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IEEE International Conference on Big Knowledge (ICBK)

作者： Xiang Guo Youquan Wang Kaiyuan Gao Jie Cao Haicheng Tao Chaoyue Chen School of Information Engineering Nanjing University of Finance and Economics Nanjing China Jiangsu Provincial Key Laboratory of E-Business Nanjing University of Finance and Economics Nanjing China Department of Mathematical Sciences Xi’an Jiaotong-Liverpool University Suzhou China College of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Neural-based text classification methods have attracted increasing attention in recent years. Unlike the standard text classification methods, neural-based text classification methods perform the representation operation and end-to-end learning on the text data. Many useful insights can be derived from neural based text classifiers as demonstrated by an ever-growing body of work focused on text mining. However, in the real-world, text can be both complex and noisy which can pose a problem for effective text classification. An effective way to deal with this issue is to incorporate self-attention and capsule networks into text mining solutions. In this paper, we propose a Bi-dynamic routing Capsule Network with Label-constraint (BCNL) model for text classification, which moves beyond the limitations of previous methods by automatically learning the task-relevant and label-relevant words of text. Specifically, we use a Bi-LSTM and self-attention with position encoder network to learn text embeddings. Meanwhile, we propose a bi-dynamic routing capsule network with label-constraint to adjust the category distribute of text capsules. Through extensive experiments on four datasets, we observe that our method outperforms state-of-the-art baseline methods.

关键词： Routing Semantics Feature extraction Heuristic algorithms Task analysis Finance Economics

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ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection 12

ECNU at SemEval-2018 Task 10: Evaluating Simple but Effectiv...

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12th International Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual Conference of the North American Chapter of the

作者： Zhou, Yunxiao Lan, Man Wu, Yuanbin Department of Computer Science and Technology East China Normal University Shanghai China Shanghai Key Laboratory of Multidimensional Information Processing China

ISBN: (纸本)9781948087209

This paper describes the system we submitted to Task 10 (Capturing Discriminative Attributes) in SemEval 2018. Given a triple (word1, word2, attribute), this task is to predict whether it exemplifies a semantic difference or not. We design and investigate several word embedding features, PMI features and WordNet features together with supervised machine learning methods to address this task. Officially released results show that our system ranks above average. © 2018 Association for Computational Linguistics

关键词： Supervised learning

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ECNU at SemEval-2018 Task 2: Leverage Traditional NLP Features and Neural Networks Methods to Address Twitter Emoji Prediction Task 12

ECNU at SemEval-2018 Task 2: Leverage Traditional NLP Featur...

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12th International Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual Conference of the North American Chapter of the

作者： Lu, Xingwu Mao, Xin Lan, Man Wu, Yuanbin Department of Computer Science and Technology East China Normal University Shanghai China Shanghai Key Laboratory of Multidimensional Information Processing China

ISBN: (纸本)9781948087209

This paper describes our submissions to Task 2 in SemEval 2018, i.e., Multilingual Emoji Prediction. We first investigate several traditional Natural Language processing (NLP) features, and then design several deep learning models. For subtask 1: Emoji Prediction in English, we combine two different methods to represent tweet, i.e., supervised model using traditional features and deep learning model. For subtask 2: Emoji Prediction in Spanish, we only use deep learning model. © 2018 Association for Computational Linguistics

关键词： Forecasting

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Are solution processed CsPbI3 perovskite nanocrystalline films similar to traditional crystalline semiconductors?

Research Square

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Research Square 2021年

作者： Kobbekaduwa, Kanishka Liu, Exian Zhao, Qian Adhikari, Pan Xie, Chendi Zhang, Jianbing Shi, Ying Zheng, Haimei Li, Dawen Sanabria, Hugo Hu, Ming Cai, Tong Chen, Ou Rao, Rahul Rao, Apparao M. Beard, Matthew C. Luther, Joseph M. Gao, Jianbo Department of Physics and Astronomy Ultrafast Photophysics of Quantum Devices Laboratory Clemson University ClemsonSC29634 United States Chemistry & Nanoscience Center National Renewable Energy Laboratory GoldenCO80401 United States School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China Institute of Atomic and Molecular Physics Jilin Provincial Key Laboratory of Applied Atomic and Molecular Spectroscopy Jilin University Changchun130012 China Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Electrical and Computer Engineering Center for Materials for Information Technology The University of Alabama TuscaloosaAL35487 United States Department of Mechanical Engineering University of South Carolina ColumbiaSC29208 United States Department of Chemistry Brown University ProvidenceRI02912 United States Materials and Manufacturing Directorate Air Force Research Laboratory Wright-Patterson Air Force Base DaytonOH45433 United States

We use ultrafast photocurrent spectroscopy with a sub-25 picosecond resolution to address a long-standing question in emergent perovskite materials: are solution-processed CsPbI3 perovskites nanocrystal films similar to their counterparts semiconductors including GaAs and Si? Prior to carrier trapping, all semiconductors show similar transition of transport mechanism from the band-like transport due to the longitudinal optical (LO) phonon scattering in the higher temperature region to a combination of band-like and hopping transport mechanisms in the lower temperature region. The critical difference is that CsPbI3 demonstrate the strongest carrier-LO phonon interaction among all semiconductors, most likely due to the unique cation-halogen bond arrangements within the highly symmetric lattice structure. This ultrafast dynamics work establish a foundation of fundamental carrier transport understating for perovskite optoelectronics. © 2021, CC BY.

关键词： Perovskite

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Remote Sensing Image Classification Based on Markov Random Field

Remote Sensing Image Classification Based on Markov Random F...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Tong Zhang Yin Zhuang Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China School of Electronic Engineering and Computer Science Peking University Beijing China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

With the continuous development of optical remote sensing technology, the segmentation of high resolution remote sensing images has become a hot research field. The high resolution and complex remote sensing images make the general natural image segmentation become a challenge task. Here, Markov random field (MRF) can well combine the spatial information of remote sensing image with the domain information between pixels for image segmentation, which is currently a hot research direction. In this paper, the wavelet and MRF are combined to achieve multi-scale analysis and produce more accurate segmentation results, the extensive experiments demonstrated that proposed method is more suitable for remote sensing image segmentation, and it provides a good boundary local mapping results and is robust with image non-stationary signal. In addition, in view of the disadvantage of fixed potential function parameters of traditional MRF, we put forward the method of variable weight. On this basis, Kullback-Leibler (KL) divergence is proposed to calculate the similarity between the segmented regions to further optimize the segmentation results.

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171MO Unraveling iCAF and tumor cell communication: Advancing novel adjuvant immunotherapy response and prognostic prediction via single-cell analysis and multimodal data-based models

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Immuno-Oncology and technology 2024年 24卷 100924-100924页

作者： Ren, W. Ouyang, W. Cai, G. Huang, Z. Lin, R. Wang, Z. Li, T. Zhang, Z. Peng, Q. Yu, Y. Yao, H. Oncology Department The Second Affiliated Hospital of Sun Yat-sen University Guangzhou China Department of Oncology The Second Affiliated Hospital/Sun Yat-sen Memorial Hospital of Sun Yat-sen University Guangzhou China Breast Cancer Sun Yat-sen Memorial Hospital Guangzhou China Oncology Sun Yat-sen Memorial Hospital Guangzhou China School of Computer and Information Engineering MUST - Macau University of Science &Technology Macao SAR China Science and Technology Department MUST - Macau University of Science &Technology Macao SAR China Oncology Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangzhou China Oncology The Second Affiliated Hospital of Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regu The Second Affiliated Hospital of Sun Yat-sen University Guangzhou China

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Saiqin Long Zhetao Li Zihao Liu Qingyong Deng Sangyoon Oh Nobuyoshi Komuro Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China Ajou University Suwon South Korea Graduate School of Advanced Integration Science Chiba University Chiba Japan

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.

关键词： Cloud computing Clustering algorithms Estimation Fingerprint recognition Throughput Partitioning algorithms Classification algorithms

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INVESTIGATION OF SINGING VOICE SEPARATION FOR SINGING VOICE DETECTION IN POLYPHONIC MUSIC

arXiv

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arXiv 2020年

作者： Sun, Yifu Zhang, Xulong Yu, Yi Chen, Xi Li, Wei School of Computer Science and Technology Fudan University Shanghai 200438 China Co. Ltd. Shenzhen518000 China Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo163-8001 Japan Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai 200433 China

Singing voice detection (SVD), to recognize vocal parts in the song, is an essential task in music information retrieval (MIR). The task remains challenging since singing voice varies and intertwines with the accompaniment music, especially for some complicated polyphonic music such as choral music recordings. To address this problem, we investigate singing voice detection while discarding the interference from the accompaniment. The proposed SVD has two steps: i. The singing voice separation (SVS) technique is first utilized to filter out the singing voice’s potential part coarsely. ii. Upon the continuity of vocal in the time domain, Long-term Recurrent Convolutional Networks (LRCN) is used to learn compositional features. Moreover, to eliminate the outliers, we choose to use a median filter for time-domain smoothing. Experimental results show that the proposed method outperforms the existing state-of-the-art works on two public datasets, the Jamendo Corpus and the RWC pop dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Music

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An automatic biomedical ontology meta-matching technique

Journal of Network Intelligence

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Journal of Network Intelligence 2019年第3期4卷 109-113页

作者： Xue, Xingsi Yang, Haiyan Zhang, Jie Zhang, Jing Chen, Dongxu College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China College of Information Science and Engineering Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou CityFujian Province350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China School of Computing Ulster University Jordanstown BT370QB United Kingdom Fujian Medical University Union Hospital No.29 Xinquan Road Gulou Fuzhou CityFujian Province350001 China

Biomedical ontology matching aims at determining the heterogeneous biomed-ical concepts, and bridging the semantic gap between heterogeneous biomedical ontologies. The foundation of a biomedical ontology matching technique is the Biomedical Concept Similarity Measure (BCSM), which calculates the similarity value between two biomed-ical concepts. Since various BCSMs have different advantages, usually several BCSMs are aggregated together to improve the result’s confidence. How to tune the aggregating weights to ensure the quality of the alignment is called biomedical ontology meta-matching problem, which is a challenge in the ontology matching domain. Currently, researchers mainly focus on how to tune the aggregating weights for various similarity measures to improve the quality of the ontology alignments. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on different similarity measures significantly reduces the alignment’s quality. To overcome this drawback, in this work, we formally define the biomedical ontology meta-matching problem, and then present an Ordered Weighted Average (OWA) based approach to automatically aggregate various biomedical concept similarity measures. In our method, the aggregating weights determined for each concept mapping is associated with the ordered position of the similarity value instead of a particular concept similarity measure. The experiment utilizes the large biomed track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the experimental results show the effectiveness of our proposal. © 2019, Taiwan Ubiquitous information. All rights reserved.

关键词： Semantics

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Improved VGG Model for Road Traffic Sign Recognition

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computers, Materials & Continua 2018年第10期57卷 11-24页

作者： Shuren Zhou Wenlong Liang Junguo Li Jeong-Uk Kim Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science&TechnologyChangsha410114China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Department of Energy Grid Sangmyung UniversitySeoul110743Korea

Road traffic sign recognition is an important task in intelligent transportation *** neural networks(CNNs)have achieved a breakthrough in computer vision tasks and made great success in traffic sign *** this paper,it presents a road traffic sign recognition algorithm based on a convolutional neural *** natural scenes,traffic signs are disturbed by factors such as illumination,occlusion,missing and deformation,and the accuracy of recognition decreases,this paper proposes a model called Improved VGG(IVGG)inspired by VGG *** IVGG model includes 9 layers,compared with the original VGG model,it is added max-pooling operation and dropout operation after multiple convolutional layers,to catch the main features and save the training *** paper proposes the method which adds dropout and Batch Normalization(BN)operations after each fully-connected layer,to further accelerate the model convergence,and then it can get better classification *** uses the German Traffic Sign Recognition Benchmark(GTSRB)dataset in the *** IVGG model enhances the recognition rate of traffic signs and robustness by using the data augmentation and transfer learning,and the spent time is also reduced greatly.

关键词： Intelligent transportation traffic sign deep learning GTSRB data augmentation

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