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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Bowen Dou, Wanchun Zhou, Xiaokang Zhang, Xuyun Qi, Lianyong Dai, Fei Chen, Chaochao Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Shiga University Faculty of Data Science Hikone Japan Macquarie University Department of Computing Sydney Australia College of Computer and Software Qingdao China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350346558

Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels in each wireless link. These wireless channels are limited resource while different channel has different cost and service ability. The dynamic changes of users' status make it harder to find an appropriate method to satisfy the BPM requirements of channel deployment. With this observation, it is a tricky challenge to make a trade-off between the system cost(rental price) and the service ability(number of users). In view of this challenge, an intelligent resource scheduling method, named EdgeIRS, is proposed in this paper. In the technical sense, the EdgeIRS method can accommodate most users at the edge with a minimum cost of deploying channel resources in an online way. Its performance is analyzed theoretically and the experiments verify the superiority of the method. © 2022 IEEE.

关键词： Cost effectiveness

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AMSC: Adaptive Multi-channel Graph Convolutional Network-Enhanced Web Services Classification 23

AMSC: Adaptive Multi-channel Graph Convolutional Network-Enh...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Qing, Yueying Cao, Buqing Peng, Mi Zhang, Lulu Kang, Guosheng Liu, Jianxun Fletcher, Kenneth K. School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China University of Massachusetts Boston Department of Computer Science Boston United States

ISBN: (纸本)9781665494571

With the development of Service Oriented Architecture (SOA), the number of Web services on the Internet is also growing rapidly. Classifying Web services accurately and efficiently is helpful to improve the quality of services discovery and promote the efficiency of service composition. However, existing deep learning-based Web services classification methods, such as graph convolutional networks (GCNs), are incapable of adaptively learning the correlation between service topology structure and service node features concurrently, resulting in unsatisfactory classification performance. To address this problem, this paper proposes an adaptive multi-channel GCN-enhanced Web services classification method. In this method, we first extract specific and shared embedding, in the Web API node isomorphic network, from the node features, topology, and combination of Web service nodes. Then, an attention mechanism is used to learn the importance weight of each embedding. By doing this, we adaptively integrate these weights to ensure the consistency and difference of each learned embedding. Finally, experimental results on real datasets from Programmable Web show that compared with LINE, Node2vec, Deep-walk, GCN, and GAT, the method proposed in this paper has an average improvement in accuracy of 19.81%, 19.35%, 19.16%, 11.56%, and 7.75% respectively. © 2021 IEEE.

关键词： Correlation Adaptive systems Network topology Smart cities Quality of service Feature extraction Topology

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EEG-based Emotion Recognition using Graph Attention Network with Dual-Branch Attention Module 46

EEG-based Emotion Recognition using Graph Attention Network ...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Li, Cheng Pun, Sio Hang Li, Jia Wen Chen, Fei University of Macau State Key Laboratory of Analog and Mixed-Signal Vlsi China Southern University of Science and Technology Department of Electrical and Electronic Engineering Shenzhen China Guangdong Polytechnic Normal University School of Computer Science Guangzhou China

ISBN: (纸本)9798350371499

EEG reveals human brain activities for emotion and becomes an important aspect of affective computing. In this study, we developed a novel approach, namely DAM-GAT, which incorporated a dual-branch attention module (DAM) into a graph attention network (GAT) for EEG-based emotion recognition. This method used the GAT to capture the local features of emotional EEG signals. To enhance the important EEG features for emotion recognition, the proposed method also included a DAM that calculated weights considering both channel and frequency information. Additionally, the relationship between EEG channels was determined using the phase-locking value (PLV) connectivity of corresponding EEG signals. Based on the SEED datasets, the proposed approach provided an accuracy of up to 94.63% for emotion recognition, demonstrating its impressive performance compared with other existing methods. © 2024 IEEE.

关键词： Emotion Recognition

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state and tendency: an empirical study of deep learning question&answer topics on Stack Overflow

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science China(Information sciences) 2021年第11期64卷 131-153页

作者： Henghui ZHAO Yanhui LI Fanwei LIU Xiaoyuan XIE Lin CHEN State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science Wuhan University

Deep learning has developed rapidly in recent years, attracting the attention of numerous researchers. Since a wide range of topics are covered in this field, we are wondering what topics researchers have concerned about. However, after investigation, we find that very few researchers have paid attention to this demand. In this paper, we conduct a large-scale study to analyze the questions faced by deep learning developers. We use Stack Overflow, one of the largest question&answer sites, as our data source, and extract32969 posts about deep learning as our studied dataset. After filtering, augmenting and pre-processing the post datasets from Stack Overflow, we use the latent Dirichlet allocation(LDA) topic model to summarize30 topics based on their text content. In addition, we measure the difficulty and popularity of each topic,compare the different issues faced by different deep learning frameworks, and analyze the development trend of each topic. Our main results are as follows:(1) developers ask a broad spectrum of questions about deep-learning, ranging from Data Shape to Object Detection;(2) Gradient Propagation is the most popular among all the topics and(3) Object Detection is the most difficult;(4) issues of Package Installation, Code Understanding and Method Introduction are common in the current different deep learning frameworks;(5) there are three trends in these topics, e.g., a significant rising trend is found in the number of discussion on Data Shape. Finally, based on our research findings, we make some targeted and valuable suggestions for developers, researchers, educators, and framework providers of deep learning.

关键词： topic model deep learning Stack Overflow question&answer

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A novel Method for Precipitation Nowcasting Based on ST-LSTM

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computers, Materials & Continua 2022年第9期72卷 4867-4877页

作者： Wei Fang Liang Shen Victor S.Sheng Qiongying Xue School of Computer&Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing100081China Department of Computer Texas Tech UniversityLubbockTX79409USA

Precipitation nowcasting is of great significance for severe convective weather *** echo extrapolation is a commonly used precipitation nowcasting ***,the traditional radar echo extrapolation methods are encountered with the dilemma of low prediction accuracy and extrapolation *** reason is that those methods cannot retain important long-term information and fail to capture short-term motion information from the long-range data *** order to solve the above problems,we select the spatiotemporal long short-term memory(ST-LSTM)as the recurrent unit of the model and integrate the 3D convolution operation in it to strengthen the model’s ability to capture short-term motion information which plays a vital role in the prediction of radar echo motion *** the purpose of enhancing the model’s ability to retain long-term important information,we also introduce the channel attention mechanism to achieve this *** the experiment,the training and testing datasets are constructed using radar data of Shanghai,we compare our model with three benchmark models under the reflectance thresholds of 15 and *** results demonstrate that the proposed model outperforms the three benchmark models in radar echo extrapolation task,which obtains a higher accuracy rate and improves the clarity of the extrapolated image.

关键词： Precipitation nowcasting radar echo extrapolation ST-LSTM attention mechanism

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Neural Machine Translation by Fusing key Information of Text

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computers, Materials & Continua 2023年第2期74卷 2803-2815页

作者： Shijie Hu Xiaoyu Li Jiayu Bai Hang Lei Weizhong Qian Sunqiang Hu Cong Zhang Akpatsa Samuel Kofi Qian Qiu Yong Zhou Shan Yang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Science and Technology on Altitude Simulation Laboratory Sichuan Gas Turbine Establishment Aero Engine Corporation of ChinaMianyang621000China School of Power and Energy Northwestern Polytechnical UniversityXi’an710072China School of Computer Science Southwest Petroleum UniversityChengdu610500China Department of Chemistry Physics and Atmospheric SciencesJackson State UniversityJacksonMS 39217USA

When the Transformer proposed by Google in 2017,it was first used for machine translation tasks and achieved the state of the art at that *** the current neural machine translation model can generate high quality translation results,there are still mistranslations and omissions in the translation of key information of long *** the other hand,the most important part in traditional translation tasks is the translation of key *** the translation results,as long as the key information is translated accurately and completely,even if other parts of the results are translated incorrect,the final translation results’quality can still be *** order to solve the problem of mistranslation and missed translation effectively,and improve the accuracy and completeness of long sentence translation in machine translation,this paper proposes a key information fused neural machine translation model based on *** model proposed in this paper extracts the keywords of the source language text separately as the input of the *** the same encoding as the source language text,it is fused with the output of the source language text encoded by the encoder,then the key information is processed and input into the *** incorporating keyword information from the source language sentence,the model’s performance in the task of translating long sentences is very *** order to verify the effectiveness of the method of fusion of key information proposed in this paper,a series of experiments were carried out on the verification *** experimental results show that the Bilingual Evaluation Understudy(BLEU)score of the model proposed in this paper on theWorkshop on Machine Translation(WMT)2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test *** experimental results show the advantages of the model proposed in this paper.

关键词： key information transformer fusion neural machine translation

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Hyperbolic Contrastive Learning with Second Order Sampling for Collaborative Filtering

Hyperbolic Contrastive Learning with Second Order Sampling f...

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IEEE International Conference on Web Services (ICWS)

作者： Longchuan Tu Shunmei Meng Lianyong Qi Xiaolong Xu Xuyun Zhang School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Lab. for Novel Software Technology Nanjing University China College of Computer Science and Technology China University of Petroleum(East China) China School of Computer and Software Nanjing University of Information Science and Technology China School of Computing Macquarie University Australia

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

Recently, hyperbolic spaces have proven beneficial for service recommendation due to their exponentially growing spatial properties conforming to power-law distributed user-item networks. Among them, the combination of hyperbolic space with graph convolution has achieved great success. However, hyperbolic convolutional models still perform multi-layer convolution in tangent space (Euclidean space), leading to the still inevitable problem of over-smoothing arising from multi-layer convolution. In addition, most of these models randomly draw negative samples from items that users have not interacted with, so that some of the samples obtained may not be well suited for model optimization. To tackle the above challenges, we propose a new Hyperbolic GCN model based on Contrastive Learning and Second-order Reachable sampling for collaborative filtering (HG-CLSR), which improve high quality of representations by exploring the distribution of users and items in hyperbolic space. Specifically, We first introduce a root alignment approach to encourage embeddings to align with the tangent space, thereby reducing distortions during the embedding mapping process in space. Then, we perform contrastive learning in hyperbolic space to motivate the spatial distribution of nodes to better fit the hyperbolic space. Moreover, we sample in the user’s second-order reachable item set, which ensures that the negative sample is more similar to the positive sample, so that the negative node can provide better information for guiding model optimization. Extensive experiments on three real-world datasets demonstrate that the HG-CLSR is significantly superior compared to existing hyperbolic models.

关键词： Degradation Graphical models Convolution Web services Collaborative filtering Design methodology Contrastive learning Distortion Optimization Distribution functions

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Deep Learning-Based Joint Channel Estimation and Implicit CSI Feedback 11th

Deep Learning-Based Joint Channel Estimation and Implicit C...

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11th China Conference on Command and Control, C2 2023

作者： Wan, Liangtian He, Jifeng Liu, Kaihui Sun, Lu Wang, Xianpeng Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China School of Electrical Engineering and Intelligentization Dongguan University of Technology Dongguan523820 China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian116026 China State Key Laboratory of Marine Resource Utilization in South China Sea and School of Information and Communication Engineering Hainan University Haikou570228 China

ISBN: (纸本)9789819990207

Previous studies often address channel estimation and channel state information (CSI) implicit feedback issues in 5G new radio (NR) system independently, posing a challenge to achieve effective coordination between the two processes in a shared environment. Consequently, we propose a deep learning-based scheme that jointly addresses channel estimation and implicit CSI feedback. In this scheme, the received pilot information from user equipment (UE) is regarded as a two-dimensional image. Channel estimation is performed employing the ResMLP architecture, whereas the Transformer architecture is utilized for implicit CSI feedback. Furthermore, a novel training approach is introduced to further enhance the performance of the proposed scheme. Simulation results illustrate a substantial improvement compared to the reference scheme employed in the 3rd Wireless Communication Artificial Intelligence (AI) Competition (WAIC). © Chinese Institute of Command and Control 2024.

关键词： Channel estimation

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Truly Bi-stable Ferroelectric Liquid Crystal Based Modulators

Truly Bi-stable Ferroelectric Liquid Crystal Based Modulator...

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International Symposium, Seminar, and Exhibition, Display Week 2024

作者： Swaminathan, Vigneshwaran Vashchenko, Valerii Vashchenko, Olena Bhadra, Debjyoti Mathew, Chris Kwok, Hoi-Sing Srivastava, Abhishek. K. State Key Laboratory of Advanced Displays and Optoelectronics Technologies and Center for Display Research Department of Electronics and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Road Kowloon Hong Kong

We developed a novel method for achieving long-term (>24 hours) true bi-stability in helix-free Ferroelectric Liquid Crystals (FLCs). Instead of employing asymmetric boundary conditions, traditionally used for achieving bi-stability, we utilized symmetric boundary conditions and carefully optimized the anchoring conditions on both the top and bottom substrates using the photoalignment technology. The key to the newly observed long term bi-stability is the twisted alignment structure. Surface roughness was found to play a crucial role in achieving sustained bi-stability. This breakthrough holds promise for developing truly bi-stable, fast modulators for photonic applications. © 2024, John Wiley and Sons Inc. All rights reserved.

关键词： Liquid crystals

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VSM: A Versatile Semi-supervised Model for Multi-modal Cell Instance Segmentation 36

VSM: A Versatile Semi-supervised Model for Multi-modal Cell ...

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Cell Segmentation Challenge in Multi-Modality High-Resolution Microscopy Images - 36th Conference on Neural Information Processing Systems, NeurIPS 2022

作者： Cai, Xiaochen Cai, Hengxing Tu, Weiwei Xu, Kele Li, Wu-Jun National Key Laboratory for Novel Software Technology National Institute of Healthcare Data Science Nanjing University Nanjing China 4 Paradigm Inc. Beijing China School of Computer National University of Defense Technology Changsha China Center for Medical Big Data Nanjing Drum Tower Hospital Nanjing China

Cell instance segmentation is a fundamental task in analyzing microscopy images, with applications in computer-aided biomedical research. In recent years, deep learning techniques have been widely used in this field. However, existing methods exhibit inadequate generalization ability towards multi-modal cellular images and require a considerable amount of manually labeled data for training. To overcome these limitations, we present VSM, a versatile semi-supervised model for multi-modal cell instance segmentation. Our method delivers high accuracy and efficiency, as verified through comprehensive experiments. Additionally, VSM achieved a top-five ranking in the Weakly Supervised Cell Segmentation category of the multi-modal High-Resolution Microscopy competition. © 2023 SPIE.

关键词： Cells

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