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Journal articles on the topic 'Contextual features'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Kunar, Melina A., Stephen J. Flusberg, and Jeremy M. Wolfe. "Contextual cuing by global features." Perception & Psychophysics 68, no. 7 (2006): 1204–16. http://dx.doi.org/10.3758/bf03193721.

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Zhou, Zhili, Meimin Wang, Yi Cao, and Yuecheng Su. "CNN Feature-Based Image Copy Detection with Contextual Hash Embedding." Mathematics 8, no. 7 (2020): 1172. http://dx.doi.org/10.3390/math8071172.

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As one of the important techniques for protecting the copyrights of digital images, content-based image copy detection has attracted a lot of attention in the past few decades. The traditional content-based copy detection methods usually extract local hand-crafted features and then quantize these features to visual words by the bag-of-visual-words (BOW) model to build an inverted index file for rapid image matching. Recently, deep learning features, such as the features derived from convolutional neural networks (CNN), have been proven to outperform the hand-crafted features in many applicatio

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Isti'anah, Arina. "The Contrastive and Contextual Analysis of the Verb Run." Journal of Language and Literature 16, no. 1 (2016): 45–54. http://dx.doi.org/10.24071/joll.v16i1.150.

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Words become the source for understanding the meanings of language. In relation to the study of meanings, this paper attempts ate presenting the contrastive and contextual analysis of the verb RUN. The contrastive analysis is conducted in order to find the distinctive features of the word run and its synonymous words. After finding out the features differentiating the verb RUN, the contextual analysis is conducted. The contextual analysis displays the features which take place in the verb RUN in various fields. Based on the analysis, RUN has features of manner, agent, situation, distance or du

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Diachok, N. "CONTEXTUAL FEATURES FOR IMPLEMENTING EPISTEMIC MODALITY." Journal “Ukrainian sense”, no. 1 (August 8, 2021): 108–15. http://dx.doi.org/10.15421/462109.

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Background. The article deals with the consideration that a speaker is the central figure for determination of modality as it is the speech subject that establishes the relations between an object and its predicated sign in an utterance. The subject of our research is epistemic modality, which is referred to a subjective type of modality; it is considered as the connection between a subject and an attribute. This connection is evaluated rationally by a speaker as his/her subjective mental construction rather than objective characteristic of the reality. The research objective is to demon

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Yuan, Jingwen, and Shugen Wang. "HCFPN: Hierarchical Contextual Feature-Preserved Network for Remote Sensing Scene Classification." Remote Sensing 15, no. 3 (2023): 810. http://dx.doi.org/10.3390/rs15030810.

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Convolutional neural networks (CNNs) have made significant advances in remote sensing scene classification (RSSC) in recent years. Nevertheless, the limitations of the receptive field cause CNNs to suffer from a disadvantage in capturing contextual information. To address this issue, vision transformer (ViT), a novel model that has piqued the interest of academics, is used to extract latent contextual information in remote sensing scene classification. However, when confronted with the challenges of large-scale variations and high interclass similarity in scene classification images, the origi

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AlJarrah, Mohammed N., Qussai M. Yaseen, and Ahmad M. Mustafa. "A Context-Aware Android Malware Detection Approach Using Machine Learning." Information 13, no. 12 (2022): 563. http://dx.doi.org/10.3390/info13120563.

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The Android platform has become the most popular smartphone operating system, which makes it a target for malicious mobile apps. This paper proposes a machine learning-based approach for Android malware detection based on application features. Unlike many prior research that focused exclusively on API Calls and permissions features to improve detection efficiency and accuracy, this paper incorporates applications’ contextual features with API Calls and permissions features. Moreover, the proposed approach extracted a new dataset of static API Calls and permission features using a large dataset

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Talibovna, Imamova Gulnora. "Synonyms and Their Semantic Features." American Journal of Philological Sciences 5, no. 5 (2025): 226–28. https://doi.org/10.37547/ajps/volume05issue05-60.

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An, Han, and Jifan Ren. "XGBDeepFM for CTR Predictions in Mobile Advertising Benefits from Ad Context." Mathematical Problems in Engineering 2020 (April 27, 2020): 1–7. http://dx.doi.org/10.1155/2020/1747315.

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The problem of click-through rate (CTR) prediction in mobile advertising is one of the most informative metrics used in mobile business activities, such as profit evaluation and resource management. In mobile advertising, CTR prediction is essential but challenging due to data sparsity. Moreover, existing methods often have difficulty in capturing the different orders of feature interactions simultaneously. In this study, a method was developed to obtain accurate CTR prediction by incorporating contextual features and feature interactions. We initially use extreme gradient boosting (XGBoost) a

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Gupta, Ashish Kumar, Ayan Seal, Pritee Khanna, Anis Yazidi, and Ondrej Krejcar. "Gated Contextual Features for Salient Object Detection." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–13. http://dx.doi.org/10.1109/tim.2021.3064423.

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Magro, Anne M. "Contextual Features of Tax Decision-Making Settings." Journal of the American Taxation Association 21, s-1 (1999): 63–73. http://dx.doi.org/10.2308/jata.1999.21.s-1.63.

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Prior research in psychology and accounting suggests that features of the decision-making task and context affect information processing, yet the decision-making context is often ignored in tax judgment and decision-making research. Two primary decision contexts in the tax setting are planning and compliance. If these two contexts differ on significant features, the information processing of tax professionals in the settings also is likely to differ. An analysis of the characteristics of tax planning and compliance contexts suggests that planning problems are generally characterized by greater

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Schlangen, D., and E. Barenholtz. "Intrinsic and contextual features in object recognition." Journal of Vision 15, no. 1 (2015): 28. http://dx.doi.org/10.1167/15.1.28.

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Schlangen, D., and E. Barenholtz. "Intrinsic versus contextual features in object recognition." Journal of Vision 14, no. 10 (2014): 1288. http://dx.doi.org/10.1167/14.10.1288.

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YALİM KELES, Hacer, and Long Ang LİM. "LEARNING DENSE CONTEXTUAL FEATURES FOR SEMANTIC SEGMENTATION." Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 62, no. 1 (2020): 26–34. http://dx.doi.org/10.33769/aupse.611958.

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Semantic segmentation, which is one of the key problems in computer vision, has been applied in various application domains such as autonomous driving, robot navigation, or medical imagery, to name a few. Recently, deep learning, especially deep neural networks, have shown significant performance improvement over conventional semantic segmentation methods. In this paper, we present a novel encoder-decoder type deep neural network-based method, namely XSeNet, that can be trained end-to-end in a supervised manner. We adapt ResNet-50 layers as the encoder and design a cascaded decoder that compos

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Rahmani, Hossein, Hendrik Blockeel, and Andreas Bender. "Predicting Genes Involved in Human Cancer Using Network Contextual Information." Journal of Integrative Bioinformatics 9, no. 1 (2012): 44–71. http://dx.doi.org/10.1515/jib-2012-210.

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Summary Protein-Protein Interaction (PPI) networks have been widely used for the task of predicting proteins involved in cancer. Previous research has shown that functional information about the protein for which a prediction is made, proximity to specific other proteins in the PPI network, as well as local network structure are informative features in this respect. In this work, we introduce two new types of input features, reflecting additional information: (1) Functional Context: the functions of proteins interacting with the target protein (rather than the protein itself); and (2) Structur

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Yue, Xi, Tao Zhou, Lei He, and Yuxia Li. "Research on Long Text Classification Model Based on Multi-Feature Weighted Fusion." Applied Sciences 12, no. 13 (2022): 6556. http://dx.doi.org/10.3390/app12136556.

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Text classification in the long-text domain has become a development challenge due to the significant increase in text data, complexity enhancement, and feature extraction of long texts in various domains of the Internet. A long text classification model based on multi-feature weighted fusion is proposed for the problems of contextual semantic relations, long-distance global relations, and multi-sense words in long text classification tasks. The BERT model is used to obtain feature representations containing global semantic and contextual feature information of text, convolutional neural netwo

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Hua-Ping Zhou, Hua-Ping Zhou, Jie Zhang Hua-Ping Zhou, Ke-Lei Sun Jie Zhang, Qi-Fen Wen Ke-Lei Sun, Qi Zhao Qi-Fen Wen, and Ying-Jie Guo Qi Zhao. "Small Object Detection in Remote Sensing Based on Contextual Information and Attention." 電腦學刊 35, no. 1 (2024): 145–57. http://dx.doi.org/10.53106/199115992024023501010.

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<p>Many small objects, for instance vehicles and small ships, are encountered in remotely sensed images. However, small object detection has been a challenging task in remote sensing because of the problem that small objects are easily missed and influenced by the background. To address this challenge, we propose a detection method based on contextual information and attention, divided into two main parts. Firstly, for purpose of further improve the backbone network features to derive more contextual information, a multi-branch feature enhancement module is constructed to fuse multiple s

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Xue, Xu, and Bruno C. d. S. Oliveira. "Contextual Typing." Proceedings of the ACM on Programming Languages 8, ICFP (2024): 880–908. http://dx.doi.org/10.1145/3674655.

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Bidirectional typing is a simple, lightweight approach to type inference that propagates known type information during typing, and can scale up to many different type systems and features. It typically only requires a reasonable amount of annotations and eliminates the need for many obvious annotations. Nonetheless the power of inference is still limited, and complications arise in the presence of more complex features. In this paper we present a generalization of bidirectional typing called contextual typing . In contextual typing not only known type information is propagated during typing, b

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Hirsch, Pamela L., and Elisabeth Hollister Sandberg. "Microdevelopment of Complex Featural and Spatial Integration with Contextual Support." Child Development Research 2015 (October 15, 2015): 1–10. http://dx.doi.org/10.1155/2015/902584.

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Complex spatial decisions involve the ability to combine featural and spatial information in a scene. In the present work, 4- through 9-year-old children completed a complex map-scene correspondence task under baseline and supported conditions. Children compared a photographed scene with a correct map and with map-foils that made salient an object feature or spatial property. Map-scene matches were analyzed for the effects of age and featural-spatial information on children’s selections. In both conditions children significantly favored maps that highlighted object detail and object perspectiv

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Kim, Joonyoung, Kangwook Lee, Haebin Shin, et al. "Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15689–95. http://dx.doi.org/10.1609/aaai.v37i13.26861.

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The more new features that are being added to smartphones, the harder it becomes for users to find them. This is because the feature names are usually short and there are just too many of them for the users to remember the exact words. The users are more comfortable asking contextual queries that describe the features they are looking for, but the standard term frequency-based search cannot process them. This paper presents a novel retrieval system for mobile features that accepts intuitive and contextual search queries. We trained a relevance model via contrastive learning from a pre-trained

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Muslimova, Malika, and M. Kh Alimova. "SOME SEMANTIC-FUNCTIONAL FEATURES OF ENGLISH ANTONYMS." CURRENT RESEARCH JOURNAL OF HISTORY 04, no. 02 (2023): 25–28. http://dx.doi.org/10.37547/history-crjh-04-02-05.

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The article deals with some peculiarities of English antonyms. The author describes 5 types of antonyms and their functions: auto-antonyms, complementary antonyms, converse antonyms, gradual antonyms, and contextual antonyms.

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Tajuddin, Jaslina Mohd, and Jamaludin Mohaiadin. "Malay Children Verbal Reception towards Contextual Features in Fast Food Advertisements." GATR Global Journal of Business Social Sciences Review 1, no. 2 (2013): 01–08. http://dx.doi.org/10.35609/gjbssr.2013.1.2(1).

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Objective This study examines the relationship between Malay children verbal reception and contextual features in fast food advertisements. Methodology/Technique There are four items have been used to measure verbal reception and 26 items to measure contextual features. Structured questionnaire was developed based on previous literatures and a total of 50 pre-school children participated in this study. Findings The findings disclosed that there is a positive correlation exists between verbal receptions and rewarding, imitation and humour and negative correlation between verbal reception, model

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Razali, Md Saifullah, Alfian Abdul Halin, Lei Ye, Shyamala Doraisamy, and Noris Mohd Norowi. "Sarcasm Detection Using Deep Learning With Contextual Features." IEEE Access 9 (2021): 68609–18. http://dx.doi.org/10.1109/access.2021.3076789.

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Turki, Hussain Mohammed, Essam Al Daoud, Ghassan Samara, et al. "Arabic fake news detection using hybrid contextual features." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 836. http://dx.doi.org/10.11591/ijece.v15i1.pp836-845.

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Technology has advanced and social media users have grown dramatically in the last decade. Because social media makes information easily accessible, some people or organizations distribute false news for political or commercial gain. This news may influence elections and attitudes. Even though English fake news is widely detected and limited, Arabic fake news is hard to recognize owing to a lack of study and data collection. Wara Arabic bidirectional encoder representations from transformers (WaraBERT), a hybrid feature extraction approach, combines word level tokenization with two Arabic bidi

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Tiemann, Christopher O. "Multipath features as a contextual clue for classification." Journal of the Acoustical Society of America 127, no. 3 (2010): 2003. http://dx.doi.org/10.1121/1.3385196.

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Savolainen, Reijo. "Approaching information-seeking habits and their contextual features." Information Research an international electronic journal 29, no. 2 (2024): 126–48. http://dx.doi.org/10.47989/ir292706.

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Introduction. In information seeking, habits manifest themselves in the ways in which people repeatedly and regularly prefer and access certain sources of information. The present study reviews how researchers in diverse field have approached such habits and characterised their contextual features. Method. Qualitative content analysis of a sample of seventy-three studies on the above topic. The research inductively identified three approaches to habits in information seeking and contextual factors affecting habitual information seeking. Results. Many of the investigations on information-seekin

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Turki, Hussain Mohammed, Essam Al Daoud, Ghassan Samara, et al. "Arabic fake news detection using hybrid contextual features." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 836–45. https://doi.org/10.11591/ijece.v15i1.pp836-845.

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Technology has advanced and social media users have grown dramatically in the last decade. Because social media makes information easily accessible, some people or organizations distribute false news for political or commercial gain. This news may influence elections and attitudes. Even though English fake news is widely detected and limited, Arabic fake news is hard to recognize owing to a lack of study and data collection. Wara Arabic bidirectional encoder representations from transformers (WaraBERT), a hybrid feature extraction app

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Li, Junda, Chunxu Zhang, and Bo Yang. "Global Contextual Dependency Network for Object Detection." Future Internet 14, no. 1 (2022): 27. http://dx.doi.org/10.3390/fi14010027.

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Current two-stage object detectors extract the local visual features of Regions of Interest (RoIs) for object recognition and bounding-box regression. However, only using local visual features will lose global contextual dependencies, which are helpful to recognize objects with featureless appearances and restrain false detections. To tackle the problem, a simple framework, named Global Contextual Dependency Network (GCDN), is presented to enhance the classification ability of two-stage detectors. Our GCDN mainly consists of two components, Context Representation Module (CRM) and Context Depen

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Liu, Jie, Cangming Liang, Jintao Feng, et al. "A Multi-Feature Fusion-Based Automatic Detection Method for High-Severity Defects." Electronics 12, no. 14 (2023): 3075. http://dx.doi.org/10.3390/electronics12143075.

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It is crucial to detect high-severity defects, such as memory leaks that can result in system crashes or severe resource depletion, in order to reduce software development costs and ensure software quality and reliability. The primary cause of high-severity defects is usually resource scheduling errors, and in the program source code, these defects have contextual features that require defect context to confirm their existence. In the context of utilizing machine learning methods for defect automatic confirmation, the single-feature label method cannot achieve high-precision defect confirmatio

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Prem, Dhanu Hasi. "Learning Enriched Features for Fast Image Enhancement." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5062–66. http://dx.doi.org/10.22214/ijraset.2024.62395.

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Abstract: In the realm of image restoration, the objective is to recover high-quality image content from degraded input images. This pursuit finds applications in diverse fields such as computational photography, surveillance, autonomous vehicles, and remote sensing. Recent years have witnessed notable progress in image restoration, primarily driven by convolutional neural networks (CNNs). Existing CNN based methods are commonly designed to operate either on full-resolution images, preserving spatial details but lacking precise contextual encoding, or on progressively lower-resolution represen

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Deepa D. "Contextual Sentiment Boosting through Lexicon Masking and Transformer Fine-Tuning." Journal of Information Systems Engineering and Management 10, no. 44s (2025): 78–90. https://doi.org/10.52783/jisem.v10i44s.8571.

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Introduction: In today's advert environment sentiment analysis plays critical for gathering insights from consumer perspectives. Typical machine learning models rely on predefined features which might struggle with feature weighting, neglect important comments and misunderstand word meanings particular to a certain domain especially when applied to small datasets. Objectives: This study aims to overwhelm the boundaries of traditional sentiment analysis by enhancing feature representation and leveraging deep learning. Also it focusses on improving the extraction of sentiment-related features, d

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Zhang, Peng, Qingyang Jing, Xinlei Zhao, et al. "A Multi-Object Tracking Approach Combining Contextual Features and Trajectory Prediction." Electronics 12, no. 23 (2023): 4720. http://dx.doi.org/10.3390/electronics12234720.

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Aiming to solve the problem of the identity switching of objects with similar appearances in real scenarios, a multi-object tracking approach combining contextual features and trajectory prediction is proposed. This approach integrates the motion and appearance features of objects. The motion features are mainly used for trajectory prediction, and the appearance features are divided into contextual features and individual features, which are mainly used for trajectory matching. In order to accurately distinguish the identities of objects with similar appearances, a context graph is constructed

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van der Smagt, Maarten J., Christian Wehrhahn, and Thomas D. Albright. "Contextual Masking of Oriented Lines: Interactions Between Surface Segmentation Cues." Journal of Neurophysiology 94, no. 1 (2005): 576–89. http://dx.doi.org/10.1152/jn.00366.2004.

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The ability of human observers to detect and discriminate a single feature of a visual image deteriorates markedly when the targeted feature is surrounded by others of a similar kind. This perceptual masking is mirrored by the suppressive effects of surround stimulation on the responses of neurons in primary visual cortex (area V1). Both perceptual and neuronal masking effects are partially relieved, however, if the targeted image feature is distinguished from surrounding features along some dimension, such as contour orientation. Masking relief is likely to play an important role in perceptua

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Hacar, Müslüm, Diego Altafini, and Valerio Cutini. "Network-Based Hierarchical Feature Augmentation for Predicting Road Classes in OpenStreetMap." ISPRS International Journal of Geo-Information 13, no. 12 (2024): 456. https://doi.org/10.3390/ijgi13120456.

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The need to enrich the semantic completeness of OpenStreetMap (OSM) data is crucial for its effective use in geographic information systems and urban studies. Addressing this challenge, our research introduces a novel hierarchical feature augmentation approach to developing machine learning classifiers by the features retrieved from various levels of road network connectivity. This method systematically augments the feature space by incorporating measure values of connected road features, thereby integrating extensive contextual information from the network hierarchy. In our evaluation, conduc

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Tian, Xuecheng, Bo Jiang, King-Wah Pang, Yu Guo, Yong Jin, and Shuaian Wang. "Solving Contextual Stochastic Optimization Problems through Contextual Distribution Estimation." Mathematics 12, no. 11 (2024): 1612. http://dx.doi.org/10.3390/math12111612.

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Stochastic optimization models always assume known probability distributions about uncertain parameters. However, it is unrealistic to know the true distributions. In the era of big data, with the knowledge of informative features related to uncertain parameters, this study aims to estimate the conditional distributions of uncertain parameters directly and solve the resulting contextual stochastic optimization problem by using a set of realizations drawn from estimated distributions, which is called the contextual distribution estimation method. We use an energy scheduling problem as the case

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Schickore, Jutta. "(Ab)Using the Past for Present Purposes: Exposing Contextual and Trans-Contextual Features of Error." Perspectives on Science 10, no. 4 (2002): 433–56. http://dx.doi.org/10.1162/106361402322288057.

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Berger, Cynthia, Scott Crossley, and Stephen Skalicky. "USING LEXICAL FEATURES TO INVESTIGATE SECOND LANGUAGE LEXICAL DECISION PERFORMANCE." Studies in Second Language Acquisition 41, no. 5 (2019): 911–35. http://dx.doi.org/10.1017/s0272263119000019.

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AbstractA large dataset of word recognition behavior from nonnative speakers (NNS) of English was collected using an online crowdsourced lexical decision task. Lexical features were used to predict NNS lexical decision latencies and accuracies. Predictors of NNS latencies and accuracy included contextual diversity, age of acquisition, and contextual distinctiveness, while length moderated the impact of contextual diversity and neighborhood size on accuracy. Results have implications for second language word recognition and demonstrate that NNS behavioral data collected through large crowdsourc

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Waheed, Talha, Muhammad Aslam, and Muhammad Awais. "Predicting Mental-Illness from Twitter Activity Using Activity Theory Based Context Ontology." Journal of Medical Imaging and Health Informatics 9, no. 6 (2019): 1224–33. http://dx.doi.org/10.1166/jmihi.2019.2722.

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In this digital era, people are becoming more comfortable to express their sentiments on social media rather than sharing them in person. The analysis of these sentiments is a doorway to understand their mental-health status. The literature proposed linguistic features-based methods for their automatic analysis to see if the user needs any psychological assistance. However, these methods analysed expressed sentiments as discrete and fragmented information, without considering the holistic context. For this, activity theory has been employed to explicate the context of sentiment expression acti

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Wu, Weidong, Hongbo Fan, Yu Fan, and Jian Wen. "Nonlinear Activation-Free Contextual Attention Network for Polyp Segmentation." Information 14, no. 7 (2023): 362. http://dx.doi.org/10.3390/info14070362.

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The accurate segmentation of colorectal polyps is of great significance for the diagnosis and treatment of colorectal cancer. However, the segmentation of colorectal polyps faces complex problems such as low contrast in the peripheral region of salient images, blurred borders, and diverse shapes. In addition, the number of traditional UNet network parameters is large and the segmentation effect is average. To overcome these problems, an innovative nonlinear activation-free uncertainty contextual attention network is proposed in this paper. Based on the UNet network, an encoder and a decoder ar

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Bottinelli, Katherine, Yena Kyeong, and Cecilia Cheung. "Adolescents’ Drawing and Divergent Thinking: Does Culture Matter?" Journal of Educational and Developmental Psychology 8, no. 2 (2018): 147. http://dx.doi.org/10.5539/jedp.v8n2p147.

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The current study examined the association between adolescents’ divergent thinking and features of their drawings in the United States and China. A total of 321 American (n = 125) and Chinese (n = 196) adolescents completed a battery of assessments on divergent thinking and free drawing adapted from the Torrance Tests of Creative Thinking (TTCT). Central (e.g., focal object) and contextual (e.g., background) features characterizing adolescents’ drawings were coded. Results indicated that Chinese adolescents included more central features in their drawings compared to their American counterpart

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Dong, Xin, and Jie Hu. "An Exploration of Impact Factors Influencing Students’ Reading Literacy in Singapore with Machine Learning Approaches." International Journal of English Linguistics 9, no. 5 (2019): 52. http://dx.doi.org/10.5539/ijel.v9n5p52.

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This study identified the contextual factors which differentiated 15-year-old students with high- and low-achieving reading literacy in Singapore based on Program for International Student Assessment (PISA) 2015. 4,015 students from Singapore were collected from the public dataset of PISA 2015, with 2,646 high-achieving students and 1,369 low-achieving students in PISA reading literacy test. The impact of the overall 49 contextual factors on reading literacy was analyzed in three levels: student level, family level and school level. Support vector machine (SVM), a machine learning approach, wa

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Ninh Khánh, Duy. "Evaluation of speaker-dependent and average-voice Vietnamese statistical speech synthesis systems." Journal of Science and Technology Issue on Information and Communications Technology 17, no. 12.1 (2019): 11. http://dx.doi.org/10.31130/jst-ud2019-035e.

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This paper describes the development and evaluation of a Vietnamese statistical speech synthesis system using the average voice approach. Although speaker-dependent systems have been applied extensively, no average voice based system has been developed for Vietnamese so far. We have collected speech data from several Vietnamese native speakers and employed state-of-the-art speech analysis, model training and speaker adaptation techniques to develop the system. Besides, we have performed perceptual experiments to compare the quality of speaker-adapted (SA) voices built on the average voice mode

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Yang, Wenhao, Liqun Kuang, Song Wang, et al. "Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining." Algorithms 18, no. 7 (2025): 383. https://doi.org/10.3390/a18070383.

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Three-dimensional semantic segmentation plays a crucial role in accurately identifying terrain features and objects by effectively extracting 3D spatial information from the environment. However, the inherent sparsity of point clouds and unclear terrain boundaries in outdoor mining environments significantly complicate the recognition process. To address these challenges, we propose a novel 3D semantic segmentation network that incorporates spatiotemporal feature aggregation. Specifically, we introduced the Gated Spatiotemporal Clue Encoder, which extracts spatiotemporal context from historica

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Dave, Kushal, and Vasudeva Varma. "Identifying Microblogs for Targeted Contextual Advertising." Proceedings of the International AAAI Conference on Web and Social Media 6, no. 1 (2021): 431–34. http://dx.doi.org/10.1609/icwsm.v6i1.14303.

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Micro-blogging sites such as Facebook, Twitter, Google+ present a nice opportunity for targeting advertisements that are contextually related to the microblog content. By virtue of the sparse and noisy text makes identifying the microblogs suitable for advertising a very hard problem. In this work, we approach the problem of identifying the microblogs that could be targeted for advertisements as a two-step classification approach. In the first pass, microblogs suitable for advertising are identified. Next, in the second pass, we build a model to find the sentiment of the advertisable microblog

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Liu, Faxue, Xuan Wang, Qiqi Chen, Jinghong Liu, and Chenglong Liu. "SiamMAN: Siamese Multi-Phase Aware Network for Real-Time Unmanned Aerial Vehicle Tracking." Drones 7, no. 12 (2023): 707. http://dx.doi.org/10.3390/drones7120707.

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In this paper, we address aerial tracking tasks by designing multi-phase aware networks to obtain rich long-range dependencies. For aerial tracking tasks, the existing methods are prone to tracking drift in scenarios with high demand for multi-layer long-range feature dependencies such as viewpoint change caused by the characteristics of the UAV shooting perspective, low resolution, etc. In contrast to the previous works that only used multi-scale feature fusion to obtain contextual information, we designed a new architecture to adapt the characteristics of different levels of features in chal

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Tianxue, Lian, and Mi-Lim Ryoo. "Textual and Contextual Features of Marine Safety Investigation Reports." Cultural Interaction Studies of Sea Port Cities 17 (October 31, 2017): 321–45. http://dx.doi.org/10.35158/cisspc.2017.10.17.321.

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HIRANO, TORU, YOSHIHIRO MATSUO, and GENICHIRO KIKUI. "Detecting Semantic Relations between Named Entities Using Contextual Features." Journal of Natural Language Processing 15, no. 4 (2008): 43–58. http://dx.doi.org/10.5715/jnlp.15.4_43.

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Purnomo, Y. W., F. S. Mastura, and K. S. Perbowo. "Contextual Features of Geometrical Problems in Indonesian Mathematics Textbooks." Journal of Physics: Conference Series 1315 (October 2019): 012048. http://dx.doi.org/10.1088/1742-6596/1315/1/012048.

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Cloyd, C. Bryan. "Discussion of Contextual Features of Tax Decision-Making Settings." Journal of the American Taxation Association 21, s-1 (1999): 74–77. http://dx.doi.org/10.2308/jata.1999.21.s-1.74.

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Tax practice has evolved over the past decade from being predominantly concerned with tax compliance to a major focus on tax planning. Significant challenges for researchers interested in tax professional judgment and decision making (JDM) are (1) understanding the current environment of tax professionals, (2) identifying research questions that are relevant to this environment, and (3) appropriately capturing the essential elements of the current environment in the experimental setting. The research question addressed in Magro (1999) is related to this first challenge—understanding the curren

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Ramírez, J., P. Yélamos, J. M. Górriz, and J. C. Segura. "SVM-based speech endpoint detection using contextual speech features." Electronics Letters 42, no. 7 (2006): 426. http://dx.doi.org/10.1049/el:20064068.

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Morago, Brittany, Giang Bui, and Ye Duan. "An Ensemble Approach to Image Matching Using Contextual Features." IEEE Transactions on Image Processing 24, no. 11 (2015): 4474–87. http://dx.doi.org/10.1109/tip.2015.2456498.

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