標題: 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: 兩邊在擴散 時間: 2025-3-21 20:07
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)學科排名
書目名稱Knowledge Science, Engineering and Management網(wǎng)絡公開度
書目名稱Knowledge Science, Engineering and Management網(wǎng)絡公開度學科排名
書目名稱Knowledge Science, Engineering and Management被引頻次
書目名稱Knowledge Science, Engineering and Management被引頻次學科排名
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書目名稱Knowledge Science, Engineering and Management年度引用學科排名
書目名稱Knowledge Science, Engineering and Management讀者反饋
書目名稱Knowledge Science, Engineering and Management讀者反饋學科排名
作者: 他去就結(jié)束 時間: 2025-3-21 20:27 作者: misanthrope 時間: 2025-3-22 00:43
Boosting LightWeight Depth Estimation via?Knowledge Distillationlexity and inference performance. In this paper, we propose a lightweight network that can accurately estimate depth maps using minimal computing resources. We achieve this by designing a compact model that maximally reduces model complexity. To improve the performance of our lightweight network, we作者: 公豬 時間: 2025-3-22 06:28
Graph Neural Network with?Neighborhood Reconnectionveness of GNNs is compromised by two limitations. First, they implicitly assume that networks are homophilous, leading to decreased performance on heterophilous or random networks commonly found in the real world. Second, they tend to ignore the known node labels, inferring node labels merely from t作者: idiopathic 時間: 2025-3-22 11:57
Critical Node Privacy Protection Based on Random Pruning of Critical Treesy obvious. Especially for critical node users, if the critical node users suffer from background knowledge attacks during data publishing, it can not only lead to the privacy information leakage of the critical user but also lead to the privacy leakage of their friends. To address this issue, we pro作者: Cosmopolitan 時間: 2025-3-22 14:16
DSEAformer: Forecasting by?De-stationary Autocorrelation with?Edgeboundl due to the increasing data volume and dimensionality. Current MLTF methods face challenges such as over-stationarization and distribution shift, affecting prediction accuracy. This paper proposes DSEAformer, a unique MLTF method that addresses distribution shift by normalizing and de-normalizing t作者: 前奏曲 時間: 2025-3-22 20:04 作者: 鋪子 時間: 2025-3-23 00:15 作者: Picks-Disease 時間: 2025-3-23 02:30
RTAD-TP: Real-Time Anomaly Detection Algorithm for?Univariate Time Series Data Based on?Two-Parameteications. However, due to the continuous influx of data streams and the dynamic changes in data patterns, real-time anomaly detection still poses challenges. Algorithms such as SPOT, DSPOT, and FluxEV are efficient unsupervised anomaly detection algorithms for data streams, but their detection perfo作者: chuckle 時間: 2025-3-23 08:44 作者: Keratin 時間: 2025-3-23 11:53
A Sparse Matrix Optimization Method for?Graph Neural Networks Traininguperior feature representation capabilities for graph data with non-Euclidean structures. These capabilities are enabled efficiently by sparse matrix-matrix multiplication (SPMM) and sparse matrix-vector multiplication (SPMV) that operate on sparse matrix representations of graph structures. However作者: HEW 時間: 2025-3-23 16:21
Dual-Dimensional Refinement of?Knowledge Graph Embedding Representation within and between triples. However, existing methods primarily focus on a single dimension of entities or relations, limiting their ability to learn knowledge facts. To address this issue, this paper proposes a dual-dimension refined representation model. At the entity level, we perform residual s作者: 微枝末節(jié) 時間: 2025-3-23 20:30 作者: Congregate 時間: 2025-3-23 23:42
Dynamic and?Static Feature-Aware Microservices Decomposition via?Graph Neural Networkstem into microservices can increase code reusability and reduce reconstruction costs. However, existing microservices decomposition approaches only utilize dynamic or static feature to represent the monolithic system, leading to low coverage of classes and inadequate information. To address these is作者: 偽書 時間: 2025-3-24 05:50 作者: Functional 時間: 2025-3-24 07:35
Low Redundancy Learning for?Unsupervised Multi-view Feature Selections on the correlation between features and data category structure, while ignoring the redundancy between features. In this paper, we propose a multi-view feature selection method based on low redundancy learning, which introduces and automatically assigns the weight of feature redundancy in each vie作者: Notify 時間: 2025-3-24 12:39
Dynamic Feed-Forward LSTMo this end, we propose the Dynamic Feed-Forward LSTM (D-LSTM). Specifically, our D-LSTM first expands the capabilities of hidden states by assigning an exclusive state vector to each word. Then, the Dynamic Additive Attention (DAA) method is utilized to adaptively compress local context words into a作者: 小卒 時間: 2025-3-24 16:02
Black-Box Adversarial Attack on?Graph Neural Networks Based on?Node Domain Knowledgepplication of GNNs in various graph tasks, it is particularly important to study the principles and implementation of graph adversarial attacks for understanding the robustness of GNNs. Previous studies have attempted to reduce the prediction accuracy of GNNs by adding small perturbations to the gra作者: corporate 時間: 2025-3-24 22:25
Tian Wang,Zhiguang Wang,Rongliang Wang,Dawei Li,Qiang Luw?rtsspirale. Zun?chst zur Abw?rtsspirale: Stadterneuerung und Regionalentwicklung sind normalerweise eigendynamische Prozesse, bei denen sich Quartiere auf neue Gegebenheiten durch den Druck des Marktes ausrichten und eine Modernisierung ohne künstliche Steuerung oder finanzielle Anreize stattfinde作者: archaeology 時間: 2025-3-25 02:13
Long Chen,Mingjian Guang,Junli Wang,Chungang Yanw?rtsspirale. Zun?chst zur Abw?rtsspirale: Stadterneuerung und Regionalentwicklung sind normalerweise eigendynamische Prozesse, bei denen sich Quartiere auf neue Gegebenheiten durch den Druck des Marktes ausrichten und eine Modernisierung ohne künstliche Steuerung oder finanzielle Anreize stattfinde作者: Ingratiate 時間: 2025-3-25 05:06 作者: cardiovascular 時間: 2025-3-25 10:50 作者: 團結(jié) 時間: 2025-3-25 12:20 作者: geriatrician 時間: 2025-3-25 17:14 作者: 遣返回國 時間: 2025-3-25 20:00
Tian Wang,Zhiguang Wang,Rongliang Wang,Dawei Li,Qiang Lub des Quartiers bzw. der Region generiert wird und auch im Quartier kaum wirtschaftliche Leistungen angeboten werden. Dies h?tte zur Folge, dass solche R?ume stark von staatlichen Transferzahlungen abh?ngig w?ren und auch in Bezug auf die Identit?tsbildung Probleme auftreten würden.作者: Glossy 時間: 2025-3-26 00:37 作者: 親密 時間: 2025-3-26 06:09
Haijia Bao,Yu Du,Ya Lib des Quartiers bzw. der Region generiert wird und auch im Quartier kaum wirtschaftliche Leistungen angeboten werden. Dies h?tte zur Folge, dass solche R?ume stark von staatlichen Transferzahlungen abh?ngig w?ren und auch in Bezug auf die Identit?tsbildung Probleme auftreten würden.作者: CRUMB 時間: 2025-3-26 11:03
Hong Jia,Jian Huangb des Quartiers bzw. der Region generiert wird und auch im Quartier kaum wirtschaftliche Leistungen angeboten werden. Dies h?tte zur Folge, dass solche R?ume stark von staatlichen Transferzahlungen abh?ngig w?ren und auch in Bezug auf die Identit?tsbildung Probleme auftreten würden.作者: 建筑師 時間: 2025-3-26 13:14
Chengkai Piao,Yuchen Wang,Jinmao Weib des Quartiers bzw. der Region generiert wird und auch im Quartier kaum wirtschaftliche Leistungen angeboten werden. Dies h?tte zur Folge, dass solche R?ume stark von staatlichen Transferzahlungen abh?ngig w?ren und auch in Bezug auf die Identit?tsbildung Probleme auftreten würden.作者: Counteract 時間: 2025-3-26 19:13
Qin Sun,Zheng Yang,Zhiming Liu,Quan Zoub des Quartiers bzw. der Region generiert wird und auch im Quartier kaum wirtschaftliche Leistungen angeboten werden. Dies h?tte zur Folge, dass solche R?ume stark von staatlichen Transferzahlungen abh?ngig w?ren und auch in Bezug auf die Identit?tsbildung Probleme auftreten würden.作者: scoliosis 時間: 2025-3-26 23:06
Joint Feature Selection and?Classifier Parameter Optimization: A Bio-Inspired Approachal HBA. Finally, a binary mechanism is adopted to make it suitable for the feature selection problem. Experiments conducted in 27 public datasets have demonstrated that the proposed approach can outperform some well-known swarm-based algorithms.作者: 健忘癥 時間: 2025-3-27 01:10
Boosting LightWeight Depth Estimation via?Knowledge Distillation guide KD, enabling the student to better learn from the teacher’s predictions. This approach helps fill the gap between the teacher and the student, resulting in improved data-driven learning. The experiments show that our method achieves comparable performance to state-of-the-art methods while usi作者: Peculate 時間: 2025-3-27 08:43 作者: 攀登 時間: 2025-3-27 12:31
Multitask-Based Cluster Transmission for?Few-Shot Text Classificationntation of basic classification features as an auxiliary task to further enhance the diversity of spatial vectors and alleviate the over-fitting problem. Experiments show that our approach further improves the performance of the few-shot text classification task.作者: 白楊 時間: 2025-3-27 14:02 作者: 含糊其辭 時間: 2025-3-27 20:32
RTAD-TP: Real-Time Anomaly Detection Algorithm for?Univariate Time Series Data Based on?Two-Parameteo determine the threshold for the residual. In addition, we use two-parameter estimation to improve the speed and accuracy of parameter estimation in automatic thresholding, addressing the limitations of SPOT, DSPOT, and FluxEV. Experimental results show that the RTAD-TP algorithm has better detecti作者: Terrace 時間: 2025-3-27 23:48
Multi-Sampling Item Response Ranking Neural Cognitive Diagnosis with?Bilinear Feature Interaction element product feature interaction with bilinear feature interaction in the multi-sampling item response ranking neural cognitive diagnosis to enhance interaction in the deep learning process. Specifically, our model is stable and can be easily applied to cognitive diagnosis. We observed improveme作者: 顧客 時間: 2025-3-28 06:07 作者: Confidential 時間: 2025-3-28 07:03
Dual-Dimensional Refinement of?Knowledge Graph Embedding Representationd UMLS. Through validation experiments, we substantiate our assumptions and analyses regarding datasets and model capabilities, thereby addressing the interpretability shortcomings of existing embedding models on underperforming datasets.作者: Laconic 時間: 2025-3-28 12:57
An Enhanced Fitness-Distance Balance Slime Mould Algorithm and?Its Application in?Feature Selectionsition update mechanism. Secondly, an elite opposition-based learning strategy is adopted in the population initialization for increasing population diversity. Then chaotic tent sequence, with traversal property, is integrated into the position updating of SMA to perturb the position and jump out of作者: Loathe 時間: 2025-3-28 16:19
Low Redundancy Learning for?Unsupervised Multi-view Feature Selectionndication matrix. Finally, an alternating iterative updating method is presented to solve the optimization problem. Experiments on different public multi-view data sets verify the effectiveness of the proposed method.作者: chalice 時間: 2025-3-28 22:30 作者: Lyme-disease 時間: 2025-3-28 23:58
Black-Box Adversarial Attack on?Graph Neural Networks Based on?Node Domain Knowledge vector. To address this issue, we propose a special adversarial graph called DK-AdvGraph, where we meticulously tailor the perturbation vector of adversarial graphs in a highly limited black-box setting. Additionally, to better confuse GNNs, we ensure a higher similarity between nodes after perturb作者: Forage飼料 時間: 2025-3-29 06:04 作者: G-spot 時間: 2025-3-29 11:03
Kai Shen,Haoyu Wang,Arin Chaudhuri,Zohreh Asgharzadeh作者: 叢林 時間: 2025-3-29 13:26
Junjie Hu,Chenyou Fan,Hualie Jiang,Xiyue Guo,Yuan Gao,Xiangyong Lu,Tin Lun Lam作者: gerrymander 時間: 2025-3-29 16:45
Kaifang Dong,Fuyong Xu,Baoxing Jiang,Hongye Li,Peiyu Liu作者: 排他 時間: 2025-3-29 22:21
Yadan Han,Guangquan Lu,Jiecheng Li,Fuqing Ling,Wanxi Chen,Liang Zhang作者: Suppository 時間: 2025-3-30 03:37
Qiyun Fan,Yan Tang,Xiaoming Ding,Qianglong Huangfu,Peihao Ding作者: 軌道 時間: 2025-3-30 06:37 作者: 令人苦惱 時間: 2025-3-30 08:58
Automatic Gaussian Bandwidth Selection for?Kernel Principal Component Analysisselection method, called the criterion of the maximum sum of eigenvalues (CMSE) method and a scalable variation (SCMSE) to handle big data. Both feature high time efficiency and achieve performance better than or comparable to that of the existing methods. We conduct both simulation study and real-world data analyses to support our conclusions.作者: 挫敗 時間: 2025-3-30 16:18
DSEAformer: Forecasting by?De-stationary Autocorrelation with?Edgeboundization based on weighted moving average helps prevent overfitting. Tests on three datasets confirm that DSEAformer outperforms existing MLTF techniques. In conclusion, DSEAformer introduces innovative ideas and methods to enhance time series prediction and offers improved practical applications.作者: Forehead-Lift 時間: 2025-3-30 19:56 作者: ALIBI 時間: 2025-3-30 23:38 作者: 大笑 時間: 2025-3-31 02:20
Contextual Information Augmented Few-Shot Relation ExtractionewRel demonstrate that our method outperforms existing methods on three different few-shot relation extraction tasks. Moreover, our method also provides a new idea for both few-shot learning and data augmentation research.作者: Medley 時間: 2025-3-31 06:39 作者: hurricane 時間: 2025-3-31 09:23
0302-9743 gineering research and applications; knowledge management systems; and emerging technologies for knowledge science, engineering and management.?.978-3-031-40282-1978-3-031-40283-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Foment 時間: 2025-3-31 15:23
Conference proceedings 2023 and selected from 395 submissions. They were organized in topical sections as follows: knowledge science with learning and AI; knowledge engineering research and applications; knowledge management systems; and emerging technologies for knowledge science, engineering and management.?.作者: overbearing 時間: 2025-3-31 20:03 作者: cutlery 時間: 2025-3-31 23:55
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/k/image/544041.jpg作者: 不愿 時間: 2025-4-1 04:03 作者: Lethargic 時間: 2025-4-1 09:47
Knowledge Science, Engineering and Management978-3-031-40283-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: avulsion 時間: 2025-4-1 10:34
