標(biāo)題: Titlebook: Computer Supported Cooperative Work and Social Computing; 18th CCF Conference, Yuqing Sun,Tun Lu,Bowen Du Conference proceedings 2024 The E [打印本頁] 作者: deliberate 時(shí)間: 2025-3-21 17:09
書目名稱Computer Supported Cooperative Work and Social Computing影響因子(影響力)
書目名稱Computer Supported Cooperative Work and Social Computing影響因子(影響力)學(xué)科排名
書目名稱Computer Supported Cooperative Work and Social Computing網(wǎng)絡(luò)公開度
書目名稱Computer Supported Cooperative Work and Social Computing網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computer Supported Cooperative Work and Social Computing被引頻次
書目名稱Computer Supported Cooperative Work and Social Computing被引頻次學(xué)科排名
書目名稱Computer Supported Cooperative Work and Social Computing年度引用
書目名稱Computer Supported Cooperative Work and Social Computing年度引用學(xué)科排名
書目名稱Computer Supported Cooperative Work and Social Computing讀者反饋
書目名稱Computer Supported Cooperative Work and Social Computing讀者反饋學(xué)科排名
作者: 鎮(zhèn)痛劑 時(shí)間: 2025-3-21 20:55
HGNN-T5 PEGASUS: A Hybrid Approach for?Chinese Long Text Summarizations introduced to T5 PEGASUS to avoid overfitting. To demonstrate the effectiveness of our model, we constructed the SCHOLAT text summarization dataset. The results of our experiments show that the proposed model outperforms other baseline models on both the NLPCC 2018 and SCHOLAT datasets.作者: profligate 時(shí)間: 2025-3-22 03:33 作者: concise 時(shí)間: 2025-3-22 07:11 作者: 禍害隱伏 時(shí)間: 2025-3-22 10:19
Similarity Metrics and Visualization of Scholars Based on Variational Graph Normalized Auto-Encodersted for experiments in this paper. The experimental results show that the model achieves the best performance on both the AUC and AP metrics on the task of measuring scholar similarity, with 98.7% and 98.8%, respectively, relative to other traditional widely used algorithms. In addition, by fusing t作者: Mets552 時(shí)間: 2025-3-22 15:03
Scholar Influence Maximization via?Opinion Leader and?Graph Embedding Regression in?Social Networksy overlapping influence ranges between the highly influential nodes in the seed set. Specifically, our framework first adopts an information diffusion model-based approach to obtain opinion leaders’ tendency evaluation and meanwhile uses variational graph auto-encoders (VGAE) to encode it into low-d作者: Mets552 時(shí)間: 2025-3-22 20:27
https://doi.org/10.1007/978-94-6300-010-9s introduced to T5 PEGASUS to avoid overfitting. To demonstrate the effectiveness of our model, we constructed the SCHOLAT text summarization dataset. The results of our experiments show that the proposed model outperforms other baseline models on both the NLPCC 2018 and SCHOLAT datasets.作者: 瘋狂 時(shí)間: 2025-3-23 01:14 作者: cumulative 時(shí)間: 2025-3-23 03:54 作者: PIZZA 時(shí)間: 2025-3-23 07:54 作者: 嘲弄 時(shí)間: 2025-3-23 10:49 作者: 鈍劍 時(shí)間: 2025-3-23 14:30 作者: 咽下 時(shí)間: 2025-3-23 18:42 作者: FAWN 時(shí)間: 2025-3-23 23:27
978-981-99-9636-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: SOB 時(shí)間: 2025-3-24 03:16 作者: LUMEN 時(shí)間: 2025-3-24 10:02 作者: 可商量 時(shí)間: 2025-3-24 13:43
Storytelling in Religious Educationecently, prompt-based learning has emerged as a powerful approach to handling a wide variety of tasks in NLP. It effectively bridges the gap between pre-trained language models (PLMs) and downstream tasks. Verbalizers are key components in prompt-based tuning. Existing manual prompts heavily rely on作者: 仇恨 時(shí)間: 2025-3-24 16:27 作者: 肉身 時(shí)間: 2025-3-24 22:33
Storytelling in Religious Educationn network communication research. In this study, we present an approach integrating the BERTopic topic model, advanced Natural Language Processing (NLP) techniques, and Social Network Analysis, to meticulously dissect the intricate dynamics of emotion propagation and evolution within collective beha作者: 乞丐 時(shí)間: 2025-3-24 23:17 作者: dandruff 時(shí)間: 2025-3-25 07:04
https://doi.org/10.1007/1-4020-4713-4ence maximization problem. Most Existing influence maximization algorithms relying on scenario-specific centrality measures often underperform when applied to diverse social networks. Many influence maximization deep learning based algorithms use topological embedding to learn the global influence i作者: sacrum 時(shí)間: 2025-3-25 09:43
https://doi.org/10.1007/1-4020-4713-4oses an incremental inductive dynamic network community detection algorithm (IINDCD). First, the algorithm uses an attention mechanism to capture node neighborhood information and learn node representations by neighborhood aggregation induction while enhancing low-order structural representations. S作者: 調(diào)整校對(duì) 時(shí)間: 2025-3-25 13:08
https://doi.org/10.1007/1-4020-4713-4mantic information are required to achieve a deeper understanding of question intent. While existing large pre-trained models can obtain character-based text representations with contextual information, the specificity of Chinese sentences makes word-based text representation superior to character-b作者: deadlock 時(shí)間: 2025-3-25 17:20 作者: infinite 時(shí)間: 2025-3-25 23:31 作者: 疼死我了 時(shí)間: 2025-3-26 03:31 作者: 最高峰 時(shí)間: 2025-3-26 07:35 作者: Intrepid 時(shí)間: 2025-3-26 11:01
Storytelling in Religious Educationity and the intensity of activity. This study highlights the significance of harnessing the combined power of BERTopic, NLP, and social network methodologies to decode the subtleties of emotional propagation and transformation.作者: JAUNT 時(shí)間: 2025-3-26 14:50 作者: Statins 時(shí)間: 2025-3-26 19:57 作者: Flu表流動(dòng) 時(shí)間: 2025-3-26 21:09 作者: 確定方向 時(shí)間: 2025-3-27 04:21
Incremental Inductive Dynamic Network Community Detectionel reuse for dynamic representation learning, constructs incremental node sets for updating the model, reduces training overhead, and quickly obtains node representation vectors for new moments of the network, then completing dynamic network community detection. IINDCD runs without reconstruction and with low retraining overhead.作者: 某人 時(shí)間: 2025-3-27 05:59
Er-EIR: A Chinese Question Matching Model Based on Word-Level and Sentence-Level Interaction Feature incorporate a co-attention mechanism to capture the interaction information between sentence pairs. By comparing our model with several baseline models on a self-built dataset of university financial question pairs, we have achieved remarkable performance.作者: collagen 時(shí)間: 2025-3-27 12:17 作者: MULTI 時(shí)間: 2025-3-27 15:01
Prompt-Based and?Two-Stage Training for?Few-Shot Text Classificationecently, prompt-based learning has emerged as a powerful approach to handling a wide variety of tasks in NLP. It effectively bridges the gap between pre-trained language models (PLMs) and downstream tasks. Verbalizers are key components in prompt-based tuning. Existing manual prompts heavily rely on作者: 偽書 時(shí)間: 2025-3-27 21:19
A Fine-Grained Image Description Generation Method Based on?Joint Objectivest provide coherent and comprehensive textual details about the image content. Currently, most of these methods face two main challenges: description repetition and omission. Moreover, the existing evaluation metrics cannot clearly reflect the performance of models on these two issues. To address the作者: 枕墊 時(shí)間: 2025-3-27 21:56
Analyzing Collective Intelligence Through Sentiment Networks in?Self-organized Douban Communitiesn network communication research. In this study, we present an approach integrating the BERTopic topic model, advanced Natural Language Processing (NLP) techniques, and Social Network Analysis, to meticulously dissect the intricate dynamics of emotion propagation and evolution within collective beha作者: 干涉 時(shí)間: 2025-3-28 05:26 作者: CULP 時(shí)間: 2025-3-28 07:39
Scholar Influence Maximization via?Opinion Leader and?Graph Embedding Regression in?Social Networksence maximization problem. Most Existing influence maximization algorithms relying on scenario-specific centrality measures often underperform when applied to diverse social networks. Many influence maximization deep learning based algorithms use topological embedding to learn the global influence i
