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Journal articles on the topic 'Human conditional learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

He, Xiao, Thomas Gumbsch, Damian Roqueiro, and Karsten Borgwardt. "Kernel conditional clustering and kernel conditional semi-supervised learning." Knowledge and Information Systems 62, no. 3 (2019): 899–925. http://dx.doi.org/10.1007/s10115-019-01334-5.

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Parikh, Pranav J., and Marco Santello. "Role of human premotor dorsal region in learning a conditional visuomotor task." Journal of Neurophysiology 117, no. 1 (2017): 445–56. http://dx.doi.org/10.1152/jn.00658.2016.

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Conditional learning is an important component of our everyday activities (e.g., handling a phone or sorting work files) and requires identification of the arbitrary stimulus, accurate selection of the motor response, monitoring of the response, and storing in memory of the stimulus-response association for future recall. Learning this type of conditional visuomotor task appears to engage the premotor dorsal region (PMd). However, the extent to which PMd might be involved in specific or all processes of conditional learning is not well understood. Using transcranial magnetic stimulation (TMS),

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Cimini, Giulio, and Angel Sánchez. "Learning dynamics explains human behaviour in Prisoner's Dilemma on networks." Journal of The Royal Society Interface 11, no. 94 (2014): 20131186. http://dx.doi.org/10.1098/rsif.2013.1186.

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Cooperative behaviour lies at the very basis of human societies, yet its evolutionary origin remains a key unsolved puzzle. Whereas reciprocity or conditional cooperation is one of the most prominent mechanisms proposed to explain the emergence of cooperation in social dilemmas, recent experimental findings on networked Prisoner's Dilemma games suggest that conditional cooperation also depends on the previous action of the player—namely on the ‘mood’ in which the player is currently in. Roughly, a majority of people behave as conditional cooperators if they cooperated in the past, whereas they

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Logan, Frank A. "Animal Learning and Motivation and Addictive Drugs." Psychological Reports 73, no. 1 (1993): 291–306. http://dx.doi.org/10.2466/pr0.1993.73.1.291.

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Highlights of a systematic analysis of the abstracts of over 1700 publications dealing with addictive drugs (primarily alcohol) in the context of animal learning and motivation are summarized under two main headings. The behavioral effects of drugs vary with the nature of the drug, the dosage, and the behavioral baseline; behavioral tolerance frequently results from continued practice in the drug state. The paradigmatic effects show that drugs can function effectively as conditional stimuli, unconditional stimuli, responses, and reinforcers. As a result, drug habits develop their own motivatio

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Wang, Zhenyi, Ping Yu, Yang Zhao, et al. "Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12281–88. http://dx.doi.org/10.1609/aaai.v34i07.6911.

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Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence models such as RNN to directly model transitions in the original action space. Due to high dimensionality and potential noise, such modeling of action transitions is particularly challenging. In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality. Conditioned on a latent sequence, actions are

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Hodder, K. I., D. N. George, A. S. Killcross, and R. C. Honey. "Representational Blending in Human Conditional Learning: Implications for Associative Theory." Quarterly Journal of Experimental Psychology Section B 56, no. 2b (2003): 223–38. http://dx.doi.org/10.1080/02724990244000269.

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In two experiments, participants were presented with pictures of different foods (A, B, C, D, X,) and learned which combinations resulted in an allergic reaction in a fictitious patient, MrX. In Problem 1, when Aor B (but not Cor D) was combined with food Xan allergic reaction occurred, and when C or D(but not A or B) was combined with Y an allergic reaction occurred. In Experiment 1, participants also received Problem 2 in which A, B, C, and Dinteracted with foods V and Weither in the same way as X and Y, respectively, or in a different way. Participants performed more proficiently in the for

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Jarecki, Jana B., Björn Meder, and Jonathan D. Nelson. "Naïve and Robust: Class-Conditional Independence in Human Classification Learning." Cognitive Science 42, no. 1 (2017): 4–42. http://dx.doi.org/10.1111/cogs.12496.

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Guo, Bin, Hao Wang, Yasan Ding, et al. "Conditional Text Generation for Harmonious Human-Machine Interaction." ACM Transactions on Intelligent Systems and Technology 12, no. 2 (2021): 1–50. http://dx.doi.org/10.1145/3439816.

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In recent years, with the development of deep learning, text-generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication. The automatically generated text is becoming more and more fluent so researchers begin to consider more anthropomorphic text-generation technology, that is, the conditional text generation, including emotional text generation, personalized text generation, and so on. Conditional Text Generation (CTG) has thus become a research hotspot. As a promising research field, we find th

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Walford, Edward. "Age differences in associative and strategic processes in human conditional learning." Journal of Cognitive Psychology 23, no. 7 (2011): 783–94. http://dx.doi.org/10.1080/20445911.2011.567190.

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Siddiqi, Muhammad Hameed, Madallah Alruwaili, Amjad Ali, Saad Alanazi, and Furkh Zeshan. "Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields." Computational Intelligence and Neuroscience 2019 (August 18, 2019): 1–14. http://dx.doi.org/10.1155/2019/8590560.

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In healthcare, the analysis of patients’ activities is one of the important factors that offer adequate information to provide better services for managing their illnesses well. Most of the human activity recognition (HAR) systems are completely reliant on recognition module/stage. The inspiration behind the recognition stage is the lack of enhancement in the learning method. In this study, we have proposed the usage of the hidden conditional random fields (HCRFs) for the human activity recognition problem. Moreover, we contend that the existing HCRF model is inadequate by independence assumpt

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Breslyn, Wayne. "An Empirically-Based Conditional Learning Progression for Climate Change." Science Education International 28, no. 3 (2017): 214–23. http://dx.doi.org/10.33828/sei.v28.i3.5.

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Climate change encompasses a broad and complex set of concepts that is often challenging for students and educators. Using a learning progressions conceptual framework, we develop a description of student learning of climate change based on our research findings and an extensive review of the science education research literature. In this exploratory study we present findings from written assessments (N=294) and in-depth interviews (n=27) with middle school students in which we examine their understanding of the role of human activity, mechanism, impacts, and adaptation and mitigation of clima

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Thapa, Keshav, Zubaer Md Abdullah Al, Barsha Lamichhane, and Sung-Hyun Yang. "A Deep Machine Learning Method for Concurrent and Interleaved Human Activity Recognition." Sensors 20, no. 20 (2020): 5770. http://dx.doi.org/10.3390/s20205770.

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Human activity recognition has become an important research topic within the field of pervasive computing, ambient assistive living (AAL), robotics, health-care monitoring, and many more. Techniques for recognizing simple and single activities are typical for now, but recognizing complex activities such as concurrent and interleaving activity is still a major challenging issue. In this paper, we propose a two-phase hybrid deep machine learning approach using bi-directional Long-Short Term Memory (BiLSTM) and Skip-Chain Conditional random field (SCCRF) to recognize the complex activity. BiLSTM

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Li, Juntao, Lidong Bing, Lisong Qiu, Dongmin Chen, Dongyan Zhao, and Rui Yan. "Learning to Write Stories with Thematic Consistency and Wording Novelty." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1715–22. http://dx.doi.org/10.1609/aaai.v33i01.33011715.

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Automatic story generation is a challenging task, which involves automatically comprising a sequence of sentences or words with a consistent topic and novel wordings. Although many attention has been paid to this task and prompting progress has been made, there still exists a noticeable gap between generated stories and those created by humans, especially in terms of thematic consistency and wording novelty. To fill this gap, we propose a cache-augmented conditional variational autoencoder for story generation, where the cache module allows to improve thematic consistency while the conditional

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Rojas-Ferrer, Isabel, and Julie Morand-Ferron. "The impact of learning opportunities on the development of learning and decision-making: an experiment with passerine birds." Philosophical Transactions of the Royal Society B: Biological Sciences 375, no. 1803 (2020): 20190496. http://dx.doi.org/10.1098/rstb.2019.0496.

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Developmental context has been shown to influence learning abilities later in life, namely through experiments with nutritional and/or environmental constraints (i.e. lack of enrichment). However, little is known about the extent to which opportunities for learning affect the development of animal cognition, even though such opportunities are known to influence human cognitive development. We exposed young zebra finches ( Taenopygia guttata ) ( n = 26) to one of three experimental conditions, i.e. an environment where (i) colour cues reliably predicted the presence of food (associative learnin

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Petrides, M. "Visuo-motor conditional associative learning after frontal and temporal lesions in the human brain." Neuropsychologia 35, no. 7 (1997): 989–97. http://dx.doi.org/10.1016/s0028-3932(97)00026-2.

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Suakkaphong, Nichalin, Zhu Zhang, and Hsinchun Chen. "Disease named entity recognition using semisupervised learning and conditional random fields." Journal of the American Society for Information Science and Technology 62, no. 4 (2011): 727–37. http://dx.doi.org/10.1002/asi.21488.

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Lipp, Ottmar V., and Mark S. Edwards. "Effect of Instructed Extinction on Verbal and Autonomic Indices of Pavlovian Learning with Fear-Relevant and Fear-Irrelevant Conditional Stimuli." Journal of Psychophysiology 16, no. 3 (2002): 176–86. http://dx.doi.org/10.1027//0269-8803.16.3.176.

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Abstract We investigated the effects of conditional stimulus fear-relevance and of instructed extinction on human Pavlovian conditioning as indexed by electrodermal responses and verbal ratings of conditional stimulus unpleasantness. Half of the participants (n = 64) were trained with pictures of snakes and spiders (fear-relevant) as conditional stimuli, whereas the others were trained with pictures of flowers and mushrooms (fear-irrelevant) in a differential aversive Pavlovian conditioning procedure. Half of the participants in each group were instructed after the completion of acquisition th

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Minh The, Nguyen, Takahiro Kawamura, Hiroyuki Nakagawa, Yasuyuki Tahara, and Akihiko Ohsuga. "Automatic Extraction and Evaluation of Human Activity Using Conditional Random Fields and Self-Supervised Learning." Transactions of the Japanese Society for Artificial Intelligence 26 (2011): 166–78. http://dx.doi.org/10.1527/tjsai.26.166.

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Adaimi, Rebecca, and Edison Thomaz. "Leveraging Active Learning and Conditional Mutual Information to Minimize Data Annotation in Human Activity Recognition." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, no. 3 (2019): 1–23. http://dx.doi.org/10.1145/3351228.

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Chen, Chen, Shuai Mu, Wanpeng Xiao, Zexiong Ye, Liesi Wu, and Qi Ju. "Improving Image Captioning with Conditional Generative Adversarial Nets." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8142–50. http://dx.doi.org/10.1609/aaai.v33i01.33018142.

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In this paper, we propose a novel conditional-generativeadversarial-nets-based image captioning framework as an extension of traditional reinforcement-learning (RL)-based encoder-decoder architecture. To deal with the inconsistent evaluation problem among different objective language metrics, we are motivated to design some “discriminator” networks to automatically and progressively determine whether generated caption is human described or machine generated. Two kinds of discriminator architectures (CNN and RNNbased structures) are introduced since each has its own advantages. The proposed alg

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Sharma, Richa, Sudha Morwal, and Basant Agarwal. "Entity-Extraction Using Hybrid Deep-Learning Approach for Hindi text." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 3 (2021): 1–11. http://dx.doi.org/10.4018/ijcini.20210701.oa1.

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This article presents a neural network-based approach to develop named entity recognition for Hindi text. In this paper, the authors propose a deep learning architecture based on convolutional neural network (CNN) and bi-directional long short-term memory (Bi-LSTM) neural network. Skip-gram approach of word2vec model is used in the proposed model to generate word vectors. In this research work, several deep learning models have been developed and evaluated as baseline systems such as recurrent neural network (RNN), long short-term memory (LSTM), Bi-LSTM. Furthermore, these baseline systems are

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Lobo, Manuel, Andre Lamurias, and Francisco M. Couto. "Identifying Human Phenotype Terms by Combining Machine Learning and Validation Rules." BioMed Research International 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/8565739.

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Named-Entity Recognition is commonly used to identify biological entities such as proteins, genes, and chemical compounds found in scientific articles. The Human Phenotype Ontology (HPO) is an ontology that provides a standardized vocabulary for phenotypic abnormalities found in human diseases. This article presents the Identifying Human Phenotypes (IHP) system, tuned to recognize HPO entities in unstructured text. IHP uses Stanford CoreNLP for text processing and applies Conditional Random Fields trained with a rich feature set, which includes linguistic, orthographic, morphologic, lexical, a

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Burton-Chellew, Maxwell N., Heinrich H. Nax, and Stuart A. West. "Payoff-based learning explains the decline in cooperation in public goods games." Proceedings of the Royal Society B: Biological Sciences 282, no. 1801 (2015): 20142678. http://dx.doi.org/10.1098/rspb.2014.2678.

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Economic games such as the public goods game are increasingly being used to measure social behaviours in humans and non-human primates. The results of such games have been used to argue that people are pro-social, and that humans are uniquely altruistic, willingly sacrificing their own welfare in order to benefit others. However, an alternative explanation for the empirical observations is that individuals are mistaken, but learn, during the game, how to improve their personal payoff. We test between these competing hypotheses, by comparing the explanatory power of different behavioural rules,

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Li, Shuyu, and Yunsick Sung. "INCO-GAN: Variable-Length Music Generation Method Based on Inception Model-Based Conditional GAN." Mathematics 9, no. 4 (2021): 387. http://dx.doi.org/10.3390/math9040387.

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Deep learning has made significant progress in the field of automatic music generation. At present, the research on music generation via deep learning can be divided into two categories: predictive models and generative models. However, both categories have the same problems that need to be resolved. First, the length of the music must be determined artificially prior to generation. Second, although the convolutional neural network (CNN) is unexpectedly superior to the recurrent neural network (RNN), CNN still has several disadvantages. This paper proposes a conditional generative adversarial

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Kozunova, G. L. "Reinforcement learning in probabilistic environment and its role in human adaptive and maladaptive behavior." Современная зарубежная психология 5, no. 4 (2016): 85–96. http://dx.doi.org/10.17759/jmfp.2016050409.

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The article discusses human training in conditions of partly uncertain outcomes of his/her actions that models one of the mechanisms of adaptive behavior in natural environment. Basic learning mechanisms are studied in details through modelling conditional reflexes of animals in experiments, where a certain behavior is reinforced similarly, immediately and repeatedly. At the same time, neurophysiological foundations of learning opportunities in humans under conditions of irregular or delayed reinforcements, despite increased interest to them in recent years, remain poorly studied. Research of

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Yang, Dongyang, and Wei Xu. "Clustering on Human Microbiome Sequencing Data: A Distance-Based Unsupervised Learning Model." Microorganisms 8, no. 10 (2020): 1612. http://dx.doi.org/10.3390/microorganisms8101612.

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Modeling and analyzing human microbiome allows the assessment of the microbial community and its impacts on human health. Microbiome composition can be quantified using 16S rRNA technology into sequencing data, which are usually skewed and heavy-tailed with excess zeros. Clustering methods are useful in personalized medicine by identifying subgroups for patients stratification. However, there is currently a lack of standardized clustering method for the complex microbiome sequencing data. We propose a clustering algorithm with a specific beta diversity measure that can address the presence-abs

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De Campos, Luis M., Juan M. Fernández-Luna, and J. Miguel Puerta. "An iterated local search algorithm for learning Bayesian networks with restarts based on conditional independence tests." International Journal of Intelligent Systems 18, no. 2 (2003): 221–35. http://dx.doi.org/10.1002/int.10085.

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Deiber, M. P., S. P. Wise, M. Honda, M. J. Catalan, J. Grafman, and M. Hallett. "Frontal and Parietal Networks for Conditional Motor Learning: A Positron Emission Tomography Study." Journal of Neurophysiology 78, no. 2 (1997): 977–91. http://dx.doi.org/10.1152/jn.1997.78.2.977.

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Deiber, M.-P., S. P. Wise, M. Honda, M. J. Catalan, J. Grafman, and M. Hallett. Frontal and parietal networks for conditional motor learning: a positron emission tomography study. J. Neurophysiol. 78: 977–991, 1997. Studies on nonhuman primates show that the premotor (PM) and prefrontal (PF) areas are necessary for the arbitrary mapping of a set of stimuli onto a set of responses. However, positron emission tomography (PET) measurements of regional cerebral blood flow (rCBF) in human subjects have failed to reveal the predicted rCBF changes during such behavior. We therefore studied rCBF while

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Abidine, M'Hamed Bilal, Lamya Fergani, Belkacem Fergani, and Anthony Fleury. "Improving Human Activity Recognition in Smart Homes." International Journal of E-Health and Medical Communications 6, no. 3 (2015): 19–37. http://dx.doi.org/10.4018/ijehmc.2015070102.

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Even if it is now simple and cheap to collect sensors information in a smart home environment, the main issue remains to infer high-level activities from these simple readings. The main contribution of this work is twofold. Firstly, the authors demonstrate the efficiency of a new procedure for learning Optimized Cost-Sensitive Support Vector Machines (OCS-SVM) classifier based on the user inputs to appropriately tackle the problem of class imbalanced data. It uses a new criterion for the selection of the cost parameter attached to the training errors. Secondly, this method is assessed and comp

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CHALI, YLLIAS, and SADID A. HASAN. "Query-focused multi-document summarization: automatic data annotations and supervised learning approaches." Natural Language Engineering 18, no. 1 (2011): 109–45. http://dx.doi.org/10.1017/s1351324911000167.

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AbstractIn this paper, we apply different supervised learning techniques to build query-focused multi-document summarization systems, where the task is to produce automatic summaries in response to a given query or specific information request stated by the user. A huge amount of labeled data is a prerequisite for supervised training. It is expensive and time-consuming when humans perform the labeling task manually. Automatic labeling can be a good remedy to this problem. We employ five different automatic annotation techniques to build extracts from human abstracts using ROUGE, Basic Element

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Yang, Z., and C. W. Chan. "Conditional iterative learning control for non-linear systems with non-parametric uncertainties under alignment condition." IET Control Theory & Applications 3, no. 11 (2009): 1521–27. http://dx.doi.org/10.1049/iet-cta.2008.0532.

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Pécot, Thierry, Alexander Alekseyenko, and Kristin Wallace. "A deep learning segmentation strategy that minimizes the amount of manually annotated images." F1000Research 10 (March 30, 2021): 256. http://dx.doi.org/10.12688/f1000research.52026.1.

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Deep learning has revolutionized the automatic processing of images. While deep convolutional neural networks have demonstrated astonishing segmentation results for many biological objects acquired with microscopy, this technology's good performance relies on large training datasets. In this paper, we present a strategy to minimize the amount of time spent in manually annotating images for segmentation. It involves using an efficient and open source annotation tool, the artificial increase of the training data set with data augmentation, the creation of an artificial data set with a conditiona

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Porter, David B. "Classroom Teaching, Implicit Learning and the Deleterious Effects of Inappropriate Explication." Proceedings of the Human Factors Society Annual Meeting 31, no. 3 (1987): 289–92. http://dx.doi.org/10.1177/154193128703100304.

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Eighty-five senior cadets participated in a class exercise involving complex decision-making in a natural context. One experimental group was induced to employ explicit decisional processing and another was allowed to simply guess appropriate responses. Decision accuracy was measured at three levels of information availability. Both groups performed significantly above the level of chance when no reliable, objective information was provided. However, neither accurate base rate information nor conditional probabilities increased the decision accuracy of either experimental group. The group allo

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Tewari, Prerna, Eugene Kashdan, Cathal Walsh, Cara M. Martin, Andrew C. Parnell, and John J. O’Leary. "Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling." PLOS Computational Biology 17, no. 8 (2021): e1009289. http://dx.doi.org/10.1371/journal.pcbi.1009289.

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The epidemic increase in the incidence of Human Papilloma Virus (HPV) related Oropharyngeal Squamous Cell Carcinomas (OPSCCs) in several countries worldwide represents a significant public health concern. Although gender neutral HPV vaccination programmes are expected to cause a reduction in the incidence rates of OPSCCs, these effects will not be evident in the foreseeable future. Secondary prevention strategies are currently not feasible due to an incomplete understanding of the natural history of oral HPV infections in OPSCCs. The key parameters that govern natural history models remain lar

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Hatten, Kyle M., Julian Amin, and Amal Isaiah. "Machine Learning Prediction of Extracapsular Extension in Human Papillomavirus–Associated Oropharyngeal Squamous Cell Carcinoma." Otolaryngology–Head and Neck Surgery 163, no. 5 (2020): 992–99. http://dx.doi.org/10.1177/0194599820935446.

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Objective To determine whether machine learning (ML) can predict the presence of extracapsular extension (ECE) prior to treatment, using common oncologic variables, in patients with human papillomavirus (HPV)–associated oropharyngeal squamous cell carcinoma (OPSCC). Study Design Retrospective database review. Setting National Cancer Database study. Methods All patients with HPV-associated OPSCC treated surgically between January 1, 2010, and December 31, 2015, were selected from the National Cancer Database. Patients were excluded if surgical pathology reports did not include information regar

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Kibria, Md Raisul, and Mohammad Abu Yousuf. "Context-driven Bengali Text Generation using Conditional Language Model." Statistics, Optimization & Information Computing 9, no. 2 (2021): 334–50. http://dx.doi.org/10.19139/soic-2310-5070-1061.

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Text generation is a rapidly evolving field of Natural Language Processing (NLP) with larger Language models proposed very often setting new state-of-the-art. These models are exorbitantly effective in learning the representation of words and their internal coherence in a particular language. However, an established context-driven, end to end text generation model is very rare, even more so for the Bengali language. In this paper, we have proposed a Bidirectional gated recurrent unit (GRU) based architecture that simulates the conditional language model or the decoder portion of the sequence t

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Mairittha, Nattaya, Tittaya Mairittha, and Sozo Inoue. "On-Device Deep Learning Inference for Efficient Activity Data Collection." Sensors 19, no. 15 (2019): 3434. http://dx.doi.org/10.3390/s19153434.

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Labeling activity data is a central part of the design and evaluation of human activity recognition systems. The performance of the systems greatly depends on the quantity and “quality” of annotations; therefore, it is inevitable to rely on users and to keep them motivated to provide activity labels. While mobile and embedded devices are increasingly using deep learning models to infer user context, we propose to exploit on-device deep learning inference using a long short-term memory (LSTM)-based method to alleviate the labeling effort and ground truth data collection in activity recognition

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Luck, Camilla C., Shannon Bramwell, Jessica Kerin, Luke J. S. Green, Belinda M. Craig, and Ottmar V. Lipp. "Temporal context cues in human fear conditioning: Unreinforced conditional stimuli can segment learning into distinct temporal contexts and drive fear responding." Behaviour Research and Therapy 108 (September 2018): 10–17. http://dx.doi.org/10.1016/j.brat.2018.06.004.

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Yang, Jianliang, Yuenan Liu, Minghui Qian, Chenghua Guan, and Xiangfei Yuan. "Information Extraction from Electronic Medical Records Using Multitask Recurrent Neural Network with Contextual Word Embedding." Applied Sciences 9, no. 18 (2019): 3658. http://dx.doi.org/10.3390/app9183658.

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Clinical named entity recognition is an essential task for humans to analyze large-scale electronic medical records efficiently. Traditional rule-based solutions need considerable human effort to build rules and dictionaries; machine learning-based solutions need laborious feature engineering. For the moment, deep learning solutions like Long Short-term Memory with Conditional Random Field (LSTM–CRF) achieved considerable performance in many datasets. In this paper, we developed a multitask attention-based bidirectional LSTM–CRF (Att-biLSTM–CRF) model with pretrained Embeddings from Language M

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Fuentes Reyes, Mario, Stefan Auer, Nina Merkle, Corentin Henry, and Michael Schmitt. "SAR-to-Optical Image Translation Based on Conditional Generative Adversarial Networks—Optimization, Opportunities and Limits." Remote Sensing 11, no. 17 (2019): 2067. http://dx.doi.org/10.3390/rs11172067.

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Due to its all time capability, synthetic aperture radar (SAR) remote sensing plays an important role in Earth observation. The ability to interpret the data is limited, even for experts, as the human eye is not familiar to the impact of distance-dependent imaging, signal intensities detected in the radar spectrum as well as image characteristics related to speckle or steps of post-processing. This paper is concerned with machine learning for SAR-to-optical image-to-image translation in order to support the interpretation and analysis of original data. A conditional adversarial network is adop

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Liao, Pin-Chao, Mei Liu, Yu-Sung Su, Hui Shi, and Xintong Luo. "Estimating the Influence of Improper Workplace Environment on Human Error: Posterior Predictive Analysis." Advances in Civil Engineering 2018 (June 6, 2018): 1–11. http://dx.doi.org/10.1155/2018/5078906.

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A model for identifying, analyzing, and quantifying the mechanisms for the influence of improper workplace environment on human error in elevator installation is proposed in this study. By combining a modification of a human error model with real-world inspection data collected by an elevator installation company, the influence paths of improper workplace environment on the conditional probability of human error were quantified using a Bayesian network parameter-learning estimation method and posterior predictive simulation. Under the condition of an improper workplace environment, the probabi

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Eisenstein, J., R. Barzilay, and R. Davis. "Gesture Salience as a Hidden Variable for Coreference Resolution and Keyframe Extraction." Journal of Artificial Intelligence Research 31 (February 29, 2008): 353–98. http://dx.doi.org/10.1613/jair.2450.

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Gesture is a non-verbal modality that can contribute crucial information to the understanding of natural language. But not all gestures are informative, and non-communicative hand motions may confuse natural language processing (NLP) and impede learning. People have little diffculty ignoring irrelevant hand movements and focusing on meaningful gestures, suggesting that an automatic system could also be trained to perform this task. However, the informativeness of a gesture is context-dependent and labeling enough data to cover all cases would be expensive. We present conditional modality fusio

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Gill, Navdeep, Patrick Hall, Kim Montgomery, and Nicholas Schmidt. "A Responsible Machine Learning Workflow with Focus on Interpretable Models, Post-hoc Explanation, and Discrimination Testing." Information 11, no. 3 (2020): 137. http://dx.doi.org/10.3390/info11030137.

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This manuscript outlines a viable approach for training and evaluating machine learning systems for high-stakes, human-centered, or regulated applications using common Python programming tools. The accuracy and intrinsic interpretability of two types of constrained models, monotonic gradient boosting machines and explainable neural networks, a deep learning architecture well-suited for structured data, are assessed on simulated data and publicly available mortgage data. For maximum transparency and the potential generation of personalized adverse action notices, the constrained models are anal

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Tao, Hai, Md Arafatur Rahman, Wang Jing, et al. "Interaction modeling and classification scheme for augmenting the response accuracy of human-robot interaction systems." Work 68, no. 3 (2021): 903–12. http://dx.doi.org/10.3233/wor-203424.

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BACKGROUND: Human-robot interaction (HRI) is becoming a current research field for providing granular real-time applications and services through physical observation. Robotic systems are designed to handle the roles of humans and assist them through intrinsic sensing and commutative interactions. These systems handle inputs from multiple sources, process them, and deliver reliable responses to the users without delay. Input analysis and processing is the prime concern for the robotic systems to understand and resolve the queries of the users. OBJECTIVES: In this manuscript, the Interaction Mo

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Kong, Quan, Bin Tong, Martin Klinkigt, Yuki Watanabe, Naoto Akira, and Tomokazu Murakami. "Active Generative Adversarial Network for Image Classification." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4090–97. http://dx.doi.org/10.1609/aaai.v33i01.33014090.

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Sufficient supervised information is crucial for any machine learning models to boost performance. However, labeling data is expensive and sometimes difficult to obtain. Active learning is an approach to acquire annotations for data from a human oracle by selecting informative samples with a high probability to enhance performance. In recent emerging studies, a generative adversarial network (GAN) has been integrated with active learning to generate good candidates to be presented to the oracle. In this paper, we propose a novel model that is able to obtain labels for data in a cheaper manner

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Chinchali, Sandeep P., Scott C. Livingston, Mo Chen, and Marco Pavone. "Multi-objective optimal control for proactive decision making with temporal logic models." International Journal of Robotics Research 38, no. 12-13 (2019): 1490–512. http://dx.doi.org/10.1177/0278364919868290.

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The operation of today’s robots entails interactions with humans, e.g., in autonomous driving amidst human-driven vehicles. To effectively do so, robots must proactively decode the intent of humans and concurrently leverage this knowledge for safe, cooperative task satisfaction: a problem we refer to as proactive decision making. However, simultaneous intent decoding and robotic control requires reasoning over several possible human behavioral models, resulting in high-dimensional state trajectories. In this paper, we address the proactive decision-making problem using a novel combination of f

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Μαυρογιάννη (Mavrogianni), Αριστέα (Aristea) Γεωργίου, Ελένη (Eleni) Βασιλάκη (Vasilaki), Ιωάννης (Ioannis) Σπαντιδάκης (Spantidakis), Απόστολος (Apostolos) Σαρρής (Sarris), Ελένη (Eleni) Παπαδάκη Μιχαηλίδη (Papadaki Michailidi), and Εμμανουήλ (Emmanuel) Γιαχνάκης (Yachnakis). "Narrative Pedagogical Agents to Enhance Reading Strategies in Geo-Histor Multimedia Learning Environment." Διεθνές Συνέδριο για την Ανοικτή & εξ Αποστάσεως Εκπαίδευση 10, no. 1A (2019): 197. http://dx.doi.org/10.12681/icodl.2344.

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This study focuses on the design and implementation of three narrative pedagogical agents, which act as descending guidance assistants for adolescent student-users of the Geo-Histor multimedia learning environment. The goal of creating and using pedagogical agents was to empower students to use strategies before, during, and after reading. The pedagogical agents that emerged from the bibliographic inquiry and students' choices were anthropomorphic, cheerful and attractive animations, with real human voice, discussing with humor and representing real-life peer grouping. Agents provide declining

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Tenekedjiev, Kiril I., Carlos A. Kobashikawa, Natalia D. Nikolova, and Kaoru Hirota. "Generic Database for Hybrid Bayesian Pattern Recognition." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 3 (2006): 419–31. http://dx.doi.org/10.20965/jaciii.2006.p0419.

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A Bayesian pattern recognition system is proposed, that processes information encoded by four types of features: discrete, pseudo-discrete, multi-normal continuous and independent continuous. This hybrid system utilizes the combined frequentist-subjective approach to probabilities, uses parametric and nonparametric techniques for the conditional likelihood estimation, and relies heavily on the fuzzy theory for data presentation, learning, and information fusion. The information for training, recognition, and prediction of the system is organized in a database, which is logically structured int

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Wang, Xiaoming, Xinbo Zhao, and Jinchang Ren. "A New Type of Eye Movement Model Based on Recurrent Neural Networks for Simulating the Gaze Behavior of Human Reading." Complexity 2019 (March 24, 2019): 1–12. http://dx.doi.org/10.1155/2019/8641074.

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Traditional eye movement models are based on psychological assumptions and empirical data that are not able to simulate eye movement on previously unseen text data. To address this problem, a new type of eye movement model is presented and tested in this paper. In contrast to conventional psychology-based eye movement models, ours is based on a recurrent neural network (RNN) to generate a gaze point prediction sequence, by using the combination of convolutional neural networks (CNN), bidirectional long short-term memory networks (LSTM), and conditional random fields (CRF). The model uses the e

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Hepburn, Alexander, Valero Laparra, Ryan McConville, and Raul Santos-Rodriguez. "Enforcing perceptual consistency on Generative Adversarial Networks by using the Normalised Laplacian Pyramid Distance." Proceedings of the Northern Lights Deep Learning Workshop 1 (February 6, 2020): 6. http://dx.doi.org/10.7557/18.5124.

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In recent years there has been a growing interest in image generation through deep learning. While an important part of the evaluation of the generated images usually involves visual inspection, the inclusion of human perception as a factor in the training process is often overlooked. In this paper we propose an alternative perceptual regulariser for image-to-image translation using conditional generative adversarial networks (cGANs). To do so automatically (avoiding visual inspection), we use the Normalised Laplacian Pyramid Distance (NLPD) to measure the perceptual similarity between the gen

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