Basis Of Information Theory Research Articles

A Text Detection and Recognition Algorithm for English Teaching Based on Deep Learning

Traditional English teaching cannot make effective use of various resources, and the scheduling ability is poor. People cannot accurately obtain the information in the English textbook text in the learning process, resulting in some people who cannot better learn and master the English language. For this problem, this study adopts deep learning algorithm and establishes an English teaching text algorithm based on association semantic rules to mine the features between sentences and phrases in the text provided by English teachers. The proposed algorithm completes the feature extraction of the English teaching text and also analyzes the association analysis between semantics in English teaching text. In fact, its essence is to get English teaching association rules on the basis of information theory. By combining with semantic similarity information, English teaching text can be accurately detected and identified. The simulation results show that the proposed algorithm can accurately extract English teaching text information, and the accuracy and convergence speed during extraction are higher than other competing algorithms.

ТЕОРЕТИЧНЕ ПІДҐРУНТЯ МЕТОДИК ВСТАНОВЛЕННЯ НЕВИЗНАЧЕНОСТІ РЕЗУЛЬТАТІВ ЕКОНОМІЧНИХ ВИМІРЮВАНЬ, ЗДІЙСНЮВАНИХ МЕТОДАМИ НЕЗАЛЕЖНОЇ ЕКСПЕРТНОЇ ОЦІНКИ

The article behaves to the field of assets and property rights independent expert appraising/valuation. Theoretical bases, on those economic measurements expert valuation results uncertainty degree methodology is based, are examined. The substantiation of the probabilistic-metrological approach methodology principles to evaluation results uncertainty degree quantitative determination, based on the evaluation result interval form presentation, is considered. The requirements of national and international evaluation standards for the evaluation results uncertainty are analyzed. The methodology of evaluation results accuracy objective quantitative indexes is described and performed. It is well-proven that the level of valuation results errors at economic measurements implementation can be set on the basis of information theory and measurement errors theory laws application. It is shown that in the case of Comparative Sales and Income approaches applying, uncertainties for given levels of confidence probability in the form of numerical values of the confidence interval limits can be established on the basis of similar property adjusted single unit value indicators or rental rate rows statistical processing. The conditions that must be met when these economic measurements performing are formulated. The main principles of the economic measurements implementation using the metrological methodical approach are considered. It is grounded on independent valuation results uncertainty degree quantitative determination on the basis of economic measurements series implementation with next results row statistical processing. Base terms, limitations, original assumptions of that evaluation works accuracy conditions are set. Possible directions of evaluation methodologies further improvement are analyzed. Attention is accented on the informative-metrological approach to valuation results uncertainty degree exposure. It is shown that it gives an opportunity to set the valuation uncertainty degree by objective quantitative indexes, as these results errors estimates. Some recommendations in relation to priority directions of further researches are offered.

Incorporating heterogeneous information in deep learning with informative meta-paths for community recommendations

Communities of interest promote knowledge sharing and discovery in social network platforms. However, platform users face difficulties of finding suitable communities, given their increasing number. Although recommendations have been proposed to help users find communities of interest, these methods ignore or exclude heterogeneous interactions between users and communities. In addition, widely used meta-paths help capture the complex semantic relation among entities but heavily rely on domain knowledge. In this study, we propose a novel recommendation model based on informative meta-path discovery in heterogeneous information networks and deep learning. Users, communities, relevant items and their relations are considered as entities in a heterogeneous information network, from where informative meta-paths are extracted on the basis of information theory to measure user-community similarities. Finally, similarities are incorporated in a deep learning model to predict whether target users join candidate communities. The proposed recommendation model is evaluated and compared against baseline methods using two data sets. Results demonstrate the superior performance of the present model in terms of precision, recall and F score.

Deep Learning for Walking Behaviour Detection in Elderly People Using Smart Footwear.

The increase in the proportion of elderly in Europe brings with it certain challenges that society needs to address, such as custodial care. We propose a scalable, easily modulated and live assistive technology system, based on a comfortable smart footwear capable of detecting walking behaviour, in order to prevent possible health problems in the elderly, facilitating their urban life as independently and safety as possible. This brings with it the challenge of handling the large amounts of data generated, transmitting and pre-processing that information and analysing it with the aim of obtaining useful information in real/near-real time. This is the basis of information theory. This work presents a complete system aiming at elderly people that can detect different user behaviours/events (sitting, standing without imbalance, standing with imbalance, walking, running, tripping) through information acquired from 20 types of sensor measurements (16 piezoelectric pressure sensors, one accelerometer returning reading for the 3 axis and one temperature sensor) and warn the relatives about possible risks in near-real time. For the detection of these events, a hierarchical structure of cascading binary models is designed and applied using artificial neural network (ANN) algorithms and deep learning techniques. The best models are achieved with convolutional layered ANN and multilayer perceptrons. The overall event detection performance achieves an average accuracy and area under the ROC curve of 0.84 and 0.96, respectively.

ЗНАК – ТЕКСТ – ІНФОРМАЦІЯ (ВІДМІННОСТІ, СПОРІДНЕНІСТЬ, ВЗАЄМООБУМОВЛЕНІСТЬ)

Розглядаються знак - текст - інформація як явище однієї природи у їх взаємозв’язку і та взаємообумовленості. Інформація має знакову природу. Процеси, пов’язані с передачею, переробкою і зберіганням інформації протікають з використанням знаків та знакових систем, тобто є семіотичними. Семіотика постає як основа теорії інформації.

Consistent and coherent treatment of uncertainties and dependencies in fatigue crack growth calculations using multi-level Bayesian models

Engineers perform fatigue assessments to support structural integrity management. Given that the purpose of these calculations is linked to problems of decision making under various sources of uncertainty, probabilistic methods are often more useful than deterministic alternatives. Guidance on the direct probabilistic application of procedures in existing industrial standards is currently limited and dependencies between marginal probabilistic models are generally not considered, despite their potential significance being acknowledged. This paper proposes the use of Bayesian data analysis as a flexible and intuitive approach to coherently and consistently account for uncertainty and dependency in fatigue crack growth rate models. Various Bayesian models are established and presented, based on the same data as the existing models in BS 7910 (a widely used industrial standard). The models are compared in terms of their out of sample predictive accuracy, using methods with a basis in information theory and cross-validation. The Bayesian models exhibit an improved performance, with the most accurate predictions resulting from multi-level (hierarchical) models, which account for variation between constituent test datasets and partially pool information.

Is There a Relationship between Energy, Amount of Information and Temperature?

Aims: To use the generally accepted formulas linking energy, temperature and information, and not requiring any additional restrictions, to introduce a practical numerical value of the energy of any specific object based on the amount of information and thermodynamic temperature.
 Place and Duration of Study: Beer-Sheba, between January 2019 and July 2019.
 Methodology: By combining the Landauer limit and Bekenstein’s proof that the amount of information of any physical system must be finite, if the object space and its energy are finite, the values of energy-matter and energy, based on the amount of information, were calculated for various elements of nature. In addition, a formula is presented for the energy of the universe containing these two components.
 Results: The energy content of an object depends not only on its mass and speed. The value of the additional independent component, due to the amount of information contained in the object, is caused by its size and the ambient temperature. This component has never been considered in the scientific literature. This means that energy is inextricably linked with both the space and the thermodynamic component of Nature.
 Conclusion: Using the generally accepted formulas linking energy, temperature and information and not requiring any additional restrictions, we have shown that it is possible to represent the energy of the universe on the basis of information theory.

Information Theory and an Entropic Approach to an Analysis of Fiscal Inequality.

In his influential study, Theil (1967) developed the notion of entropy on the basis of information theory. He then advocated the use of entropy-based measure for the analysis of income inequality. In this paper, the first of its kind, we apply Theil’s notion of entropy to public finances in multi-tiered governments, in particular for a measurement of fiscal decentralisation, which is currently very crude in terms of the ratio between local government revenue and total revenue. It is the claim of this paper that such an approach of measuring fiscal decentralisation completely ignores important distributional aspects of fiscal arrangements. Findings from this paper indicate that studies on measuring various aspects of fiscal activities—such as fiscal decentralisation—should carefully take into account the dispersion of revenue (and expenditure) across regions. On that basis, the entropic approach developed in this paper is able to accommodate these dispersions across subnational governments. As an illustration for the case of Vietnam, the true degree of fiscal decentralization has effectively been decreased in comparison with estimates from other simple measurements due to the presence of substantial dispersions of revenue and expenditure from the subnational governments across 63 provinces in Vietnam.

Thermodynamics for trajectories of a mass point

On the basis of information theory, a new formalism of classical non-relativistic mechanics of a mass point is proposed. The particle trajectories of a general dynamical system defined on an (1+n)-dimensional smooth manifold are treated geometrically as dynamical variables. Statistical mechanics of particle trajectories are constructed in a classical manner. Thermodynamic variables are introduced through a partition function based on a canonical ensemble of trajectories. Within this theoretical framework, classical mechanics can be interpreted as an equilibrium state of statistical mechanics. The relationships between classical and quantum mechanics are discussed from this statistical mechanical viewpoint. The maximum entropy principle is shown to provide a unified view of both classical and quantum mechanics.

Optimal sampling network for monitoring the representative water quality of an entire reservoir on the basis of information theory

This study presents a method for optimal selection of sampling stations for representative and effective water quality monitoring of a reservoir on the basis of information theory. We adopt an objective function used for the design of other hydrological monitoring networks such as rain gauge networks. Unlike rain gauges, in which only a single variable of rainfall is of interest, water quality monitoring stations measure multiple water quality variables. To consider this nature, a new concept of multi-variate weighted total information is proposed. This allows us to consider the relative importance between multiple water quality variables in the design of water quality monitoring networks. The proposed methodology is applied to Lake Yongdam, South Korea, where water quality has been extensively observed at several points. The optimal combination of sampling sites selected with the proposed method is found to show little redundancy in comparison with a previous independent study that presents statistical analysis of the same dataset. Further, the water quality data averaged over all stations are very close to those averaged over the selected sites only, implying that the optimal combination of sampling sites is representative of all sites.

Mark Burgin’s Theory of Information

A review of a major, definitive source book on the basis of information theory is presented.

Minimum relative entropy, Bayes and Kapur

SUMMARY The focus of this paper is to illustrate important philosophies on inversion and the similarly and differences between Bayesian and minimum relative entropy (MRE) methods. The development of each approach is illustrated through the general-discrete linear inverse. MRE differs from both Bayes and classical statistical methods in that knowledge of moments are used as ‘data’ rather than sample values. MRE like Bayes, presumes knowledge of a prior probability distribution and produces the posterior pdf itself. MRE attempts to produce this pdf based on the information provided by new moments. It will use moments of the prior distribution only if new data on these moments is not available. It is important to note that MRE makes a strong statement that the imposed constraints are exact and complete. In this way, MRE is maximally uncommitted with respect to unknown information. In general, since input data are known only to within a certain accuracy, it is important that any inversion method should allow for errors in the measured data. The MRE approach can accommodate such uncertainty and in new work described here, previous results are modified to include a Gaussian prior. A variety of MRE solutions are reproduced under a number of assumed moments and these include second-order central moments. Various solutions of Jacobs & van der Geest were repeated and clarified. Menke's weighted minimum length solution was shown to have a basis in information theory, and the classic least-squares estimate is shown as a solution to MRE under the conditions of more data than unknowns and where we utilize the observed data and their associated noise. An example inverse problem involving a gravity survey over a layered and faulted zone is shown. In all cases the inverse results match quite closely the actual density profile, at least in the upper portions of the profile. The similar results to Bayes presented in are a reflection of the fact that the MRE posterior pdf, and its mean are constrained not by d=Gm but by its first moment E(d=Gm), a weakened form of the constraints. If there is no error in the data then one should expect a complete agreement between Bayes and MRE and this is what is shown. Similar results are shown when second moment data is available (e.g. posterior covariance equal to zero). But dissimilar results are noted when we attempt to derive a Bayesian like result from MRE. In the various examples given in this paper, the problems look similar but are, in the final analysis, not equal. The methods of attack are different and so are the results even though we have used the linear inverse problem as a common template.

Laws of Information in Decentralized Control Systems

This paper deals with the laws of information in decentralized control systems. In the case of decentralized control of complex systems, often referred to hierarchical systems, theoretically analyze the possibility of using an n-dimensional information theory to deal with such systems. N-dimensional information theory is an interesting tool to deal with these systems. On the basis of information theory the partition laws of information rates are listed. These laws are fundamental requirements for complex systems that must be met and by their quantification one can be able to compare these complex control systems and track how different parts of the system communicate and collaborate with others, etc. In the final section the deterministic partition law of information rates and its possibility of use for dynamical systems are presented.

Repeat Sequences and Base Correlations in Human Y Chromosome Palindromes

On the basis of information theory and statistical methods, we use mutual information, n-tuple entropy and conditional entropy, combined with biological characteristics, to analyze the long range correlation and short range correlation in human Y chromosome palindromes. The magnitude distribution of the long range correlation which can be reflected by the mutual information is P5>P5a>P5b (P5a and P5b are the sequences that replace solely Alu repeats and all interspersed repeats with random uncorrelated sequences in human Y chromosome palindrome 5, respectively); and the magnitude distribution of the short range correlation which can be reflected by the n-tuple entropy and the conditional entropy is P5>P5a>P5b>random uncorrelated sequence. In other words, when the Alu repeats and all interspersed repeats replace with random uncorrelated sequence, the long range and short range correlation decrease gradually. However, the random uncorrelated sequence has no correlation. This research indicates that more repeat sequences result in stronger correlation between bases in human Y chromosome. The analyses may be helpful to understand the special structures of human Y chromosome palindromes profoundly.

In Silico Analysis of Phosphoproteome Data Suggests a Rich-get-richer Process of Phosphosite Accumulation over Evolution

Recent phosphoproteome analyses using mass spectrometry-based technologies have provided new insights into the extensive presence of protein phosphorylation in various species and have raised the interesting question of how this protein modification was gained evolutionarily on such a large scale. We investigated this issue by using human and mouse phosphoproteome data. We initially found that phosphoproteins followed a power-law distribution with regard to their number of phosphosites: most of the proteins included only a few phosphosites, but some included dozens of phosphosites. The power-law distribution, unlike more commonly observed distributions such as normal and log-normal distributions, is considered by the field of complex systems science to be produced by a specific rich-get-richer process called preferential attachment growth. Therefore, we explored the factors that may have promoted the rich-get-richer process during phosphosite evolution. We conducted a bioinformatics analysis to evaluate the relationship of amino acid sequences of phosphoproteins with the positions of phosphosites and found an overconcentration of phosphosites in specific regions of protein surfaces and implications that in many phosphoproteins these clusters of phosphosites are activated simultaneously. Multiple phosphosites concentrated in limited spaces on phosphoprotein surfaces may therefore function biologically as cooperative modules that are resistant to selective pressures during phosphoprotein evolution. We therefore proposed a hypothetical model by which the modularization of multiple phosphosites has been resistant to natural selection and has driven the rich-get-richer process of the evolutionary growth of phosphosite numbers.

Information processing and signal integration in bacterial quorum sensing

Bacteria communicate using secreted chemical signaling molecules called autoinducers in a process known as quorum sensing. The quorum-sensing network of the marine bacterium Vibrio harveyi uses three autoinducers, each known to encode distinct ecological information. Yet how cells integrate and interpret the information contained within these three autoinducer signals remains a mystery. Here, we develop a new framework for analyzing signal integration on the basis of information theory and use it to analyze quorum sensing in V. harveyi. We quantify how much the cells can learn about individual autoinducers and explain the experimentally observed input–output relation of the V. harveyi quorum-sensing circuit. Our results suggest that the need to limit interference between input signals places strong constraints on the architecture of bacterial signal-integration networks, and that bacteria probably have evolved active strategies for minimizing this interference. Here, we analyze two such strategies: manipulation of autoinducer production and feedback on receptor number ratios.

Effect of microstructural complexity on the hot deformation behavior of aluminum alloy 2024

Abstract Powder metallurgy (P/M) aluminum alloy 2024 (Al-2024) reinforced with SiC whiskers is difficult to shape-form because of microstructural complexities introduced by rapid solidification and whisker additions. In the present investigation, four forms of Al-2024, viz. ingot, wrought, P/M, and P/M with 20 v/o silicon-carbide whiskers (SiC w ) were examined to understand the influence of structural complexity on the hot deformation microstructural mechanisms. The analyses using the concepts of activation energy and dynamic stability criteria revealed that the safe hot working window of Al-2024 narrows as the microstructural complexity increases. This experimental observation was also explained on the basis of information theory. Safe processing window identified for the composite was validated using scaled-up extrusion experiments and the influence of processing conditions on microstructural control was demonstrated.

Measuring the quality of digital soil maps using information criteria

One of the purposes of a soil map is to present information regarding the spatial variation of soil to the end user. In the past, statistical methods of uncertainty have been used to indicate map quality. In this paper, the information content of a map is proposed as a more suitable measure of map quality. The theory has its basis in information theory, more particularly Shannon's information criterion. A modification of Shannon's information criterion is presented. Other than being used as an indicator of map quality, the method is particularly useful as an aid during the map production process, for example, choosing block size or grid spacing. Four examples illustrate the concept and its potential use in soil science.

Metrological analysis of two-component systems for laser doppler anemometry of turbulent flows

A metrological analysis of three- and two-channel two-component laser Doppler anemometry measurements of turbulent flow parameters is made on the basis of information theory and parametric optimization.

The information-theory aspect of target program planning

A new method is proposed for target program planning in the development of means of measurement on the basis of information theory and statistical physics.