The effects of uncertain context inference on motor adaptation

Mapping Intimacies ◽

10.1101/2021.11.30.470569 ◽

2021 ◽

Author(s):

Darío Cuevas Rivera ◽

Stefan J. Kiebel

Keyword(s):

Bayesian Inference ◽

Spontaneous Recovery ◽

Contextual Information ◽

Sudden Change ◽

Motor Adaptation ◽

Proprioceptive Information ◽

Sensory Cues ◽

Proposed Model ◽

Training History ◽

Exact Bayesian Inference

Humans have been shown to adapt their movements when a sudden change to the dynamics of the environment is introduced, a phenomenon called motor adaptation. If the change is reverted, the adaptation is also quickly reverted. Human are also able to adapt to multiple changes in dynamics presented separately, and to be able to switch between adapted movements on the fly. Such switching relies on contextual information which is often noisy or misleading, which affects the switch between adaptations. In this work, we introduce a computational model to explain the behavioral phenomena effected by uncertain contextual information. Specifically, we present a hierarchical model for motor adaptation based on exact Bayesian inference. This model explicitly takes into account contextual information and how the dynamics of context inference affect adaptation and action selection. We show how the proposed model provides a unifying explanation for four different experimentally-established phenomena: (i) effects of sensory cues and proprioceptive information on switching between tasks, (ii) the effects of previously-learned adaptations on switching between tasks, (iii) the effects of training history on behavior in new contexts, in addition to (iv) the well-studied savings, de-adaptation and spontaneous recovery.

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A Robust Lane Detection Model Using Vertical Spatial Features and Contextual Driving Information

Sensors ◽

10.3390/s21030708 ◽

2021 ◽

Vol 21 (3) ◽

pp. 708

Author(s):

Wenbo Liu ◽

Fei Yan ◽

Jiyong Zhang ◽

Tao Deng

Keyword(s):

Information Exchange ◽

Information Transfer ◽

Spatial Information ◽

Contextual Information ◽

Great Influence ◽

Light Condition ◽

Lane Detection ◽

Detection Model ◽

Spatial Features ◽

Proposed Model

The quality of detected lane lines has a great influence on the driving decisions of unmanned vehicles. However, during the process of unmanned vehicle driving, the changes in the driving scene cause much trouble for lane detection algorithms. The unclear and occluded lane lines cannot be clearly detected by most existing lane detection models in many complex driving scenes, such as crowded scene, poor light condition, etc. In view of this, we propose a robust lane detection model using vertical spatial features and contextual driving information in complex driving scenes. The more effective use of contextual information and vertical spatial features enables the proposed model more robust detect unclear and occluded lane lines by two designed blocks: feature merging block and information exchange block. The feature merging block can provide increased contextual information to pass to the subsequent network, which enables the network to learn more feature details to help detect unclear lane lines. The information exchange block is a novel block that combines the advantages of spatial convolution and dilated convolution to enhance the process of information transfer between pixels. The addition of spatial information allows the network to better detect occluded lane lines. Experimental results show that our proposed model can detect lane lines more robustly and precisely than state-of-the-art models in a variety of complex driving scenarios.

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Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201644 ◽

2021 ◽

pp. 1-17

Author(s):

J. Shobana ◽

M. Murali

Keyword(s):

Particle Swarm Optimization ◽

Sentiment Analysis ◽

Language Processing ◽

Short Term Memory ◽

Contextual Information ◽

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

Adaptive Particle Swarm Optimization ◽

Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

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Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3422668 ◽

2021 ◽

Vol 17 (1s) ◽

pp. 1-18 ◽

Cited By ~ 1

Author(s):

Huimin Lu ◽

Rui Yang ◽

Zhenrong Deng ◽

Yonglin Zhang ◽

Guangwei Gao ◽

...

Keyword(s):

Feature Extraction ◽

Contextual Information ◽

Image Features ◽

Context Information ◽

Global Information ◽

Image Description ◽

Image Captioning ◽

Image Content ◽

Single Feature ◽

Proposed Model

Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model’s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU , BLEU , METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.

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Context-Aware Neural Machine Translation for Korean Honorific Expressions

Electronics ◽

10.3390/electronics10131589 ◽

2021 ◽

Vol 10 (13) ◽

pp. 1589

Author(s):

Yongkeun Hwang ◽

Yanghoon Kim ◽

Kyomin Jung

Keyword(s):

Machine Translation ◽

Deep Neural Networks ◽

Contextual Information ◽

Context Aware ◽

Neural Machine Translation ◽

Translation Quality ◽

Sentence Level ◽

Proposed Model ◽

The Given ◽

The Relationship

Neural machine translation (NMT) is one of the text generation tasks which has achieved significant improvement with the rise of deep neural networks. However, language-specific problems such as handling the translation of honorifics received little attention. In this paper, we propose a context-aware NMT to promote translation improvements of Korean honorifics. By exploiting the information such as the relationship between speakers from the surrounding sentences, our proposed model effectively manages the use of honorific expressions. Specifically, we utilize a novel encoder architecture that can represent the contextual information of the given input sentences. Furthermore, a context-aware post-editing (CAPE) technique is adopted to refine a set of inconsistent sentence-level honorific translations. To demonstrate the efficacy of the proposed method, honorific-labeled test data is required. Thus, we also design a heuristic that labels Korean sentences to distinguish between honorific and non-honorific styles. Experimental results show that our proposed method outperforms sentence-level NMT baselines both in overall translation quality and honorific translations.

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Does proprioceptive acuity influence the extent of implicit sensorimotor adaptation in young and older adults?

Journal of Neurophysiology ◽

10.1152/jn.00636.2020 ◽

2021 ◽

Author(s):

Koenraad Vandevoorde ◽

Jean-Jacques Orban de Xivry

Keyword(s):

Older Adults ◽

Sensory Integration ◽

Prediction Error ◽

Motor Adaptation ◽

Sensorimotor Adaptation ◽

Proprioceptive Information ◽

Age Related ◽

Sensory Prediction

The ability to adjust movements to changes in the environment declines with aging. This age-related decline is caused by the decline of explicit adjustments. However, implicit adaptation remains intact and might even be increased with aging. Since proprioceptive information has been linked to implicit adaptation, it might well be that an age-related decline in proprioceptive acuity might be linked to the performance of older adults in implicit adaptation tasks. Indeed, age-related proprioceptive deficits could lead to altered sensory integration with an increased weighting of the visual sensory-prediction error. Another possibility is that reduced proprioceptive acuity results in an increased reliance on predicted sensory consequences of the movement. Both these explanations led to our preregistered hypothesis: we expected a relation between the decline of proprioception and the amount of implicit adaptation across ages. However, we failed to support this hypothesis. Our results question the existence of reliability-based integration of visual and proprioceptive signals during motor adaptation.

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Learned rather than online relative weighting of visual-proprioceptive sensory cues

Journal of Neurophysiology ◽

10.1152/jn.00338.2017 ◽

2018 ◽

Vol 119 (5) ◽

pp. 1981-1992 ◽

Cited By ~ 9

Author(s):

Laura Mikula ◽

Valérie Gaveau ◽

Laure Pisella ◽

Aarlenne Z. Khan ◽

Gunnar Blohm

Keyword(s):

Multisensory Integration ◽

Visual Information ◽

Target Location ◽

Hand Position ◽

Proprioceptive Information ◽

Sensory Cues ◽

Reaching Movement ◽

Left And Right ◽

Neuronal Representation ◽

Relative Weighting

When reaching to an object, information about the target location as well as the initial hand position is required to program the motor plan for the arm. The initial hand position can be determined by proprioceptive information as well as visual information, if available. Bayes-optimal integration posits that we utilize all information available, with greater weighting on the sense that is more reliable, thus generally weighting visual information more than the usually less reliable proprioceptive information. The criterion by which information is weighted has not been explicitly investigated; it has been assumed that the weights are based on task- and effector-dependent sensory reliability requiring an explicit neuronal representation of variability. However, the weights could also be determined implicitly through learned modality-specific integration weights and not on effector-dependent reliability. While the former hypothesis predicts different proprioceptive weights for left and right hands, e.g., due to different reliabilities of dominant vs. nondominant hand proprioception, we would expect the same integration weights if the latter hypothesis was true. We found that the proprioceptive weights for the left and right hands were extremely consistent regardless of differences in sensory variability for the two hands as measured in two separate complementary tasks. Thus we propose that proprioceptive weights during reaching are learned across both hands, with high interindividual range but independent of each hand’s specific proprioceptive variability. NEW & NOTEWORTHY How visual and proprioceptive information about the hand are integrated to plan a reaching movement is still debated. The goal of this study was to clarify how the weights assigned to vision and proprioception during multisensory integration are determined. We found evidence that the integration weights are modality specific rather than based on the sensory reliabilities of the effectors.

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Augmented Context-Based Conceptual User Modeling for Personalized Recommendation System in Online Social Networks

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.2020070101 ◽

2020 ◽

Vol 14 (3) ◽

pp. 1-19

Author(s):

Ammar Alnahhas ◽

Bassel Alkhatib

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Recommendation System ◽

Contextual Information ◽

Detailed Comparison ◽

Personalized Recommendation ◽

User Models ◽

Proposed Model ◽

Recommendation Algorithms ◽

Novel Method

As the data on the online social networks is getting larger, it is important to build personalized recommendation systems that recommend suitable content to users, there has been much research in this field that uses conceptual representations of text to match user models with best content. This article presents a novel method to build a user model that depends on conceptual representation of text by using ConceptNet concepts that exceed the named entities to include the common-sense meaning of words and phrases. The model includes the contextual information of concepts as well, the authors also show a novel method to exploit the semantic relations of the knowledge base to extend user models, the experiment shows that the proposed model and associated recommendation algorithms outperform all previous methods as a detailed comparison shows in this article.

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A Context-Aware Location Recommendation System for Tourists Using Hierarchical LSTM Model

Sustainability ◽

10.3390/su12104107 ◽

2020 ◽

Vol 12 (10) ◽

pp. 4107

Author(s):

Wafa Shafqat ◽

Yung-Cheol Byun

Keyword(s):

Recommendation System ◽

Short Term Memory ◽

Contextual Information ◽

Optimal Size ◽

Effective Parameters ◽

Travel History ◽

Proposed Model ◽

Processing Information ◽

Lstm Network ◽

Gated Recurrent Unit

The significance of contextual data has been recognized by analysts and specialists in numerous disciplines such as customization, data recovery, ubiquitous and versatile processing, information mining, and management. While a generous research has just been performed in the zone of recommender frameworks, by far most of the existing approaches center on prescribing the most relevant items to customers. It usually neglects extra-contextual information, for example time, area, climate or the popularity of different locations. Therefore, we proposed a deep long-short term memory (LSTM) based context-enriched hierarchical model. This proposed model had two levels of hierarchy and each level comprised of a deep LSTM network. In each level, the task of the LSTM was different. At the first level, LSTM learned from user travel history and predicted the next location probabilities. A contextual learning unit was active between these two levels. This unit extracted maximum possible contexts related to a location, the user and its environment such as weather, climate and risks. This unit also estimated other effective parameters such as the popularity of a location. To avoid feature congestion, XGBoost was used to rank feature importance. The features with no importance were discarded. At the second level, another LSTM framework was used to learn these contextual features embedded with location probabilities and resulted into top ranked places. The performance of the proposed approach was elevated with an accuracy of 97.2%, followed by gated recurrent unit (GRU) (96.4%) and then Bidirectional LSTM (94.2%). We also performed experiments to find the optimal size of travel history for effective recommendations.

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