標(biāo)題: Titlebook: Computer Supported Cooperative Work and Social Computing; 16th CCF Conference, Yuqing Sun,Tun Lu,Liping Gao Conference proceedings 2022 Spr [打印本頁] 作者: Forbidding 時間: 2025-3-21 16:17
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: 誘使 時間: 2025-3-21 22:57
Cache Optimization Based on Linear Regression and Directed Acyclic Task Graphred a bottleneck. The current algorithms show poor results on large-scale datasets. In this paper, machine learning technology is applied to the hit strategy of Cache. The calculation task is converted into a DAG graph, and then the DAG graph is represented by an adjacency matrix, and the multiple l作者: visual-cortex 時間: 2025-3-22 03:56 作者: Obligatory 時間: 2025-3-22 07:06
Multi-objective Optimization of?Ticket Assignment Problem in?Large Data Centersucial to design multi-objective ticket scheduling algorithms to maximize the total matching degree and minimize the total flowtime. However, most of existing methods for assignment problems only consider single objective, while some methods optimizing multi-objectives are not for the same objectives作者: Negligible 時間: 2025-3-22 12:13 作者: 軍火 時間: 2025-3-22 16:33 作者: 軍火 時間: 2025-3-22 19:56 作者: crescendo 時間: 2025-3-22 21:48 作者: heartburn 時間: 2025-3-23 03:26 作者: reperfusion 時間: 2025-3-23 07:44 作者: notion 時間: 2025-3-23 13:18 作者: 大方一點 時間: 2025-3-23 14:50
Academic Article Classification Algorithm Based on?Pre-trained Model and?Keyword Extractionive source of academic information and play an important role in the process of delivering latest academic information. On social media, these academic articles will generate considerable academic news, translated articles, tutorial articles, etc. How to classify these academic articles has become m作者: 補助 時間: 2025-3-23 20:09 作者: CHOP 時間: 2025-3-24 02:02
Extractive-Abstractive: A Two-Stage Model for Long Text Summarizationxts with a clear structure, while abstractive method is suitable for short texts. In this paper, we aim to address the problems of missing key words and incomplete overview that are usually caused by abstractive method in the face of long texts. To solve this problem, we propose a two-stage model th作者: gusher 時間: 2025-3-24 06:01
A Random-Walk-Based Heterogeneous Attention Network for?Community Detectionommunity have more dense connections than those in different communities, which can be utilized to analyze the function of complex networks. In addition, heterogeneous networks are ubiquitous in the real world. For example, academic networks have different types of nodes such as authors, papers, and作者: 極端的正確性 時間: 2025-3-24 09:38 作者: DAUNT 時間: 2025-3-24 13:35
Adaptive Seed Expansion Based on?Composite Similarity for?Community Detection in?Attributed Networksle for real-world networks, it is useful to detect communities in attributed networks. Recently, many algorithms consider combinating node attributes and network topology, and the effect of these methods is better than using only one information source. However, the existing algorithms still have so作者: Visual-Field 時間: 2025-3-24 18:25
Conclusion: Creating a New Discourseons as feature information from high-dimensional data as well as multi-label data. Discriminative feature learning strengthens discrimination between sample features. Therefore, the feature information of samples can be better discriminated against in algorithms. In this paper, we propose a new unsu作者: 俗艷 時間: 2025-3-24 20:53 作者: gastritis 時間: 2025-3-25 03:03
,High Economic Growth, 1955–70, by investigating common university and college information service platforms, we find a problem that users cannot quickly access key information. Inspired by user profile and corporate portraits, we propose a university portrait system incorporating academic social networks. We first collect two ty作者: 老巫婆 時間: 2025-3-25 06:13 作者: Prostaglandins 時間: 2025-3-25 09:16 作者: Factorable 時間: 2025-3-25 13:41
Soviet Policy in the Middle Eastn deep neural networks have shown the favorable performance. However, existing models mainly depend on large-scale labelled data and are unfit for the innovative drug discovery study because of local optimum on pre-training. This paper proposes a new deep learning model to predict the drug-target in作者: 凹室 時間: 2025-3-25 16:06 作者: ANA 時間: 2025-3-25 22:06 作者: 我吃花盤旋 時間: 2025-3-26 01:15 作者: Functional 時間: 2025-3-26 05:52 作者: 孵卵器 時間: 2025-3-26 09:03
Natural Resources, Geography, and Climate, the performance of the actors that only use their own local observations with centralized critics is prone to bottlenecks in complex scenarios. Recent research has shown that agents learn when to communicate to share information efficiently, that agents communicate with each other in a right time 作者: concert 時間: 2025-3-26 16:30 作者: gout109 時間: 2025-3-26 20:41
Leonid Limonov,Denis Kadochnikovd achieved excellent performance, but their model uses only a single 2D convolutional layer. Instead, we think that the network should go deeper. In this case, we propose the ResConvE model, which takes reference from the application of residual networks in computer vision, and deepens the neural ne作者: insipid 時間: 2025-3-26 23:48 作者: initiate 時間: 2025-3-27 03:41
How Do Scholars and Academics Differ?ommunity have more dense connections than those in different communities, which can be utilized to analyze the function of complex networks. In addition, heterogeneous networks are ubiquitous in the real world. For example, academic networks have different types of nodes such as authors, papers, and作者: 遠(yuǎn)足 時間: 2025-3-27 09:22
The Contemporary Scholar in Higher Educationure and node attributes, facilitating various downstream inference tasks. However, most existing attribute network embedding methods base on random walk usually sample many redundant samples and suffer from inconsistency between node structure and attributes. In this paper, we propose a novel attrib作者: 小官 時間: 2025-3-27 10:25
The Contemporary Scholar in Higher Educationle for real-world networks, it is useful to detect communities in attributed networks. Recently, many algorithms consider combinating node attributes and network topology, and the effect of these methods is better than using only one information source. However, the existing algorithms still have so作者: –吃 時間: 2025-3-27 15:35 作者: 立即 時間: 2025-3-27 21:23
Communications in Computer and Information Sciencehttp://image.papertrans.cn/c/image/233945.jpg作者: 花費 時間: 2025-3-28 01:34
https://doi.org/10.1007/978-981-19-4549-6artificial intelligence; machine learning; information networks; information security; cryptology; inform作者: 顧客 時間: 2025-3-28 03:36
978-981-19-4548-9Springer Nature Singapore Pte Ltd. 2022作者: 天氣 時間: 2025-3-28 09:01 作者: Inordinate 時間: 2025-3-28 11:46
Schriften des Deutschen Orient - Institutsof polymerization of . in .. By embedding relation information into each . representation and concatenating . representations in ., this proposed novel relation embedding method addresses the problem that GAT-based models only consider aggregating the neighboring entities and ignore the effect of re作者: 阻止 時間: 2025-3-28 18:08 作者: 瑪瑙 時間: 2025-3-28 21:58
The Contemporary Russian Economyector-wise in both implicit and explicit ways, but also learn both low-order and high-order feature interactions. xDeepFM can effectively enhance recommendation accuracy. Finally, the recommendation model is embedded in the system for testing. Evaluate on the public dataset of DiDi, we compare diffe作者: 反饋 時間: 2025-3-28 23:40 作者: JIBE 時間: 2025-3-29 05:38
How Do Scholars and Academics Differ?ion, which requires prior knowledge to set metapaths in advance. This paper proposes a novel random-walk-based heterogeneous attention network (RHAN) for community detection on heterogeneous networks. Random walk is used to generate the neighbor nodes set of nodes, and heterogeneous information is c作者: GROG 時間: 2025-3-29 09:08 作者: FLAG 時間: 2025-3-29 15:19
Computer Supported Cooperative Work and Social Computing16th CCF Conference,作者: IVORY 時間: 2025-3-29 16:40
Joint Embedding Multiple Feature and Rule for Paper Recommendations papers are recommended according to the relatedness between user interests and paper embeddings. We conduct experiments on the ACM academic paper dataset. The results show that our model outperforms baseline methods on personalized recommendation. We also analyze the influence of model structure a作者: 混雜人 時間: 2025-3-29 22:15 作者: LVAD360 時間: 2025-3-30 03:33
Deep Bug Triage Model Based on Multi-head Self-attention Mechanismport, and further quantifies the influence of fixers with similar activities on bug triage through fixer sequence. We conducted texts on four open source software projects. We can get the MSDBT has clear strength over the previous model in recall index.作者: 異教徒 時間: 2025-3-30 05:25
Taxi Pick-Up Area Recommendation via Integrating Spatio-Temporal Contexts into XDeepFMector-wise in both implicit and explicit ways, but also learn both low-order and high-order feature interactions. xDeepFM can effectively enhance recommendation accuracy. Finally, the recommendation model is embedded in the system for testing. Evaluate on the public dataset of DiDi, we compare diffe作者: intimate 時間: 2025-3-30 08:21 作者: 不易燃 時間: 2025-3-30 13:20
A Random-Walk-Based Heterogeneous Attention Network for?Community Detectionion, which requires prior knowledge to set metapaths in advance. This paper proposes a novel random-walk-based heterogeneous attention network (RHAN) for community detection on heterogeneous networks. Random walk is used to generate the neighbor nodes set of nodes, and heterogeneous information is c作者: Flat-Feet 時間: 2025-3-30 18:54 作者: Jingoism 時間: 2025-3-30 23:14 作者: 傀儡 時間: 2025-3-31 00:55 作者: 有法律效應(yīng) 時間: 2025-3-31 08:32
Conclusion: Creating a New Discourseses the discriminative of features. Specifically, we propose the restructure cost function as an objective function by adding constraint conditions about discrimination to standard function, which is solved by using stochastic gradient descent and momentum gradient descent algorithms combined with standard LLE.
