摘要：
A novel low-power distributed Visual Sensor Network (VSN) system is proposed, which performs real-time collaborative barcode localization, tracking, and robust identification. Due to a dynamic triggering mechanism and efficient transmission protocols, communication is organized amongst the nodes themselves rather than being orchestrated by a single sink node, achieving lower congestion and significantly reducing the vulnerability of the overall system. Specifically, early detection of the moving barcode is achieved through a dynamic triggering mechanism. A hierarchical transmission protocol is designed, within which different communication protocols are used, depending on the type of data exchanged among nodes. Real-Time Transport Protocol (RTP) is employed for video communication, while the Transmission Control Protocol (TCP) and Long Range (LoRa) protocol are used for passing messages amongst the nodes in the VSN. Through an extensive experimental evaluation, we demonstrate that the proposed distributed VSN brings substantial advantages in terms of accuracy, power savings, and time complexity compared to an equivalent system performing centralized processing.
摘要：
Dialogue state tracking (DST) is a core component of task-oriented dialogue systems. Recent works focus mainly on end-to-end DST models that omit the spoken language understanding (SLU) module to directly obtain the dialogue state based on a user’s dialogue. However, the slot information detected by slot filling in SLU is closely tied to the slot–value pair that needs to be updated in DST. Efficient use of the key slot semantic knowledge obtained by slot filling contributes to improving the performance of DST. Based on this idea, we introduce slot filling as a subtask and build an end-to-end joint model to explicitly integrate the slot information detected by slot filling, which further guides DST. In this article, a novel stack-propagation framework with slot filling for multidomain DST is proposed. The stack-propagation framework is introduced to jointly model slot filling and DST. The framework directly feeds the key slot semantic knowledge detected by slot filling into the DST module. In addition, a slot-masked attention mechanism is designed to enable DST to focus on the key slot information obtained by slot filling. When the slot value is updated, a slot–value softcopy mechanism is designed to enhance the influence of the words marked by key slots. Experiments show that our approach outperforms previous methods and performs outstandingly on two benchmark datasets. IEEE
期刊：
INTERNET AND HIGHER EDUCATION,2022年55:100875 ISSN：1096-7516
通讯作者：
Jin Zhou
作者机构：
[Ye, Jun -min] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Zhou, Jin] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.
通讯机构：
[Jin Zhou] F;Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China
摘要：
Evidence suggests that learning sentiments are inextricably related to cognitive processing, and the exploration of the relationship remains to be an important research topic. This study collected discourse data from 40 college students in online collaborative learning activities. Epistemic network analysis (ENA) was employed to explore the connection between learning sentiments and cognitive processing and compare the ENA network characteristics of the higher- and lower-engagement groups. The results indicated that there was a joint connection between understand-analyze-neutral, and insightful sentiments had more association with neutral sentiments and understanding. Besides, distinctions existed between higher- and lower-engagement groups with respect to the association between learning sentiments and cognitive processing. The higher-engagement group had stronger associations around positive and confused sentiments, while the lower-engagement group had stronger associations around off-topic discussion. The findings of this research may serve as a reference for designing and implementing collaborative learning activities to increase cognitive levels.
期刊：
IEEE/ACM Transactions on Computational Biology and Bioinformatics,2022年19(1):513-521 ISSN：1545-5963
通讯作者：
Zhang, XF
作者机构：
[Tan, Yu-Ting; Zhang, Xiao-Fei] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei, Peoples R China.;[Tan, Yu-Ting; Zhang, Xiao-Fei] Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan 430079, Hubei, Peoples R China.;[Ou-Yang, Le] Shenzhen Univ, Coll Informat Engn, Shenzhen 518060, Guangdong, Peoples R China.;[Ou-Yang, Le] Shenzhen Univ, Shenzhen Key Lab Media Secur, Shenzhen 518060, Guangdong, Peoples R China.;[Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Zhang, XF ] C;Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan 430079, Hubei, Peoples R China.
摘要：
It is an important task to learn how gene regulatory networks change under different conditions. Several Gaussian graphical model-based methods have been proposed to deal with this task by inferring differential networks from gene expression data. However, most existing methods define the differential networks as the difference of precision matrices, which may include false differential edges caused by the change of conditional variances. In addition, prior information about the condition-specific networks and the differential networks can be obtained from other domains. It is useful to incorporate prior information into differential network analysis. In this study, we propose a new differential network analysis method to address the above challenges. Instead of using the precision matrices, we define the differential networks as the difference of partial correlations, which can exclude the spurious differential edges due to the variants of conditional variances. Furthermore, prior information from multiple hypothesis testing is incorporated using a weighted fused penalty. Simulation studies show that our method outperforms the competing methods. We also apply our method to identify the differential network between luminal A and basal-like subtypes of breast cancers and the differential network between acute myeloid leukemia tumors and normal samples. The hub genes in the differential networks identified by our method carry out important biological functions.
期刊：
IEEE Transactions on Image Processing,2022年31:4251-4265 ISSN：1057-7149
通讯作者：
Lu, X.
作者机构：
[Lu, Xiaoqiang; Zheng, Xiangtao] Xi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Key Laboratory of Spectral Imaging Technology CAS, Xi'an, 710119, China;University of Chinese Academy of Sciences, Beijing, 100049, China;[Xie, Wei] Central China Normal University, Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, National Language Resources Monitoring and Research Center for Network Media, School of Computer, Wuhan, 430079, China;[Sun, Hao] Xi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Key Laboratory of Spectral Imaging Technology CAS, Xi'an, 710119, China, University of Chinese Academy of Sciences, Beijing, 100049, China
通讯机构：
[Lu, X.] X;Xi'An Institute of Optics and Precision Mechanics, China
摘要：
Hyperspectral image (HSI) classification refers to identifying land-cover categories of pixels based on spectral signatures and spatial information of HSIs. In recent deep learning-based methods, to explore the spatial information of HSIs, the HSI patch is usually cropped from original HSI as the input. And $3 \times 3$ convolution is utilized as a key component to capture spatial features for HSI classification. However, the $3 \times 3$ convolution is sensitive to the spatial rotation of inputs, which results in that recent methods perform worse in rotated HSIs. To alleviate this problem, a rotation-invariant attention network (RIAN) is proposed for HSI classification. First, a center spectral attention (CSpeA) module is designed to avoid the influence of other categories of pixels to suppress redundant spectral bands. Then, a rectified spatial attention (RSpaA) module is proposed to replace $3 \times 3$ convolution for extracting rotation-invariant spectral-spatial features from HSI patches. The CSpeA module, the $1 \times 1$ convolution and the RSpaA module are utilized to build the proposed RIAN for HSI classification. Experimental results demonstrate that RIAN is invariant to the spatial rotation of HSIs and has superior performance, e.g., achieving an overall accuracy of 86.53% (1.04% improvement) on the Houston database. The codes of this work are available at https://github.com/spectralpublic/RIAN .
作者：
Hao Sheng*;Hu Yin Zhang;Fei Yang;Cheng Hao Li;Jing Wang
期刊：
数字通信与网络：英文版,2022年 ISSN：2352-8648
通讯作者：
Hao Sheng
作者机构：
School of Computer Science, Central China Normal University, Wuhan, 430079, PR China;National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan, 430079, PR China;[Hu Yin Zhang; Fei Yang] School of Computer Science, Wuhan University, Wuhan, 430079, China;[Cheng Hao Li] School of Information Science and Engineering, Shandong Normal University, Jinan, 250000, China;[Jing Wang] School of Computer Science, Hubei University of Technology, Wuhan, 430079, China
通讯机构：
[Hao Sheng] S;School of Computer Science, Central China Normal University, Wuhan, 430079, PR China<&wdkj&>National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan, 430079, PR China
摘要：
Hybrid Power-line/Visible-light Communication (HPVC) network has been one of the most promising Cooperative Communication (CC) technologies for constructing Smart Home due to its superior communication reliability and hardware efficiency. Current research on HPVC networks focuses on the performance analysis and optimization of the Physical (PHY) layer, where the Power Line Communication (PLC) component only serves as the backbone to provide power to light Emitting Diode (LED) devices. Hence, it's still a great challenge to design a medium access control (MAC) protocol for HPVC network, that allows PLC and VLC (Visible-light communication) components to operate the data transmission simultaneously, i.e., realizing a true CC. To solve this problem, we propose a new HPC network MAC protocol (HPVC MAC) based on Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) by combining IEEE 802.15.7 and IEEE 1901 standards. Firstly, we add an Additional Assistance (AA) layer to provide the channel selection strategies for sensor stations, so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism, respectively. Based on this, we give a detailed working principle of the HPVC MAC, followed by the construction of a joint analytical model for mathematical-mathematical validation of the HPVC MAC. In the modeling process, the impacts of PHY layer settings (including channel fading types and additive noise feature), CSMA/CA mechanisms of 802.15.7 and 1901, and practical configurations (such as traffic rate, transit buffer size) are comprehensively taken into consideration. Moreover, we prove the proposed analytical model has the solvability. Finally, through extensive simulations, we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62% between the simulation and analytical results.
作者机构：
[Zhong, Duo; Jiang, Xingpeng; Li, Bojing] Cent China Normal Univ, Hubei Key Lab Artificial Intelligence & Smart Lear, Wuhan, Peoples R China.;[Zhong, Duo; Jiang, Xingpeng; Li, Bojing] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Qiao, Jimei] Shanghai Normal Univ, Math & Sci Coll, Shanghai, Peoples R China.;[Jiang, Xingpeng] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan, Peoples R China.
通讯机构：
[Xingpeng Jiang] H;Hubei Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China<&wdkj&>School of Computer, Central China Normal University, Wuhan, China<&wdkj&>National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, China
摘要：
Microorganisms play important roles in our lives especially on metabolism and diseases. Determining the probability of human suffering from specific diseases and the severity of the disease based on microbial genes is the crucial research for understanding the relationship between microbes and diseases. Previous could extract the topological information of phylogenetic trees and integrate them to metagenomic datasets, thus enable classifiers to learn more information in limited datasets and thus improve the performance of the models. In this paper, we proposed a GNPI model to better learn the structure of phylogenetic trees. GNPI maintained the original vector format of metagenomic datasets, while previous research had to change the input form to matrices. The vector-like form of the input data can be easily adopted in the baseline machine learning models and is available for deep learning models. The datasets processed with GNPI help enhance the accuracy of machine learning and deep learning models in three different datasets. GNPI is an interpretable data processing method for host phenotype prediction and other bioinformatics tasks.
摘要：
Recent years have witnessed the rapid development of service-oriented computing technologies. The boom of Web services increases software developers' selection burden in developing new service-based systems such as mashups. Timely recommending appropriate component services for developers to build new mashups has become a fundamental problem in service-oriented software engineering. Existing service recommendation approaches are mainly designed for mashup development in the single-round scenario. It is hard for them to effectively update recommendation results according to developers' requirements and behaviours (e.g. instant service selection). To address this issue, the authors propose a service bundle recommendation framework based on deep learning, DLISR, which aims to capture the interactions among the target mashup to build, selected (component) services, and the following service to recommend. Moreover, an attention mechanism is employed in DLISR to weigh selected services when recommending a candidate service. The authors also design two separate models for learning interactions from the perspectives of content and invocation history, respectively, and a hybrid model called HISR. Experiments on a real-world dataset indicate that HISR can outperform several state-of-the-art service recommendation methods to develop new mashups iteratively.
期刊：
Information and Software Technology,2022年150:106987 ISSN：0950-5849
通讯作者：
Po Hu<&wdkj&>Ran Mo
作者机构：
[Hu, Po; Wei, Shaozhi; Cheng, Wuyan; Mo, Ran] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Hu, Po; Mo, Ran] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan, Hubei, Peoples R China.;[Hu, Po] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan, Hubei, Peoples R China.
通讯机构：
[Po Hu; Ran Mo] H;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, China<&wdkj&>School of Computer, Central China Normal University, Wuhan, Hubei, China<&wdkj&>National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei, China<&wdkj&>Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, China<&wdkj&>School of Computer, Central China Normal University, Wuhan, Hubei, China
摘要：
Question answering over temporal knowledge graphs (TKGQA) has attracted great attentions in natural language processing community. One of the key challenges is how to effectively model the representations of questions and the candidate answers associated with timestamp constraints. Many existing methods attempt to learn temporal knowledge graph embedding for entities, relations and timestamps. However, these existing methods cannot effectively exploiting temporal knowledge graph embeddings to capture time intervals (e.g., "WWII" refers to 1939-1945) as well as temporal relation words (e.g., "first" and "last") appeared in complex questions, resulting in the sub-optimal results. In this paper, we propose a temporal-sensitive information for complex question answering (TSIQA) framework to tackle these problems. We employ two alternative approaches to augment questions embeddings with question-specific time interval information, which consists of specific start and end timestamps. We also present auxiliary contrastive learning to contrast the answer prediction and prior knowledge regarding time approximation for questions that only differ by the temporal relation words. To evaluate the effectiveness of our proposed method, we conduct the experiments on CRONQUESTION. The results show that our proposed model achieves better improvements over the state-of-the-art models that require multiple steps of reasoning.
作者：
Zhang, Yi;Zhou, Guangyou;Xie, Zhiwen;Huang, Jimmy Xiangji
期刊：
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,2022年30:816-828 ISSN：2329-9290
通讯作者：
Zhou, GY
作者机构：
[Zhang, Yi] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Zhou, Guangyou; Zhou, GY] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.;[Xie, Zhiwen] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.;[Huang, Jimmy Xiangji] York Univ, Sch Informat Technol, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON M3J 1P3, Canada.
通讯机构：
[Zhou, GY ] C;Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
关键词：
Mathematical models;Decoding;Natural languages;Encoding;Electronic mail;Arithmetic;Task analysis;Math word problem;natural language processing;text mining;representation learning
摘要：
Designing algorithms to solve math word problems (MWPs) is an important research topic in natural language processing and smart education domains. The task of solving MWPs involves transforming math problem texts into math equations. Although recent Graph2Tree-based models, which adopt homogeneous graph encoders to learn quantity representations, have obtained very promising results in generating math equations, they do not consider the heterogeneous issue and the long-distance dependencies of heterogeneous nodes. In this paper, we propose a novel hierarchical heterogeneous graph encoding called HGEN for MWPs. Specifically, HGEN first introduces a heterogeneous graph consisting of a node-level attention layer and a type-aware attention layer to learn the heterogeneous node embedding. HGEN then captures the long-distance dependent information by propagating the multi-hop nodes in a hierarchical manner. We conduct extensive experiments on two popular MWP datasets. Our empirical results show that HGEN significantly outperforms the state-of-the-art Graph2Tree-based models in the literature.
