Position recovery on a circle based on coding theory

2005 ◽  
pp. 87-101
Author(s):  
Benjamin Arazi
Keyword(s):  
Coding Theory

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.

Chemistry instructors’ intentions toward developing, teaching, and assessing student representational competence skills

2021 ◽  
Author(s):  
Maia Popova ◽  
Tamera Jones
Keyword(s):  
Professional Development ◽  
Coding Theory ◽  
Assessment Practices ◽  
Self Awareness ◽  
Scientific Practices ◽  
Information Processing Model ◽  
Representational Competence ◽  
Evidence Based Practices ◽  
Factors Affecting ◽  
The Us

Representational competence is one's ability to use disciplinary representations for learning, communicating, and problem-solving. These skills are at the heart of engagement in scientific practices and were recognized by the ACS Examinations Institute as one of ten anchoring concepts. Despite the important role that representational competence plays in student success in chemistry and the considerable number of investigations into students’ ability to reason with representations, very few studies have examined chemistry instructors’ approaches toward developing student representational competence. This study interviewed thirteen chemistry instructors from eleven different universities across the US about their intentions to develop, teach, and assess student representational competence skills. We found that most instructors do not aim to help students develop any representational competence skills. At the same time, participants’ descriptions of their instructional and assessment practices revealed that, without realizing it, most are likely to teach and assess several representational competence skills in their courses. A closer examination of these skills revealed a focus on lower-level representational competence skills (e.g., the ability to interpret and generate representations) and a lack of a focus on higher-level meta-representational competence skills (e.g., the ability to describe affordances and limitations of representations). Finally, some instructors reported self-awareness about their lack of knowledge about effective teaching about representations and the majority expressed a desire for professional development opportunities to learn about differences in how experts and novices conceptualize representations, about evidence-based practices for teaching about representations, and about how to assess student mastery of representational competence skills. This study holds clear implications for informing chemistry instructors’ professional development initiatives. Such training needs to help instructors take cognizance of relevant theories of learning (e.g., constructivism, dual-coding theory, information processing model, Johnstone's triangle), and the key factors affecting students’ ability to reason with representations, as well as foster awareness of representational competence skills and how to support students in learning with representations.

scholarly journals The encoding of stochastic regularities is facilitated by action-effect predictions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Betina Korka ◽  
Erich Schröger ◽  
Andreas Widmann
Keyword(s):  
Coding Theory ◽  
Predictive Coding ◽  
Event Related Potentials ◽  
Random Order ◽  
Generative Models ◽  
Higher Order ◽  
Action Effect ◽  
Related Potentials ◽  
Sensory Prediction ◽  
Action Intention

AbstractOur brains continuously build and update predictive models of the world, sources of prediction being drawn for example from sensory regularities and/or our own actions. Yet, recent results in the auditory system indicate that stochastic regularities may not be easily encoded when a rare medium pitch deviant is presented between frequent high and low pitch standard sounds in random order, as reflected in the lack of sensory prediction error event-related potentials [i.e., mismatch negativity (MMN)]. We wanted to test the implication of the predictive coding theory that predictions based on higher-order generative models—here, based on action intention, are fed top-down in the hierarchy to sensory levels. Participants produced random sequences of high and low pitch sounds by button presses in two conditions: In a “specific” condition, one button produced high and the other low pitch sounds; in an “unspecific” condition, both buttons randomly produced high or low-pitch sounds. Rare medium pitch deviants elicited larger MMN and N2 responses in the “specific” compared to the “unspecific” condition, despite equal sound probabilities. These results thus demonstrate that action-effect predictions can boost stochastic regularity-based predictions and engage higher-order deviance detection processes, extending previous notions on the role of action predictions at sensory levels.

scholarly journals Spectral gap in random bipartite biregular graphs and applications

2021 ◽  
pp. 1-39
Author(s):  
Gerandy Brito ◽  
Ioana Dumitriu ◽  
Kameron Decker Harris
Keyword(s):  
Community Detection ◽  
Adjacency Matrix ◽  
Moment Method ◽  
Coding Theory ◽  
High Probability ◽  
Spectral Gap ◽  
Matrix Completion ◽  
Full Rank ◽  
The Moment ◽  
Deterministic Matrix

Abstract We prove an analogue of Alon’s spectral gap conjecture for random bipartite, biregular graphs. We use the Ihara–Bass formula to connect the non-backtracking spectrum to that of the adjacency matrix, employing the moment method to show there exists a spectral gap for the non-backtracking matrix. A by-product of our main theorem is that random rectangular zero-one matrices with fixed row and column sums are full rank with high probability. Finally, we illustrate applications to community detection, coding theory, and deterministic matrix completion.

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