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Found 19 papers, 3 papers with code
Application of Liquid Rank Reputation System for Twitter Trend Analysis on Bitcoin
The paper proposes the implementation, challenges, and future scope of the liquid rank reputation model.
Evolution of Efficient Symbolic Communication Codes
The paper presents preliminary results of cross-lingual word-level segmentation tokenisation study for Russian, Chinese and English as well as subword segmentation or morphological parsing study for English.
Self-tuning hyper-parameters for unsupervised cross-lingual tokenization
In case of Chinese, we find a significant correlation between the F 1 score and the compression factor.
Cognitive Architecture for Decision-Making Based on Brain Principles Programming (in Russian)
We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity, with their implementation in three subsystems: logical-probabilistic inference, probabilistic formal concepts, and functional systems theory.
Application of Liquid Rank Reputation System for Content Recommendation
This paper analyzes the dataset based on cryptocurrency news on Twitter to find the opinion leader using the liquid rank reputation system.
Unsupervised Tokenization Learning
In the presented study, we discover that the so-called "transition freedom" metric appears superior for unsupervised tokenization purposes in comparison to statistical metrics such as mutual information and conditional probability, providing F-measure scores in range from 0. 71 to 1. 0 across explored multilingual corpora.
Adaptive Multi-Strategy Market-Making Agent For Volatile Markets
Crypto-currency market uncertainty drives the need to find adaptive solutions to maximise gain or at least to avoid loss throughout the periods of trading activity.
Causal Analysis of Generic Time Series Data Applied for Market Prediction
We explore the applicability of the causal analysis based on temporally shifted (lagged) Pearson correlation applied to diverse time series of different natures in context of the problem of financial market prediction.
Social Media Sentiment Analysis for Cryptocurrency Market Prediction
In this paper, we explore the usability of different natural language processing models for the sentiment analysis of social media applied to financial market prediction, using the cryptocurrency domain as a reference.
Cognitive Architecture for Decision-Making Based on Brain Principles Programming
We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity, with their implementation in three subsystems: logical-probabilistic inference, probabilistic formal concepts, and functional systems theory.
Brain Principles Programming
In this paper, for the description and modeling of Brain Principles Programming, it is proposed to apply mathematical models and algorithms developed by us earlier that model cognitive functions, which are based on well-known physiological, psychological and other natural science theories.
High-performance automatic categorization and attribution of inventory catalogs
Techniques of machine learning for automatic text categorization are applied and adapted for the problem of inventory catalog data attribution, with different approaches explored and optimal solution addressing the tradeoff between accuracy and performance is selected.
Architecture of Automated Crypto-Finance Agent
We present the cognitive architecture of an autonomous agent for active portfolio management in decentralized finance, involving activities such as asset selection, portfolio balancing, liquidity provision, and trading.
Natural Language Generation Using Link Grammar for General Conversational Intelligence
Many current artificial general intelligence (AGI) and natural language processing (NLP) architectures do not possess general conversational intelligence--that is, they either do not deal with language or are unable to convey knowledge in a form similar to the human language without manual, labor-intensive methods such as template-based customization.
Interpretable Natural Language Segmentation Based on Link Grammar
Natural language segmentation (NLS), or text segmentation, refers to the process of dividing written text into meaningful units.
Controlled Language and Baby Turing Test for General Conversational Intelligence
General conversational intelligence appears to be an important part of artificial general intelligence.
Latent Semantic Search and Information Extraction Architecture
The motivation, concept, design and implementation of latent semantic search for search engines have limited semantic search, entity extraction and property attribution features, have insufficient accuracy and response time of latent search, may impose privacy concerns and the search results are unavailable in offline mode for robotic search operations.
Representing scenarios for process evolution management
In the following writing we discuss a conceptual framework for representing events and scenarios from the perspective of a novel form of causal analysis.
A Reputation System for Artificial Societies
One approach to achieving artificial general intelligence (AGI) is through the emergence of complex structures and dynamic properties arising from decentralized networks of interacting artificial intelligence (AI) agents.
