scholarly journals Security Aspects of Information Structures in the Information Warfare Context

2018 ◽  
Vol 21 ◽  
pp. 49-53
Author(s):  
Pēteris Grabusts
Keyword(s):  
Neural Networks ◽  
Information And Communication Technologies ◽  
Adaptive Learning ◽  
Information Structure ◽  
Point Of View ◽  
Information Warfare ◽  
Information Structures ◽  
Theory Point ◽  
Concept Of Information ◽  
Types Of Information

In the modern sense, the concept of information warfare includes the use and management of information and communication technologies to achieve a competitive advantage compared to the opponent. An information warfare is a manipulation with information that trusts a goal without an objective understanding, so that the goal is to take decisions against its own interests in the interests of the opponents. Information structures are considered as systems that produce and process various types of information, provide the storage of information and access to users. Such information structures may include neural networks, adaptive learning systems, etc. They must be prepared to train, respond to threats and ensure the safety of their existence, which is very topical during modern information warfare. This analytical article will cover more theoretical aspects related to the security of information systems from the system theory point of view. Knowledge base of the information structure can be a neural network, in which training should be provided from external threats.The author considers artificial neural networks as one of the potential threats in the context of information warfare.

scholarly journals INFORMATIONAL WARFARE – INFLUENCE ON INFORMATIONAL STRUCTURES

2019 ◽  
Vol 2 ◽  
pp. 56
Author(s):  
Peter Grabusts ◽  
Aleksejs Zorins ◽  
Artis Teilāns
Keyword(s):  
Neural Networks ◽  
Information And Communication Technologies ◽  
Communication Technologies ◽  
Point Of View ◽  
Information Warfare ◽  
Information Structures ◽  
Theory Point ◽  
Information And Communication ◽  
Concept Of Information ◽  
Types Of Information

The concept of information warfare encompasses the use of information and communication technologies to gain an advantage over a potential opponent. The information warfare is the manipulation with the information that trusts the goal, so that the goal should make decisions about its interests in the interests of opponents. Information structures are treated as systems that process different types of information, provide storage and access to users. Such structures may enclose neural networks, self-learning systems etc. They need to be ready to learn, respond to threats and ensure their safety, which is topical in today's information warfare. This paper will address aspects related to the security of information systems from a system theory point of view. The knowledge base of information structures can be elements of artificial intelligence, which security must be protected against various threats. The authors considers artificial neural networks to be one of the potential threats in the context of information warfare.

Information structures and entropy measurement for a fuzzy probabilistic information system

2021 ◽  
pp. 1-19
Author(s):  
Yanling He ◽  
Chunji Yao
Keyword(s):  
Information System ◽  
Information Structure ◽  
Granular Computing ◽  
Basic Structure ◽  
Fuzzy Relations ◽  
Information Structures ◽  
Inclusion Degree ◽  
Probabilistic Information ◽  
Entropy Measurement ◽  
Types Of Information

An information system (IS), an important model in the field of artificial intelligence, takes information structure as the basic structure. A fuzzy probabilistic information system (FPIS) is the combination of some fuzzy relations in the same universe that satisfy probability distribution. A FPIS as an IS with fuzzy relations includes three types of uncertainties (i.e., roughness, fuzziness and probability). This paper studies information structures in a FPIS from the perspective of granular computing (GrC). Firstly, two types of information structures in a FPIS are defined by set vectors. Then, equality, dependence and independence between information structures in a FPIS are proposed, and they are depicted by means of the inclusion degree. Next, information distance between information structures in a FPIS is presented. Finally, entropy measurement for a FPIS is investigated based on the proposed information structures. These results may be helpful for understanding the nature of structures and uncertainty in a FPIS.

Information structures in a fuzzy β-covering information system

2021 ◽  
pp. 1-26
Author(s):  
Rui-Lin Liu ◽  
Hai-Long Yang ◽  
Li-Juan Zhang
Keyword(s):  
Information System ◽  
Information Systems ◽  
Data Compression ◽  
Information Structure ◽  
Information Structures ◽  
Concept Of Information

This paper studies information structures in a fuzzy β-covering information system. We introduce the concepts of a fuzzy β-covering information system and homomorphism between them, and investigate related properties. The concept of information structure of a fuzzy β-covering information system is given. We discuss the relationships between information structures from the view of dependence and separation. Then granularity measures for a fuzzy β-covering information system are studied. Finally, we discuss invariance of fuzzy β-covering information systems under homomorphism and illustrate its application on data compression.

scholarly journals Trójwarstwowa struktura informacji w treści wypowiedzi1 (szkic o programie Gramatyki Rozproszonej)

2016 ◽  
pp. 73-112 ◽  
Author(s):  
André Włodarczyk ◽  
Hélène Włodarczyk
Keyword(s):  
Computer Science ◽  
Information Structure ◽  
Predicate Logic ◽  
The Body ◽  
Formal Concept ◽  
Information Structures ◽  
Formal Concepts ◽  
Meta Information ◽  
Spatio Temporal ◽  
Concept Of Information

The idea of the three-tier information structure in the content of utterances is the result of research carried out in a transdisciplinary spirit, within sciences such as linguistics, psychology, logic and computer science. The theory of Meta-Informative Centering (MIC) of attention, which borrows the concept of attention centering from psychology and the concept of information from computer science, makes it possible to treat ―information structures" or "thematic-rhematic structures" as strictly dependent on the canonical form of utterances (as the manifestation of predication). Thus, an important feature of the MIC theory is its reference to predication, and in particular to the language of classical logic (first-order predicate logic). In the light of MIC theory, "information structure" appears in fact to be a structure of meta-information (representing "information about information") with respect to orthoinformation (proper information; i.e. situation frame, its types and aspects, roles, spatio-temporal anchors etc.) contained in the utterances. It is the theory of Associative Semantics (AS), which is being developed in parallel to the theory of MIC, which constitutes the layer of ortho-information. However, the foundational role in the structure of information content of utterances is played by para-information ("information which is similar or related to some other information"). In order to treat systematically the structure of information (ortho-, meta- and para-information) as contained in language utterances, and for representing their fundamental components, we proposed using ―formal concepts‖ belonging to the rapidly developing new field of research in computer science under the general name of ―Formal Concept Analysis‖ (FCA). The most important operations (responsible for the structure of information contained in the body of the utterances) are as follows: (a) selection (for building conceptual areas of interest in the para-information tier), (b) configuration (for building relationships between concepts in the ortho-information tier) and (c) centering (secondary selection of concepts for transmitting in the meta-information tier), while the most important operations (responsible for the structure of utterances) are: (a) nesting (for building utterances as units of discourse in the profile, across the tiers) and (b) encapsulation (for designating the conceptual space and combining conceptual representations with the language). In this paper we present one of the many areas of application of the described theoretical foundation, namely: word order in linguistic typology. Research taking into account the abovementioned three kinds of information constitutes a contribution to the construction of a coherent theoretical framework for Distributed Grammar (i.e. multi-tier grammar with a generalized idiomatics).

DIFFUSION AND ADOPTION OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN SOUTH AFRICAN TELECENTRES: SELECTED TELECENTRES IN KWAZULU-NATAL

Mousaion ◽  
2016 ◽  
Vol 33 (1) ◽  
pp. 103-120 ◽  
Author(s):  
Blessing Mbatha
Keyword(s):  
Information And Communication Technologies ◽  
Diffusion Of Innovations ◽  
Local Community ◽  
Information And Communications Technologies ◽  
Community Members ◽  
Kwazulu Natal ◽  
Information And Communication ◽  
Ict Tools ◽  
The Government ◽  
Types Of Information

This study investigated the usage and types of information and communications technologies (ICTs) accessible to community members in four selected Thusong Service Centres (TSCs or telecentres) in KwaZulu-Natal (KZN). The telecentres that participated in the study were: Nhlazuka, Mbazwane, Dududu and Malangeni. The study was informed by Rogers’ (1995) Diffusion of Innovations (DoI) theory. Through a survey, four TSCs were purposively selected. A questionnaire was used to collect data from community members in the four telecentres involved. The data collected was tabulated under the various headings and presented using tables, frequencies, percentiles and generalisations with the help of the Statistical Package for the Social Sciences (SPSS). The results indicated that a variety of ICT tools have been adopted in the TSCs to provide the local community with the much-needed access to information and improved communication. The government should ensure that adequate varieties and levels of ICT competence are offered to all the citizens. In conclusion, there is a need for sufficient and coherent government policies regulating the training of the local community to use these ICTs effectively.

Chemical analysis: Information contribution of results

1991 ◽  
Vol 56 (3) ◽  
pp. 505-559 ◽  
Author(s):  
Karel Eckschlager
Keyword(s):  
Analytical Chemistry ◽  
Information Theory ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Information Gain ◽  
Analytical Signal ◽  
Point Of View ◽  
System A ◽  
Review Analysis ◽  
Theory Point

In this review, analysis is treated as a process of gaining information on chemical composition, taking place in a stochastic system. A model of this system is outlined, and a survey of measures and methods of information theory is presented to an extent as useful for qualitative or identification, quantitative and trace analysis and multicomponent analysis. It is differentiated between information content of an analytical signal and information gain, or amount of information, obtained by the analysis, and their interrelation is demonstrated. Some notions of analytical chemistry are quantified from the information theory and system theory point of view; it is also demonstrated that the use of fuzzy set theory can be suitable. The review sums up the principal results of the series of 25 papers which have been published in this journal since 1971.

scholarly journals RSPCN: Super-Resolution of Digital Elevation Model Based on Recursive Sub-Pixel Convolutional Neural Networks

2021 ◽  
Vol 10 (8) ◽  
pp. 501
Author(s):  
Ruichen Zhang ◽  
Shaofeng Bian ◽  
Houpu Li
Keyword(s):  
Neural Networks ◽  
Digital Elevation Model ◽  
Convolutional Neural Networks ◽  
Adaptive Learning ◽  
Nearest Neighbor ◽  
Super Resolution ◽  
Recursion Theory ◽  
Research Issues ◽  
Digital Elevation ◽  
Elevation Model

The digital elevation model (DEM) is known as one kind of the most significant fundamental geographical data models. The theory, method and application of DEM are hot research issues in geography, especially in geomorphology, hydrology, soil and other related fields. In this paper, we improve the efficient sub-pixel convolutional neural networks (ESPCN) and propose recursive sub-pixel convolutional neural networks (RSPCN) to generate higher-resolution DEMs (HRDEMs) from low-resolution DEMs (LRDEMs). Firstly, the structure of RSPCN is described in detail based on recursion theory. This paper explores the effects of different training datasets, with the self-adaptive learning rate Adam algorithm optimizing the model. Furthermore, the adding-“zero” boundary method is introduced into the RSPCN algorithm as a data preprocessing method, which improves the RSPCN method’s accuracy and convergence. Extensive experiments are conducted to train the method till optimality. Finally, comparisons are made with other traditional interpolation methods, such as bicubic, nearest-neighbor and bilinear methods. The results show that our method has obvious improvements in both accuracy and robustness and further illustrate the feasibility of deep learning methods in the DEM data processing area.

scholarly journals Hardware‐Friendly Stochastic and Adaptive Learning in Memristor Convolutional Neural Networks

2021 ◽  
pp. 2100041
Author(s):  
Wei Zhang ◽  
Lunshuai Pan ◽  
Xuelong Yan ◽  
Guangchao Zhao ◽  
Hong Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Adaptive Learning

scholarly journals Two Remarks on Blackwell's Theorem

2008 ◽  
Vol 45 (02) ◽  
pp. 580-586 ◽  
Author(s):  
Ehud Lehrer ◽  
Eran Shmaya
Keyword(s):  
Decision Maker ◽  
Information Structure ◽  
Decision Problem ◽  
Partial Information ◽  
The Other ◽  
Actual State ◽  
Information Structures ◽  
Comparison Of Experiments ◽  
Classical Characterization ◽  
Blackwell's Theorem

In a decision problem with uncertainty a decision maker receives partial information about the actual state via an information structure. After receiving a signal, he is allowed to withdraw and gets zero profit. We say that one structure is better than another when a withdrawal option exists if it may never happen that one structure guarantees a positive profit while the other structure guarantees only zero profit. This order between information structures is characterized in terms that are different from those used by Blackwell's comparison of experiments. We also treat the case of a malevolent nature that chooses a state in an adverse manner. It turns out that Blackwell's classical characterization also holds in this case.

Deep neural network based on generalized neo-fuzzy neurons and its learning based on backpropagation

2021 ◽  
Vol 26 (jai2021.26(1)) ◽  
pp. 32-41
Author(s):  
Bodyanskiy Y ◽  
◽  
Antonenko T ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Process ◽  
Activation Function ◽  
Point Of View ◽  
Basic Unit ◽  
Theoretical Point ◽  
Activation Functions ◽  
Approximation Properties ◽  
Network Training

Modern approaches in deep neural networks have a number of issues related to the learning process and computational costs. This article considers the architecture grounded on an alternative approach to the basic unit of the neural network. This approach achieves optimization in the calculations and gives rise to an alternative way to solve the problems of the vanishing and exploding gradient. The main issue of the article is the usage of the deep stacked neo-fuzzy system, which uses a generalized neo-fuzzy neuron to optimize the learning process. This approach is non-standard from a theoretical point of view, so the paper presents the necessary mathematical calculations and describes all the intricacies of using this architecture from a practical point of view. From a theoretical point, the network learning process is fully disclosed. Derived all necessary calculations for the use of the backpropagation algorithm for network training. A feature of the network is the rapid calculation of the derivative for the activation functions of neurons. This is achieved through the use of fuzzy membership functions. The paper shows that the derivative of such function is a constant, and this is a reason for the statement of increasing in the optimization rate in comparison with neural networks which use neurons with more common activation functions (ReLU, sigmoid). The paper highlights the main points that can be improved in further theoretical developments on this topic. In general, these issues are related to the calculation of the activation function. The proposed methods cope with these points and allow approximation using the network, but the authors already have theoretical justifications for improving the speed and approximation properties of the network. The results of the comparison of the proposed network with standard neural network architectures are shown

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