標(biāo)題: Titlebook: Neural Information Processing; 28th International C Teddy Mantoro,Minho Lee,Achmad Nizar Hidayanto Conference proceedings 2021 Springer Nat [打印本頁] 作者: autoantibodies 時(shí)間: 2025-3-21 18:11
書目名稱Neural Information Processing影響因子(影響力)
書目名稱Neural Information Processing影響因子(影響力)學(xué)科排名
書目名稱Neural Information Processing網(wǎng)絡(luò)公開度
書目名稱Neural Information Processing網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Neural Information Processing被引頻次
書目名稱Neural Information Processing被引頻次學(xué)科排名
書目名稱Neural Information Processing年度引用
書目名稱Neural Information Processing年度引用學(xué)科排名
書目名稱Neural Information Processing讀者反饋
書目名稱Neural Information Processing讀者反饋學(xué)科排名
作者: Flavouring 時(shí)間: 2025-3-21 21:04
A Region Descriptive Pre-training Approach with?Self-attention Towards Visual Question Answering as a bridge to map the text and image inputs. The addition of region description makes our model perform better against some recent state-of-the-art models. Experiments demonstrated in this paper show that our model significantly outperforms most of these models.作者: 初次登臺(tái) 時(shí)間: 2025-3-22 02:12
Prediction of?Inefficient BCI Users Based on?Cognitive Skills and?Personality TraitsThe most accurate model was based on: Spatial Ability, Visuospatial Memory, Autonomy, and Vividness of Visual Imagery. Further correlation analyses showed that a novice user’s . suppression during a MI-BCI task can be predicted based on their visuospatial memory, as measured by the Design Organization Test (DOT).作者: 眉毛 時(shí)間: 2025-3-22 06:54
UED: A Unified Encoder Decoder Network for?Visual DialogD is unified in that (1) it fully exploiting the interaction between different modalities to support answer ranking and generation in a single transformer based model, and (2) it uses the answers as anchors to facilitate both two settings. We evaluate the proposed UED on the VisDial dataset, where our model outperforms the state-of-the-art.作者: intoxicate 時(shí)間: 2025-3-22 09:35 作者: 模仿 時(shí)間: 2025-3-22 13:48
Conference proceedings 2021n Processing, ICONIP 2021, held in Sanur, Bali, Indonesia, in December 2021.* The volume also presents papers from the?workshop on Artificial Intelligence and Cyber Security, held during the ICONIP 2021.?.The 176 short and workshop papers presented in this volume were carefully reviewed and selected作者: Optometrist 時(shí)間: 2025-3-22 17:28
1865-0929 Information Processing, ICONIP 2021, held in Sanur, Bali, Indonesia, in December 2021.* The volume also presents papers from the?workshop on Artificial Intelligence and Cyber Security, held during the ICONIP 2021.?.The 176 short and workshop papers presented in this volume were carefully reviewed an作者: 吹牛需要藝術(shù) 時(shí)間: 2025-3-22 22:47
Video Captioning with?External Knowledge Assistance and?Multi-feature Fusionain more informative video features and higher quality semantic features. Experimental results on the MSVD and MSRVTT datasets show that the proposed method can greatly improve the caption diversity and model performance, surpass all previous models in all evaluation metrics, and achieve the new state-of-the-art results.作者: apiary 時(shí)間: 2025-3-23 04:04 作者: senile-dementia 時(shí)間: 2025-3-23 08:06
BCI Speller on Smartphone Device with Diminutive-Sized Visual Stimulific spatial-temporal features. The results showed 96.8% of spelling accuracy with a maximum ITR of 31.6 [bits/min], which is comparable or even superior to conventional speller systems. Our study demonstrated the feasibility to create more reliable and practical BCI spelling systems in the future.作者: 使人煩燥 時(shí)間: 2025-3-23 13:13 作者: 夜晚 時(shí)間: 2025-3-23 15:18
Xuecheng Zhang,Xuanying Zhu major carbon compound within most plant tissues and increases during active photosynthesis and decreases as it is enzymatically converted into sugars. Amylase catalyzes the hydrolytic depolymerization of polysaccharides in soil (Tu and Miles 1976). Starch-hydrolyzing enzymes are usually extracellul作者: 弄臟 時(shí)間: 2025-3-23 19:14 作者: 陰謀 時(shí)間: 2025-3-24 00:53 作者: 赦免 時(shí)間: 2025-3-24 02:48
Scale-Aware Multi-stage Fusion Network for Crowd Countingd noise, accurate crowd counting is still very difficult. In this paper, we raise a simple but efficient network named SMFNet, which focuses on dealing with the above two problems of highly congested noisy scenes. SMFNet consists of two main components: multi-scale dilated convolution block (MDCB) f作者: THE 時(shí)間: 2025-3-24 08:56 作者: EXALT 時(shí)間: 2025-3-24 12:19
A Novel Transfer-Learning Network for?Image Inpaintinge mask inpainting rely on deep learning methods to retrieve specific image attributes. However, due to the lack of a key remainder, the quality of image restoration remains at a low level. For instance, when the mask is large enough, traditional deep learning methods cannot imagine and fill a car on作者: Inertia 時(shí)間: 2025-3-24 17:07
BPFNet: A Unified Framework for Bimodal Palmprint Alignment and Fusionn property. For bimodal palmprint recognition and verification, the ROI detection and ROI alignment of palmprint region-of-interest (ROI) are two crucial points for bimodal palmprint matching. Most existing plamprint ROI detection methods are based on keypoint detection algorithms, however the intri作者: 急性 時(shí)間: 2025-3-24 21:26 作者: Capitulate 時(shí)間: 2025-3-25 00:04
Dynamical Characteristics of State Transition Defined by Neural Activity of Phase in Alzheimer’s Dis and deficits in cognitive functions. Recently, we introduced the instantaneous phase difference between electroencephalography (EEG) signals (called the dynamical phase synchronization (DPS) approach) and succeeded in detecting moment-to-moment dFC dynamics. In this approach, neural interactions in作者: tangle 時(shí)間: 2025-3-25 07:16
Robot Arm Control Using Reward-Modulated Hebbian Learningerform delicate tasks that only humans can do. On the other hand, it is challenging to control. Therefore, in this research, we focused on reservoir computing with a biologically inspired learning algorithm. Reward-modulated Hebbian learning, one of the reservoir computing frameworks, is based on He作者: Monolithic 時(shí)間: 2025-3-25 09:52 作者: 負(fù)擔(dān) 時(shí)間: 2025-3-25 14:27
A Region Descriptive Pre-training Approach with?Self-attention Towards Visual Question Answering-answer) and image inputs in a forced manner. In this paper, we introduce a region descriptive pre-training approach with self-attention towards VQA. The model is a new learning method that uses the image region descriptions combined with object labels to create a proper alignment between the text(q作者: Armory 時(shí)間: 2025-3-25 19:17
Prediction of?Inefficient BCI Users Based on?Cognitive Skills and?Personality Traitslls and personality traits correlate with MI-BCI real-time performance. Other studies have examined sensorimotor rhythm changes (known as . suppression) as a valuable indicator of successful execution of the motor imagery task. This research aims to combine these insights by investigating whether co作者: 變形 時(shí)間: 2025-3-25 22:41
Analysis of Topological Variability in Mouse Neuronal Populations Based on Fluorescence Microscopy Ias occupied by ensembles of cell groups in mouse brain tissue. Recognition of mouse neuronal populations was performed on the basis of visual properties of fluorescence-activated cells. In our study 60 fluorescence microscopy datasets obtained from 23 mice ex vivo were analyzed. Based on data from l作者: 分期付款 時(shí)間: 2025-3-26 00:26 作者: 摻假 時(shí)間: 2025-3-26 05:48 作者: 策略 時(shí)間: 2025-3-26 08:36 作者: Anterior 時(shí)間: 2025-3-26 14:27
Explaining Neural Network Results by?Sensitivity Analysis for?Deception Detectionervers, we train a three-layer neural network, a long short-term memory (LSTM) and a multi-tasking learning neural network (MTL-NN). We demonstrate that examined models are able to identify deception with an accuracy up?to 62%, surpassing the average accuracy of human deception detection. The superi作者: OVER 時(shí)間: 2025-3-26 20:02
Stress Recognition with?EEG Signals Using Explainable Neural Networks and?a?Genetic Algorithm for?Fedeterioration, it is important for researchers to understand and improve its detection. This paper uses neural network techniques to classify whether an individual is stressed, based on signals from an electroencephalogram (EEG), a popular physiological sensor. We also overcome two prominent limitat作者: 競(jìng)選運(yùn)動(dòng) 時(shí)間: 2025-3-26 23:11
BCI Speller on Smartphone Device with Diminutive-Sized Visual Stimulileads to a reduction of P300 amplitude, causing lower system performance. The purpose of this study is to propose a state-of-the-art BCI speller where diminutive (less than 1?mm) visual stimuli were implemented in a smartphone interface. To boost the task-relevant brain components, participants perf作者: 內(nèi)行 時(shí)間: 2025-3-27 03:42
Communications in Computer and Information Sciencehttp://image.papertrans.cn/n/image/663638.jpg作者: Lumbar-Stenosis 時(shí)間: 2025-3-27 05:46
https://doi.org/10.1007/978-3-030-92310-5artificial intelligence; communication systems; computer networks; computer systems; computer vision; dat作者: 恫嚇 時(shí)間: 2025-3-27 12:46
978-3-030-92309-9Springer Nature Switzerland AG 2021作者: tendinitis 時(shí)間: 2025-3-27 17:06 作者: 飛行員 時(shí)間: 2025-3-27 17:57 作者: Integrate 時(shí)間: 2025-3-27 22:55
Jiao-Wei Miao,Huan Shao,Yi Ji,Ying Li,Chun-Ping Liu作者: cogitate 時(shí)間: 2025-3-28 04:19 作者: Deceit 時(shí)間: 2025-3-28 06:38
Itsuki Machida,Atsushi Kodama,Kouji Kimura,Motofumi Shishikura,Hiroshi Tamura,Ko Sakai作者: panorama 時(shí)間: 2025-3-28 11:21 作者: athlete’s-foot 時(shí)間: 2025-3-28 15:11 作者: 狂亂 時(shí)間: 2025-3-28 20:57 作者: 詞匯記憶方法 時(shí)間: 2025-3-29 00:28 作者: lethargy 時(shí)間: 2025-3-29 03:04 作者: 喪失 時(shí)間: 2025-3-29 07:40 作者: extinct 時(shí)間: 2025-3-29 13:23 作者: 露天歷史劇 時(shí)間: 2025-3-29 15:45 作者: Oscillate 時(shí)間: 2025-3-29 23:20 作者: 虛構(gòu)的東西 時(shí)間: 2025-3-30 01:20
BPFNet: A Unified Framework for Bimodal Palmprint Alignment and Fusionhe detection network directly regresses the palmprint ROIs and conducts alignment by estimating translation. In the downstream, the fusion network conducts bimodal ROI image fusion leveraging a novel cross-modal selection scheme. To demonstrate the effectiveness of BPFNet, we implement experiments o作者: 值得 時(shí)間: 2025-3-30 05:43 作者: ineptitude 時(shí)間: 2025-3-30 10:58 作者: Delectable 時(shí)間: 2025-3-30 12:26 作者: syncope 時(shí)間: 2025-3-30 16:47
Analysis of Topological Variability in Mouse Neuronal Populations Based on Fluorescence Microscopy Irescence microscopy records were selected based on their brightness in grayscale mode. Frequently occurring patterns formed by individual elements were classified and found in other sets of images: this way we built a training sample and classified the optogenetics data. The presence of training sam作者: blight 時(shí)間: 2025-3-30 22:00 作者: 尖 時(shí)間: 2025-3-31 04:45
A SSA-Based Attention-BiLSTM Model for?COVID-19 Predictionn, and use the SSA to optimize the critical parameters of the model for matching the characteristics of COVID-19 data, enhance the interpretability of the model parameters. This study is based on daily confirmed cases collected from six countries: Egypt, Ireland, Iran, Japan, Russia, and the UK. The作者: adipose-tissue 時(shí)間: 2025-3-31 06:28 作者: CARE 時(shí)間: 2025-3-31 11:32 作者: Indict 時(shí)間: 2025-3-31 15:54
978-3-031-25890-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
