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A multilayer network diffusion-based model for reviewer recommendation

Chinese Physics B 2024年第3期33卷 700-717页

作者：黄羿炜徐舒琪蔡世民吕琳媛 Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Dataspace Hefei Comprehensive National Science CenterHefei 230088China Big Data Research Center University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Cyber Science and Technology University of Science and Technology of ChinaHefei 230026China

With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy *** systems are powerful tools developed in computer science and information science to deal with this ***,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform *** this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual *** network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among *** results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking *** work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

关键词： reviewer recommendation multilayer network network diffusion model recommender systems complex networks

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Attention-based network embedding with higher-order weights and node attributes

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CAAI Transactions on Intelligence technology 2024年第2期9卷 440-451页

作者： Xian Mo Binyuan Wan Rui Tang Junkai Ding Guangdi Liu School of Information Engineering Ningxia UniversityYinchuanChina School of Cyber Science and Engineering Sichuan UniversityChengduChina Computer Science and Information Technology University of MalayaKuala LumpurMalaysia Academy of Medical Sciences Zhengzhou UniversityZhengzhouChina

Network embedding aspires to learn a low-dimensional vector of each node in networks,which can apply to diverse data mining *** real-life,many networks include rich attributes and temporal ***,most existing embedding approaches ignore either temporal information or network attributes.A self-attention based architecture using higher-order weights and node attributes for both static and temporal attributed network embedding is presented in this article.A random walk sampling algorithm based on higher-order weights and node attributes to capture network topological features is *** static attributed networks,the algorithm incorporates first-order to k-order weights,and node attribute similarities into one weighted graph to preserve topological features of *** temporal attribute networks,the algorithm incorporates previous snapshots of networks containing first-order to k-order weights,and nodes attribute similarities into one weighted *** addition,the algorithm utilises a damping factor to ensure that the more recent snapshots allocate a greater *** features are then incorporated into topological ***,the authors adopt the most advanced architecture,Self-Attention Networks,to learn node *** results on node classification of static attributed networks and link prediction of temporal attributed networks reveal that our proposed approach is competitive against diverse state-of-the-art baseline approaches.

关键词： data mining deep neural networks social network

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Mining User Preferences from Online Reviews with the Genre-aware Personalized Neural Topic Model 25

Mining User Preferences from Online Reviews with the Genre-a...

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34th ACM Web Conference, WWW 2025

作者： Wang, Rui Lu, Jiahao Lv, Xincheng Chang, Shuyu Wu, Yansheng Yao, Yuanzhi Huang, Haiping Sun, Guozi School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China

ISBN: (纸本)9798400712746

Customer-generated reviews on e-commerce websites often contain valuable insights into users’ interests in product genres and provide a rich source for mining user preferences. However, most existing neural topic models tend to generate meaningless topics that share low correlations with product genres. Furthermore, they often fail to mine user preferences and discover personalized topic profiles due to the absence of explicit user modeling. To address these limitations, we propose a novel Genre-aware Personalized neural Topic Model (GPTM), which incorporates product genre information into the topic modeling process to ensure the relevance between mined topics and product genres. Moreover, it could produce a personalized topic profile for each user by performing user preference modeling. Extensive experimental results on three publicly available Amazon review corpora validate the effectiveness of the proposed GPTM in genre-aware topic modeling. Furthermore, GPTM surpasses state-of-the-art baselines in user preference mining and generates high-quality personalized topic profiles. © 2025 Copyright held by the owner/author(s).

关键词： Text mining

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PBCounter:weighted model counting on pseudo-boolean formulas

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Frontiers of computer science 2025年第3期19卷 55-63页

作者： Yong LAI Zhenghang XU Minghao YIN College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China School of Computer Science and Information Technology Northeast Normal UniversityChangchun 130017China

In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its *** current WMC solvers work on Conjunctive Normal Form(CNF)***,CNF is not a natural representation for human-being in many *** by the stronger expressive power of Pseudo-Boolean(PB)formulas than CNF,we propose to perform WMC on PB *** on a recent dynamic programming algorithm framework called ADDMC for WMC,we implement a weighted PB counting tool *** compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD,ExactMC,D4,and ADDMC,where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF *** experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas.

关键词： weighted model counting pseudo-boolean constraint algebraic decision diagrams preprocessing techniques

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TCAS: A Two-Channel Text Classification Model Based on Adversarial Samples

TCAS: A Two-Channel Text Classification Model Based on Adver...

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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Zhang, Jiajing Zou, Kun Chen, Jinlan Ma, Tinghuai School of Electronics and Information Engineering Anhui Jianzhu University Hefei China School of Mechanical and Electrical Engineering Anhui Jianzhu University Hefei China School of Computer Science Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350360585

Text categorization has always been a research hotspot in the field of natural language processing. Although text classification models based on deep learning have achieved good results, the method of removing noise in the text preprocessing stage is usually relatively simple, which can affect the accuracy of classification when there is noise in the sample. In order to solve this problem, this paper proposes a two-channel text classification model that fuses adversarial samples. First, the samples to be classified are input into the adversarial network to generate adversarial samples, and the generated adversarial samples are feature extracted using DeBERTa and MLP. Meanwhile, feature extraction is performed on the original samples using RoBERTa and Bi-LSTM. Finally, a weighted fusion operation is performed on the features of these two channels to obtain the classification output. After experimental validation, the two-channel text classification model classification based on fused adversarial samples achieves 90.38% and 95.66% accuracy on MR and SST-Binary datasets, which is 4.15% and 4.12% higher than the baseline model, respectively. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybrid Optimization Algorithm for Handwritten Document Enhancement

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computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

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Gaining Technological Autonomy and Social-emotional Support: A Case Study of How and Why Chinese Older Adults Engage with a Semi-acquaintance Online Community

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Proceedings of the ACM on Human-computer Interaction 2024年第CSCW2期8卷 1-35页

作者： Qian, Zhigu Fu, Jiaojiao Zhou, Yangfan School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai China School of Information Science and Engineering East China University of Science and Technology Shanghai China

Older adults are often underserved and marginalized in technology engagement due to their reluctance and the barriers they face in adopting and engaging with mainstream technology. However, Pinxiaoquan, a social feature of an e-commerce platform in China, has gained a large number of older users. This work investigates how and why Chinese older adults use Pinxiaoquan, aiming to unveil the underlying logic and inspire technology-inclusive design for older adults. To this end, we conducted a mixed-methods qualitative study over two years, which included online observation, and semi-interview. We found that Pinxiaoquan’s success among Chinese older adults is mainly due to its ability to inspire technological autonomy and provide social-emotional support. Rather than simply lowering technical barriers or asking them to seek assistance outside the platform, Pinxiaoquan builds a semi-acquaintance online community based on location and social ties that allows older adults to realize technical support mutually. Pinxiaoquan also fulfills their social-emotional needs, such as free online expression, memory creation and preservation, relationship expansion and maintenance, and a sense of value. Our research contributes to the HCI community by highlighting the importance of improving older adults’ technology autonomy following their specific social and cultural background and social-emotional needs. This work also provides unique insights and implications for building inclusive technology for the growing aging population. © 2024 Copyright held by the owner/author(s).

关键词： Marketplaces

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CPFU-Net: A Channel Position Wide Focus Mechanism based on U-Net for Building Image Segmentation Algorithm 24

CPFU-Net: A Channel Position Wide Focus Mechanism based on U...

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4th International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024

作者： Liu, Cheng Pang, Yue Li, Tiezhu Sun, Jiawei Zuo, Xianyu Dang, Lanxue Zhou, Liming Qiao, Baojun School of Computer and Information Engineering Henan University Kaifeng Henan China School of Computer Science and Technology Anhui University Hefei Anhui China

ISBN: (纸本)9798400710247

Building image segmentation is widely used in building area estimation and urban construction planning. However, there are problems in the building segmentation in high-resolution remote sensing images, such as small targets misclassified easily, and edge details lost easily. To address the front issues, an improvement channel -position-focus-U-Net (CPFU-Net) model is proposed. A wide focus module (WFM) is designed to increase the receptive field which can achieve higher edge integrity of building segmentation through preserving edge details and focusing on the area of interest. The Squeeze-and-Excitation module is integrated into the channel attention convolution module (CACM) which improves the sensitivity of the model to channel information features. The CPFU-Net uses attention mechanisms and wide focus module (WFM) to improve skip connections which can fully utilize low-level and high-level semantic information to extract more accurate building features. Large numbers of experiments are implemented on both Satellite datasets and Massachusetts Building datasets to verify the effectiveness of CPFU-Net. The experimental results show that CPFU-Net reduces the probability of small target misclassification and preserve more edge details in building segmentation which improves the accuracy of building segmentation and achieves higher performance in OA, Recall, IoU, and F1-Score metrics. © 2024 Copyright held by the owner/author(s).

关键词： Urban planning

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Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

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science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

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An AAE-based Proactive Content Chunked Caching Scheme with Mobility Prediction in VANETs 23

An AAE-based Proactive Content Chunked Caching Scheme with M...

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Jin, Limei Zhu, Chenchen Yu, Lei Zhang, Benhong School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Medical Information Technology Anhui University of Chinese Medicine Hefei China

ISBN: (纸本)9798400700705

In Vehicular Ad hoc Networks(VANETs), edge caching can meet the increasing needs of users for vehicular applications and reduce the network burden. However, the existing studies rarely consider the differences in popularity caused by different regions and the high mobility of vehicles. To improve the efficiency of edge caching and reduce the content retrieval cost, an AAE-based proactive content chunked caching scheme(APCCMP) with mobility prediction is proposed. Firstly, according to the local historical request matrix and user preference matrix, Adversarial Autoencoder(AAE) is used to predict local content popularity. Then, considering the moving trajectory, RSUs predict the transition probability of vehicles on the Markov model. Finally, according to the prediction results and vehicles' speeds, the content chunks are cached on multiple RSUs. The experimental results show that the proposed scheme is significantly superior to other baseline schemes. © 2023 ACM.

关键词： Vehicular ad hoc networks

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