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8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019

作者： Lin, Xiang Liao, Xiangwen Xu, Tong Pian, Wenjing Wong, Kam-Fai College of Mathematics and Computer Science Fuzhou University Fuzhou China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Hefei China The Chinese University of Hong Kong Sha Tin Hong Kong

ISBN: (纸本)9783030322359

Automatic rumor detection for events on online social media has attracted considerable attention in recent years. Usually, the events on social media are divided into several time segments, and for each segment, corresponding text will be converted as vectors for various neural network models to detect rumors. During this process, however, only sentence-level embedding has been considered, while the contextual information at the word level has been largely ignored. To address that issue, in this paper, we propose a novel rumor detection method based on a hierarchical recurrent convolutional neural network, which integrates contextual information for rumor detection. Specifically, with dividing events on social media into time segments, recurrent convolution neural network is adapted to learn the contextual representation information. Along this line, a bidirectional GRU network with attention mechanism is integrated to learn the time period information via combining event feature vectors. Experiments on real-world data sets validate that our solution could outperform several state-of-the-art methods. © 2019, Springer Nature Switzerland AG.

关键词： Convolution

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Homogeneous and Heterogeneous Graph Learning for Hybrid Beamforming in mmWave Systems

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IEEE Transactions on Wireless Communications 2025年

作者： Li, Yuhang Lu, Yang Zhang, Guangyang Ai, Bo Niyato, Dusit Cui, Shuguang State Key Laboratory of Advanced Rail Autonomous Operation China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Shenzhen518066 China

Hybrid analog and digital beamforming (HBF) is a cost-efficient technique to achieve high data rates in millimeterwave (mmWave) communication systems. This paper applies the emerging graph neural networks (GNNs) to HBF by leveraging the topological information in wireless networks for better adaptation to dynamic environments. To address the issue of limited feature extraction capability of the existing single-type GNNs, such as node-GNN or edge-GNN, we model the mmWave communication systems as both homogeneous and heterogeneous graphs, and separate the HBF design into node- and edge-level subtasks. Then, the two graphs are presented by two novel models based on homogeneous graph attention network (GAT) and heterogeneous GAT (HGAT), respectively, and mapped to the desired power allocation, radio frequency precoder and baseband precoder. Both the proposed GAT and HGAT are generalizable in the user variation scenarios, while the HGAT is also generalizable in antenna variation scenarios through the incorporation of a complex embedding layer. Furthermore, we introduce a constraint adaptive layer in the GAT and HGAT to ensure feasible outputs. Extensive numerical results based on the public dataset DeepMIMO are provided to evaluate the GAT and HGAT. The proposed approaches generally outperform existing baselines in terms of adaptability to system settings and generalizability to (unseen) problem parameters/sizes, while the HGAT can even achieve faster and better inference than traditional optimization approaches. © 2002-2012 IEEE.

关键词： Network theory (graphs)

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Volatile organic compounds exposure, biological ageing, and increased sarcopenia risk: Association, interaction, and mechanistic exploration

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Journal of Hazardous Materials 2025年 495卷 138929页

作者： Le Hong Qing Liu Wei Liu Jiahao Song Shuhui Wan Zhiying Huo Yongfang Zhang Da Shi Ruyi Liang Bingdong Chen Yani Xiong Feifei Wang Pengfei Chen Weihong Chen Bin Wang Department of Occupational and Environmental Health School of Public Health Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei 430030 China Key Laboratory of Environment and Health Ministry of Education & Ministry of Environmental Protection and State Key Laboratory of Environmental Health (Incubating) School of Public Health Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei 430030 China Medical Big Data and Bioinformatics Research Centre First Affiliated Hospital of Gannan Medical University Ganzhou 341000 China School of Public Health and Health Management Gannan Medical University Ganzhou Jiangxi 341000 China Faculty of Agricultural Life and Environmental Sciences University of Alberta Edmonton Alberta T6G 2P5 Canada

Evidence on the impacts of volatile organic compounds (VOCs), a class of ubiquitous environmental toxics, on sarcopenia, a major age-related global health issue, remains scarce. data from 3007 adults in the National Health and Nutrition Examination Survey (2011–2018) were analyzed to assess the individual and mixture effects of 16 VOCs metabolites (VOCMs) on sarcopenia using multivariate logistic regression, weighted quantile sum (WQS), and Bayesian kernel machine regression (BKMR) models. Network pharmacology was employed to explore potential underlying mechanisms. The phenotypic age (PhenoAge), a biomarker of biological ageing, was used to evaluate its interaction with VOCMs. Several VOCMs, including 2-aminothiazoline-4-carboxylic acid (ATCA; odds ratio: 1.30, 95 % confidence interval: 1.07–1.57), N-acetyl-S-(2-carboxyethyl)-l-cysteine (CEMA; 1.40, 1.01–1.94), N-acetyl-S-(3,4-dihydroxybutyl)-l-cysteine (DHBMA; 2.35, 1.45–3.82), and 3-methylhippuric acid & 4-methylhippuric acid (3,4MHA; 1.32, 1.05–1.64), were associated with an increased risk of sarcopenia. The mixture of 16 VOCMs was linked to a higher sarcopenia risk in both BKMR and WQS (1.79, 1.30–2.49) models. The tumor necrosis factor (TNF) signaling pathway was identified as a key mechanism underlying the VOCs-sarcopenia relationship. PhenoAge was associated with an elevated sarcopenia risk (1.05, 1.01–1.09) and intensified the adverse effects of VOCMs ( P interaction <0.05). This study is the first to demonstrate that VOCs exposure, in interaction with biological ageing, may elevate sarcopenia risk, potentially through the TNF signaling pathway, offering novel insights into sarcopenia prevention in the context of VOCs exposure.

关键词： Environmental health Mixture effect Network pharmacology Phenotypic age TNF signaling pathway

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Discovering attractive segments in the user generated video streams 3rd

Discovering attractive segments in the user generated video ...

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3rd APWeb and WAIM Joint Conference on Web and big data, APWeb-WAIM 2019

作者： Zhou, Jie Ai, Jiangbo Wang, Zheng Chen, Si Wei, Qin Guizhou Provincial Key Laboratory of Public Big Data GuiZhou University GuiyangGuizhou55002 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Information Science Academy China Electronics Technology Group Corporation Chengdu China

ISBN: (纸本)9783030260743

With the rapid development of digital equipment and the continuous upgrading of online media, a growing number of people are willing to post videos on the web to share their daily lives [1, 2]. Generally, not all video segments are popular with audiences, some of which may be boring. In recent years, crowd-sourced time-sync video comments have emerged worldwide, supporting further research on temporal video labelling. In this paper, we propose a novel framework to achieve the following goal: Predicting which segment in a newly generated video stream will be popular among the audiences. At last, experimental results on real-world data demonstrate the effectiveness of the proposed framework and justify the idea of predicting the popularities of segments in a video exploiting crowd-sourced time-sync comments as a bridge to analyse videos. © 2019, Springer Nature Switzerland AG.

关键词： Forecasting

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Automatic Generation Method of Ancient Poetry Based on LSTM

Automatic Generation Method of Ancient Poetry Based on LSTM

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Hanshuang Zhang Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

This paper mainly focuses on the literary genre of ancient poetry with a certain rhythm and cadence, and proposes a novel automatic generation model of ancient poetry. The model uses about 300,000 Tang poems and Song poems as training data, uses One-hot encoding to process data, and uses long short-term memory networks (LSTMs) to learn the semantics of ancient poetry texts and conduct research across a single RNN structure. According to the user's requirements, the model can automatically calculate the most relevant coherent words in the context of the selected context, and generate common ancient poetry and specified words. The method of BLEU automatic evaluation and manual evaluation finally demonstrates the effectiveness of the experiment.

关键词： Industrial electronics Semantics Training data Manuals Rhythm Encoding Periodic structures

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Recovering Accurate Labeling Information from Partially Valid data for Effective Multi-Label Learning

arXiv

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arXiv 2020年

作者： Li, Ximing Wang, Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China School of Computer Sci & Information Engineering Hefei University of Technology China

Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, i.e., the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to recover the ground-truth labels, i.e., estimating the ground-truth confidences, from the label enrichment, composed of the relevance degrees of candidate labels and irrelevance degrees of non-candidate labels. Upon this observation, we further develop a novel two-stage PML method, namely Partial MultiLabel Learning with Label Enrichment-Recovery (PML3ER), where in the first stage, it estimates the label enrichment with unconstrained label propagation, then jointly learns the ground-truth confidence and multi-label predictor given the label enrichment. Experimental results validate that PML3ER outperforms the state-of-the-art PML methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Recovery

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A reduced collatz dynamics maps to a residue class, and its count of x/2 over count of 3*x+1 is larger than ln3/ln2

TechRxiv

引用

TechRxiv 2020年

作者： Ren, Wei School of Computer Science China University of Geosciences Wuhan China Key Laboratory of Network Assessment Technology CAS [Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Guizhou Provincial Key Laboratory Public Big Data Guizhou University Guizhou China

We propose Reduced Collatz conjecture and prove that it is equivalent to Collatz conjecture but more primitive due to reduced dynamics. We study reduced dynamics (that consists of occurred computations from any starting integer to the first integer less than it), because it is the component of original dynamics (from any starting integer to 1). Reduced dynamics is denoted as a sequence of "I" that represents (3*x+1)/2 and "O" that represents x/2. Here 3*x+1 and x/2 are combined together because 3*x+1 is always even and thus followed by x/2. We discover and prove two key properties on reduced dynamics: (1) Reduced dynamics is invertible. That is, given a reduced dynamics, a residue class that presents such reduced dynamics, can be computed directly by our derived formula. (2) Reduced dynamics can be constructed algorithmically, instead of by computing concrete 3*x+1 and x/2 step by step. We discover the sufficient and necessary condition that guarantees a sequence consisting of "I" and "O" to be a reduced dynamics. Counting from the beginning of a sequence, if and only if the count of x/2 over the count of 3*x+1 is larger than ln3/ln2, reduced dynamics will be obtained (i.e., current integer will be less than starting integer). © 2020, CC BY.

关键词： Dynamics

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Collatz dynamics is partitioned by residue class regularly

TechRxiv

引用

TechRxiv 2020年

We propose Reduced Collatz Conjecture that is equivalent to Collatz Conjecture, which states that every positive integer can return to an integer less than it, instead of 1. Reduced Collatz Conjecture should be easier because some properties are presented in reduced dynamics, rather than in original dynamics (e.g., ratio and period). Reduced dynamics is a computation sequence from starting integer to the first integer less than it, and original dynamics is a computation sequence from starting integer to 1. Reduced dynamics is a component of original dynamics. We denote dynamics of x as a sequence of either computations in terms of "I" that represents (3*x+1)/2 and "O" that represents x/2. Here 3*x+1 and x/2 are combined together, because 3*x+1 is always even and followed by x/2. We formally prove that all positive integers are partitioned into two halves and either presents "I" or "O" in next ongoing computation. More specifically, (1) if any positive integer x that is i module 2t (i is an odd integer) is given, then the first t computations (each one is either "I" or "O" corresponding to whether current integer is odd or even) will be identical with that of i. (2) If current integer after t computations (in terms of "I" or "O") is less than x, then reduced dynamics of x is available. Otherwise, the residue class of x (namely, i module 2t) can be partitioned into two halves (namely, i module 2t+1 and i+2t module 2t+1), and either half presents "I" or "O" in intermediately forthcoming (t+1)-th computation. © 2020, CC BY.

关键词： Dynamics

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Reduced collatz dynamics is periodical and the period equals 2 to the power of the count of x/2

TechRxiv

引用

TechRxiv 2020年

作者： Ren, Wei School of Computer Science China University of Geosciences Wuhan China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guizhou China

We propose Reduced Collatz Conjecture that is equivalent to Collatz Conjecture but is easier to explore, because reduced dynamics is more primitive than original dynamics and presents better structures (e.g., period and ratio). Reduced dynamics (that are occurred computation sequence from a starting integer to the first integer less than the starting integer) is the component of original dynamics (from a starting integer to 1). Reduced dynamics of x is represented by a sequence of computation that is either (3*x+1)/2 or x/2, because 3*x+1 is always even and followed by x/2. We prove that reduced dynamics is periodical and its period equals 2 to the power of the count of x/2. More specifically, if there exists reduced dynamics of x, then there exists reduced dynamics of x+P, where P equals 2L and L is the total count of x/2 computations in reduced dynamics of x (equivalently, L is the length of the sequence). © 2020, CC BY.

关键词： Dynamics

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Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement

arXiv

引用

arXiv 2021年

作者： Luo, Hongming Zhou, Fei Liao, Guangsen Qiu, Guoping College of Electronics and Information Engineering Shenzhen University China Peng Cheng Laboratory Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Key Laboratory of Digital Creative Technology China Shenzhen Institute for Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Guangdong Shenzhen China

In real-world applications, such as sharing photos on social media platforms, images are always not only sub-sampled but also heavily compressed thus often containing various artefacts. Simple methods for enhancing the resolution of such images will exacerbate the artefacts, rendering them visually objectionable. In spite of its high practical values, super-resolving compressed images is not well studied in the literature. In this paper, we propose a novel compressed image super resolution (CISR) framework based on parallel and series integration of artefacts removal and resolution enhancement. Based on a mathematical inference model for estimating a clean low-resolution (LR) image and a clean high-resolution (HR) image from a down-sampled and compressed observation, we have designed a CISR architecture consisting of two deep neural network modules: the artefacts removal module (ARM) and the resolution enhancement module (REM). The ARM and the REM work in parallel with both taking the compressed LR image as their inputs, at the same time they also work in series with the REM taking the output of the ARM as one of its inputs and the ARM taking the output of the REM as its other input. A technique called unfolding is introduced to recursively suppress the compression artefacts and restore the image resolution. A unique feature of our CISR system is that it exploits the parallel and series connections between the ARM and the REM, and recursive optimization to reduce the model's dependency on specific types of degradation thus making it possible to train a single model to super-resolve images compressed by different methods to different qualities. Experiments are conducted on a mixture of JPEG and WebP compressed images without assuming apriori compression type and compression quality factor. To demonstrate our technique's real-world application value, we have also applied the trained models directly to restore social media images which have undergone scaling and compre

关键词： Deep neural networks

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