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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hao Fei Yafeng Ren Donghong Ji Ministry of Education Key Laboratory of Aerospace Information Security and Trusted Computing School of Cyber Science and Engineering Wuhan University Wuhan China Guangdong Collaborative Innovation Center for Language Research and Services Guangdong University of Foreign Studies Guangzhou China

Many named entities usually contain nested entities in biomedical texts. Nested entities pose challenge to the task of named entity recognition. Traditional methods try to solve the problem as a graph-structure prediction problem. However, these methods fail to sufficiently capture the boundaries information between nested entities, which limits the performance of the task. In this paper, we take a different view by solving each unique entity type as a separate task, using multi-task learning with dispatched attention to facilitate information exchange between tasks. Results on GENIA corpus show that the proposed method is highly effective, obtaining the best results in the literature.

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Student recognition of interference and diffraction patterns: An eye-tracking study

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Physical Review Physics Education Research 2020年第2期16卷 020133-020133页

作者： Ana Susac Maja Planinic Andreja Bubic Lana Ivanjek Marijan Palmovic Department of Applied Physics Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 10000 Zagreb Croatia Department of Physics Faculty of Science University of Zagreb Bijenicka 32 10000 Zagreb Croatia Chair for Psychology Faculty of Humanities and Social Sciences University of Split Sinjska 2 21000 Split Croatia Faculty of Physics University of Vienna Porzellangasse 4 1090 Vienna Austria Laboratory for Psycholinguistic Research Department of Speech and Language Pathology University of Zagreb Borongajska cesta 83h 10000 Zagreb Croatia

Previous studies have demonstrated that students have difficulties in applying the wave model of light to explain single-slit diffraction and double-slit interference patterns. In this study, we investigated if students could recognize typical interference and diffraction patterns at all. Eye movements of high-school students were measured while they were identifying patterns produced by monochromatic light on a double slit, single slit, and diffraction grating, and by white light on a diffraction grating. Most students had difficulties with recognizing double-slit interference pattern and diffraction grating pattern of monochromatic light. Identification of the single-slit diffraction pattern was easier probably due to its distinguishable central maximum. The easiest task for students was recognizing the diffraction pattern of white light on an optical grating. Eye-tracking data suggested that even students who incorrectly answered this question were aware that the diffraction grating separates white light into colors. Additionally, eye tracking revealed that students who identified patterns correctly attended more the correct pattern than other options, thus corroborating previous findings. Overall, the results indicate that the recognition of interference and diffraction patterns is quite demanding for students, suggesting that more attention should be paid to observing and understanding basic wave optics phenomena.

关键词： Concepts & principles Scientific reasoning & problem solving

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Stacked auto encoder based Deep Reinforcement Learning for online resource scheduling in large-scale MEC networks

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Speech-to-Noise Ratio and Voice-to-Noise Ratio of Voice Databases With Implications for Acoustic Voice Analysis

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Journal of voice : official journal of the Voice Foundation 2025年

作者： Duy Duong Nguyen Rijul Gupta Dhanshree R Gunjawate John Holik Craig Jin Catherine Madill Sydney Voice Lab Discipline of Speech Pathology Sydney School of Health Sciences Faculty of Medicine and Health University of Sydney Camperdown New South Wales Australia National Hospital of Otorhinolaryngology Hanoi Vietnam. Electronic address: duong.nguyen@sydney.edu.au. Computing and Audio Research Laboratory School of Electrical and Information Engineering The University of Sydney Camperdown New South Wales Australia. Department of Audiology and Speech-Language Pathology Kasturba Medical College Mangalore Manipal Academy of Higher Education Manipal India. Sydney Voice Lab Discipline of Speech Pathology Sydney School of Health Sciences Faculty of Medicine and Health University of Sydney Camperdown New South Wales Australia.

OBJECTIVES:This study aimed to examine the speech-to-noise ratio (SNR) and voice-to-noise ratio (VNR) in five freely available databases and one university voice database to clarify whether they meet the signal quality requirements for use in acoustic voice analysis. METHODS:This was a retrospective study that extracted the prolonged vowel /ɑ/, short phrases, and connected speech in 977 nondisordered and voice-disordered speakers from 6 preexisting voice and speech databases, including Advanced Voice Function Assessment Databases (AVFAD), Massachusetts Eye and Ear Infirmary (MEEI), Perceptual Voice Qualities Database (PVQD), Saarbruecken Voice Database (SVD), Uncommon Voice, and University of Sydney Voice Assessment Clinic (USVAC). These vocal tasks were extracted from randomly selected study identitys and were used to measure SNR and VNR using a Praat script. SNR and VNR were described using descriptive statistics and compared across databases using multivariate analysis of variance. Vocal task effects and group effects within each database were calculated. RESULTS:There was great variability in signal quality across databases with wide ranges of SNR and VNR from low to high values. Except the Uncommon Voice database, in databases with measurable SNR and VNR from available tasks, there were statistically significant effects of tasks and groups on both SNR and VNR in AVFAD, USVAC, and PVQD. Overall, the vowel /ɑ/ and phrases had higher SNR and VNR than connected speech. In databases with single measurable task, group effects were significant for MEEI and not significant for SVD. CONCLUSIONS:These databases provided voice samples with variable signal quality, ranging from low to high levels compared with recommended values. The decreased values of SNR and VNR in connected speech tasks in these databases suggested the effects of articulation factors on these two measures. The differences in the two measures between nondisordered and disordered groups in some of the da

关键词： Acoustic voice analysis—Voice database—Signal-to-noise ratio—Voice-to-noise ratio— Acoustic signals—Signal quality

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Dynamic maintenance of decision rules for decision attribute values’ changing 25th

Dynamic maintenance of decision rules for decision attribute...

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25th International Conference on Neural Information Processing, ICONIP 2018

作者： Wang, Yingyao Dai, Jianhua Shi, Hong School of Computer Science and Technology Tianjin University Tianjin300350 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China

ISBN: (纸本)9783030041816

Rule induction method based on rough set theory (RST) which can generate a minimal set of decision rules by using attribute reduction and approximations has received much attention recently. In real-life, the variation of objects, attributes and attributes’ values affects reducts and approximations, e.g., the coarsening and refining of attributes’ values. The goal of this paper is dynamic maintenance of decision rules for decision attribute values’ coarsening and refining. Two incremental rough-set based methods are proposed to deal with this issue by updating assignment discernibility matrix dynamically without recomputing the reducts from the beginning, which increases the efficiency. © Springer Nature Switzerland AG 2018.

关键词： Rough set theory

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Label distribution learning based on ensemble neural networks 25th

Label distribution learning based on ensemble neural network...

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25th International Conference on Neural Information Processing, ICONIP 2018

作者： Zhai, Yansheng Dai, Jianhua Shi, Hong School of Computer Science and Technology Tianjin University Tianjin300350 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China

ISBN: (纸本)9783030041816

Label distribution learning (LDL), as an extension of multi-label learning, is a new arising machine learning technique to deal with label ambiguity problems. The maximum entropy model is commonly used in label distribution learning. However, it does not consider the correlation between the labels and is not suitable for nonlinear relationships, and the prediction performance is also limited. In this paper, we propose a label distribution learning algorithm based on ensemble neural networks. The algorithm trains neural networks with preferences using training sets with different label sets to construct base learners, and combines the base learners with the weights, which is learned by the combined learner to obtain the final learning results. Experimental results show that the proposed algorithm is effective for label distribution data. © Springer Nature Switzerland AG 2018.

关键词： Learning algorithms

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Ai driven heterogeneous MEC system for dynamic environment - Challenges and solutions

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

—By taking full advantage of computing, Communication and Caching (3C) resources at the network edge, Mobile Edge computing (MEC) is envisioned as one of the key enablers for the next generation network and services. However, current fixed-location MEC architecture may not be able to make real-time decision in dynamic environment, especially in large-scale scenarios. To address this issue, in this paper, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, e.g., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions are discussed in this paper. In addition, an AI-based joint Resource schEduling (ARE) framework is proposed, with simulation results shown to verify the efficiency of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Systematic Resource Allocation in Cloud RAN with Caching as a Service under Two Timescales

Systematic Resource Allocation in Cloud RAN with Caching as ...

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作者： Tang, Jianhua Quek, Tony Q. S. Chang, Tsung-Hui Shim, Byonghyo Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China Institute of New Media and Communications Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CCC) together. In this paper, we dissect the interactions of CCC in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CCC. © 1972-2012 IEEE.

关键词： Integer programming

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Coherence-based automated essay scoring using self-attention 17th

Coherence-based automated essay scoring using self-attention

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17th China National Conference on Computational Linguistics, CCL 2018 and 6th International Symposium on Natural language Processing Based on Naturally Annotated Big Data, NLP-NABD 2018

作者： Li, Xia Chen, Minping Nie, Jianyun Liu, Zhenxing Feng, Ziheng Cai, Yingdan Key Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China School of Information Science and Technology/School of Cyber Security Guangdong University of Foreign Studies Guangzhou China Department of Computer Science and Operations Research University of Montreal Montreal Canada

ISBN: (纸本)9783030017156

Automated essay scoring aims to score an essay automatically without any human assistance. Traditional methods heavily rely on manual feature engineering, making it expensive to extract the features. Some recent studies used neural-network-based scoring models to avoid feature engineering. Most of them used CNN or RNN to learn the representation of the essay. Although these models can cope with relationships between words within a short distance, they are limited in capturing long-distance relationships across sentences. In particular, it is difficult to assess the coherence of the essay, which is an essential criterion in essay scoring. In this paper, we use self-attention to capture useful long-distance relationships between words so as to estimate a coherence score. We tested our model on two datasets (ASAP and a new non-native speaker dataset). In both cases, our model outperforms the existing state-of-the-art models. © Springer Nature Switzerland AG 2018.

关键词： Automation

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SOURCE-AWARE CONTEXT NETWORK FOR SINGLE-CHANNEL MULTI-SPEAKER SPEECH SEPARATION

SOURCE-AWARE CONTEXT NETWORK FOR SINGLE-CHANNEL MULTI-SPEAKE...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zeng-Xi Li Yan Song Li-Rong Dai Ian McLoughlin National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei China School of Computing University of Kent Medway UK

ISBN: (纸本)9781538646595

Deep learning based approaches have achieved promising performance in speaker-dependent single-channel multi-speaker speech separation. However, partly due to the label permutation problem, they may encounter difficulties in speaker-independent conditions. Recent methods address this problem by some assignment operations. Different from them, we propose a novel source-aware context network, which explicitly inputs speech sources as well as mixture signal. By exploiting the temporal dependency and continuity of the same source signal, the permutation order of outputs can be easily determined without any additional post-processing. Furthermore, a Multi-time-step Prediction Training strategy is proposed to address the mismatch between training and inference stages. Experimental results on benchmark WSJ0-2mix dataset revealed that our network achieved comparable or better results than state-of-the-art methods in both closed-set and open-set conditions, in terms of Signal-to-Distortion Ratio (SDR) improvement.

关键词： Speech Separation Deep Learning Label Permutation Problem Speech Network Permutation signal-to-distortion ratio speaker-dependent

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