標題: Titlebook: Web Information Systems Engineering – WISE 2021; 22nd International C Wenjie Zhang,Lei Zou,Lu Chen Conference proceedings 2021 Springer Nat [打印本頁] 作者: JAZZ 時間: 2025-3-21 19:19
書目名稱Web Information Systems Engineering – WISE 2021影響因子(影響力)
書目名稱Web Information Systems Engineering – WISE 2021影響因子(影響力)學(xué)科排名
書目名稱Web Information Systems Engineering – WISE 2021網(wǎng)絡(luò)公開度
書目名稱Web Information Systems Engineering – WISE 2021網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Web Information Systems Engineering – WISE 2021被引頻次
書目名稱Web Information Systems Engineering – WISE 2021被引頻次學(xué)科排名
書目名稱Web Information Systems Engineering – WISE 2021年度引用
書目名稱Web Information Systems Engineering – WISE 2021年度引用學(xué)科排名
書目名稱Web Information Systems Engineering – WISE 2021讀者反饋
書目名稱Web Information Systems Engineering – WISE 2021讀者反饋學(xué)科排名
作者: Thyroxine 時間: 2025-3-21 23:35
Interactive Pose Attention Network for?Human Pose Transferf the network comprises a sequence of interactive pose attention (IPA) blocks to transfer the attended regions regarding to intermedia poses progressively, and retain the texture details of the unattended regions for subsequent pose transfer. More specifically, we design an attention mechanism by in作者: 加入 時間: 2025-3-22 00:44
Efficient Feature Interactions Learning with Gated Attention Transformerlearn feature interactions (i.e., cross features) for building an accurate prediction model. Recently, several self-attention based transformer methods are proposed to learn feature interactions automatically. However, those approaches are hindered by two drawbacks. First, Learning high-order featur作者: Gleason-score 時間: 2025-3-22 06:11 作者: Coterminous 時間: 2025-3-22 10:43
Interactive Pose Attention Network for?Human Pose Transferf the network comprises a sequence of interactive pose attention (IPA) blocks to transfer the attended regions regarding to intermedia poses progressively, and retain the texture details of the unattended regions for subsequent pose transfer. More specifically, we design an attention mechanism by in作者: 過時 時間: 2025-3-22 16:04 作者: Barter 時間: 2025-3-22 19:30 作者: 偶然 時間: 2025-3-22 23:48
Performance Evaluation of Pre-trained Models in Sarcasm Detection Taskction plays an important role in many domains of semantic analysis, such as stance detection and sentiment analysis. Recently, pre-trained models (PTMs) on large unlabelled corpora have shown excellent performance in various tasks of NLP. PTMs have learned universal language representations and can 作者: 閑逛 時間: 2025-3-23 03:27 作者: malapropism 時間: 2025-3-23 05:34
Performance Evaluation of Pre-trained Models in Sarcasm Detection Taskction plays an important role in many domains of semantic analysis, such as stance detection and sentiment analysis. Recently, pre-trained models (PTMs) on large unlabelled corpora have shown excellent performance in various tasks of NLP. PTMs have learned universal language representations and can 作者: NAVEN 時間: 2025-3-23 12:20
News Popularity Prediction with Local-Global Long-Short-Term Embeddings. This paper presents ., a neural model to predict news popularity by learning news embedding from global, local, long-term and short-term factors. . integrates a sentence encoding module to represent the local context of each news story; a heterogeneous graph-based module to capture the short-term作者: 眉毛 時間: 2025-3-23 15:13 作者: accordance 時間: 2025-3-23 20:45 作者: atrophy 時間: 2025-3-24 01:44 作者: 在前面 時間: 2025-3-24 04:54 作者: 廚師 時間: 2025-3-24 07:14 作者: 表示向下 時間: 2025-3-24 12:02 作者: TAIN 時間: 2025-3-24 15:36 作者: 慢慢沖刷 時間: 2025-3-24 22:42 作者: 名次后綴 時間: 2025-3-25 03:00
Comparison the Performance of Classification Methods for Diagnosis of Heart Disease and Chronic Condds for chronic diseases. The five categories of chronic disease datasets were collected from UCI and GitHub database include heart disease, breast cancer, diabetic retinopathy, Parkinson’s disease and diabetes. Machine learning (ML) methods including six individual learners (logistic regression (LR)作者: impale 時間: 2025-3-25 04:59 作者: BOOR 時間: 2025-3-25 09:21
Capturing Multi-granularity Interests with Capsule Attentive Network for Sequential Recommendation intricate sequential dependencies and user’s various interests underneath the interactions. Existing works regard each item that the user interacts with as an interest unit and apply advanced deep learning techniques to learn a unified interest representation. However, user’s interests vary in mult作者: 頭盔 時間: 2025-3-25 15:25
Multi-Task Learning with Personalized Transformer for Review Recommendationor popularity order without personalization. The review recommendation model provides users with attractive reviews and efficient consumption experience, allowing users to grasp the characteristics of items in seconds. However, the sparsity of interactions between users and reviews appears to be a m作者: Eosinophils 時間: 2025-3-25 19:46
Multi-Task Learning with Personalized Transformer for Review Recommendationor popularity order without personalization. The review recommendation model provides users with attractive reviews and efficient consumption experience, allowing users to grasp the characteristics of items in seconds. However, the sparsity of interactions between users and reviews appears to be a m作者: Acumen 時間: 2025-3-25 21:13 作者: 大廳 時間: 2025-3-26 01:26 作者: foppish 時間: 2025-3-26 05:56 作者: 脆弱么 時間: 2025-3-26 11:27
MGSAN: A Multi-granularity Self-attention Network for Next POI Recommendationods usually exploit the individual-level POI sequences but failed to utilize the information of collective-level POI sequences. Since collective-level POIs, like shopping malls or plazas, are common in the real world, we argue that only the individual-level POI sequences cannot represent more semant作者: TEM 時間: 2025-3-26 14:03
HRFA: Don’t Ignore Strangers with Different Views methods resort to supplementary reviews written by similar users, which only leverage homogeneous preferences. However, users holding different views could also supply valuable information with heterogeneous preferences. In this paper, we propose a recommendation model for rating prediction, named 作者: 發(fā)炎 時間: 2025-3-26 19:35 作者: ethnology 時間: 2025-3-27 00:48
Conference proceedings 2021rne, VIC, Australia, in October 2021..The 55 full, 29 short and?5 demo papers, plus 2 tutorials?were carefully reviewed and selected from 229 submissions. The papers are organized in the following topical sections:.Part I: BlockChain and Crowdsourcing; Database System and Workflow; Data Mining and A作者: 孵卵器 時間: 2025-3-27 01:54
0302-9743 in?Melbourne, VIC, Australia, in October 2021..The 55 full, 29 short and?5 demo papers, plus 2 tutorials?were carefully reviewed and selected from 229 submissions. The papers are organized in the following topical sections:.Part I: BlockChain and Crowdsourcing; Database System and Workflow; Data Mi作者: Initiative 時間: 2025-3-27 08:17
Conference proceedings 2021Learning (1),?Deep Learning (2),?Recommender Systems (1),?Recommender Systems (2),?Text Mining (1),?Text Mining (2),?Service Computing and Cloud Computing (1),?Service Computing and Cloud Computing (2), Tutorial and Demo..作者: 夾克怕包裹 時間: 2025-3-27 12:42 作者: CON 時間: 2025-3-27 15:47 作者: 洞察力 時間: 2025-3-27 21:43 作者: Occipital-Lobe 時間: 2025-3-28 00:24 作者: 變色龍 時間: 2025-3-28 05:58 作者: Bouquet 時間: 2025-3-28 06:18 作者: Pericarditis 時間: 2025-3-28 13:38 作者: POWER 時間: 2025-3-28 17:02
Jiarui Si,Haohan Zou,Chuanyi Huang,Huan Feng,Honglin Liu,Guangyu Li,Shuaijun Hu,Hong Zhang,Xin Wang作者: sulcus 時間: 2025-3-28 21:52
Yepeng Li,Xuefeng Xian,Pengpeng Zhao,Yanchi Liu,Victor S. Sheng作者: 縱火 時間: 2025-3-28 23:36
eringer Variations-Breite, Gleiches ausdrückt? Wenn das zu bejahen ist, dann mü?te ein erfahrener Fachmann alle Mythen entschlüsseln, also mit Eindeutigkeit in die Ebene mitteilbarer Rationalit?t übersetzen k?nnen. Und es ist wohl bekannt, da? dieser Anspruch. mit Ernst und Zuversicht erhoben wird.作者: 遺傳 時間: 2025-3-29 07:06
Shenghao Zheng,Xuefeng Xian,Yongjing Hao,Victor S. Sheng,Zhiming Cui,Pengpeng Zhaowentieth century until it was demonstrated that the injected materials were contaminated with Gram-negative bacteria and hence contained bacterial lipopolysaccharide (reviewed in B. and B. 1950; A. and B. 1974, 1979).作者: Uncultured 時間: 2025-3-29 10:53
esamtschulen, die binnendifferenziert-integrativ arbeiten. Diese in der allt?glichen Schulp?dagogik realisierte Vielschichtigkeit findet kaum eine Entsprechung in der p?dagogischen Theoriebildung und den damit einhergehenden erziehungswissenschaftlichen Diskursen.作者: 動脈 時間: 2025-3-29 14:03 作者: 清晰 時間: 2025-3-29 18:48 作者: ingenue 時間: 2025-3-29 21:20 作者: FAR 時間: 2025-3-30 00:38 作者: cuticle 時間: 2025-3-30 07:11 作者: FRONT 時間: 2025-3-30 12:03 作者: Foment 時間: 2025-3-30 16:05 作者: 飲料 時間: 2025-3-30 17:49 作者: 檔案 時間: 2025-3-31 00:23 作者: CULP 時間: 2025-3-31 01:05 作者: 粉筆 時間: 2025-3-31 07:25
elbar in das Erkranken hereinspielen; da? andererseits nicht selten ein ?rtlich und zeitlich geh?uftes gleichartiges Verhalten, speziell Erkranken zahlreicher, ja vieler Menschen allein auf eine gemeinsame ?u?ere Krankheitsursache, also einen Umwelteinflu? zurückgeführt werden müsse.作者: Gossamer 時間: 2025-3-31 11:43 作者: 得意人 時間: 2025-3-31 15:38
Zihan Song,Jiahao Yuan,Xiaoling Wang,Wendi Jis Forschers führte dazu, da? der Gegenstand der Erkenntnis ebenfalls immer edler und vollkommener werden mu?te, wohingegen der Ideologieverdacht die Neigung zur Abstraktion und Formelhaftigkeit vorantrieb: nur die reine Struktur stand jenseits aller Ideologie.作者: 華而不實 時間: 2025-3-31 17:51 作者: ASTER 時間: 2025-4-1 00:56
Efficient Feature Interactions Learning with Gated Attention Transformerose a novel model named Gated Attention Transformer. In our method, .-order cross features are generated by crossing .-order cross features and .-order features, which uses the vanilla attention mechanism instead of the self-attention mechanism and is more explainable and efficient. In addition, as 作者: Aura231 時間: 2025-4-1 05:07 作者: filicide 時間: 2025-4-1 09:32
Efficient Feature Interactions Learning with Gated Attention Transformerose a novel model named Gated Attention Transformer. In our method, .-order cross features are generated by crossing .-order cross features and .-order features, which uses the vanilla attention mechanism instead of the self-attention mechanism and is more explainable and efficient. In addition, as 作者: Memorial 時間: 2025-4-1 13:30
Exploiting Intra and?Inter-field Feature Interaction with?Self-Attentive Network for?CTR Predictionntion mechanism to aggregate all interactive embeddings. Finally, we assign DNNs in the prediction layer to generate the final output. Extensive experiments on three real public datasets show that IISAN achieves better performance than existing state-of-the-art approaches for CTR prediction.作者: tendinitis 時間: 2025-4-1 14:39 作者: Capitulate 時間: 2025-4-1 20:21 作者: nutrition 時間: 2025-4-2 01:25 作者: 廢墟 時間: 2025-4-2 03:58
Performance Evaluation of Pre-trained Models in Sarcasm Detection Tasktection task when computing resources are limited. However, XLNet may not be suitable for sarcasm detection task. In addition, we implement detailed grid search for four hyperparameters to investigate their impact on PTMs. The results show that learning rate is the most important hyperparameter. Fur作者: 離開真充足 時間: 2025-4-2 09:21
AMBD: Attention Based Multi-Block Deep Learning Model for Warehouse Dwell Time Predictionepresent the loading task statuses of different trucks. On the basis of that, we propose a deep learning based multi-block dwell time prediction model, called .. It incorporates the loading ability of warehouse and the execution process of loading tasks of preceding trucks in the queue. Moreover, to作者: dapper 時間: 2025-4-2 14:58 作者: AMOR 時間: 2025-4-2 16:31 作者: GLEAN 時間: 2025-4-2 21:18
An Efficient Method for?Indoor Layout Estimation with?FPNpixel error respectively. Besides, the advanced two-step method is only . better than our result on key point error. Both the high efficiency and accuracy make our method a good choice for some real-time room layout estimation tasks.作者: Herd-Immunity 時間: 2025-4-3 01:20
Lightweight Network Traffic Classification Model Based on Knowledge Distillationples and different degrees of difficulty in classification. To enhance the learning efficiency, we design an adaptive temperature function to soften the labels at each training stage. Experiments show that compared with the teacher model, the recognition speed of the student model is increased by 72作者: GRATE 時間: 2025-4-3 06:32
Lightweight Network Traffic Classification Model Based on Knowledge Distillationples and different degrees of difficulty in classification. To enhance the learning efficiency, we design an adaptive temperature function to soften the labels at each training stage. Experiments show that compared with the teacher model, the recognition speed of the student model is increased by 72作者: 繁重 時間: 2025-4-3 11:12 作者: Forage飼料 時間: 2025-4-3 15:44
