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Journal articles on the topic 'Attentive Mechanisms'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Yin, Wenpeng, and Hinrich Schütze. "Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms." Transactions of the Association for Computational Linguistics 6 (December 2018): 687–702. http://dx.doi.org/10.1162/tacl_a_00249.

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In NLP, convolutional neural networks (CNNs) have benefited less than recurrent neural networks (RNNs) from attention mechanisms. We hypothesize that this is because the attention in CNNs has been mainly implemented as attentive pooling (i.e., it is applied to pooling) rather than as attentive convolution (i.e., it is integrated into convolution). Convolution is the differentiator of CNNs in that it can powerfully model the higher-level representation of a word by taking into account its local fixed-size context in the input text t x. In this work, we propose an attentive convolution network,

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Spekreijse, Henk. "Pre-attentive and attentive mechanisms in vision." Vision Research 40, no. 10-12 (2000): 1179–82. http://dx.doi.org/10.1016/s0042-6989(00)00060-2.

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Von Grünau, M. W. "Attentive mechanisms in visual search." Spatial Vision 17, no. 4 (2004): 353–71. http://dx.doi.org/10.1163/1568568041920177.

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Tervaniemi, Mari. "215 Pre-attentive and attentive brain mechanisms of complex-sound perception." International Journal of Psychophysiology 30, no. 1-2 (1998): 85. http://dx.doi.org/10.1016/s0167-8760(98)90215-x.

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Jaaskelainen, Iiro P., Giorgio Bonmassar, Hennifer Melcher, et al. "Cortical mechanisms of pre-attentive auditory “Gating”." NeuroImage 13, no. 6 (2001): 321. http://dx.doi.org/10.1016/s1053-8119(01)91664-7.

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Milanese, Ruggero. "Attentive mechanisms for dynamic and static scene analysis." Optical Engineering 34, no. 8 (1995): 2428. http://dx.doi.org/10.1117/12.205668.

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YU, YUANLONG, GEORGE K. I. MANN, and RAYMOND G. GOSINE. "A SINGLE-OBJECT TRACKING METHOD FOR ROBOTS USING OBJECT-BASED VISUAL ATTENTION." International Journal of Humanoid Robotics 09, no. 04 (2012): 1250030. http://dx.doi.org/10.1142/s0219843612500302.

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It is a quite challenging problem for robots to track the target in complex environment due to appearance changes of the target and background, large variation of motion, partial and full occlusion, motion of the camera and so on. However, humans are capable to cope with these difficulties by using their cognitive capability, mainly including the visual attention and learning mechanisms. This paper therefore presents a single-object tracking method for robots based on the object-based attention mechanism. This tracking method consists of four modules: pre-attentive segmentation, top-down atten

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Sleem, Ahmed, and Ibrahim Elhenawy. "An Attentive Convolutional Recurrent Network for Fake News Detection." International Journal of Advances in Applied Computational Intelligence 2, no. 1 (2023): 08–14. http://dx.doi.org/10.54216/ijaaci.020101.

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With the rapid growth of social media and online news platforms, the spread of fake news has become a major problem, leading to misinformation and distrust. In this paper, we propose an attentive convolutional recurrent network (ACRN) for fake news detection, which combines convolutional learning and recurrent learning capabilities to capture both local and global temporal information. Additionally, we incorporate attention mechanisms to focus on important features and reduce noise. We evaluate our model on a publicly available dataset and compare it with state-of-the-art methods. The results

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Himabindu, Dakshayani D., and Praveen S. Kumar. "A Streamlined Attention Mechanism for Image Classification and Fine-Grained Visual Recognition." MENDEL 27, no. 2 (2021): 59–67. http://dx.doi.org/10.13164/mendel.2021.2.059.

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In the recent advancements attention mechanism in deep learning had played a vital role in proving better results in tasks under computer vision. There exists multiple kinds of works under attention mechanism which includes under image classification, fine-grained visual recognition, image captioning, video captioning, object detection and recognition tasks. Global and local attention are the two attention based mechanisms which helps in interpreting the attentive partial. Considering this criteria, there exists channel and spatial attention where in channel attention considers the most attent

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Kalaska, John F. "Central Neural Mechanisms of Touch and Proprioception." Canadian Journal of Physiology and Pharmacology 72, no. 5 (1994): 542–45. http://dx.doi.org/10.1139/y94-078.

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The argument is made that somesthesia is not a strictly passive process, and its central neuronal mechanisms cannot be studied in all their complexity and subtlety by applying passive stimuli to uninterested or unconscious animals. The case is clear for kinesthesia. Peripheral proprioceptive signals are altered by active muscle contractions, and the central mechanisms of kinesthetic sensations should be studied during active movements. A similar case can be made for tactile discrimination. Ascending tactile afferents are subject to modulation during movement. Moreover, the generation of a cent

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Rajan Prasad Tripathi. "A Novel CNN Architecture with Integrated Self-Attention Mechanism for Mammogram Classification: Self-Attentive Convolutional Neural Network." Advances in Nonlinear Variational Inequalities 28, no. 5s (2025): 725–38. https://doi.org/10.52783/anvi.v28.4036.

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This study introduces a Self-Attentive Convolutional Neural Network (SACNN), a novel CNN architecture integrated with a self-attention mechanism, tailored for enhanced mammogram classification. Unlike traditional CNNs, SACNN leverages self-attention to provide a refined feature map that emphasizes salient features critical for accurate diagnosis. We evaluated SACNN on two benchmark datasets, achieving a classification accuracy improvement of 3.5% over conventional CNNs. Our findings suggest that the integration of self-attention mechanisms can significantly improve the interpretability and per

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Liu, Yichu, Haifeng Hu, and Dihu Chen. "Attentive Part-Based Alignment Network for Vehicle Re-Identification." Electronics 11, no. 10 (2022): 1617. http://dx.doi.org/10.3390/electronics11101617.

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Vehicle Re-identification (Re-ID) has become a research hotspot along with the rapid development of video surveillance. Attention mechanisms are utilized in vehicle Re-ID networks but often miss the attention alignment across views. In this paper, we propose a novel Attentive Part-based Alignment Network (APANet) to learn robust, diverse, and discriminative features for vehicle Re-ID. To be specific, in order to enhance the discrimination of part features, two part-level alignment mechanisms are proposed in APANet, consisting of Part-level Orthogonality Loss (POL) and Part-level Attention Alig

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Cheng, Fan-Yin, Julia Campbell, and Chang Liu. "Auditory Sensory Gating: Effects of Noise." Biology 13, no. 6 (2024): 443. http://dx.doi.org/10.3390/biology13060443.

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Cortical auditory evoked potentials (CAEPs) indicate that noise degrades auditory neural encoding, causing decreased peak amplitude and increased peak latency. Different types of noise affect CAEP responses, with greater informational masking causing additional degradation. In noisy conditions, attention can improve target signals’ neural encoding, reflected by an increased CAEP amplitude, which may be facilitated through various inhibitory mechanisms at both pre-attentive and attentive levels. While previous research has mainly focused on inhibition effects during attentive auditory processin

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Coco, Marinella, Andrea Buscemi, Matej Tušak, et al. "Attentive Processes and Blood Lactate in the Sambo." International Journal of Environmental Research and Public Health 19, no. 3 (2022): 1113. http://dx.doi.org/10.3390/ijerph19031113.

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Background: Sambo is a martial art and combat sport that originated in the Soviet Union. There are two main stiles, Sport Sambo and Combat Sambo which resembles modern mixed martial arts. Very little literature is available about physiological aspects of Sambo and, in particular, on the possible effects on cognitive domains. The purpose of the present research was to determine if there is a correlation between a blood lactate increase and the intensity and/or selectivity of attentions. Methods: Sixteen male athletes practicing Sambo for at least 5 years participated voluntarily in the study. E

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Zhaoping, Li. "Pre–attentive segmentation and correspondence in stereo." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 357, no. 1428 (2002): 1877–83. http://dx.doi.org/10.1098/rstb.2002.1158.

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Traditional stereo grouping models have focused on the problem of stereo correspondence between monocular inputs. Recent physiological data revealed that the disparity selective V2 cells increase their responses when (random–dot stereograms) stimuli within their receptive fields are at or near the boundary of a depth surface. Such highlights to depth (non–luminance) edges are seemingly not computationally required for the correspondence problem. Computationally, these highlights make the boundaries of a depth surface more salient, serving pre–attentive segmentation (between depth planes) and a

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Chen, Yining, Laura E. Matheson, and Jon T. Sakata. "Mechanisms underlying the social enhancement of vocal learning in songbirds." Proceedings of the National Academy of Sciences 113, no. 24 (2016): 6641–46. http://dx.doi.org/10.1073/pnas.1522306113.

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Social processes profoundly influence speech and language acquisition. Despite the importance of social influences, little is known about how social interactions modulate vocal learning. Like humans, songbirds learn their vocalizations during development, and they provide an excellent opportunity to reveal mechanisms of social influences on vocal learning. Using yoked experimental designs, we demonstrate that social interactions with adult tutors for as little as 1 d significantly enhanced vocal learning. Social influences on attention to song seemed central to the social enhancement of learni

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Chen, Yin. "An Alzheimer’s Disease Identification and Classification Model Based on the Convolutional Neural Network with Attention Mechanisms." Traitement du Signal 38, no. 5 (2021): 1557–64. http://dx.doi.org/10.18280/ts.380533.

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MRI image analysis of brain regions based on deep learning can effectively reduce the workload of doctors in reading films and improve the accuracy of diagnosis. Therefore, deep learning models have great application prospects in the classification and prediction of Alzheimer’s patients and normal people. However, the existing research has ignored the correlation between small abnormalities in local brain regions and changes in brain tissues. To this end, this paper studies an Alzheimer’s disease identification and classification model based on the convolutional neural network (CNN) with atten

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Ewe, Edmond Li Ren, Chin Poo Lee, Kian Ming Lim, Lee Chung Kwek, and Ali Alqahtani. "LAVRF: Sign language recognition via Lightweight Attentive VGG16 with Random Forest." PLOS ONE 19, no. 4 (2024): e0298699. http://dx.doi.org/10.1371/journal.pone.0298699.

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Sign language recognition presents significant challenges due to the intricate nature of hand gestures and the necessity to capture fine-grained details. In response to these challenges, a novel approach is proposed—Lightweight Attentive VGG16 with Random Forest (LAVRF) model. LAVRF introduces a refined adaptation of the VGG16 model integrated with attention modules, complemented by a Random Forest classifier. By streamlining the VGG16 architecture, the Lightweight Attentive VGG16 effectively manages complexity while incorporating attention mechanisms that dynamically concentrate on pertinent

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Lamba, Sangeeta, and Neelam Sharma. "Advanced Online Proctoring: Facial Emotion Monitoring with Attentive-Net." Advances in Artificial Intelligence and Machine Learning 05, no. 02 (2025): 3646–62. https://doi.org/10.54364/aaiml.2025.52207.

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The Attentive Proctoring System proposed in this paper addresses the growing need for reliable remote examination solutions amid the global shift toward online learning. Traditional methods of human proctoring are often constrained by scalability issues and resource demands, rendering them inefficient in the face of large-scale online assessments. Leveraging advanced deep learning techniques, our framework aims to ensure exam integrity through a multi-phase approach. By preprocessing video frames captured from students’ webcams and employing techniques such as background subtraction and face d

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Feng, Ling, and Ananta Sinchai. "Deep context-attentive transformer transfer learning for financial forecasting." PeerJ Computer Science 11 (June 30, 2025): e2983. https://doi.org/10.7717/peerj-cs.2983.

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This study presents 2CAT (CNN-Correlation-based Attention Transformer), a deep learning model for financial time-series forecasting. The model integrates signal decomposition, convolutional layers, and correlation-based attention mechanisms to capture temporal patterns. A transfer learning framework is incorporated to enhance generalization across markets through pretraining, encoder freezing, and fine-tuning. Evaluation on six stock indices—Dow Jones Industrial Average (DJIA), Nikkei 225 (N225), Hang Seng Index (HSI), Shanghai Stock Exchange (SSE), Bombay Stock Exchange (BSE), and the Stock E

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Falleti, Tulia G., and Julia F. Lynch. "Context and Causal Mechanisms in Political Analysis." Comparative Political Studies 42, no. 9 (2009): 1143–66. http://dx.doi.org/10.1177/0010414009331724.

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Political scientists largely agree that causal mechanisms are crucial to understanding causation. Recent advances in qualitative and quantitative methodology suggest that causal explanations must be contextually bounded. Yet the relationship between context and mechanisms and this relationship's importance for causation are not well understood. This study defines causal mechanisms as portable concepts that explain how and why a hypothesized cause, in a given context, contributes to a particular outcome. In turn, it defines context as the relevant aspects of a setting in which an array of initi

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Fu, Junwei, and Jun Liang. "Virtual View Generation Based on 3D-Dense-Attentive GAN Networks." Sensors 19, no. 2 (2019): 344. http://dx.doi.org/10.3390/s19020344.

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A binocular vision system is a common perception component of an intelligent vehicle. Benefiting from the biomimetic structure, the system is simple and effective. Which are extremely snesitive on external factors, especially missing vision signals. In this paper, a virtual view-generation algorithm based on generative adversarial networks (GAN) is proposed to enhance the robustness of binocular vision systems. The proposed model consists of two parts: generative network and discriminator network. To improve the quality of a virtual view, a generative network structure based on 3D convolutiona

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Avanesi, Vino. "Second-screen subsumption: Aesthetics of control and the second-screen facilitation of hyper-attentive watching-labour." Convergence: The International Journal of Research into New Media Technologies 26, no. 2 (2018): 369–85. http://dx.doi.org/10.1177/1354856518781650.

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The article at hand theorizes the political economic shift between the aesthetic strategies deployed by network television in the broadcast-era and those deployed in the matrix-era. It argues that where in the former era certain strategies allowed for the disciplining of viewers’ sensory dimensions and in turn allowed for the ‘factory’ process of watching-labour to penetrate the private lives of people, in the latter era this factory process has not only collapsed into the home but also into viewers’ sensoria. And, that this is due to the nature of the control mechanisms embedded in the new ae

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Wu, Wenqing, Zhenfang Zhu, Qiang Lu, Dianyuan Zhang, and Qiangqiang Guo. "Introducing External Knowledge to Answer Questions with Implicit Temporal Constraints over Knowledge Base." Future Internet 12, no. 3 (2020): 45. http://dx.doi.org/10.3390/fi12030045.

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Knowledge base question answering (KBQA) aims to analyze the semantics of natural language questions and return accurate answers from the knowledge base (KB). More and more studies have applied knowledge bases to question answering systems, and when using a KB to answer a natural language question, there are some words that imply the tense (e.g., original and previous) and play a limiting role in questions. However, most existing methods for KBQA cannot model a question with implicit temporal constraints. In this work, we propose a model based on a bidirectional attentive memory network, which

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Huang, Lingfeng, Jieyu Zhao, and Yu Chen. "MixFormer: A Self-Attentive Convolutional Network for 3D Mesh Object Recognition." Algorithms 16, no. 3 (2023): 171. http://dx.doi.org/10.3390/a16030171.

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3D mesh as a complex data structure can provide effective shape representation for 3D objects, but due to the irregularity and disorder of the mesh data, it is difficult for convolutional neural networks to be directly applied to 3D mesh data processing. At the same time, the extensive use of convolutional kernels and pooling layers focusing on local features can cause the loss of spatial information and dependencies of low-level features. In this paper, we propose a self-attentive convolutional network MixFormer applied to 3D mesh models. By defining 3D convolutional kernels and vector self-a

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Zheng, Lei. "A Visual Cortex-Attentive Deep Convolutional Neural Network for Digital Image Design." Volume 31, Issue 1 31, no. 1 (2024): 21–37. http://dx.doi.org/10.20532/cit.2023.1005695.

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With the proliferation of advanced visualization techniques in visual communication, enhancing digital image quality remains a persistent challenge. This study presents a sophisticated Convolutional Neural Network (CNN) model to optimize image processing. The model incorporates a multi-stage architecture attentive to biological visual pathways. Inter-subnetwork connections enable integrated feature learning, guided by adaptive weighting of luminance, color, orientation, and edge maps. Spatial and channel attention modules further enrich feature interplay. When evaluated on the LIVE 3D Phase da

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Chen, Long, Ziyu Guan, Wei Zhao, et al. "Answer Identification from Product Reviews for User Questions by Multi-Task Attentive Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 45–52. http://dx.doi.org/10.1609/aaai.v33i01.330145.

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Online Shopping has become a part of our daily routine, but it still cannot offer intuitive experience as store shopping. Nowadays, most e-commerce Websites offer a Question Answering (QA) system that allows users to consult other users who have purchased the product. However, users still need to wait patiently for others’ replies. In this paper, we investigate how to provide a quick response to the asker by plausible answer identification from product reviews. By analyzing the similarity and discrepancy between explicit answers and reviews that can be answers, a novel multi-task deep learning

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Ladinig, Olivia, Henkjan Honing, Gáábor Hááden, and Istváán Winkler. "Probing Attentive and Preattentive Emergent Meter in Adult Listeners without Extensive Music Training." Music Perception 26, no. 4 (2009): 377–86. http://dx.doi.org/10.1525/mp.2009.26.4.377.

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BEAT AND METER INDUCTION ARE CONSIDERED important structuring mechanisms underlying the perception of rhythm. Meter comprises two or more levels of hierarchically ordered regular beats with different periodicities. When listening to music, adult listeners weight events within a measure in a hierarchical manner. We tested if listeners without advanced music training form such hierarchical representations for a rhythmical sound sequence under different attention conditions (Attend, Unattend, and Passive). Participants detected occasional weakly and strongly syncopated rhythmic patterns within th

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Gross, Neil. "The Structure of Causal Chains." Sociological Theory 36, no. 4 (2018): 343–67. http://dx.doi.org/10.1177/0735275118811377.

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Sociologists are increasingly attentive to the mechanisms responsible for cause-and-effect relationships in the social world. But an aspect of mechanistic causality has not been sufficiently considered. It is well recognized that most phenomena of interest to social science result from multiple mechanisms operating in sequence. However, causal chains—sequentially linked mechanisms and their enabling background conditions—vary not just substantively, by the kind of causal work they do, but also structurally, by their formal properties. In this article, the author examines the nature of causal c

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Said, Imran, Vasit Sagan, Kyle T. Peterson, et al. "Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models." Sensors 25, no. 2 (2025): 303. https://doi.org/10.3390/s25020303.

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Wheat is a globally cultivated cereal crop with substantial protein content present in its seeds. This research aimed to develop robust methods for predicting seed protein concentration in wheat seeds using bench-top hyperspectral imaging in the visible, near-infrared (VNIR), and shortwave infrared (SWIR) regions. To fully utilize the spectral and texture features of the full VNIR and SWIR spectral domains, a computer-vision-aided image co-registration methodology was implemented to seamlessly align the VNIR and SWIR bands. Sensitivity analyses were also conducted to identify the most sensitiv

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Tiwari, Prayag, Amit Kumar Jaiswal, Sahil Garg, and Ilsun You. "SANTM: Efficient Self-attention-driven Network for Text Matching." ACM Transactions on Internet Technology 22, no. 3 (2022): 1–21. http://dx.doi.org/10.1145/3426971.

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Self-attention mechanisms have recently been embraced for a broad range of text-matching applications. Self-attention model takes only one sentence as an input with no extra information, i.e., one can utilize the final hidden state or pooling. However, text-matching problems can be interpreted either in symmetrical or asymmetrical scopes. For instance, paraphrase detection is an asymmetrical task, while textual entailment classification and question-answer matching are considered asymmetrical tasks. In this article, we leverage attractive properties of self-attention mechanism and proposes an

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Obermeyer, Jessica A., and Lisa A. Edmonds. "Attentive Reading With Constrained Summarization Adapted to Address Written Discourse in People With Mild Aphasia." American Journal of Speech-Language Pathology 27, no. 1S (2018): 392–405. http://dx.doi.org/10.1044/2017_ajslp-16-0200.

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Purpose The purpose of this study was to examine the preliminary efficacy of Attentive Reading and Constrained Summarization–Written (ARCS-W) in people with mild aphasia. ARCS-W adapts an existing treatment, ARCS (Rogalski & Edmonds, 2008), to address discourse level writing in mild aphasia. ARCS-W focuses on the cognitive and linguistic skills required for discourse production. Method This study was a within-subject pre–postdesign. Three people with mild aphasia participated. ARCS-W integrates attentive reading or listening with constrained summarization of discourse level material in spo

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Lao, Liming, Dangkui Du, and Pengzhan Chen. "Predicting Pedestrian Trajectories with Deep Adversarial Networks Considering Motion and Spatial Information." Algorithms 16, no. 12 (2023): 566. http://dx.doi.org/10.3390/a16120566.

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This paper proposes a novel prediction model termed the social and spatial attentive generative adversarial network (SSA-GAN). The SSA-GAN framework utilizes a generative approach, where the generator employs social attention mechanisms to accurately model social interactions among pedestrians. Unlike previous methodologies, our model utilizes comprehensive motion features as query vectors, significantly enhancing predictive performance. Additionally, spatial attention is integrated to encapsulate the interactions between pedestrians and their spatial context through semantic spatial features.

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Dzhurabekova, Aziza Takhirovna, Ksenia Vladimirovna Shmyrina, and Natalya Fedorovna Vyazikova. "DIAGNOSTICS OF WALKING DISORDERS IN PATIENTS WITH HYPERTENSION AND CHRONIC BRAIN ISCHEMIA." Journal of neurology and neurosergery 4, no. 2 (2023): 3. https://doi.org/10.5281/zenodo.7826190.

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The article presents examination data of patients with hypertension, chronic brain ischemia and Leriche's syndrome. It is noted that in patients with concomitant Leriche's syndrome, the process of dysfunction of the central nervous system is aggravated. The effect of this syndrome on aspects of reduced physical activity, deterioration of cognitive functions (attention and memory), and increased pathological fatigue were reliably revealed. Due to multimorbidity, features of the course of the disease, heterogeneity of pathogenetic mechanisms and risk factors, it is necessary to include p

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Hong Wang, Yin Ang, Yilin Kang, Shasha Tian, Lu Zheng, and Aifei Wang. "The lane detection algorithm based on multiscale aggregated attention fusion network." ITU Journal on Future and Evolving Technologies 4, no. 4 (2023): 619–30. http://dx.doi.org/10.52953/sgml9321.

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High accuracy and high stability are key elements of the lane detection algorithm in an autonomous driving system. Traditional algorithms are having difficulty extracting detailed features due to the complex geometric structure and background interference of lanes in real scenarios. Therefore, this paper proposes a Multiscale Aggregated Attention Fusion (MAAF) network, which integrates attention mechanisms to improve the accuracy and robustness of lane detection. Firstly, the Recurrent Feature-Shift Aggregator for Lane Detection (RESA) is improved to increase the effective sensory field and im

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Chen, Xin, Jianqin Wang, and Ji Lu. "A Cross-Language Study of Tonal Variants in Mandarin in Different Attentional Conditions." Behavioral Sciences 15, no. 3 (2025): 304. https://doi.org/10.3390/bs15030304.

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This study used an electrophysiological technique to investigate the perception mechanism of Mandarin native speakers and learners from non-tonal language backgrounds when processing the third tone (T3) and its variants in Mandarin. The experiments used a 2 × 2 two-factor mixed design to examine the perception of T3 and its variants and the processing mechanisms of learners and native speakers under different levels of attention. Differences in attention and language backgrounds in the perception of Mandarin tones were further investigated. These results provide evidence that there are no sign

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Ames, Justin, Dustin Bluhm, James Gaskin, and Kalle Lyytinen. "The impact of moral attentiveness on manager’s turnover intent." Society and Business Review 15, no. 3 (2020): 189–209. http://dx.doi.org/10.1108/sbr-03-2020-0025.

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Purpose With the rise in public awareness of corporate social responsibility, business leaders are increasingly expected to recognize the needs and demands of multiple stakeholders. There may, however, be unintended consequences of this expectation for organizational managers who engage these needs and demands with a high level of moral attentiveness. This study aims to investigate the indirect effect of managerial moral attentiveness on managerial turnover intent, serially mediated by moral dissonance and moral stress. Design/methodology/approach Multi-phase survey data were collected from 13

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Pinna, Baingio, Daniele Porcheddu, and Jurģis Šķilters. "Accentuation and Attention: From Perceptual Organization to Consciousness." Brain Sciences 15, no. 3 (2025): 243. https://doi.org/10.3390/brainsci15030243.

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Background: This study investigates the complex relationship between accentuation and attention in visual perception, extending classical Gestalt principles by introducing dissimilarity as a complementary mechanism to similarity in perceptual organization. Objectives and Methods: Through a series of phenomenological experiments, we demonstrate how accentuation, driven by dissimilarity, plays a crucial role in shaping visual experience and guiding attention. Results: Our findings reveal that accentuation serves as a pre-attentive mechanism for highlighting salient features, influencing initial

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Lux, Nicholas, Jayne Downey, Jennifer Luebeck, and Joe Hicks. "Digital Rural Community Walks: Building Preservice Teachers’ Rural Engagement through Technology Integration." Journal of Technology and Teacher Education 30, no. 2 (2022): 135–43. https://doi.org/10.70725/150661miisnj.

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Rural communities around the United States are facing a critical shortage of teachers due largely to challenges with recruitment and retention. Our vision for responding to this growing crisis it to consider how technology-rich early rural field experiences that emphasize place-attentive technology integration can build preservice teachers’ interest in and commitment to teaching in rural spaces. More specifically, we propose that teacher educators consider how to provide preservice teachers opportunities for embedded rural field experiences where they build digital videos that document the ass

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Wang, Dong, Bing Liu, Yong Zhou, Mingming Liu, Peng Liu, and Rui Yao. "Separate Syntax and Semantics: Part-of-Speech-Guided Transformer for Image Captioning." Applied Sciences 12, no. 23 (2022): 11875. http://dx.doi.org/10.3390/app122311875.

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Transformer-based image captioning models have recently achieved remarkable performance by using new fully attentive paradigms. However, existing models generally follow the conventional language model of predicting the next word conditioned on the visual features and partially generated words. They treat the predictions of visual and nonvisual words equally and usually tend to produce generic captions. To address these issues, we propose a novel part-of-speech-guided transformer (PoS-Transformer) framework for image captioning. Specifically, a self-attention part-of-speech prediction network

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Knight, Carly R., and Isaac Ariail Reed. "Meaning and Modularity: The Multivalence of “Mechanism” in Sociological Explanation." Sociological Theory 37, no. 3 (2019): 234–56. http://dx.doi.org/10.1177/0735275119869969.

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Mechanisms are ubiquitous in sociological explanation. Recent theoretical work has sought to extend mechanistic explanation further still: into cultural and interpretative analysis. Yet it is not clear that the concept of mechanism can coherently unify interpretation and causal explanation within a single explanatory framework. We note that sociological mechanistic explanation is marked by a crucial disjuncture. Specifically, we identify two conflicting mechanistic approaches: Modular mechanism models depict counterfactual dependence among independent causal chains, whereas meaningful mechanis

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Demiris, Yiannis, and Bassam Khadhouri. "Content-based control of goal-directed attention during human action perception." Interaction Studies 9, no. 2 (2008): 353–76. http://dx.doi.org/10.1075/is.9.2.10dem.

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During the perception of human actions by robotic assistants, the robotic assistant needs to direct its computational and sensor resources to relevant parts of the human action. In previous work we have introduced HAMMER (Hierarchical Attentive Multiple Models for Execution and Recognition) (Demiris and Khadhouri, 2006), a computational architecture that forms multiple hypotheses with respect to what the demonstrated task is, and multiple predictions with respect to the forthcoming states of the human action. To confirm their predictions, the hypotheses request information from an attentional

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Moura, Aline A. "On Emotion: Exchanges Between Neurosciences and Literary Studies." Scripta 26, no. 57 (2022): 222–42. http://dx.doi.org/10.5752/p.2358-3428.2022v26n57p222-242.

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Taking into consideration an intellectual atmosphere attentive to the interchanges between embodied cognition and literature, the objective of this paper is to introduce definitions, mechanisms, and functionalities of bodily reactions to human existence and the production of knowledge based mainly on the investigations of the Portuguese neuroscientist Antonio Damasio. He has published several books and is one of the most influential researchers on the reciprocity among body, brain, mind, and situated experiences. By analyzing his investigations, it becomes possible to develop theoretical and a

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Imbriaco, Raffaele, Clint Sebastian, Egor Bondarev, and Peter de With. "Aggregated Deep Local Features for Remote Sensing Image Retrieval." Remote Sensing 11, no. 5 (2019): 493. http://dx.doi.org/10.3390/rs11050493.

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Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requ

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Ayub, Hina, Murad-Ali Khan, Syed Shehryar Ali Naqvi, et al. "Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities." Bioengineering 11, no. 6 (2024): 533. http://dx.doi.org/10.3390/bioengineering11060533.

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The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and pe

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Ivanov, D. D. "How much salt should i recommend to a patient with CKD: the real mechanisms of regulation of sodium metabolism in kidney pathology." Nephrology (Saint-Petersburg) 28, no. 3 (2024): 62–70. http://dx.doi.org/10.36485/1561-6274-2024-28-3-62-70.

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Dietary considerations for salt (sodium chloride) in people with chronic kidney disease are based on multiple guidelines and suggest an intake of <90 mmol sodium per day, or <5 g sodium chloride per day. It is assumed that excess sodium intake contributes to the formation/maintenance of hypertension and edema syndrome. However, WHO data and cohort studies show that average salt intake is approximately twice the recommended level. In people with chronic kidney disease, a paradoxical situation often arises when, with recommended, but poorly followed, recommendations, 15-36 % of patients ma

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GOOD, MARION. "Acute Pain." Annual Review of Nursing Research 17, no. 1 (1999): 107–32. http://dx.doi.org/10.1891/0739-6686.17.1.107.

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The review of acute pain describes the problem of unresolved pain and its effects on the neural, autonomic, and immune systems. Conceptualizations and mechanisms of pain are reviewed as well as theories of pain management. Descriptive studies of patient and nurse factors that inhibit effective pain management are discussed, followed by studies of pharmacological and nonpharmacological interventions. Critical analysis reveals that most studies were atheoretical, and therefore, this proliferation of information lacked conceptual coherence and organization. Furthermore, the nature and extent of b

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Schwartze, Michael, and Sonja A. Kotz. "The Timing of Regular Sequences: Production, Perception, and Covariation." Journal of Cognitive Neuroscience 27, no. 9 (2015): 1697–707. http://dx.doi.org/10.1162/jocn_a_00805.

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The temporal structure of behavior provides information that allows the tracking of temporal regularity in the sensory and sensorimotor domains. In turn, temporal regularity allows the generation of predictions about upcoming events and to adjust behavior accordingly. These mechanisms are essential to ensure behavior beyond the level of mere reaction. However, efficient temporal processing is required to establish adequate internal representations of temporal structure. The current study used two simple paradigms, namely, finger-tapping at a regular self-chosen rate (spontaneous motor tempo) a

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Chan, Agnes S., Yvonne M. Y. Han, Sophia L. Sze, Queenie Y. Wong, and Mei-chun Cheung. "A Randomized Controlled Neurophysiological Study of a ChineseChan-Based Mind-Body Intervention in Patients with Major Depressive Disorder." Evidence-Based Complementary and Alternative Medicine 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/812096.

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Our previous studies have reported the therapeutic effects of 10-session ChineseChan-basedDejianmind-body interventions (DMBI) in reducing the intake of antidepressants, improving depressive symptoms, and enhancing the attentional abilities of patients with depression. This study aims to explore the possible neuroelectrophysiological mechanisms underlying the previously reported treatment effects of DMBI in comparison with those of cognitive behavioral therapy (CBT). Seventy-five age-, gender-, and education-matched participants with depression were randomly assigned to receive either CBT or D

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Luberadzka, Joanna, Hendrik Kayser, and Volker Hohmann. "Making sense of periodicity glimpses in a prediction-update-loop—A computational model of attentive voice tracking." Journal of the Acoustical Society of America 151, no. 2 (2022): 712–37. http://dx.doi.org/10.1121/10.0009337.

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Humans are able to follow a speaker even in challenging acoustic conditions. The perceptual mechanisms underlying this ability remain unclear. A computational model of attentive voice tracking, consisting of four computational blocks: (1) sparse periodicity-based auditory features (sPAF) extraction, (2) foreground-background segregation, (3) state estimation, and (4) top-down knowledge, is presented. The model connects the theories about auditory glimpses, foreground-background segregation, and Bayesian inference. It is implemented with the sPAF, sequential Monte Carlo sampling, and probabilis

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