標(biāo)題: Titlebook: Knowledge Science, Engineering and Management; 16th International C Zhi Jin,Yuncheng Jiang,Wenjun Ma Conference proceedings 2023 The Editor [打印本頁] 作者: 搖尾乞憐 時(shí)間: 2025-3-21 17:50
書目名稱Knowledge Science, Engineering and Management影響因子(影響力)
書目名稱Knowledge Science, Engineering and Management影響因子(影響力)學(xué)科排名
書目名稱Knowledge Science, Engineering and Management網(wǎng)絡(luò)公開度
書目名稱Knowledge Science, Engineering and Management網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Knowledge Science, Engineering and Management被引頻次
書目名稱Knowledge Science, Engineering and Management被引頻次學(xué)科排名
書目名稱Knowledge Science, Engineering and Management年度引用
書目名稱Knowledge Science, Engineering and Management年度引用學(xué)科排名
書目名稱Knowledge Science, Engineering and Management讀者反饋
書目名稱Knowledge Science, Engineering and Management讀者反饋學(xué)科排名
作者: faucet 時(shí)間: 2025-3-21 21:18
0302-9743 KSEM 2023, which was held in Guangzhou, China, during August 16–18, 2023.?.The 114 full papers and 30 short papers included in this book were carefully reviewed and selected from 395 submissions. They were organized in topical sections as follows: knowledge science with learning and AI; knowledge en作者: BORE 時(shí)間: 2025-3-22 03:12 作者: 反抗者 時(shí)間: 2025-3-22 06:17 作者: 堅(jiān)毅 時(shí)間: 2025-3-22 12:37 作者: 盲信者 時(shí)間: 2025-3-22 16:18 作者: Focus-Words 時(shí)間: 2025-3-22 19:48 作者: Harpoon 時(shí)間: 2025-3-22 22:22
Federated Prompting and?Chain-of-Thought Reasoning for?Improving LLMs Answeringificantly more accurate answers for all user queries without requiring sophisticated model-tuning. Through extensive experiments, we demonstrate that our proposed methods can significantly enhance question accuracy by fully exploring the synonymous nature of the questions and the consistency of the answers.作者: Annotate 時(shí)間: 2025-3-23 04:43
Jian Yang,Xinyu Hu,Weichun Huang,Hao Yuan,Yulong Shen,Gang Xiaochaden durch ungeprüftes Umsetzen von Forschungsergebnissen in Technologien angerichtet, da? es ernsthafter und umfangreicher Forschungsarbeiten bedarf, die Sch?den zu erkennen, zu beseitigen und durch verbesserte Technologien dauerhaft zu vermeiden.作者: nephritis 時(shí)間: 2025-3-23 05:40
Xianwei Zhou,Xin Ye,Kun Zhang,Songsen Yuchaden durch ungeprüftes Umsetzen von Forschungsergebnissen in Technologien angerichtet, da? es ernsthafter und umfangreicher Forschungsarbeiten bedarf, die Sch?den zu erkennen, zu beseitigen und durch verbesserte Technologien dauerhaft zu vermeiden.作者: Acupressure 時(shí)間: 2025-3-23 10:12 作者: 虛度 時(shí)間: 2025-3-23 15:55 作者: 松軟 時(shí)間: 2025-3-23 19:49 作者: MOT 時(shí)間: 2025-3-23 23:20
Zhaohuan Wang,Yi Xu,Liangzhe Han,Tongyu Zhu,Leilei Sun作者: flaunt 時(shí)間: 2025-3-24 05:59 作者: 相一致 時(shí)間: 2025-3-24 07:28
Wenjun Peng,Derong Xu,Tong Xu,Jianjin Zhang,Enhong Chen作者: 消毒 時(shí)間: 2025-3-24 11:03 作者: 口訣 時(shí)間: 2025-3-24 16:39 作者: osculate 時(shí)間: 2025-3-24 20:40 作者: 話 時(shí)間: 2025-3-25 01:08
Advancing Domain Adaptation of?BERT by?Learning Domain Term Semantics gap between the original vocabulary and domain terms in the embedding space. We evaluate our method on both general and biomedical NLP tasks, and experimental results demonstrate a significant improvement in BERT’s performance across all biomedical NLP tasks without affecting its performance on gen作者: 不利 時(shí)間: 2025-3-25 06:34
TCMCoRep: Traditional Chinese Medicine Data Mining with?Contrastive Graph Representation LearningM diagnosis in real life. Hybridization of homogeneous and heterogeneous graph convolutions is able to preserve graph heterogeneity preventing the possible damage from early augmentation, to convey strong samples for contrastive learning. Experiments conducted in practical datasets demonstrate our p作者: NOT 時(shí)間: 2025-3-25 08:30 作者: Anonymous 時(shí)間: 2025-3-25 12:49
PRACM: Predictive Rewards for?Actor-Critic with?Mixing Function in?Multi-Agent Reinforcement Learnin action space, PRACM uses Gumbel-Softmax. And to promote cooperation among agents and to adapt to cooperative environments with penalties, the predictive rewards is introduced. PRACM was evaluated against several baseline algorithms in “Cooperative Predator-Prey” and the challenging “SMAC” scenarios作者: cyanosis 時(shí)間: 2025-3-25 16:22
A Cybersecurity Knowledge Graph Completion Method for?Scalable Scenariosn matrix and multi-head attention mechanism to explore the relationships between samples. To mitigate the catastrophic forgetting problem, a new self-distillation algorithm is designed to enhance the robustness of the trained model. We construct knowledge graph based on cybersecurity data, and condu作者: PACK 時(shí)間: 2025-3-25 21:40 作者: 把…比做 時(shí)間: 2025-3-26 01:20 作者: apropos 時(shí)間: 2025-3-26 05:40 作者: VEN 時(shí)間: 2025-3-26 11:32
Importance-Based Neuron Selective Distillation for?Interference Mitigation in?Multilingual Neural Mahe important ones representing general knowledge of each language and the unimportant ones representing individual knowledge of each low-resource language. Then, we prune the pre-trained model, retaining only the important neurons, and train the pruned model supervised by the original complete model作者: opinionated 時(shí)間: 2025-3-26 14:41
Are GPT Embeddings Useful for?Ads and?Recommendation?embedding aggregation, and as a pre-training task (EaaP) to replicate the capability of LLMs, respectively. Our experiments demonstrate that, by incorporating GPT embeddings, basic PLMs can improve their performance in both ads and recommendation tasks. Our code is available at 作者: metropolitan 時(shí)間: 2025-3-26 17:49 作者: diathermy 時(shí)間: 2025-3-26 21:05 作者: Ruptured-Disk 時(shí)間: 2025-3-27 01:06 作者: 缺陷 時(shí)間: 2025-3-27 08:52 作者: dry-eye 時(shí)間: 2025-3-27 09:42 作者: Anterior 時(shí)間: 2025-3-27 17:26
k in die Zukunft der Unternehmensführung schlie?t. Jedes Kapitel bietet zur Veranschaulichung Beispiele aus der beruflichen Praxis des Autors und erl?utert die konkrete Anwendung der entwickelten Methode, die auch auf den Arbeitsalltag von Freiberuflern übertragbar ist.? ??.978-3-658-26157-3978-3-658-26158-0作者: triptans 時(shí)間: 2025-3-27 18:32
Xiangyang Liu,Tianqi Pang,Chenyou Fank in die Zukunft der Unternehmensführung schlie?t. Jedes Kapitel bietet zur Veranschaulichung Beispiele aus der beruflichen Praxis des Autors und erl?utert die konkrete Anwendung der entwickelten Methode, die auch auf den Arbeitsalltag von Freiberuflern übertragbar ist.? ??.978-3-658-26157-3978-3-658-26158-0作者: Immortal 時(shí)間: 2025-3-28 01:03
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/k/image/544049.jpg作者: Flinch 時(shí)間: 2025-3-28 04:30
https://doi.org/10.1007/978-3-031-40292-0artificial intelligence; computational linguistics; computer networks; data mining; databases; directed g作者: callous 時(shí)間: 2025-3-28 06:29 作者: Rheumatologist 時(shí)間: 2025-3-28 11:47 作者: Abutment 時(shí)間: 2025-3-28 16:36 作者: Blood-Clot 時(shí)間: 2025-3-28 20:44
Advancing Domain Adaptation of?BERT by?Learning Domain Term SemanticsNatural Language Processing (NLP) tasks. However, these models yield an unsatisfactory results in domain scenarios, particularly in specialized fields like biomedical contexts, where they cannot amass sufficient semantics of domain terms. To tackle this problem, we present a semantic learning method作者: Macronutrients 時(shí)間: 2025-3-28 23:58
Deep Reinforcement Learning for?Group-Aware Robot Navigation in?Crowdspredictable. Previous research has addressed the problem of navigating in dense crowds by modelling the crowd and using a self-attention mechanism to assign different weights to each individual. However, in reality, crowds do not only consist of individuals, but more often appear as groups, so avoid作者: Congeal 時(shí)間: 2025-3-29 05:56
An Enhanced Distributed Algorithm for?Area Skyline Computation Based on?Apache Sparkta grows larger, these computations become slower and more challenging. To address this issue, we propose an efficient algorithm that uses Apache Spark, a platform for distributed processing, to perform area skyline computations faster and more salable. Our algorithm consists of three main phases: c作者: Assignment 時(shí)間: 2025-3-29 10:48 作者: 過剩 時(shí)間: 2025-3-29 12:46 作者: 設(shè)施 時(shí)間: 2025-3-29 16:34
PRACM: Predictive Rewards for?Actor-Critic with?Mixing Function in?Multi-Agent Reinforcement Learninnificant progress in tackling cooperative problems with discrete action spaces. Nevertheless, many existing algorithms suffer from significant performance degradation when faced with large numbers of agents or more challenging tasks. Furthermore, some specific scenarios, such as cooperative environm作者: 去掉 時(shí)間: 2025-3-29 21:06 作者: CONE 時(shí)間: 2025-3-30 03:56
Research on?Remote Sensing Image Classification Based on?Transfer Learning and?Data Augmentation sensing image classification algorithm based on convolutional neural net-work architecture needs a significant amount of annotated datasets, and the creation of these training data is labor-intensive and time-consuming. Therefore, using a small sample dataset and a mix of transfer learning and data作者: Nonconformist 時(shí)間: 2025-3-30 04:52 作者: 旅行路線 時(shí)間: 2025-3-30 08:15 作者: blight 時(shí)間: 2025-3-30 13:04 作者: hauteur 時(shí)間: 2025-3-30 20:12
Are GPT Embeddings Useful for?Ads and?Recommendation?se services is semantic modeling, which involves extracting useful knowledge or information from text. Large language models (LLMs) such as GPT-3 and LaMDA have incredible natural language understanding capabilities and their text embeddings have achieved excellent performance in various NLP tasks. 作者: Arthropathy 時(shí)間: 2025-3-30 23:02
