標題: Titlebook: Artificial Intelligence and Mobile Services – AIMS 2022; 11th International C Xiuqin Pan,Ting Jin,Liang-Jie Zhang Conference proceedings 20 [打印本頁] 作者: 縮寫 時間: 2025-3-21 17:21
書目名稱Artificial Intelligence and Mobile Services – AIMS 2022影響因子(影響力)
書目名稱Artificial Intelligence and Mobile Services – AIMS 2022影響因子(影響力)學科排名
書目名稱Artificial Intelligence and Mobile Services – AIMS 2022網(wǎng)絡公開度
書目名稱Artificial Intelligence and Mobile Services – AIMS 2022網(wǎng)絡公開度學科排名
書目名稱Artificial Intelligence and Mobile Services – AIMS 2022被引頻次
書目名稱Artificial Intelligence and Mobile Services – AIMS 2022被引頻次學科排名
書目名稱Artificial Intelligence and Mobile Services – AIMS 2022年度引用
書目名稱Artificial Intelligence and Mobile Services – AIMS 2022年度引用學科排名
書目名稱Artificial Intelligence and Mobile Services – AIMS 2022讀者反饋
書目名稱Artificial Intelligence and Mobile Services – AIMS 2022讀者反饋學科排名
作者: 有機體 時間: 2025-3-21 21:04 作者: 他日關稅重重 時間: 2025-3-22 01:18
Conference proceedings 2022but not limited to, wireless & sensor networks, mobile & wearable computing, mobile enterprise & eCommerce, ubiquitous collaborative & social services, machine-to-machine & Internet-of-things clouds, cyber-physical integration, and big data analytics for mobility-enabled services..作者: 切碎 時間: 2025-3-22 06:43 作者: NOCT 時間: 2025-3-22 12:43
Frequently Asked Question Pair Generation for?Rule and?Regulation Documentmework for FAQ pair generation by deep learning. For experiments, we collect and annotate a Chinese FAQ pair generation dataset from documents of China Merchants Securities Co., Ltd. The results show that our method can generate proper FAQ pairs and achieve competitive performance in both automatic and human evaluation.作者: 俗艷 時間: 2025-3-22 16:45
Push-Based Forwarding Scheme Using Fuzzy Logic to?Mitigate the?Broadcasting Storm Effect in?VNDN simply disseminating the content over the network. VNDN supports only a pull-based data forwarding model. In the pull-based mode, the content is forwarded upon request. However, in critical situations, a push-based data forwarding model is essential to design in order to broadcast the critical data作者: 人充滿活力 時間: 2025-3-22 20:09 作者: 小步舞 時間: 2025-3-22 22:06 作者: aesthetic 時間: 2025-3-23 05:12
Frequently Asked Question Pair Generation for?Rule and?Regulation Documentstomers and employers to quickly gain knowledge of them and provides a potential corpus for question-answering robots. While previous work focuses on web texts (e.g., Wiki), we generate FAQ pairs from the formal and verbose rule and regulation documents, which is significant in real scenarios. To ta作者: 死貓他燒焦 時間: 2025-3-23 07:34
Chinese Text Classification Using BERT and Flat-Lattice Transformerormation have achieved state-of-the-art performance in most downstream natural language processing (NLP) tasks, including named entity recognition (NER), English text classification and sentiment analysis. For Chinese text classification, the existing methods have also tried such kinds of models. Ho作者: 連鎖 時間: 2025-3-23 12:29
Indicator-Specific Recurrent Neural Networks with?Co-teaching for?Stock Trend Predictiond prediction and achieve impressive results. However, these methods still suffer from two limitations: 1) Various types of technical indicators are input into a single model, making it difficult for the model to learn differentiated features. 2) Noisy data in the stocks is not handled effectively. T作者: 忍耐 時間: 2025-3-23 14:24
SATMeas - Object Detection and Measurement: Canny Edge Detection Algorithmhouses, courier companies, airport containers at airports etc. cannot always get precise/accurate measurement using human hands. In our research we have developed an application SATMeas to detect the object and give the measurements of the object in real-time. We have utilized the canny edge detecti作者: 柔美流暢 時間: 2025-3-23 21:22
Multi-Classification of Electric Power Metadata based on Prompt-tuning, meteorology, satellites, etc. These industrial data are rich in value, and will be the foundation of digital economy and information management. Due to the particularity of the industry, the exploitation of big data mainly faces the following challenges that degrade the performance of mainstream g作者: 紀念 時間: 2025-3-23 22:51
Dual-Branch Network Fused with Attention Mechanism for Clothes-Changing Person Re-identificationcs of people, such as gait and body shape. Most of the current methods assume that persons’ clothes will not change in a short period of time, so these methods are not applicable when changing clothes. Based on this situation, this paper proposes a dual-branch network clothes-changing person re-iden作者: 極肥胖 時間: 2025-3-24 03:28
Infant Cry Classification Based-On Feature Fusion and Mel-Spectrogram Decomposition with CNNsom transfer learning convolutional neural network model and mel-spectrogram features extracted from mel-spectrogram decomposition model are fused and fed into a multiple layer perception for better classification accuracy. The mel-spectrogram decomposition method feeds band-wise crops of the mel-spe作者: NATAL 時間: 2025-3-24 07:07 作者: commute 時間: 2025-3-24 10:40
https://doi.org/10.1007/978-3-030-53051-8 to each other, which leads to the broadcast storm effect on the network. Therefore, this paper proposes a Fuzzy logic-based scheme to mitigate the broadcast storm effect. The novelty of this paper is the suggestion and application of a Fuzzy logic approach in order to mitigate critical data broadca作者: commodity 時間: 2025-3-24 17:25
Gesetz über elektronische Wertpapiere (eWpG) achieves 45% and 42% UAR?(Unweighted Average Recall), on the development dataset. After model fusion, DCRNNX achieves 46.89% UAR and 37.0% UAR on development and test datasets, respectively. The performance of our method on the development dataset is nearly 6% better than the baselines. Especially,作者: Angioplasty 時間: 2025-3-24 22:34
Gesetz über elektronische Wertpapiere (eWpG)above two parts. Besides, since translation can not be incorporated into the bilinear model directly, we introduce translation matrix as the equivalent. Theoretical analysis proves that STaR is capable of modeling all patterns and handling complex relations simultaneously, and experiments demonstrat作者: Diastole 時間: 2025-3-24 23:41
Christian Conreder,Johannes Meierr to integrate both of the two-level vector representations. Experimental results on two datasets demonstrate that our proposed method outperforms the baseline methods over 1.38–21.82% and 3.42–20.7% in terms of relative F1-measure on two Chinese text classification benchmarks, respectively.作者: crescendo 時間: 2025-3-25 06:03 作者: 被詛咒的人 時間: 2025-3-25 08:26
Victoria Sch?nefeld,Tobias Altmannedge outline. This is done after edge detection and closing any gaps between edges. We determine pixels per metric variable by relying on a reference object. The Euclidean distance between sets of center points was then determined to get the calculations. Putting it all together, we developed an app作者: Gum-Disease 時間: 2025-3-25 11:49
https://doi.org/10.1007/978-1-4614-4669-9ediction task on the unlabeled text dataset of the power industry to enable the pre-training model to acquire new vocabulary and knowledge of the industry; 2. The prompt-tuning model uses the continuous depth prompt technology as the backbone, which helps to bring the pre-training model closer to th作者: 友好 時間: 2025-3-25 19:46 作者: minion 時間: 2025-3-25 20:13 作者: FLINT 時間: 2025-3-26 00:10
0302-9743 l services, machine-to-machine & Internet-of-things clouds, cyber-physical integration, and big data analytics for mobility-enabled services..978-3-031-23503-0978-3-031-23504-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 漂白 時間: 2025-3-26 04:45 作者: 集聚成團 時間: 2025-3-26 10:05
DCRNNX: Dual-Channel Recurrent Neural Network with?Xgboost for?Emotion Identification Using Nonspeec achieves 45% and 42% UAR?(Unweighted Average Recall), on the development dataset. After model fusion, DCRNNX achieves 46.89% UAR and 37.0% UAR on development and test datasets, respectively. The performance of our method on the development dataset is nearly 6% better than the baselines. Especially,作者: Little 時間: 2025-3-26 15:08 作者: 參考書目 時間: 2025-3-26 17:19 作者: 玷污 時間: 2025-3-26 23:42 作者: 漂泊 時間: 2025-3-27 04:09
SATMeas - Object Detection and Measurement: Canny Edge Detection Algorithmedge outline. This is done after edge detection and closing any gaps between edges. We determine pixels per metric variable by relying on a reference object. The Euclidean distance between sets of center points was then determined to get the calculations. Putting it all together, we developed an app作者: 組成 時間: 2025-3-27 08:04
Multi-Classification of Electric Power Metadata based on Prompt-tuningediction task on the unlabeled text dataset of the power industry to enable the pre-training model to acquire new vocabulary and knowledge of the industry; 2. The prompt-tuning model uses the continuous depth prompt technology as the backbone, which helps to bring the pre-training model closer to th作者: narcotic 時間: 2025-3-27 11:39
Dual-Branch Network Fused with Attention Mechanism for Clothes-Changing Person Re-identificationular clothes-changing person re-identification dataset PRCC, and the experimental results show that the method in this paper is more advanced than popular methods. This paper also conducts experiments on LaST, an ultra-large-scale cross-space-time dataset, and also achieves competitive result result作者: dilute 時間: 2025-3-27 15:36
Infant Cry Classification Based-On Feature Fusion and Mel-Spectrogram Decomposition with CNNsssification error rate compared with the result using single mel-spectrogram images with CNN model on Baby Chillanto database and our testing accuracy reaches 99.26%, which outperforms all other methods with this five-category classification task. The gender classification experiment on Baby2020 dat作者: Mammal 時間: 2025-3-27 19:05 作者: Ascribe 時間: 2025-3-28 00:51
Gesetz über elektronische Wertpapiere (eWpG) a long time. Based on this, we propose a Dual-channel Recurrent Neural Network with Xgboost (DCRNNX) to solve emotion recognition using nonspeech vocalizations. The DCRNNX mainly combines two Backbone models. The first model is a two-channel neural network model based on the Deep Neural Network (DN作者: Decongestant 時間: 2025-3-28 05:27 作者: Extemporize 時間: 2025-3-28 06:17
Christian Conreder,Johannes Meierstomers and employers to quickly gain knowledge of them and provides a potential corpus for question-answering robots. While previous work focuses on web texts (e.g., Wiki), we generate FAQ pairs from the formal and verbose rule and regulation documents, which is significant in real scenarios. To ta作者: 調色板 時間: 2025-3-28 13:48
Christian Conreder,Johannes Meierormation have achieved state-of-the-art performance in most downstream natural language processing (NLP) tasks, including named entity recognition (NER), English text classification and sentiment analysis. For Chinese text classification, the existing methods have also tried such kinds of models. Ho作者: 團結 時間: 2025-3-28 15:53 作者: 脫毛 時間: 2025-3-28 19:23 作者: 不合 時間: 2025-3-28 23:37 作者: Crumple 時間: 2025-3-29 06:05
Mary E. Abood,Roger G Sorensen,Nephi Stellacs of people, such as gait and body shape. Most of the current methods assume that persons’ clothes will not change in a short period of time, so these methods are not applicable when changing clothes. Based on this situation, this paper proposes a dual-branch network clothes-changing person re-iden作者: 細查 時間: 2025-3-29 10:30
Mary E. Abood,Roger G. Sorensen,Nephi Stellaom transfer learning convolutional neural network model and mel-spectrogram features extracted from mel-spectrogram decomposition model are fused and fed into a multiple layer perception for better classification accuracy. The mel-spectrogram decomposition method feeds band-wise crops of the mel-spe作者: 種植,培養(yǎng) 時間: 2025-3-29 14:43 作者: Aqueous-Humor 時間: 2025-3-29 17:34 作者: LAIR 時間: 2025-3-29 21:57
https://doi.org/10.1007/978-3-031-23504-7artificial intelligence; computational linguistics; computer networks; computer vision; data mining; data
