書(shū)目名稱(chēng)Collaborative Computing: Networking, Applications and Worksharing影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Collaborative Computing: Networking, Applications and Worksharing網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Collaborative Computing: Networking, Applications and Worksharing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Collaborative Computing: Networking, Applications and Worksharing被引頻次
書(shū)目名稱(chēng)Collaborative Computing: Networking, Applications and Worksharing被引頻次學(xué)科排名
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書(shū)目名稱(chēng)Collaborative Computing: Networking, Applications and Worksharing讀者反饋
書(shū)目名稱(chēng)Collaborative Computing: Networking, Applications and Worksharing讀者反饋學(xué)科排名
作者: 抱狗不敢前 時(shí)間: 2025-3-21 23:33 作者: CORE 時(shí)間: 2025-3-22 00:25 作者: armistice 時(shí)間: 2025-3-22 07:54
Defeating the?Non-stationary Opponent Using Deep Reinforcement Learning and?Opponent Modelingscenario, it is not easy to capture its behavior strategy when confronted with a long-term latent, highly dynamic and unpredictable opponent. FlipIt game can model the stealth interaction of advanced persistent threat. However, it is insufficient for traditional reinforcement learning approach to so作者: deviate 時(shí)間: 2025-3-22 10:37 作者: 有其法作用 時(shí)間: 2025-3-22 15:44
D-AE: A Discriminant Encode-Decode Nets for?Data Generationmain. The first is to use algorithms to learn the main features of minority class samples, and the second is to differentiate the generated data from the majority class samples. To tackle these challenges in binary classification, we propose the Discriminant-Autoencoder (D-AE) algorithm. It has two 作者: 有其法作用 時(shí)間: 2025-3-22 19:28 作者: 使?jié)M足 時(shí)間: 2025-3-22 22:22 作者: 留戀 時(shí)間: 2025-3-23 04:46
MD-TransUNet: TransUNet with?Multi-attention and?Dilated Convolution for?Brain Stroke Lesion Segment large difference in the volume of stroke lesion areas and the great similarity between lesion areas and normal tissues, most of the existing methods for lesion segmentation cannot deal with these problems well. This paper proposes a novel network named MD-TransUNet for the segmentation of stroke le作者: 訓(xùn)誡 時(shí)間: 2025-3-23 06:03 作者: Antagonism 時(shí)間: 2025-3-23 12:44 作者: 商業(yè)上 時(shí)間: 2025-3-23 16:48
NPGraph: An Efficient Graph Computing Model in?NUMA-Based Persistent Memory Systems of main memory (DRAM). Fortunately, a promising solution has emerged in the form of hybrid memory systems (HMS) which combine DRAM and persistent memory (PMEM) to enable data-centric graph computing. However, directly transitioning existing DRAM-based models to HMS can lead to inefficiency issues, 作者: 保守 時(shí)間: 2025-3-23 19:14
tHR-Net: A Hybrid Reasoning Framework for?Temporal Knowledge Graphre events is to understand historical trends and extract the information most likely to affect the future, i.e., the TKG reasoning task is both influenced by the trends of time-evolving graphs and directly driven by the facts relevant to a specific query. Existing methods mostly build models separat作者: malign 時(shí)間: 2025-3-24 00:17 作者: Vaginismus 時(shí)間: 2025-3-24 02:42 作者: cardiovascular 時(shí)間: 2025-3-24 08:41
Robustness-Enhanced Assertion Generation Method Based on?Code Mutation and?Attack Defenseout the development cycle. However, the low readability of existing automated test case tools hinders developers from directly using them. In addition, current approaches exhibit sensitivity to individual words in the input code, often producing completely different results for minor changes in the 作者: cogitate 時(shí)間: 2025-3-24 12:57 作者: 果仁 時(shí)間: 2025-3-24 17:12 作者: 意外 時(shí)間: 2025-3-24 20:32 作者: 要控制 時(shí)間: 2025-3-25 00:13
ECCRG: A Emotion- and Content-Controllable Response Generation Modeld adversarial learning loss to jointly train the model. Experimental results show that ECCRG can embody the set target content in the generated responses, allowing us to achieve controllability on both emotion and textual content.作者: Constant 時(shí)間: 2025-3-25 04:16 作者: 協(xié)奏曲 時(shí)間: 2025-3-25 10:15
Sexuelle Identit?t und Toleranzes a content fitness algorithm for estimating the priority of caching content with high user fitness and a collaborative caching strategy built upon a multi-agent deep reinforcement learning model. Empirical results clearly show that MAACC outperforms its peers regarding cache hit rate and transfer delay time.作者: Pastry 時(shí)間: 2025-3-25 13:16
,Sexualit?ten in Afrika, Asien und Ozeanien,s function, Discriminant-., balances the discriminant and reconstruction losses. Results from experiments on three datasets show that D-AE outperforms baseline algorithms and improves dataset applicability.作者: 殺蟲(chóng)劑 時(shí)間: 2025-3-25 19:05
Heike Pantelmann,Sabine Blackmore a K-means clustering algorithms were validated, and the experimental results demonstrated the effectiveness of proposed schemes, in which the K-means clustering algorithm in the plaintext state, the proposed scheme still maintains an accuracy up to 84.1%.作者: Banister 時(shí)間: 2025-3-25 22:20
A Multi-Agent Deep Reinforcement Learning-Based Approach to?Mobility-Aware Cachinges a content fitness algorithm for estimating the priority of caching content with high user fitness and a collaborative caching strategy built upon a multi-agent deep reinforcement learning model. Empirical results clearly show that MAACC outperforms its peers regarding cache hit rate and transfer delay time.作者: hermitage 時(shí)間: 2025-3-26 01:05 作者: 包租車(chē)船 時(shí)間: 2025-3-26 05:51 作者: 圣人 時(shí)間: 2025-3-26 08:46
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engihttp://image.papertrans.cn/c/image/229419.jpg作者: Addictive 時(shí)間: 2025-3-26 14:17
Collaborative Computing: Networking, Applications and Worksharing978-3-031-54528-3Series ISSN 1867-8211 Series E-ISSN 1867-822X 作者: 規(guī)范要多 時(shí)間: 2025-3-26 17:02
https://doi.org/10.1007/978-3-031-54528-3ad hoc networks; artificial intelligence; blockchain; cloud computing; communication channels; computer n作者: 我不怕?tīng)奚?nbsp; 時(shí)間: 2025-3-27 00:55 作者: Serenity 時(shí)間: 2025-3-27 02:32
,Pornos – die (un)heimlichen Miterzieher, is of great significance to the lives of trapped persons. This paper studies the problem of unmanned aerial vehicle (UAV) equipped with mobile edge computing (MEC) servers to provide communication and computing services for ground users in the scenario where the ground infrastructure is destroyed. 作者: 食物 時(shí)間: 2025-3-27 05:56 作者: insurrection 時(shí)間: 2025-3-27 10:46 作者: 敘述 時(shí)間: 2025-3-27 15:22 作者: 微塵 時(shí)間: 2025-3-27 19:42 作者: 感激小女 時(shí)間: 2025-3-27 22:16
,Sexualit?ten in Afrika, Asien und Ozeanien,main. The first is to use algorithms to learn the main features of minority class samples, and the second is to differentiate the generated data from the majority class samples. To tackle these challenges in binary classification, we propose the Discriminant-Autoencoder (D-AE) algorithm. It has two 作者: 斷斷續(xù)續(xù) 時(shí)間: 2025-3-28 03:35 作者: 神化怪物 時(shí)間: 2025-3-28 06:59
Class and Filipino-Australians,from one region to another and capture the passengers’ mobility patterns. This problem is challenging because it requires forecasting not only the number of demands within a region, but the origin and destination of each trip as well. To address this challenge, we propose an effective model, ODCRN (作者: Oscillate 時(shí)間: 2025-3-28 11:18
The Filipino Elderly: To Love Is to Labour, large difference in the volume of stroke lesion areas and the great similarity between lesion areas and normal tissues, most of the existing methods for lesion segmentation cannot deal with these problems well. This paper proposes a novel network named MD-TransUNet for the segmentation of stroke le作者: LAST 時(shí)間: 2025-3-28 17:51
https://doi.org/10.1007/978-3-031-57009-4ry physical objects that can be independently addressed can be interconnected. In the face of the IoT produces a large of time series data, which is very necessary to detect anomaly data. Transformer has proven to be a powerful tool in several areas, but still has some limitations, such as the predi作者: outset 時(shí)間: 2025-3-28 21:38 作者: Flatter 時(shí)間: 2025-3-29 01:04
Heike Pantelmann,Sabine Blackmore of main memory (DRAM). Fortunately, a promising solution has emerged in the form of hybrid memory systems (HMS) which combine DRAM and persistent memory (PMEM) to enable data-centric graph computing. However, directly transitioning existing DRAM-based models to HMS can lead to inefficiency issues, 作者: ASSET 時(shí)間: 2025-3-29 04:31
Heike Pantelmann,Sabine Blackmorere events is to understand historical trends and extract the information most likely to affect the future, i.e., the TKG reasoning task is both influenced by the trends of time-evolving graphs and directly driven by the facts relevant to a specific query. Existing methods mostly build models separat作者: 共和國(guó) 時(shí)間: 2025-3-29 10:44
https://doi.org/10.1007/978-3-658-40467-3 naturally represented as graphs, Graph Neural Networks (GNNs) have proven highly effective for learning graph representations of source code. Pooling, as an essential operation for GNN-based models, is limited in its ability to leverage the rich hierarchical information presented in tree-like graph作者: Cholecystokinin 時(shí)間: 2025-3-29 12:53 作者: 清真寺 時(shí)間: 2025-3-29 15:49 作者: Myocyte 時(shí)間: 2025-3-29 20:37
Task Offloading in?UAV-to-Cell MEC Networks: Cell Clustering and?Path PlanningD position of the UAV is determined according to the quality of user service, and the double deep Q-network (DDQN) algorithm is used to determine the trajectory of the UAV. Simulation experiments demonstrate the effectiveness and efficiency of our proposed strategy by comparing it with the baselines作者: 臭了生氣 時(shí)間: 2025-3-30 03:31
LAMB: Label-Induced Mixed-Level Blending for?Multimodal Multi-label Emotion Detectionerent labels to attend to the most relevant blended tokens adaptively using a transformer-based decoder, which facilitates the exploration of label-to-modality dependency. Unlike common low-order strategies in multi-label learning, correlations among multiple labels can be learned by self-attention 作者: BRACE 時(shí)間: 2025-3-30 04:59 作者: Acupressure 時(shí)間: 2025-3-30 09:00
Defeating the?Non-stationary Opponent Using Deep Reinforcement Learning and?Opponent Modelingtead of explicitly identifying the opponent’s intention, the defense agent observes the opponent’s last move actions from the game environment, stores the information in its knowledge, then perceives the opponent’s strategy and finally makes a decision to maximize its benefits. We show the excellent作者: SLAY 時(shí)間: 2025-3-30 13:29 作者: 打折 時(shí)間: 2025-3-30 18:47
MD-TransUNet: TransUNet with?Multi-attention and?Dilated Convolution for?Brain Stroke Lesion Segmentes to suppress useless information expression in skip connections and upsampling processes while focusing more on effective spatial and channel information in features. The experiments show that our proposed network gets superior performance than benchmark methods and indicates the generalization an作者: 枯萎將要 時(shí)間: 2025-3-30 23:26 作者: 絕緣 時(shí)間: 2025-3-31 02:43 作者: ingrate 時(shí)間: 2025-3-31 08:54 作者: 關(guān)心 時(shí)間: 2025-3-31 10:07
