Feature-Based Analysis of the Impact of Ground Coat and Varnish on Violin Tone Qualities

2017 ◽  
Vol 103 (1) ◽  
pp. 80-93 ◽  
Author(s):  
Francesco Setragno ◽  
Massimiliano Zanoni ◽  
Fabio Antonacci ◽  
Augusto Sarti ◽  
Marco Malagodi ◽  
...  
Keyword(s):  
Feature Based ◽  
The Impact ◽  
Ground Coat

scholarly journals Comparing word embedding models for Arabic aspect category detection using a deep learning-based approach

2021 ◽  
Vol 297 ◽  
pp. 01072
Author(s):  
Rajae Bensoltane ◽  
Taher Zaki
Keyword(s):  
Deep Learning ◽  
Vector Representation ◽  
Rule Based ◽  
External Resources ◽  
Unit Model ◽  
Proposed Model ◽  
Feature Based ◽  
The Impact ◽  
Gated Recurrent Unit ◽  
Machine Learning Models

Aspect category detection (ACD) is a task of aspect-based sentiment analysis (ABSA) that aims to identify the discussed category in a given review or sentence from a predefined list of categories. ABSA tasks were widely studied in English; however, studies in other low-resource languages such as Arabic are still limited. Moreover, most of the existing Arabic ABSA work is based on rule-based or feature-based machine learning models, which require a tedious task of feature-engineering and the use of external resources like lexicons. Therefore, the aim of this paper is to overcome these shortcomings by handling the ACD task using a deep learning method based on a bidirectional gated recurrent unit model. Additionally, we examine the impact of using different vector representation models on the performance of the proposed model. The experimental results show that our model outperforms the baseline and related work models significantly by achieving an enhanced F1-score of more than 7%.

Feature-Based Uncertainty Visualization

Big Data ◽  
2016 ◽  
pp. 261-287
Author(s):  
Keqin Wu ◽  
Song Zhang
Keyword(s):  
Scientific Data ◽  
Data Sets ◽  
Data Set ◽  
Uncertainty Visualization ◽  
Scalar Data ◽  
Critical Issues ◽  
Contour Level ◽  
Feature Based ◽  
2D Data ◽  
The Impact

While uncertainty in scientific data attracts an increasing research interest in the visualization community, two critical issues remain insufficiently studied: (1) visualizing the impact of the uncertainty of a data set on its features and (2) interactively exploring 3D or large 2D data sets with uncertainties. In this chapter, a suite of feature-based techniques is developed to address these issues. First, an interactive visualization tool for exploring scalar data with data-level, contour-level, and topology-level uncertainties is developed. Second, a framework of visualizing feature-level uncertainty is proposed to study the uncertain feature deviations in both scalar and vector data sets. With quantified representation and interactive capability, the proposed feature-based visualizations provide new insights into the uncertainties of both data and their features which otherwise would remain unknown with the visualization of only data uncertainties.

scholarly journals Statistical regularities cause attentional suppression with target-matching distractors

2020 ◽  
Author(s):  
Dirk Kerzel ◽  
Stanislas Huynh Cong
Keyword(s):  
High Frequency ◽  
Target Location ◽  
Low Frequency ◽  
Target Display ◽  
Distractor Location ◽  
Target Feature ◽  
Statistical Regularities ◽  
Feature Based ◽  
The Impact ◽  
Target Matching

AbstractVisual search may be disrupted by the presentation of salient, but irrelevant stimuli. To reduce the impact of salient distractors, attention may suppress their processing below baseline level. While there are many studies on the attentional suppression of distractors with features distinct from the target (e.g., a color distractor with a shape target), there is little and inconsistent evidence for attentional suppression with distractors sharing the target feature. In this study, distractor and target were temporally separated in a cue–target paradigm, where the cue was shown briefly before the target display. With target-matching cues, RTs were shorter when the cue appeared at the target location (valid cues) compared with when it appeared at a nontarget location (invalid cues). To induce attentional suppression, we presented the cue more frequently at one out of four possible target positions. We found that invalid cues appearing at the high-frequency cue position produced less interference than invalid cues appearing at a low-frequency cue position. Crucially, target processing was also impaired at the high-frequency cue position, providing strong evidence for attentional suppression of the cued location. Overall, attentional suppression of the frequent distractor location could be established through feature-based attention, suggesting that feature-based attention may guide attentional suppression just as it guides attentional enhancement.

A FEATURE-BASED SCHEME FOR DETECTING AND CLASSIFYING VIDEO-SHOT TRANSITIONS BASED ON SPATIO-TEMPORAL ANALYSIS AND FUZZY CLASSIFICATION

2009 ◽  
Vol 23 (06) ◽  
pp. 1179-1200 ◽  
Author(s):  
GUO-SHIANG LIN ◽  
MIN-KUAN CHANG ◽  
SHIEN-TANG CHIU
Keyword(s):  
Feature Extraction ◽  
Temporal Analysis ◽  
Fuzzy Classification ◽  
Model Parameters ◽  
Fuzzy Classifier ◽  
Video Shot ◽  
Feature Based ◽  
Spatio Temporal ◽  
Shot Transitions ◽  
The Impact

In this paper, we propose a feature-based scheme for detecting different genres of video shot transitions based on spatio-temporal analysis and model parameter estimation. In feature extraction, the histogram difference and its modified versions are calculated from the effectiveness of detecting cuts and reducing the impact of fleeting lights. We propose a hybrid algorithm composed of adaptive thresholding, parameter calculation, and transition duration refinement to measure model parameters. Some properties of the associated model parameters of each transition are computed as features. A feature measuring the time gap between two consecutive shots is also adopted. After feature extraction, a fuzzy classifier integrates these features to distinguish nontransitions, cuts, and dissolve-type features from one to another. Many test videos having different types of shots are used for performance evaluation. The experimental results demonstrate that the proposed scheme not only detects cuts, dissolves, and fades well, but also accurately locates the duration of each dissolve-type transition. In addition, the proposed scheme outperforms some existing methods in terms of cut and dissolve detection.

Spatial and Temporal Feature-Based Reduced Reference Quality Assessment for Rate-Varying Videos in Wireless Networks

2019 ◽  
Vol 33 (13) ◽  
pp. 1950021 ◽  
Author(s):  
Wenjuan Shi ◽  
Yanjing Sun ◽  
Song Li ◽  
Qi Cao ◽  
Bowen Wang
Keyword(s):  
Wireless Networks ◽  
Quality Assessment ◽  
Temporal Variation ◽  
User Satisfaction ◽  
Video Quality ◽  
Orientation Selectivity ◽  
Original Video ◽  
Feature Based ◽  
The Impact ◽  
Temporal Feature

For the impact of the bitrate change of video streaming services according to the available bandwidth on user satisfaction, in this paper, we propose a spatial and temporal feature-based reduced reference (RR) quality assessment for rate-varying videos in wireless networks called STRQAW. First, simulating the orientation selectivity mechanism of the human visual system (HVS), the histogram of the orientation selectivity-based visual pattern in each frame is extracted as the spatial feature. The histogram similarity between the rate-varying video and the original video is computed as the spatial metric. Second, we extract the temporal variation of the DCT coefficients of the consecutive frame differences as the temporal feature. The temporal variation similarity between the rate-varying video and the original video is calculated as the temporal metric. Finally, we take into account the recency effect and assess the overall quality by combining the temporal and spatial metric. The experimental results using the Laboratory for Image and Video Engineering (LIVE) mobile video quality assessment (VQA) database show that STRQAW is consistent with the subjective assessment results, which means it reflects human subjective feelings well and it provides an evaluation for adjusting compression-coding rates in real time. STRQAW can be used to guide video application providers and network operators working towards satisfying end-user experiences.

scholarly journals Shallow Feature Based Dense Attention Network for Crowd Counting

2020 ◽  
Vol 34 (07) ◽  
pp. 11765-11772 ◽  
Author(s):  
Yunqi Miao ◽  
Zijia Lin ◽  
Guiguang Ding ◽  
Jungong Han
Keyword(s):  
Absolute Error ◽  
Image Features ◽  
Feature Maps ◽  
Attention Network ◽  
Crowd Counting ◽  
Attention Model ◽  
Crowd Density ◽  
Benchmark Datasets ◽  
Feature Based ◽  
The Impact

While the performance of crowd counting via deep learning has been improved dramatically in the recent years, it remains an ingrained problem due to cluttered backgrounds and varying scales of people within an image. In this paper, we propose a Shallow feature based Dense Attention Network (SDANet) for crowd counting from still images, which diminishes the impact of backgrounds via involving a shallow feature based attention model, and meanwhile, captures multi-scale information via densely connecting hierarchical image features. Specifically, inspired by the observation that backgrounds and human crowds generally have noticeably different responses in shallow features, we decide to build our attention model upon shallow-feature maps, which results in accurate background-pixel detection. Moreover, considering that the most representative features of people across different scales can appear in different layers of a feature extraction network, to better keep them all, we propose to densely connect hierarchical image features of different layers and subsequently encode them for estimating crowd density. Experimental results on three benchmark datasets clearly demonstrate the superiority of SDANet when dealing with different scenarios. Particularly, on the challenging UCF_CC_50 dataset, our method outperforms other existing methods by a large margin, as is evident from a remarkable 11.9% Mean Absolute Error (MAE) drop of our SDANet.

scholarly journals Pseudo-Invariant Feature-Based Linear Regression Model (PIF-LRM): An Effective Normalization Method to Evaluate Urbanization Impacts on Land Surface Temperature Changes

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1540
Author(s):  
Zhengwu Cai ◽  
Chao Fan ◽  
Falin Chen ◽  
Xiaoma Li
Keyword(s):  
Linear Regression ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temperature Changes ◽  
Invariant Feature ◽  
Multi Temporal ◽  
Feature Based ◽  
Impact Of Urbanization ◽  
The Impact

The Landsat land surface temperature (LST) product is widely used to understand the impact of urbanization on surface temperature changes. However, directly comparing multi-temporal Landsat LST is challenging, as the observed LST might be strongly affected by climatic factors. This study validated the utility of the pseudo-invariant feature-based linear regression model (PIF-LRM) in normalizing multi-temporal Landsat LST to highlight the urbanization impact on temperature changes, based on five Landsat LST images during 2000–2018 in Changsha, China. Results showed that LST of PIFs between the reference and the target images was highly correlated, indicating high applicability of the PIF-LRM to relatively normalize LST. The PIF-LRM effectively removed the temporal variation of LST caused by climate factors and highlighted the impacts of urbanization caused land use and land cover changes. The PIF-LRM normalized LST showed stronger correlations with the time series of normalized difference of vegetation index (NDVI) than the observed LST and the LST normalized by the commonly used mean method (subtracting LST by the average, respectively for each image). The PIF-LRM uncovered the spatially heterogeneous responses of LST to urban expansion. For example, LST decreased in the urban center (the already developed regions) and increased in the urbanizing regions. PIF-LRM is highly recommended to normalize multi-temporal Landsat LST to understand the impact of urbanization on surface temperature changes from a temporal point of view.

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