scholarly journals Introduction to coding theory(6); BCH code and RS code(I).

1991 ◽  
Vol 45 (1) ◽  
pp. 53-60
Author(s):  
Toshinobu Kaneko
Keyword(s):  
Coding Theory ◽  
Bch Code ◽  
Rs Code

Coding Theory

2004 ◽  
Author(s):  
San Ling ◽  
Chaoping Xing
Keyword(s):  
Coding Theory

The sense of presence in virtual environments:

2003 ◽  
Vol 15 (2) ◽  
pp. 69-71 ◽  
Author(s):  
Thomas W. Schubert
Keyword(s):  
Virtual Environments ◽  
Coding Theory ◽  
Model Building ◽  
Self Report ◽  
Survey Study ◽  
Spatial Presence ◽  
Component Structure ◽  
Sense Of Presence ◽  
Being There ◽  
Measure Sense

Abstract. The sense of presence is the feeling of being there in a virtual environment. A three-component self report scale to measure sense of presence is described, the components being sense of spatial presence, involvement, and realness. This three-component structure was developed in a survey study with players of 3D games (N = 246) and replicated in a second survey study (N = 296); studies using the scale for measuring the effects of interaction on presence provide evidence for validity. The findings are explained by the Potential Action Coding Theory of presence, which assumes that presence develops from mental model building and suppression of the real environment.

scholarly journals Discovering the maximum k-clique on social networks using bat optimization algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Akram Khodadadi ◽  
Shahram Saeidi
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Coding Theory ◽  
Evaluation Criteria ◽  
Convergence Speed ◽  
Optimization Approach ◽  
Computational Results ◽  
Complete Subgraph ◽  
Np Complete

AbstractThe k-clique problem is identifying the largest complete subgraph of size k on a network, and it has many applications in Social Network Analysis (SNA), coding theory, geometry, etc. Due to the NP-Complete nature of the problem, the meta-heuristic approaches have raised the interest of the researchers and some algorithms are developed. In this paper, a new algorithm based on the Bat optimization approach is developed for finding the maximum k-clique on a social network to increase the convergence speed and evaluation criteria such as Precision, Recall, and F1-score. The proposed algorithm is simulated in Matlab® software over Dolphin social network and DIMACS dataset for k = 3, 4, 5. The computational results show that the convergence speed on the former dataset is increased in comparison with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) approaches. Besides, the evaluation criteria are also modified on the latter dataset and the F1-score is obtained as 100% for k = 5.

scholarly journals Deep Neural Networks Classification via Binary Error-Detecting Output Codes

2021 ◽  
Vol 11 (8) ◽  
pp. 3563
Author(s):  
Martin Klimo ◽  
Peter Lukáč ◽  
Peter Tarábek
Keyword(s):  
Neural Networks ◽  
Categorical Data ◽  
Coding Theory ◽  
Hamming Distance ◽  
Activation Function ◽  
Block Codes ◽  
Ease Of Use ◽  
Linear Block Codes ◽  
Binary Coding ◽  
The One

One-hot encoding is the prevalent method used in neural networks to represent multi-class categorical data. Its success stems from its ease of use and interpretability as a probability distribution when accompanied by a softmax activation function. However, one-hot encoding leads to very high dimensional vector representations when the categorical data’s cardinality is high. The Hamming distance in one-hot encoding is equal to two from the coding theory perspective, which does not allow detection or error-correcting capabilities. Binary coding provides more possibilities for encoding categorical data into the output codes, which mitigates the limitations of the one-hot encoding mentioned above. We propose a novel method based on Zadeh fuzzy logic to train binary output codes holistically. We study linear block codes for their possibility of separating class information from the checksum part of the codeword, showing their ability not only to detect recognition errors by calculating non-zero syndrome, but also to evaluate the truth-value of the decision. Experimental results show that the proposed approach achieves similar results as one-hot encoding with a softmax function in terms of accuracy, reliability, and out-of-distribution performance. It suggests a good foundation for future applications, mainly classification tasks with a high number of classes.

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