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International Conference on Pattern Recognition

作者： Xunzhu Tang Rujie Zhu Tiezhu Sun Shi Wang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China University of Central Florida Orlando FL USA Momenta Suzhou China Institute of Computing Technology Chinese Academy beijing China

Recently, language representation techniques have achieved great performances in text classification. However, most existing representation models are specifically designed for English materials, which may fail in Chinese because of the huge difference between these two languages. Actually, few existing methods for Chinese text classification process texts at a single level. However, as a special kind of hieroglyphics, radicals of Chinese characters are good semantic carriers. In addition, Pinyin codes carry the semantic of tones, and Wubi reflects the stroke structure information, etc. Unfortunately, previous researches neglected to find an effective way to distill the useful parts of these four factors and to fuse them. In our works, we propose a novel model called Moto: Enhancing Embedding with Multiple Joint Factors. Specifically, we design an attention mechanism to distill the useful parts by fusing the four-level information above more effectively. We conduct extensive experiments on four popular tasks. The empirical results show that our Moto achieves SOTA 0.8316 (F₁-score, 2.11% improvement) on Chinese news titles, 96.38 (1.24% improvement) on Fudan Corpus and 0.9633 (3.26% improvement) on THUCNews.

关键词： Fuses Text categorization Semantics Pattern recognition Task analysis

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Multi-task Learning for Multi-turn Dialogue Generation with Topic Drift Modeling

Multi-task Learning for Multi-turn Dialogue Generation with ...

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IEEE International Conference on big Knowledge (ICBK)

作者： Hongwei Zeng Zhenjie Hong Jun Liu Bifan Wei School of Computer Science and Technology Xi'an Jiaotong University Xi'an China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Xi'an China School of Continuing Education Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9781665438599

Multi-turn dialogue generation aims to generate natural and fluent responses that should be consistent with multiple consecutive utterances history. It is a more challenging task compared to its single-turn counterpart since it requires the model to capture the topic drift along with the multi-turn dialogue history. In this paper, we propose a multi-turn dialogue generation model which incorporates topic drift aware information into a hierarchical encoder-decoder framework to generate coherent responses. This model first utilizes a Convolutional Neural Network (CNN) based topic model to obtain the topic representation of each utterance. Then a topic drift model is employed to encode the sequential topics of multi-turn dialogue history to infer the topic of response. During the response generation, a specially designed topic drift aware generator is proposed to dynamically balance the impact of the inferred topic of response and local word structure. Fur-thermore, we employ multi-task learning to optimize the topic drift model and dialogue generation simultaneously. Extensive experimental results on two benchmark datasets (i.e. Cornell Movie Dialog Corpus and Ubuntu Dialogue dataset) indicate that our proposed model can generate more coherent responses, and significantly outperform other dialogue generation models.

关键词： Conferences Coherence Logic gates Multitasking Motion pictures Generators History

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An Implementation of Face Recognition System with Face Presentation Attack Detection on Raspberry Pi

An Implementation of Face Recognition System with Face Prese...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Wenjun Wang Xiangkang Huang Xin Shu Shuping Li Huichou Huang Qingyao Wu School of Software Engineering South China University of Technology Guangzhou China School of Data Science and Information Engineering Guizhou Minzu University Guiyang China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China City University of Hong Kong Hong Kong China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665444194

With the popularity of unlocking and payment of using the face-swiping function, the era of face recognition has arrived. However, the current face recognition technology can identify the identity of the face image but cannot accurately distinguish the authenticity of the input face on embedded devices. Recently, face recognition with face presentation attack detection has become a research hotspot. In this paper, we develop an intelligent face recognition system on Raspberry Pi to handle this issue. Specifically, we first add a face presentation attack detection process to form a three-stage face recognition method to realize face recognition with face anti-spoofing. Second, we select a face detection model, a face presentation attack detection model and a face recognition model suitable for Raspberry Pi, and transplant these models previously trained in the Python environment to the C++ environment. Then we integrate and deploy these three models on Raspberry Pi and use the NCNN inference framework for efficient reasoning within limited memory and computing resources. Finally, we implement the face recognition system and conduct real-world tests to verify the performance. The experimental results demonstrate that our system can be applied in practice, and can achieve good effects.

关键词： Face recognition Computational modeling Conferences Memory management C++ languages Cognition Organ transplantation

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Exploring A CAM - Based Approach for Weakly Supervised Fire Detection Task

Exploring A CAM - Based Approach for Weakly Supervised Fire ...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Lvlong Lai Jian Chen Huichou Huang Qingyao Wu School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education City University of Hong Kong Hong Kong China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665444194

Most existing works in fire detection literature use available detectors like Faster RCNN, SSD, YOLO, etc. to localize the fire in images. These approaches work well but require object-level annotation for training, which is created manually and is very expensive. In this paper, we explore the weakly supervised fire detection task (WSFD) in which only the image-level annotation is given. We propose an approach based on class activation map (CAM). The CAM-based approach firstly trains a deep neural network as the classifier for identifying fire and non-fire images. For a fire image in the inference stage, it uses the classifier to create a CAM and then further generates the bounding boxes according to the CAM. To evaluate the effectiveness of our approach, we collect and construct a benchmark dataset named WS-FireNet and conduct comprehensive experiments on it. The experiment results show that in a way the performance of our approach is satisfactory.

关键词： Training Deep learning Annotations IEEE Sections Conferences Neural networks Detectors

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Research on Practical Wireless Sensor Network Query Processing technology

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Journal of Physics: Conference Series 2021年第1期1732卷

作者： Changjian Guo Xiaoxuan Wu School of Artificial Intelligence and Big Data Hefei University Hefei China Anhui Engineering Lab of Big Data Technology Application for Urban Infrastructure Hefei University Hefei China

WSN(Wireless Sensor Network) is intended for obtaining sensory data so that the query processing technology of high efficiency is the precondition of widely used WSN. Existing query processing technology assumes that the WSN deployed in two-dimensional or three-dimensional free space are not suitable for those deployed in such limited circumstances as roads, pipelines and indoor environments. This paper explores the problems of existing query processing technology, does research on perception data query processing method and technology, and seeks out the existing problem and its solution by means of using the WSN actually deployed in a building as an experimental platform.

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Deep Fusion of Shifted MLP and CNN for Medical Image Segmentation

Deep Fusion of Shifted MLP and CNN for Medical Image Segment...

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

作者： Chengyu Yuan Hao Xiong Guoqing Shangguan Hualei Shen Dong Liu Haojie Zhang Zhonghua Liu Kun Qian Bin Hu Björn W. Schuller Yoshiharu Yamamoto Shlomo Berkovsky College of Computer and Information Engineering Henan Normal University China Key Laboratory of Artificial Intelligence and Personalized Learning in Education Henan China Big Data Engineering Lab of Teaching Resources & Assessment of Education Quality Henan China Centre for Health Informatics Australian Institute of Health Innovation Macquarie University Australia Key Laboratory of Brain Health Intelligent Evaluation and Intervention (BIT) Ministry of Education China School of Medical Technology Beijing Institute of Technology China School of Information Engineering Zhejiang Ocean University China GLAM – Group on Language Audio & Music Imperial College London U.K. Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany Educational Physiology Laboratory Graduate School of Education The University of Tokyo Japan

Medical image segmentation is an important task in modern analysis of medical images. Current methods tend to extract either local features with convolutions or global features with Transformers. However, few of them are able to effectively fuse global and local features to facilitate segmentation. In this work, we propose a novel hybrid network that involves three main branches: the Multi-Layer Perception (MLP) branch, the Convolutional Neural Network (CNN) branch, and a Fusion branch. The MLP and CNN branches aim to learn global and local features, respectively. To fuse these, the fusion branch introduces a novel hierarchical fusion that performs multi-layered fusions that generate high-level representations to enhance segmentation. Our evaluation with two datasets shows strong performance of the proposed method compared to state-of-the-art baselines.

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Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering 15th

Discovering Spatio-Temporal Patterns in Precision Agricultur...

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15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020

作者： Melgar-García, Laura Godinho, Maria Teresa Espada, Rita Gutiérrez-Avilés, David Brito, Isabel Sofia Martínez-Álvarez, Francisco Troncoso, Alicia Rubio-Escudero, Cristina Data Science & Big Data Lab Pablo de Olavide University Seville41013 Spain Department of Mathematical and Physical Sciences Polytechnic Institute of Beja Beja Portugal Center for Mathematics Fundamental Applications and Operations Research University of Lisboa Lisbon Portugal Associação dos Agricultores do Baixo Alentejo Beja Portugal Department of Engineering Polytechnic Institute of Beja Beja Portugal Instituto de Desenvolvimiento de Novas Tecnologias - Centre of Technology and Systems Lisbon Portugal Department of Computer Languages and Systems University of Seville Seville Spain

ISBN: (纸本)9783030578015

Agriculture has undergone some very important changes over the last few decades. The emergence and evolution of precision agriculture has allowed to move from the uniform site management to the site-specific management, with both economic and environmental advantages. However, to be implemented effectively, site-specific management requires within-field spatial variability to be well-known and characterized. In this paper, an algorithm that delineates within-field management zones in a maize plantation is introduced. The algorithm, based on triclustering, mines clusters from temporal remote sensing data. data from maize crops in Alentejo, Portugal, have been used to assess the suitability of applying triclustering to discover patterns over time, that may eventually help farmers to improve their harvests. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Precision agriculture

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Low-Temperature Growing Anatase TiO2/SnO2 Multi-dimensional Heterojunctions at MXene Conductive Network for High-Efficient Perovskite Solar Cells

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Nano-Micro Letters 2020年第3期12卷 199-217页

作者： Linsheng Huang Xiaowen Zhou Rui Xue Pengfei Xu Siliang Wang Chao Xu Wei Zeng Yi Xiong Hongqian Sang Dong Liang National Engineering Research Center for Agro‑Ecological Big Data Analysis and Application School of Electronics and Information EngineeringAnhui UniversityNo.111 Jiulong RoadHefei 230601People’s Republic of China Science and Technology Institute Hubei Key Laboratory of Biomass Fibers and Eco‑Dyeing and FinishingWuhan Textile UniversityWuhan 430073People’s Republic of China School of Physics and Technology MOE Key Laboratory of Artificial Micro‑and Nano‑Structures and Center for Electron MicroscopyWuhan UniversityWuhan 430072People’s Republic of China Department of Physics King’s College LondonThe StrandLondon WC2R 2LSUK

A multi-dimensional conductive heterojunction structure,composited by TiO2,SnO2,and Ti3C2TX MXene,is facilely designed and applied as electron transport layer in efficient and stable planar perovskite solar *** on an oxygen vacancy scramble effect,the zero-dimensional anatase TiO2 quantum dots,surrounding on two-dimensional conductive Ti3C2TX sheets,are in situ rooted on three-dimensional SnO2 nanoparticles,constructing nanoscale TiO2/SnO2 *** fabrication is implemented in a controlled lowtemperature anneal method in air and then in N2 *** the optimal MXene content,the optical property,the crystallinity of perovskite layer,and internal interfaces are all facilitated,contributing more amount of carrier with effective and rapid transferring in *** champion power conversion efficiency of resultant perovskite solar cells achieves 19.14%,yet that of counterpart is just 16.83%.In addition,it can also maintain almost 85%of its initial performance for more than 45 days in 30–40%humidity air;comparatively,the counterpart declines to just below 75%of its initial performance.

关键词： In situ fabrication Multi-dimensional heterojunction Oxygen vacancy scramble effect Electron transport layer Perovskite solar cells

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Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approach

arXiv

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arXiv 2021年

作者： Wang, Xiong Ye, Jiancheng Lui, John C.S. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Network Technology Lab Hong Kong Research Center Huawei Technologies Co. Ltd. Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Mobile edge computing facilitates users to offload computation tasks to edge servers for meeting their stringent delay requirements. Previous works mainly explore task offloading when system-side information is given (e.g., server processing speed, cellular data rate), or centralized offloading under system uncertainty. But both generally fall short to handle task placement involving many coexisting users in a dynamic and uncertain environment. In this paper, we develop a multi-user offloading framework considering unknown yet stochastic system-side information to enable a decentralized user-initiated service placement. Specifically, we formulate the dynamic task placement as an online multi-user multi-armed bandit process, and propose a decentralized epoch based offloading (DEBO) to optimize user rewards which are subjected under network delay. We show that DEBO can deduce the optimal user-server assignment, thereby achieving a close-to-optimal service performance and tight O(log T) offloading regret. Moreover, we generalize DEBO to various common scenarios such as unknown reward gap, dynamic entering or leaving of clients, and fair reward distribution, while further exploring when users' offloaded tasks require heterogeneous computing resources. Particularly, we accomplish a sub-linear regret for each of these instances. Real measurements based evaluations corroborate the superiority of our offloading schemes over state-of-the-art approaches in optimizing delay-sensitive rewards. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

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Internal search of the evolution matrix in QUasi-Affine TRansformation Evolution (QUATRE) algorithm

Internal search of the evolution matrix in QUasi-Affine TRan...

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作者： Chu, Shu-Chuan Chen, Yuxin Meng, Fanjia Yang, Chen Pan, Jeng-Shyang Meng, Zhenyu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Institute of Artificial Intelligence Fujian University of Technology Fuzhou China Guanzhuang Central Primary School of Zhangqiu District Jinan China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China

Optimization demands exist everywhere in the real world especially in science studies and engineering practices, and it is important that the method to deal with intricacy optimization problems should itself be relative simple. Particle Swarm Optimization (PSO) and Differential Evolution (DE) both are simple evolutionary algorithms (EAs) which are proposed for single-objective optimization and both of them have been proved to be efficient methods for optimizing applications, however, there are still some weakness existing within them. A innovative evolutionary method named QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm which is derived from, also tackles some weaknesses of PSO and DE algorithm, and obtains better performance on commonly used test suites. The key characteristic of QUATRE is that an automatically generated matrix named evolution matrix M is implemented in evolutionary process, which is taken as an alternative of employing the crossover rate CR. Here in this paper, we present a novel QUATRE variant, named IS-QUATRE, which can explore the search area in a better way comparison with the previous method, and relatively good optimization ability can be obtained by our proposed IS-QUATRE algorithm under CEC2013 test suit. And the conducted experimental results validate that our proposed IS-QUATRE is competitive with some other famous PSO and DE variants. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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