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Journal articles on the topic 'Shortcut learning'

Author: Grafiati

Published: 25 May 2024

Last updated: 19 July 2025

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1

Lyu, Zhaoyan, and Miguel R. D. Rodrigues. "Exploring the Impact of Additive Shortcuts in Neural Networks via Information Bottleneck-like Dynamics: From ResNet to Transformer." Entropy 26, no. 11 (2024): 974. http://dx.doi.org/10.3390/e26110974.

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Deep learning has made significant strides, driving advances in areas like computer vision, natural language processing, and autonomous systems. In this paper, we further investigate the implications of the role of additive shortcut connections, focusing on models such as ResNet, Vision Transformers (ViTs), and MLP-Mixers, given that they are essential in enabling efficient information flow and mitigating optimization challenges such as vanishing gradients. In particular, capitalizing on our recent information bottleneck approach, we analyze how additive shortcuts influence the fitting and com

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Kim, Doyoung, Dongmin Park, Yooju Shin, Jihwan Bang, Hwanjun Song, and Jae-Gil Lee. "Adaptive Shortcut Debiasing for Online Continual Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13122–31. http://dx.doi.org/10.1609/aaai.v38i12.29211.

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We propose a novel framework DropTop that suppresses the shortcut bias in online continual learning (OCL) while being adaptive to the varying degree of the shortcut bias incurred by continuously changing environment. By the observed high-attention property of the shortcut bias, highly-activated features are considered candidates for debiasing. More importantly, resolving the limitation of the online environment where prior knowledge and auxiliary data are not ready, two novel techniques---feature map fusion and adaptive intensity shifting---enable us to automatically determine the appropriate

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Li, Zihao, Ruixiang Tang, Lu Cheng, Shuaiqiang Wang, Dawei Yin, and Mengnan Du. "DBR: Divergence-Based Regularization for Debiasing Natural Language Understanding Models." ACM SIGKDD Explorations Newsletter 27, no. 1 (2025): 1–9. https://doi.org/10.1145/3748239.3748241.

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Pre-trained language models (PLMs) have achieved impressive results on various natural language processing tasks. However, recent research has revealed that these models often rely on superficial features and shortcuts instead of developing a genuine understanding of language, especially for natural language understanding (NLU) tasks. Consequently, the models struggle to generalize to out-of-domain data. In this work, we propose Divergence Based Regularization (DBR) to mitigate this shortcut learning behavior. Our method measures the divergence between the output distributions for original exa

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Nauta, Meike, Ricky Walsh, Adam Dubowski, and Christin Seifert. "Uncovering and Correcting Shortcut Learning in Machine Learning Models for Skin Cancer Diagnosis." Diagnostics 12, no. 1 (2021): 40. http://dx.doi.org/10.3390/diagnostics12010040.

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Machine learning models have been successfully applied for analysis of skin images. However, due to the black box nature of such deep learning models, it is difficult to understand their underlying reasoning. This prevents a human from validating whether the model is right for the right reasons. Spurious correlations and other biases in data can cause a model to base its predictions on such artefacts rather than on the true relevant information. These learned shortcuts can in turn cause incorrect performance estimates and can result in unexpected outcomes when the model is applied in clinical

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Geirhos, Robert, Jörn-Henrik Jacobsen, Claudio Michaelis, et al. "Shortcut learning in deep neural networks." Nature Machine Intelligence 2, no. 11 (2020): 665–73. http://dx.doi.org/10.1038/s42256-020-00257-z.

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POTAPOV, ALEXEI B., and M. K. ALI. "LEARNING, EXPLORATION AND CHAOTIC POLICIES." International Journal of Modern Physics C 11, no. 07 (2000): 1455–64. http://dx.doi.org/10.1142/s0129183100001309.

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We consider different versions of exploration in reinforcement learning. For the test problem, we use navigation in a shortcut maze. It is shown that chaotic ∊-greedy policy may be as efficient as a random one. The best results were obtained with a model chaotic neuron. Therefore, exploration strategy can be implemented in a deterministic learning system such as a neural network.

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Fay, Louisa, Erick Cobos, Bin Yang, Sergios Gatidis, and Thomas Küstner. "Avoiding Shortcut-Learning by Mutual Information Minimization in Deep Learning-Based Image Processing." IEEE Access 11 (2023): 64070–86. http://dx.doi.org/10.1109/access.2023.3289397.

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HAN, FANG, MARIAN WIERCIGROCH, JIAN-AN FANG, and ZHIJIE WANG. "EXCITEMENT AND SYNCHRONIZATION OF SMALL-WORLD NEURONAL NETWORKS WITH SHORT-TERM SYNAPTIC PLASTICITY." International Journal of Neural Systems 21, no. 05 (2011): 415–25. http://dx.doi.org/10.1142/s0129065711002924.

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Excitement and synchronization of electrically and chemically coupled Newman-Watts (NW) small-world neuronal networks with a short-term synaptic plasticity described by a modified Oja learning rule are investigated. For each type of neuronal network, the variation properties of synaptic weights are examined first. Then the effects of the learning rate, the coupling strength and the shortcut-adding probability on excitement and synchronization of the neuronal network are studied. It is shown that the synaptic learning suppresses the over-excitement, helps synchronization for the electrically co

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MORIHIRO, KOICHIRO, NOBUYUKI MATSUI, and HARUHIKO NISHIMURA. "CHAOTIC EXPLORATION EFFECTS ON REINFORCEMENT LEARNING IN SHORTCUT MAZE TASK." International Journal of Bifurcation and Chaos 16, no. 10 (2006): 3015–22. http://dx.doi.org/10.1142/s0218127406016616.

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Reinforcement learning is usually required in the process of trial and error called exploration, and the uniform pseudorandom number generator is considered effective in that process. As a generator for the exploration, chaotic sources are also useful in creating a random-like sequence such as in the case of stochastic sources. In this research, we investigate the efficiency of the deterministic chaotic generator for the exploration in learning a nonstationary shortcut maze problem. As a result, it is found that the deterministic chaotic generator based on the logistic map is better in the per

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Du, Mengnan, Fengxiang He, Na Zou, Dacheng Tao, and Xia Hu. "Shortcut Learning of Large Language Models in Natural Language Understanding." Communications of the ACM 67, no. 1 (2023): 110–20. http://dx.doi.org/10.1145/3596490.

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11

Hu, Ruilin, Yajun Du, Jingrong Hu, and Hui Li. "Cross-community shortcut detection based on network representation learning and structural features." Intelligent Data Analysis 27, no. 3 (2023): 709–32. http://dx.doi.org/10.3233/ida-216513.

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As social networks continue to expand, an increasing number of people prefer to use social networks to post their comments and express their feelings, and as a result, the information contained in social networks has grown explosively. The effective extraction of valuable information from social networks has attracted the attention of many researchers. It can mine hidden information from social networks and promote the development of social network structures. At present, many ranking node approaches, such as structural hole spanners and opinion leaders, are widely adopted to extract valuable

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Zhong, Yujie, Xiao Li, Jiangjian Xie, and Junguo Zhang. "A Lightweight Automatic Wildlife Recognition Model Design Method Mitigating Shortcut Learning." Animals 13, no. 5 (2023): 838. http://dx.doi.org/10.3390/ani13050838.

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Recognizing wildlife based on camera trap images is challenging due to the complexity of the wild environment. Deep learning is an optional approach to solve this problem. However, the backgrounds of images captured from the same infrared camera trap are rather similar, and shortcut learning of recognition models occurs, resulting in reduced generality and poor recognition model performance. Therefore, this paper proposes a data augmentation strategy that integrates image synthesis (IS) and regional background suppression (RBS) to enrich the background scene and suppress the existing backgroun

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Dobos, Petra, and Péter Pongrácz. "Body Awareness Does Not Need a Pedigree: Mixed-Breed Dogs Rely More on Self-Representation Than Social Learning in a Spatial Task." Animals 15, no. 3 (2025): 432. https://doi.org/10.3390/ani15030432.

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Body awareness allows the individual to negotiate spatial tasks by referencing their own body. Here, we tested whether biologically meaningful factors, such as an alternative solution (detour around an obstacle) and learning from a human demonstrator, would affect dogs’ reliance on their body size in an aperture test. We hypothesized that the dogs would choose the socially reinforced solution over a shortcut, and they would choose the shortcut when the opening was comfortably large. We tested N = 45 adult, mixed-breed dogs, by using a 3 m long, transparent fence. The dogs had three trials with

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Lao, Mingrui, Nan Pu, Yu Liu, Kai He, Erwin M. Bakker, and Michael S. Lew. "COCA: COllaborative CAusal Regularization for Audio-Visual Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 12995–3003. http://dx.doi.org/10.1609/aaai.v37i11.26527.

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Audio-Visual Question Answering (AVQA) is a sophisticated QA task, which aims at answering textual questions over given video-audio pairs with comprehensive multimodal reasoning. Through detailed causal-graph analyses and careful inspections of their learning processes, we reveal that AVQA models are not only prone to over-exploit prevalent language bias, but also suffer from additional joint-modal biases caused by the shortcut relations between textual-auditory/visual co-occurrences and dominated answers. In this paper, we propose a COllabrative CAusal (COCA) Regularization to remedy this mor

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Trivedi, Anusua, Caleb Robinson, Marian Blazes, et al. "Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement." PLOS ONE 17, no. 10 (2022): e0274098. http://dx.doi.org/10.1371/journal.pone.0274098.

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In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteri

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Natsir, Siti Zahra Mulianti, Bibin Rubini, Didit Ardianto, and Nurhaedah Madjid. "Interactive Learning Multimedia: A Shortcut for Boosting Gen-Z’s Digital literacy in Science Classroom." Jurnal Penelitian Pendidikan IPA 8, no. 5 (2022): 2168–75. http://dx.doi.org/10.29303/jppipa.v8i5.1897.

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This study aims to develop interactive multimedia learning to sharpen Gen- Z’s digital literacy on the material of substance pressure. The method used is research and development with research instruments including multimedia validation sheets, digital literacy questionnaires, teachers’ response questionnaires, and Gen- Z response questionnaires. The research began with need analysis, then continued with the multimedia design stage. The multimedia design stage produces the first draft of multimedia and research instruments. The first draft of the multimedia was validated at the development sta

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Yang, Shuo, Bardh Prenkaj, and Gjergji Kasneci. "RAZOR: Sharpening Knowledge by Cutting Bias with Unsupervised Text Rewriting." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 25633–41. https://doi.org/10.1609/aaai.v39i24.34754.

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Despite the widespread use of LLMs due to their superior performance in various tasks, their high computational costs often lead potential users to opt for the pretraining-finetuning pipeline. However, biases prevalent in manually constructed datasets can introduce spurious correlations between tokens and labels, creating so-called shortcuts and hindering the generalizability of fine-tuned models. Existing debiasing methods often rely on prior knowledge of specific dataset biases, which is challenging to acquire a priori. We propose RAZOR (Rewriting And Zero-bias Optimization Refinement), a no

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18

Fathima, Sheeba. "Music Genre Classification using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 66–71. http://dx.doi.org/10.22214/ijraset.2021.36087.

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Many subjects are affected by digital music production., including music genre prediction. Machine learning techniques were used to classify music genres in this research. Deep neural networks (DNN) have recently been demonstrated to be effective in a variety of classification tasks. Including music genre classification. In this paper, we propose two methods for boosting music genre classification with convolutional neural networks: 1) using a process inspired by residual learning to combine peak- and average pooling to provide more statistical information to higher level neural networks; and

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19

Rees, Simon, Megan Bruce, and Steven Bradley. "Utilising data-driven learning in chemistry teaching: A shortcut to improving chemical language comprehension." New Directions in the Teaching of Physical Sciences, no. 10 (June 1, 2014): 12–19. http://dx.doi.org/10.29311/ndtps.v0i10.511.

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This article describes the development of the FOCUS project and specific pedagogical strategies to improve understanding of the language of chemistry. The importance of language comprehension skills for success in learning chemistry has recently been highlighted by Pyburn et al. (2013). The FOCUS project has involved the construction of a database of student writings (from foundation to Ph.D. level) to create a corpus that can then be analysed for the occurrence of key words in context. Using the principles of concordance and data-driven learning (where student becomes language researcher) a n

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Selby, Ian, Eduardo González Solares, Anna Breger, et al. "Improving the generalisation of radiographic AI using automated data curation to mitigate shortcut learning." Royal College of Radiologists Open 3 (January 2025): 100232. https://doi.org/10.1016/j.rcro.2024.100232.

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Rees, Simon, Megan Bruce, and Steven Bradley. "Utilising Data-driven Learning in Chemistry Teaching: a Shortcut to Improving Chemical Language Comprehension." New Directions 10, no. 1 (2014): 12–19. http://dx.doi.org/10.11120/ndir.2014.00028.

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22

Wilkinson, Anna, Karin Kuenstner, Julia Mueller, and Ludwig Huber. "Social learning in a non-social reptile ( Geochelone carbonaria )." Biology Letters 6, no. 5 (2010): 614–16. http://dx.doi.org/10.1098/rsbl.2010.0092.

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The ability to learn from the actions of another is adaptive, as it is a shortcut for acquiring new information. However, the evolutionary origins of this trait are still unclear. There is evidence that group-living mammals, birds, fishes and insects can learn through observation, but this has never been investigated in reptiles. Here, we show that the non-social red-footed tortoise ( Geochelone carbonaria ) can learn from the actions of a conspecific in a detour task; non-observer animals (without a conspecific demonstrator) failed. This result provides the first evidence that a non-social sp

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23

Mengue-Topio, Hursula, Yannick Courbois, Emily K. Farran, and Pascal Sockeel. "Route learning and shortcut performance in adults with intellectual disability: A study with virtual environments." Research in Developmental Disabilities 32, no. 1 (2011): 345–52. http://dx.doi.org/10.1016/j.ridd.2010.10.014.

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24

Clegg, Benjamin A. "Stimulus-Specific Sequence Representation in Serial Reaction Time Tasks." Quarterly Journal of Experimental Psychology Section A 58, no. 6 (2005): 1087–101. http://dx.doi.org/10.1080/02724980443000485.

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Some recent evidence has favoured purely response-based implicit representation of sequences in serial reaction time tasks. Three experiments were conducted using serial reaction time tasks featuring four spatial stimuli mapped in categories to two responses. Deviant items from the expected sequence that required the expected response resulted in increased response latencies. The findings demonstrated a stimulus-specific form of representation that operates in the serial reaction time task. No evidence was found to suggest that the stimulus-specific learning was contingent on explicit knowledg

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Song, Rui, Fausto Giunchiglia, Yingji Li, Mingjie Tian, and Hao Xu. "TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 18999–9007. http://dx.doi.org/10.1609/aaai.v38i17.29866.

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Cross-domain text classification aims to transfer models from label-rich source domains to label-poor target domains, giving it a wide range of practical applications. Many approaches promote cross-domain generalization by capturing domaininvariant features. However, these methods rely on unlabeled samples provided by the target domains, which renders the model ineffective when the target domain is agnostic. Furthermore, the models are easily disturbed by shortcut learning in the source domain, which also hinders the improvement of domain generalization ability. To solve the aforementioned iss

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Husain, Arshi, and Virendra P. Vishvakarma. "Optimized deterministic multikernel extreme learning machine for classification of COVID-19 chest Xray images." Journal of Information and Optimization Sciences 44, no. 4 (2023): 771–93. http://dx.doi.org/10.47974/jios-1319.

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In this paper, a novel technique has been proposed to exploit the capability of residual network (ResNet) deep learning model to extract the features. It is utilized neither in pretrained form nor as a transfer learning model. ResNet uses shortcut connections to create shortcut blocks in order to skip blocks of convolutional layers (residual blocks). These stacked residual blocks significantly increase training effectiveness and address the degradation issue. For the purpose of classification, a multiple kernel learning based deterministic extreme learning machine (MKD-ELM) which uses a linear

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Suparjan, Suparjan, and Nining Ismiyani. "The Use of Tanjungpura University’s e-Learning-Moodle LMS during Online Learning: Problems, Solutions and Continuation." Ta'dib 26, no. 1 (2023): 71. http://dx.doi.org/10.31958/jt.v26i1.7902.

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This study aims to investigate the problems, solutions, and opinions about the use of the Tanjungpura University’s e-learning-Moodle LMS as learning media during the covid-19 pandemic by students of the Elementary School Teacher Education Study Program at the Faculty of Teacher Training and Education of Tanjungpura University in Pontianak, West Kalimantan. Respondents in this study were selected based on the characteristics of gender, and semester classifications. The qualitative research approach was employed in this study, utilizing semi-structured interviews where the interview questions un

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Sudirman and Merlin Novayanti Nepa Burenia. "Peningkatan Kemampuan Berpikir Kritis Menyimak Video Melalui Media Wordwall“Open The Box” Peserta Didik X C SMAN 5 Kupang." Transformatika: Jurnal Bahasa, Sastra, dan Pengajarannya 9, no. 1 (2024): 254–68. https://doi.org/10.31002/transformatika.v9i1.2163.

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This research aims to improve students' critical thinking skills in listening to observation reports about certain cultural objects presented in video form. Based on the phenomena that occur, students are less critical in thinking and tend to look for shortcuts in critical thinking. The shortcut in question is that students prefer to describe the objects they observe according to the results of their research on online sites, so that the content and results of listening can be said to be different. The formulation of the problem in this research is how students' critical thinking capacity is w

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Xu, Chendong, Weigang Wang, Yunwei Zhang, Jie Qin, Shujuan Yu, and Yun Zhang. "An Indoor Localization System Using Residual Learning with Channel State Information." Entropy 23, no. 5 (2021): 574. http://dx.doi.org/10.3390/e23050574.

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With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-r

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Arun, K., and A. Srinagesh. "Multilingual twitter sentiment analysis using machine learning." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5992. http://dx.doi.org/10.11591/ijece.v10i6.pp5992-6000.

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Twitter sentiment analysis is one of the leading research fields. Most of the researchers were contributed to twitter sentiment analysis in English tweets, but few researchers focus on the multilingual twitter sentiment analysis. Some challenges are hoping for the research solutions in multilingual twitter sentiment analysis. This study presents the implementation of sentiment analysis in multilingual twitter data and improves the data classification up to the adequate level of accuracy. Twitter is the sixth leading social networking site in the world. Active users for twitter in a month are 3

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Zheng, Hui, Yizhi Cao, Min Sun, et al. "Mixed Structure with 3D Multi-Shortcut-Link Networks for Hyperspectral Image Classification." Remote Sensing 14, no. 5 (2022): 1230. http://dx.doi.org/10.3390/rs14051230.

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A hyperspectral image classification method based on a mixed structure with a 3D multi-shortcut-link network (MSLN) was proposed for the features of few labeled samples, excess noise, and heterogeneous homogeneity of features in hyperspectral images. First, the spatial–spectral joint features of hyperspectral cube data were extracted through 3D convolution operation; then, the deep network was constructed and the 3D MSLN mixed structure was used to fuse shallow representational features and deep abstract features, while the hybrid activation function was utilized to ensure the integrity of non

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Wang, Zitong, Zeyi Wang, and Pengyu Sun. "Deep learning model for gastrointestinal polyp segmentation." PeerJ Computer Science 11 (May 28, 2025): e2924. https://doi.org/10.7717/peerj-cs.2924.

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One of the biggest hazards to cancer-related mortality globally is colorectal cancer, and improved patient outcomes are greatly influenced by early identification. Colonoscopy is a highly effective screening method, yet segmentation and detection remain challenging aspects due to the heterogeneity and variability of readers’ interpretations of polyps. In this work, we introduce a novel deep learning architecture for gastrointestinal polyp segmentation in the Kvasir-SEG dataset. Our method employs an encoder-decoder structure with a pre-trained ConvNeXt model as the encoder to learn multi-scale

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Nibedita Patra. "Deep Learning-Based Single Image De-Raining Using Discrete Hartley Transformation." Journal of Information Systems Engineering and Management 10, no. 26s (2025): 395–408. https://doi.org/10.52783/jisem.v10i26s.4242.

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In computer vision, the removal of rain streaks from individual photographs has drawn a lot of interest. A saturated picture is broken down to an underlying image without any rain and a map of the rain streak to represent the de-raining challenge as an image decomposition assignment. This study introduced a DLD: Deep Learning-Based Single Image De-Raining Using Discrete Hartley Transformation, which is different from the majority of de-raining techniques now in use. The data cleansing phase of this study uses contrast-limited adaptive histogram equalization to smooth out the image and lower no

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K., Arun, and Srinagesh A. "Multi-lingual Twitter sentiment analysis using machine learning." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5992–6000. https://doi.org/10.11591/ijece.v10i6.pp5992-6000.

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Twitter Sentiment Analysis is one of the leading research fields nowadays. Most of the researchers have contributed to the research in twitter sentiment analysis in English tweets, but few researchers have focused on the multilingual twitter sentiment analysis. Still, some more challenges are present and not yet addressed in the domain of multilingual twitter sentiment analysis (MLTSA). Research is highly warranted in these unexplored areas. This study presents the implementation of sentiment analysis in multilingual twitter data and improves the data classification up to the adequate level of

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Holmberg, Linn. "Right and Wrong Ways of Knowing." 1700-tal: Nordic Journal for Eighteenth-Century Studies 20 (December 20, 2023): 8–33. http://dx.doi.org/10.7557/4.7203.

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This article explores how the eighteenth-century ‘dictionary craze’ – the explosive proliferation of alphabetically organized reference works – can be understood as part of a wider conflict of learning. Drawing on a wide mix of sources, I show that dictionaries, more than any other factual genre of the time, challenged established conventions about what constituted right and wrong ways of reading, learning, and ultimately knowing, and that this was a crucial reason for both the controversy and success of the genre. After an overview of early modern norms of learning, the article examines how e

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Sun, Chaoyue, Ruogu Fang, Marco Salemi, Mattia Prosperi, and Brittany Rife Magalis. "DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction." PLOS Computational Biology 20, no. 4 (2024): e1011351. http://dx.doi.org/10.1371/journal.pcbi.1011351.

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In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful in reconstructing the evolutionary dynamics of an epidemic across space and ti

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Welte, Peter O. "Caveat Examiner: Beware Clever Students." Perceptual and Motor Skills 77, no. 3_suppl (1993): 1213–14. http://dx.doi.org/10.2466/pms.1993.77.3f.1213.

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The administration procedures for the Design Sequences subtest from the Detroit Tests of Learning Aptitude (Third Edition) contain an anomaly which makes it possible for astute examinees to discover a significant shortcut to solving the visual memory task. Discovery of this serendipitous “key” would give some examinees a distinct advantage on the subtest. The time needed to locate target designs is shortened thereby decreasing the time interval during which forgetting occurs. Once the key is uncovered, a significant portion of the memory task evaporates. This could yield artificially inflated

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Pollock, Mica. "Flipping Our Scripts about Undocumented Immigration." Genealogy 4, no. 1 (2020): 29. http://dx.doi.org/10.3390/genealogy4010029.

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This critical family history explores a common script about undocumented immigration: that undocumented immigrants unfairly have refused to “stand in line” for official, sanctioned immigration and instead have broken rules that the rest of “our” families have followed. Noting a hole in her knowledge base, the author put herself on a steep learning curve to “clean her lenses”—to learn more information about opportunities past and present, so she could see and discuss the issue more clearly. The author sought new and forgotten information about immigration history, new information about her own

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Mah, Christopher, Hillary Walker, Lena Phalen, Sarah Levine, Sarah W. Beck, and Jaylen Pittman. "Beyond CheatBots: Examining Tensions in Teachers’ and Students’ Perceptions of Cheating and Learning with ChatGPT." Education Sciences 14, no. 5 (2024): 500. http://dx.doi.org/10.3390/educsci14050500.

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As artificial intelligence (AI) is increasingly integrated into educational technologies, teachers and students must acquire new forms of AI literacy, including an understanding of responsible use of AI. In this study, we explored tensions in teachers’ and students’ opinions about what constitutes learning and cheating with AI. Using qualitative methods, we asked Pre-K through postsecondary writing teachers (n = 16) and a linguistically diverse group of students (n = 12) to consider examples of how students might use ChatGPT, rank them in order of how much they thought each student learned and

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Thanuja, B. "Machine Learning Based Crime Rate Analysis Using Python." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1312–16. http://dx.doi.org/10.22214/ijraset.2022.47574.

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Abstract: Crime is obvious that the rate of crimes were increasing day by day in all societies in world, but we personally do believe that there are a lot which can be done by both the governments and the individuals to reduce the crimes in communities. Crime analysis is a well-organized way of detecting and examining patterns and trends in crime. We should give utmost importance to study the reasons behind the crimes, so that we can prevent various crimes occurring and we can be able to find suitable solutions to prevent them. When people cannot find work, they have all the free time in the w

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Petrov, Sergei, Tapan Mukerji, Xin Zhang, and Xinfei Yan. "Shape Carving Methods of Geologic Body Interpretation from Seismic Data Based on Deep Learning." Energies 15, no. 3 (2022): 1064. http://dx.doi.org/10.3390/en15031064.

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The task of seismic data interpretation is a time-consuming and uncertain process. Machine learning tools can help to build a shortcut between raw seismic data and reservoir characteristics of interest. Recently, techniques involving convolutional neural networks have started to gain momentum. Convolutional neural networks are particularly efficient at pattern recognition within images, and this is why they are suitable for seismic facies classification and interpretation tasks. We experimented with three different architectures based on convolutional layers and compared them with different sy

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Wang, Tong, Yuan Yao, Feng Xu, Miao Xu, Shengwei An, and Ting Wang. "Inspecting Prediction Confidence for Detecting Black-Box Backdoor Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (2024): 274–82. http://dx.doi.org/10.1609/aaai.v38i1.27780.

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Backdoor attacks have been shown to be a serious security threat against deep learning models, and various defenses have been proposed to detect whether a model is backdoored or not. However, as indicated by a recent black-box attack, existing defenses can be easily bypassed by implanting the backdoor in the frequency domain. To this end, we propose a new defense DTInspector against black-box backdoor attacks, based on a new observation related to the prediction confidence of learning models. That is, to achieve a high attack success rate with a small amount of poisoned data, backdoor attacks

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Algabri, Malek, and Firdaus Alhrazi. "Approach to Plagiarism Detection in Programming Assignments." Journal of Engineering and Technological Sciences - JOEATS 3, no. 1 (2025): 91–100. https://doi.org/10.59421/joeats.v3i1.2478.

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People tend to shortcut ways that save them time and effort to do the tasks required by them, either by taking tasks ready-made online, or stealing someone’s work as their own. Since everything now is connected to the Internet, there is a very high potential of duplicating or stealing someone else’s work, which is known as plagiarism. With the advancement of technology, it has become quite simple to do all tasks through the Internet. Plagiarism is the copying of other people’s ideas and actions; it is considered a crime. Plagiarism occurs due to laziness, fear of failure, and the desire to per

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Nizamli, Yasser, Yasser Nizamli, Anton Filatov, Weaam Fadel, Yulia Shichkina, and Kinda Mreish. "A Lightweight CNN Architecture for Efficient Brain Tumor Detection in MRI Scans." International Journal of Electrical and Electronics Research 13, no. 2 (2025): 296–305. https://doi.org/10.37391/ijeer.130213.

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The intricate morphology of brain tumors poses significant diagnostic challenges in MRI interpretation. While AI-driven systems offer potential for automation, balancing accuracy with computational efficiency remains critical for clinical adoption. This work introduces a lightweight convolutional neural network optimized for brain tumor detection and classification in MRI scans. The architecture’s design emphasizes a systematic exploration of layer-ordering strategies, with experiments revealing that batch normalization in post-activation mode (Post-BN) outperforms Pre-BN in training stability

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Sulistyo, Totok, and Rohmat Fauzi. "Soil Infiltration Rate Prediction using Machine Learning Regression Model: A Case Study on Sepinggan River Basin, Balikpapan, Indonesia." Indonesian Journal on Geoscience 10, no. 3 (2023): 335–47. http://dx.doi.org/10.17014/ijog.10.3.335-347.

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The infiltration rate of soil data is important in a wide range of planning, such as city planning, drainage design, landuse planning, flood prediction, flood disaster mitigation, etc. Collecting data of infiltration through in-site direct measurements is time consuming and costly. Indeed, inferring the infiltration rate using available parameters and the fittest model is needed. The model can shortcut the field measurement to get a predicted accurate infiltration rate that is worthy to support vital planning. This research aims to develop a model of infiltration rate based on initial water co

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KURAKAMI, Takeru, Kazuyoshi SOUMA, Takashi MIYAMOTO, Takahiko FURUYA, Jun MAGOME, and Hiroshi ISHIDAIRA. "APPLICATION OF A DEEP-LEARNING METHOD INCLUDING SHORTCUT PATHS TO CORRECT THE PRECIPITATION OUTPUTS OF A NUMERICAL WEATHER PREDICTION MODEL." Journal of Japan Society of Civil Engineers, Ser. G (Environmental Research) 76, no. 5 (2020): I_471—I_478. http://dx.doi.org/10.2208/jscejer.76.5_i_471.

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López-Cabrera, José Daniel, Rubén Orozco-Morales, Jorge Armando Portal-Díaz, Orlando Lovelle-Enríquez, and Marlén Pérez-Díaz. "Current limitations to identify covid-19 using artificial intelligence with chest x-ray imaging (part ii). The shortcut learning problem." Health and Technology 11, no. 6 (2021): 1331–45. http://dx.doi.org/10.1007/s12553-021-00609-8.

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Ramsgaard Thomsen, Mette, Paul Nicholas, Martin Tamke, Sebastian Gatz, Yuliya Sinke, and Gabriella Rossi. "Towards machine learning for architectural fabrication in the age of industry 4.0." International Journal of Architectural Computing 18, no. 4 (2020): 335–52. http://dx.doi.org/10.1177/1478077120948000.

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Machine Learning (ML) is opening new perspectives for architectural fabrication, as it holds the potential for the profession to shortcut the currently tedious and costly setup of digital integrated design to fabrication workflows and make these more adaptable. To establish and alter these workflows rapidly becomes a main concern with the advent of Industry 4.0 in building industry. In this article we present two projects, which presents how ML can lead to radical changes in generation of fabrication data and linking these directly to design intent. We investigate two different moments of impl

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Asghar, Ali Chandio, Leghari Mehwish, Orangzeb Panhwar Ali, Zaman Nizamani Shah, and Leghari Mehjabeen. "Deep learning-based isolated handwritten Sindhi character recognition." Indian Journal of Science and Technology 13, no. 25 (2020): 2565–74. https://doi.org/10.17485/IJST/v13i25.914.

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Abstract <strong>Motivation :</strong> The problem of handwritten text recognition is vastly studied since last few decades. Many innovative ideas have been developed, where state-of-the-art accuracy is achieved for the English, Chinese or Indian scripts.The recent developments for the cursive scripts such as Arabic and Urdu handwritten text recognition have achieved remarkable accuracy. However, for the Sindhi script, existing systems have not shown significant results and the problem is still an open challenge. Several challenges such as variations in writing styles, joined text, ligatu

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Zhao, Yu, Rennong Yang, Guillaume Chevalier, Ximeng Xu, and Zhenxing Zhang. "Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors." Mathematical Problems in Engineering 2018 (December 30, 2018): 1–13. http://dx.doi.org/10.1155/2018/7316954.

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Human activity recognition (HAR) has become a popular topic in research because of its wide application. With the development of deep learning, new ideas have appeared to address HAR problems. Here, a deep network architecture using residual bidirectional long short-term memory (LSTM) is proposed. The advantages of the new network include that a bidirectional connection can concatenate the positive time direction (forward state) and the negative time direction (backward state). Second, residual connections between stacked cells act as shortcut for gradients, effectively avoiding the gradient v

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