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17th IEEE International Conference on Dependable, Autonomic and Secure computing, IEEE 17th International Conference on Pervasive Intelligence and computing, IEEE 5th International Conference on Cloud and Big Data computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019

作者： Liu, Kaiqiang Wang, Bin Liu, Yuan Wu, Ranfeng Zhang, Xun Liu, Xin Zhang, Qiang Yin, Zhixiang Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China School of Mathematics and Big Data Anhui University of Science and Technology Dalian China

ISBN: (纸本)9781728130255;9781728130248

American scientist Adelman first used DNA molecules to solve the NP-complete problem, thus creating a new research field. As an advanced interdisciplinary subject, it has attracted many scholars because of its high parallelism, great storage density and low energy consumption. DNA sequence design is critical in the field of DNA computing. The success of DNA computing depends on the quality of DNA sequence. In this paper, we proposed an improved gravitational search algorithm for DNA sequence design. We improved the gravitational coefficient and introduced chaotic system and Gaussian mutation to the position update of the particles. We compared the result from our improved algorithm with the result obtained by other algorithms. The result shows that our proposed algorithm can generate high quality DNA sequence for DNA computing. © 2019 IEEE.

关键词： DNA computing DNA sequence design Gravitational search algorithm NP-complete problem

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Speech Emotion Recognition Based on Image Enhancement

Speech Emotion Recognition Based on Image Enhancement

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Dongyan Wang Jing Dong Dongsheng Zhou Xiaopeng Wei Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9781728123486

ISBN: (纸本)9781728123493

The performance of an emotion recognition system is determined by the quality of emotional features. In this paper, we propose a feature optimization algorithm based on image enhancement and present a convolutional recurrent model to realize emotional recognition of natural speech. For three-dimensional (3-D) log-Mel spectrum and 3-D spectrogram features, the fast gamma transformation with an adaptive threshold is adopted for feature enhancement to make full use of the dynamic characteristics of non-stationary speech signals. Meanwhile, the model combining Convolutional Neural Network (CNN) with the rectangular kernels and Long Short-Term Memory (LSTM) is used to complete speech emotion recognition tasks. Experiments are carried out on two public emotional datasets, and results demonstrate the good generalization ability and recognition performance of our proposed model.

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Image Super-resolution Reconstruction Based on Deep Residual Network

Image Super-resolution Reconstruction Based on Deep Residual...

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2018 2nd International Conference on Artificial Intelligence: Technologies and Applications（ICAITA2018）

作者： Bin Sun Jian Lu Xiaopeng Wei Laboratory of Advanced Design and Intelligent Computing Dalian University

Having powerful ability to learn and represent features, deep convolutional neural networks(CNN) can get better results in image super-resolution reconstruction. However, deep networks also exist some problems. For example, the gradient will gradually vanish through the network, which makes the training difficult to converge. To address these problems, this paper presents a deep network model that is suitable for superresolution reconstruction. By applying residual network modules, the model realizes the transmission of information across one or more layers. This residual network structure can not only avoid the loss of effective information in the network, but also speed up the training convergence. Compared with the previous classical methods, the proposed model converges faster, and the subjective and objective evaluations have been improved to a certain extent.

关键词： super-resolution reconstruction deep learning residual network

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Ladle Furnace Temperature Prediction Model Based on Large-scale Data With Random Forest

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 770-774页

作者： Xiaojun Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of EducationDalian UniversityDalian 116622 China

In ladle furnace, the prediction of the liquid steel temperature is always a hot topic for the researchers. The most of the existing temperature prediction models use small sample set. Today, the precision of them can not satisfy practical production. Fortunately, the large sample set is accumulated from the practical production process. However, a large sample set makes it difficult to build a liquid steel temperature model. To deal with the issue, the random forest method is preferred in this paper, which is a powerful regression method with low complexity and can be designed very quickly. It is with the parallel ensemble structure,uses sample subsets,and employs a simple learning algorithm of sub-models. Then, the random forest method is applied to establish a temperature model by using the data sampled from the production process. The experiments show that the random forest temperature model is more precise than other temperature models.

关键词： Ladle furnace random forest regression tree temperature prediction

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Fused behavior recognition model based on attention mechanism

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Visual computing for industry, biomedicine, and art 2020年第1期3卷 7页

作者： Lei Chen Rui Liu Dongsheng Zhou Xin Yang Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Dalian University Dalian 116622 China. Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Dalian University Dalian 116622 China. liurui@***. School of Computer Science and Technology Dalian University of Technology Dalian 116024 China.

With the rapid development of deep learning technology, behavior recognition based on video streams has made great progress in recent years. However, there are also some problems that must be solved: (1) In order to improve behavior recognition performance, the models have tended to become deeper, wider, and more complex. However, some new problems have been introduced also, such as that their real-time performance decreases; (2) Some actions in existing datasets are so similar that they are difficult to distinguish. To solve these problems, the ResNet34-3DRes18 model, which is a lightweight and efficient two-dimensional (2D) and three-dimensional (3D) fused model, is constructed in this study. The model used 2D convolutional neural network (2DCNN) to obtain the feature maps of input images and 3D convolutional neural network (3DCNN) to process the temporal relationships between frames, which made the model not only make use of 3DCNN's advantages on video temporal modeling but reduced model complexity. Compared with state-of-the-art models, this method has shown excellent performance at a faster speed. Furthermore, to distinguish between similar motions in the datasets, an attention gate mechanism is added, and a Res34-SE-IM-Net attention recognition model is constructed. The Res34-SE-IM-Net achieved 71.85%, 92.196%, and 36.5% top-1 accuracy (The predicting label obtained from model is the largest one in the output probability vector. If the label is the same as the target label of the motion, the classification is correct.) respectively on the test sets of the HMDB51, UCF101, and Something-Something v1 datasets.

关键词： Action recognition Attention mechanism Res34-SE-IM-net ResNet34-3DRes18

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Chinese Historical Term Translation Pairs Extraction Using Modern Chinese As a Pivot Language

Chinese Historical Term Translation Pairs Extraction Using M...

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第十八届中国计算语言学大会暨中国中文信息学会2019学术年会

作者： Xiaoting Wu Hanyu Zhao Lei Jing Chao Che State Grid Info & Telecom Group Beijing China-Power Information Technology Co. LTDBeijingChina Key Laboratory of Advanced Design and Intelligent Computing Ministry of EducationDalian University Chiping Vocational Education School LiaochengChina

ISBN: (纸本)9783030323806

Term translation of Chinese historical classics is very difficult and time-consuming work,and using term alignment methods to extract term translation pairs is of great help for historical term ***,the limited bilingual corpora resources of historical classics and special morphology of the ancient Chinese result in poor performance of term *** this end,this paper proposes a historical term alignment method using modern Chinese as a pivot *** method first identifies English terms by rules,then aligns them from English to modern Chinese and then from modern Chinese to ancient *** use of English-modern Chinese corpus and modern-ancient Chinese corpus instead of English-ancient Chinese corpus solves the shortage problem of the parallel ***,using modern Chinese as a pivot language effectively reduces the alignment errors caused by the abbreviations and the interchangeable characters of ancient *** the term alignment experiment on Shiji,our method outperformed the direct alignment method significantly,which proves the validity of our method.

关键词： Chinese Historical Classics Term Alignment Pivot Language

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Term Translation Extraction from Historical Classics Using Modern Chinese Explanation

Term Translation Extraction from Historical Classics Using M...

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第十七届全国计算语言学学术会议暨第六届基于自然标注大数据的自然语言处理国际学术研讨会(CCL 2018)

作者： Xiaoting Wu Hanyu Zhao Chao Che Dalian University Key Laboratory of Advanced Design and Intelligent ComputingMinistry of EducationDalian116622China

ISBN: (纸本)9783030017156

Extracting term translation pairs is of great help for Chinese histori-cal classics translation since term translation is the most time-consuming and challenging part in the translation of historical ***,it is tough to recognize the terms directly from ancient Chinese due to the flexible syntactic of ancient Chinese and the word segmentation errors of ancient Chinese will lead to more errors in term translation *** most of the terms in ancient Chinese are still reserved in modern Chinese and the terms in modern Chinese are more easily to be identified,we propose a term translation extract-ing method using multi-features based on character-based model to extract his-torical term translation pairs from modern Chinese-English corpora instead of ancient Chinese-English ***,we first employ character-based BiLSTM-CRF model to identify historical terms in modern Chinese without word segmentation,which avoids word segmentation error spreading to the term *** we extract English terms according to initial capitaliza-tion *** last,we align the English and Chinese terms based on co-occurrence frequency and transliteration *** experiment on Shiji demonstrates that the performance of the proposed method is far superior to the traditional method,which confirms the effectiveness of using modern Chinese as a substitute.

关键词： BiLSTM-CRF Co-occurrence frequency Transliteration features Term translation extraction

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Constructing DNA Barcode Sets Based on Particle Swarm Optimization

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IEEE/ACM transactions on computational biology and bioinformatics 2018年第3期15卷 999-1002页

作者： Bin Wang Xuedong Zheng Shihua Zhou Changjun Zhou Xiaopeng Wei Qiang Zhang Ziqi Wei the Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian China Department of Computing Science University of Alberta Edmonton AB Canada

Following the completion of the human genome project, a large amount of high-throughput bio-data was generated. To analyze these data, massively parallel sequencing, namely next-generation sequencing, was rapidly developed. DNA barcodes are used to identify the ownership between sequences and samples when they are attached at the beginning or end of sequencing reads. Constructing DNA barcode sets provides the candidate DNA barcodes for this application. To increase the accuracy of DNA barcode sets, a particle swarm optimization (PSO) algorithm has been modified and used to construct the DNA barcode sets in this paper. Compared with the extant results, some lower bounds of DNA barcode sets are improved. The results show that the proposed algorithm is effective in constructing DNA barcode sets.

关键词： Bioinformatics DNA Particle Swarm Optimisation Particle Swarm Optimization Next Generation Sequencing DNA Barcode Sets DNA Sequential Analysis Algorithm design And Analysis Next Generation Networking Particle Swarm Optimization Hamming Distance Filtering Algorithms DNA Barcodes Edit Distance Particle Swarm Optimization PSO Lower Bound

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Accurate RNA 3D structure prediction using a language model-based deep learning approach

arXiv

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arXiv 2022年

作者： Shen, Tao Hu, Zhihang Sun, Siqi Liu, Di Wong, Felix Wang, Jiuming Chen, Jiayang Wang, Yixuan Hong, Liang Xiao, Jin Zheng, Liangzhen Krishnamoorthi, Tejas King, Irwin Wang, Sheng Yin, Peng Collins, James J. Li, Yu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Zelixir Biotech Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA United States Department of Systems Biology Harvard Medical School BostonMA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States Synthetic Biology Center Massachusetts Institute of Technology CambridgeMA United States Integrated Biosciences Redwood CityCA United States OneAIM Ltd. Hong Kong Shanghai AI Laboratory Shanghai China The CUHK Shenzhen Research Institute Hi-Tech Park Shenzhen Nanshan China Shenzhen Institute of Advanced Technology Guangdong Shenzhen China School of Molecular Sciences Center for Molecular Design and Biomimetics the Biodesign Institute Arizona State University TempeAZ United States School of Computing and Augmented Intelligence Arizona State University TempeAZ United States

Accurate prediction of RNA three-dimensional (3D) structure remains an unsolved challenge. Determining RNA 3D structures is crucial for understanding their functions and informing RNA-targeting drug development and synthetic biology design. The structural flexibility of RNA, which leads to scarcity of experimentally determined data, complicates computational prediction efforts. Here, we present RhoFold+, an RNA language model-based deep learning method that accurately predicts 3D structures of single-chain RNAs from sequences. By integrating an RNA language model pre-trained on ∼23.7 million RNA sequences and leveraging techniques to address data scarcity, RhoFold+ offers a fully automated end-to-end pipeline for RNA 3D structure prediction. Retrospective evaluations on RNA-Puzzles and CASP15 natural RNA targets demonstrate RhoFold+’s superiority over existing methods, including human expert groups. Its efficacy and generalizability are further validated through cross-family and cross-type assessments, as well as time-censored benchmarks. Additionally, RhoFold+ predicts RNA secondary structures and inter-helical angles, providing empirically verifiable features that broaden its applicability to RNA structure and function studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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N-dimensional matrices operation based on DNA strand displacement

N-dimensional matrices operation based on DNA strand displac...

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International Workshop on advanced Computational Intelligence (IWACI)

作者： Liu Yang Changjun Zhou Hui Lv Qiang Zhang Ministry of Education Key Laboratory of Advanced Design and Intelligent Computing (Dalian University) Dalian China Dalian University of Technology Dalian China

In the field of biological computing, the DNA strand displacement technique performs well in computation and programming. DNA strand displacement can be used to simulate calculations. Models based on the characteristics of DNA strand displacement are established to simulate n-dimensional matrices operations in this paper. The input and output of these computational models are analog, which means that the values of the individual elements in the n-dimensional matrices can be expressed directly by the concentration of the input and output DNA strands. Simulation experiment is performed by Visual DSD, and their results with high accuracy are consistent to the results of matrices operations in higher algebra, which prove correctness and validity of the models. The proposed models show calculation ability of DNA strand displacement and can be applied to the field of intelligent computing.

关键词： Handheld computers Computational intelligence

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