captioning, which aims to automatically generate a sentence description for an image, has attracted much research attention in cognitive computing. The task is rather challenging, since it requires cognitively combining the techniques from both computer vision and natural language processing domains. Existing CNN-RNN framework-based methods suffer from two main problems: in the training phase, all the words of captions are treated equally without considering the importance of different words;in the caption generation phase, the semantic objects or scenes might be misrecognized. In our paper, we propose a method based on the encoder-decoder framework, named Reference based Long Short Term Memory (R-LSTM), aiming to lead the model to generate a more descriptive sentence for the given image by introducing reference information. Specifically, we assign different weights to the words according to the correlation between words and images during the training phase. We additionally maximize the consensus score between the captions generated by the captioning model and the reference information from the neighboring images of the target image, which can reduce the misrecognition problem. We have conducted extensive experiments and comparisons on the benchmark datasets MS COCO and Flickr30k. The results show that the proposed approach can outperform the state-of-the-art approaches on all metrics, especially achieving a 10.37% improvement in terms of CIDEr on MS COCO. By analyzing the quality of the generated captions, we come to a conclusion that through the introduction of reference information, our model can learn the key information of images and generate more trivial and relevant words for images.

关键词： Image captioning Reference Long short-term memory encoder-decoder

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Refined Spatial Network for Human Action Recognition

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IEEE ACCESS 2019年 7卷 111043-111052页

作者： Wu, Chunlei Cao, Haiwen Zhang, Weishan Wang, Leiquan Wei, Yiwei Peng, Zexin China Univ Petr Coll Comp & Commun Engn Qingdao 266580 Shandong Peoples R China China Univ Petr Sch Petr Engn Beijing 102249 Peoples R China

Effective video representation is a key ingredient in action recognition, but how to learn effective spatial features remains a fundamental and challenging task. The existing CNN-based methods apply low-resolution feature maps to get the high-level semantic labels. However, the slenderer spatial information for action representation has lost. In this paper, we propose a novel stacked spatial network (SSN), which integrates multi-layer feature maps in an end-to-end manner. Spatial features extraction network based on encoder-decoder is firstly used to obtain multi-level and multi-resolution spatial features under the supervision of high-level sematic labels. The multi-level features are aggregated through a stacked spatial fusion layer, which intrinsically refines the traditional convolutional neural network. Then, refined spatial network (RSN) is proposed to aggregate spatial network and SSN. Particularly, the learned representation of RSN comprises two components for representing semantic label information and local slenderer spatial information. Extensive experimental results on UCF-101 and HMDB-51 datasets demonstrate the effectiveness of the proposed RSN.

关键词： Action recognition encoder-decoder spatial features

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Attention-Based Convolutional LSTM for Describing Video

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IEEE ACCESS 2020年 8卷 133713-133724页

作者： Liu, Zhongyu Chen, Tian Ding, Enjie Liu, Yafeng Yu, Wanli China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221008 Jiangsu Peoples R China China Univ Min & Technol IOT Percept Mine Res Ctr Xuzhou 221000 Jiangsu Peoples R China Univ Bremen Inst Electrodynam & Microelect D-28359 Bremen Germany

Video description technique has been widely used in the computer community for many applications. The typical approaches are mainly based on the encode-decode framework: the fixed-length video representation vectors are extracted by the encoder using the upper layer output of pre-trained convolutional neural networks (CNNs);The decoder uses the recurrent neural networks to generate a textual sentence. However, the upper layers of convolutional neural networks contain low-resolution, semantically strong, while the lower layers contain high-resolution, semantically weak features. In the existing method, the multi-scale information of CNNs is hardly considered to be used in the video description. Ignoring this information will lead to the problem that the video description is not detailed and comprehensive. This paper applies the hierarchical convolutional long short-term memory (ConvLSTM) in the encode-decode framework to conduct feature extraction of the upper and lower layers in CNNs. Moreover, multiple network structures are designed to explore the Spatio-temporal feature extraction performance of ConvLSTM, which can approach the optimal accuracy in the three-layer ConvLSTM. In order to efficiently improve the language quality of video description, the attention mechanism focuses on visual feature output by ConvLSTM. The extensive experimental results demonstrate that the proposed method outperforms the existing approaches.

关键词： Feature extraction Visualization Decoding Task analysis Semantics Recurrent neural networks Convolution Video description long short-term memory encoder-decoder attention mechanism

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Hybrid Attention Distribution and Factorized Embedding Matrix in Image Captioning

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IEEE ACCESS 2020年 8卷 154453-154460页

作者： Wang, Jian Feng, Jie Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou 310018 Peoples R China

Self-attention mechanisms are widely used in current encoder-decoder frameworks of image-captioning tasks. These mechanisms use the transformer module as the basic unit to compute the similarity between query and key vectors, and normalize them to obtain the attention distribution matrix, weighting and reconstructing the rich semantic query vectors. However, a single attention distribution matrix cannot express more complex intrinsic relations between query and key vectors. Some very important intrinsic relations can thus become lost. We therefore propose a hybrid attention distribution (HAD) that allows multiple distributions to be reconstructed to express deeper internal relations and avoids a single shallow attention distribution. In addition, to reduce the number of model parameters, we factorize the word-embedding matrix. This effectively increases the training efficiency and prevents the metrics from decreasing. We extensively evaluate our proposals using the MS-COCO image-captioning dataset. Our results outperform existing state-of-the-art methods on some metrics.

关键词： Self-attention image captioning transformer encoder-decoder

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A Multi-Level Attention Model for Remote Sensing Image Captions

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REMOTE SENSING 2020年第6期12卷 939-939页

作者： Li, Yangyang Fang, Shuangkang Jiao, Licheng Liu, Ruijiao Shang, Ronghua Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist EducSch Artificial Intelligence Int Res Ctr Intelligent Percept & ComputatJoint Xian 710071 Peoples R China

The task of image captioning involves the generation of a sentence that can describe an image appropriately, which is the intersection of computer vision and natural language. Although the research on remote sensing image captions has just started, it has great significance. The attention mechanism is inspired by the way humans think, which is widely used in remote sensing image caption tasks. However, the attention mechanism currently used in this task is mainly aimed at images, which is too simple to express such a complex task well. Therefore, in this paper, we propose a multi-level attention model, which is a closer imitation of attention mechanisms of human beings. This model contains three attention structures, which represent the attention to different areas of the image, the attention to different words, and the attention to vision and semantics. Experiments show that our model has achieved better results than before, which is currently state-of-the-art. In addition, the existing datasets for remote sensing image captioning contain a large number of errors. Therefore, in this paper, a lot of work has been done to modify the existing datasets in order to promote the research of remote sensing image captioning.

关键词： remote sensing image image caption attention encoder-decoder

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Segment boundary detection directed attention for online end-to-end speech recognition

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EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING 2020年第1期2020卷 3-3页

作者： Hou, Junfeng Guo, Wu Song, Yan Dai, Li-Rong Univ Sci & Technol China Natl Engn Lab Speech & Language Informat Proc Hefei 230022 Peoples R China

Attention-based encoder-decoder models have recently shown competitive performance for automatic speech recognition (ASR) compared to conventional ASR systems. However, how to employ attention models for online speech recognition still needs to be explored. Different from conventional attention models wherein the soft alignment is obtained by a pass over the entire input sequence, attention models for online recognition must learn online alignment to attend part of input sequence monotonically when generating output symbols. Based on the fact that every output symbol is corresponding to a segment of input sequence, we propose a new attention mechanism for learning online alignment by decomposing the conventional alignment into two parts: segmentation-segment boundary detection with hard decision-and segment-directed attention-information aggregation within the segment with soft attention. The boundary detection is conducted along the time axis from left to right, and a decision is made for each input frame about whether it is a segment boundary or not. When a boundary is detected, the decoder generates an output symbol by attending the inputs within the corresponding segment. With the proposed attention mechanism, online speech recognition can be realized. The experimental results on TIMIT and WSJ dataset show that our proposed attention mechanism achieves comparable online performance with state-of-the-art models.

关键词： encoder-decoder Online recognition Boundary detection Attention mechanism Reinforcement learning Policy gradient

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SELF-ATTENTION ALIGNER: A LATENCY-CONTROL END-TO-END MODEL FOR ASR USING SELF-ATTENTION NETWORK AND CHUNK-HOPPING 44

SELF-ATTENTION ALIGNER: A LATENCY-CONTROL END-TO-END MODEL F...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Dong, Linhao Wang, Feng Xu, Bo Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781479981311

Self-attention network, an attention-based feedforward neural network, has recently shown the potential to replace recurrent neural networks (RNNs) in a variety of NLP tasks. However, it is not clear if the self-attention network could be a good alternative of RNNs in automatic speech recognition (ASR), which processes the longer speech sequences and may have online recognition requirements. In this paper, we present a RNN-free end-to-end model: self-attention aligner (SAA), which applies the self-attention networks to a simplified recurrent neural aligner (RNA) framework. We also propose a chunk-hopping mechanism, which enables the SAA model to encode on segmented frame chunks one after another to support online recognition. Experiments on two Mandarin ASR datasets show the replacement of RNNs by the self-attention networks yields a 8.4%-10.2% relative character error rate (CER) reduction. In addition, the chunk-hopping mechanism allows the SAA to have only a 2.5% relative CER degradation with a 320ms latency. After jointly training with a self-attention network language model, our SAA model obtains further error rate reduction on multiple datasets. Especially, it achieves 24.12% CER on the Mandarin ASR benchmark (HKUST), exceeding the best end-to-end model by over 2% absolute CER.

关键词： Speech Recognition End-to-End Latency-Control Self-Attention Network encoder-decoder

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