The Effect of Information Characteristics on Information Trust, Information Acceptance, and Choice of Beauty Institutes Portal Sites

2021 ◽  
Vol 15 (2) ◽  
pp. 10-24
Author(s):  
Yung Ji Sim ◽  
◽  
Yun-Kyoung Lim
Keyword(s):  
Information Trust ◽  
Information Characteristics ◽  
Trust Information ◽  
Information Acceptance

Spectral and Space-Frequency Information Characteristics of Decoding of Aquatic Landscapes in Aerospace Images

2003 ◽  
Vol 39 (1) ◽  
pp. 39-47
Author(s):  
A. D. Fedorovskiy ◽  
L. A. Sirenko ◽  
K. Yu. Sukhanov ◽  
V. G. Yakimchuk
Keyword(s):  
Frequency Information ◽  
Space Frequency ◽  
Information Characteristics ◽  
Aquatic Landscapes ◽  
Aerospace Images

scholarly journals Spatial Memory in a Spiking Neural Network with Robot Embodiment

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2678
Author(s):  
Sergey A. Lobov ◽  
Alexey I. Zharinov ◽  
Valeri A. Makarov ◽  
Victor B. Kazantsev
Keyword(s):  
Neural Network ◽  
Spatial Memory ◽  
Internal Representation ◽  
Learning Curves ◽  
Global Network ◽  
Spiking Neural Network ◽  
Interference Phenomenon ◽  
Brain Functioning ◽  
Information Characteristics ◽  
Catastrophic Interference

Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then shaped by Hebbian-type synaptic plasticity. The network receives stimuli at specific loci, while the memory retrieval operates as a functional SNN response in the form of population bursts. The SNN function is explored through its embodiment in a robot moving in an arena with safe and dangerous zones. We propose a measure of the global network memory using the synaptic vector field approach to validate results and calculate information characteristics, including learning curves. We show that after training, the SNN can effectively control the robot’s cognitive behavior, allowing it to avoid dangerous regions in the arena. However, the learning is not perfect. The robot eventually visits dangerous areas. Such behavior, also observed in animals, enables relearning in time-evolving environments. If a dangerous zone moves into another place, the SNN remaps positive and negative areas, allowing escaping the catastrophic interference phenomenon known for some AI architectures. Thus, the robot adapts to changing world.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

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