Information Generation Process Research Articles

Semantic Communications in Networked Systems: A Data Significance Perspective

We present our vision for a departure from the established way of architecting and assessing communication networks, by incorporating the semantics of information, defined not necessarily as the meaning of the messages, but as their significance, possibly within a real-time constraint, relative to the purpose of the data exchange. We argue that research efforts must focus on laying the theoretical foundations of a redesign of the entire process of information generation, transmission, and usage for networked systems in unison by developing advanced semantic metrics for communications and control systems; an optimal sampling theory combining signal sparsity and timeliness, for real-time prediction/reconstruction/control under communication constraints and delays; temporally effective compressed sensing techniques for decision making and inference directly in the compressed domain; and semantic-aware data generation, channel coding, packetization, feedback, and multiple and random access schemes that reduce the volume of data and the energy consumption, increasing the number of supportable devices. This paradigm shift targets jointly optimal information gathering, information dissemination, and decision making policies in networked systems.

Modeling Information Support for Startup: Theoretical Aspect

The business provides many alternatives, and an investor-entrepreneur has always to make a decision on how best to use his fund. The first challenge for a potential investor is that the business environment is constantly changing, becoming more complex. The process of data acquisition and subsequent information generation is continuously cyclical, time and cost consuming. The second challenge is analytics. The typical small business owner makes decisions by trial-and-error. In today's data-driven AI-first environment to make frequent and quick strategic, tactical, and operational decisions, the owner/entrepreneur needs advice and guidance on startup specific issues, which should only be provided by professional experts. The goal of this research is to elaborate a theoretical foundation of an information support model for a startup – the Startup-Information-Support Model (SISM). The study uses a deep theoretical study - a qualitative method - by applying content analysis of relevant literature and primary research techniques. PESTEL and the Porter's five forces model are employed as analytical tools of expert-opinion technique. PESTEL provides a framework for information collection and data analysis at mega level, while industry attractiveness analysis is based on the Porter's five forces model. The strength of each factor (each force) is analyzed through well-defined concrete indicators, and then is measured by scoring their values. Finally, the SISM presents the whole portrait of attractiveness of specific industry. The SISM equips consultant companies with an effective tool for conducting business intelligence and analytics. The companies would be able to offer additional value added to B2B services to potential investor-entrepreneurs. The Model has the potential to employing AI, which can more systematically incorporate environmental issues at both levels – mega forces and industry factors - into the monitoring process. Equipped with such tools, decision makers can perform complex simulations, test many possible scenarios, and quickly evaluate various impacts at low cost.

Accuracy and Interpretability: Struggling with the Epistemic Foundations of Machine Learning-Generated Medical Information and Their Practical Implications for the Doctor-Patient Relationship

The initial successes in recent years in harnessing machine learning (ML) technologies to improve medical practice and benefit patients have attracted attention in a wide range of healthcare fields. Particularly, it should be achieved by providing automated decision recommendations to the treating clinician. Some hopes placed in such ML-based systems for healthcare, however, seem to be unwarranted, at least partially because of their inherent lack of transparency, although their results seem convincing in accuracy and reliability. Skepticism arises when the physician as the agent responsible for the implementation of diagnosis, therapy, and care is unable to access the generation of findings and recommendations. There is widespread agreement that, generally, a complete traceability is preferable to opaque recommendations; however, there are differences about addressing ML-based systems whose functioning seems to remain opaque to some degree—even if so-called explicable or interpretable systems gain increasing amounts of interest. This essay approaches the epistemic foundations of ML-generated information specifically and medical knowledge generally to advocate differentiations of decision-making situations in clinical contexts regarding their necessary depth of insight into the process of information generation. Empirically accurate or reliable outcomes are sufficient for some decision situations in healthcare, whereas other clinical decisions require extensive insight into ML-generated outcomes because of their inherently normative implications.

Variational Autoencoder-Based Hybrid Recommendation With Poisson Factorization for Modeling Implicit Feedback

Hybrid recommendation, which is based on collaborative filtering and supplemented with auxiliary content information, is being actively researched due to its ability to overcome the cold-start problem. Many proposed hybrid methods make recommendations using Gaussian distribution-based collaborative filtering even though they handle variables that tend to be non-Gaussian, such as the number of interactions. We present a method that uses a hybrid recommendation framework based on collaborative filtering that models the number of interactions as a Poisson-distributed and variational autoencoder-based content information generation process that shares latent variables with collaborative filtering. As a prior for the shared latent variables, we use a gamma distribution, which is a conjugate prior of a Poisson distribution. An implicit-derivative-based reparameterization trick enables the use of a gamma distribution in a variational autoencoder. The latent variables in the generative model are inferred using the stochastic gradient variational Bayes approach, taking the number of interactions corresponding to users and items and content information as input. In accordance with the inference, unobserved interactions between users and items are predicted for recommendation. The use of a neural-network-based generative model for content information enables the framework to handle various types of content information. Experimental results show that the proposed method utilizes content information effectively for predicting the number of interactions and that it should aid in overcoming the cold-start problem.

Designing an algorithm for supporting information generation process optimization

The paper considers the structural elements for an automated information reference decision support system (AIRDSS) in hierarchical multilevel complex organizational systems (HMLCOS). The task to ensure the functioning of the AIRDSS has been formulated. To solve it the main steps have been designated, which comprise the calculation of the importance coefficients for the supporting information (SI) elements and ordering of options based on a decision-maker’s preference followed by the choice of the utility prospective one. On the basis of the steps considered, the authors propose the algorithm to form the optimal structure of the AIRDSS procedural component’ elements for obtaining SI; the algorithm has a number of advantages: calculation simplicity for various experiments, a relatively simple formalization of expert knowledge into numerical values of importance.

Role of RFID technologies in transportation projects: a review

Advancement in sensing and transmitting technologies has significantly reduced their cost and increased their use in various industries, including the architectural, engineering, and construction industry. These advancements in sensor and systemisation technologies are revolutionising the information generation process in the industries they are applied. Radio frequency identification (RFID) is one such wireless transmission technology that enables sensing, counting, measuring, documenting, identifying, locating, and transmitting information in real-time. These features can help managers to manage projects more effectively. However, there exists a significant gap between RFID technologies' current acceptance versus the expected acceptance in the transportation industry. This paper presents the findings from an extensive literature review, aimed to investigate the application of RFID systems to various phases of transportation projects. This research enabled identifying the advantages and barriers to RFID adoption. Lastly, action-items are recommended for transportation agencies that will help them in adopting RFID systems in the transportation industry.

How to Improve the Science and Engineering Education in Islamic Countries?

The evolutions of science, education and especially engineering training had different trends stating from the very root of human history. The future improvements in scientific and engineering education require extensive literature review to assess objectively the contributions of different civilizations among which Islamic education system had a unique feature in the past towards the researches in astronomy, physics, chemistry, algebra, robotics, medicine, etc. The universities are the highest educational institutions which provide every nation with enlightened individuals; promote intellectual development; advent and promote innovations and discoveries in field and laboratory. Prior to all these activities, it is necessary that the same universities should produce philosophical schools of thought, literature, artistic and social trends. Researchers should be able to put forward theories of good, right and beauty with ideas and orientations that serve as a reference for future policy makers in different sectors for the prosperity of the nation and humanity in general. After long centuries of slackness, the higher education is gaining momentum in the Muslim World which is striving to effect comprehensive developments in the present century. Almost in every aspects of life highly-trained manpower are required. Unfortunately, scientific philosophical thinking and logical refinement of scientific information aspects are missing in almost all the universities in Muslim countries. Especially, three T-principle as Takhayyul (imagination), Tasawwur (design, geometrical shape description) and Tafakkur (idea generation) do exist very rarely in our universities. In fact, the 3-T principle is the trigger of scientific information generation process. This paper elaborates on the education systems during the past Islamic periods, which ignited the educational system development in the western countries; its decline; and the present situation with ever increasing momentum towards the future. It is emphasized that the higher education and scientific research should have philosophy, logic, geometry and natural event visualizations prior to mathematical and computational calculations in engineering education.

Z-VIKOR Method Based on a New Comprehensive Weighted Distance Measure of Z-Number and Its Application

Reliability of information can directly affect the accuracy of decision-making. Compared with classical fuzzy sets, Z-number takes into account the uncertainty in information generation process and reliability of information. Z-number can also be an intuitive and effective description form of decision information. Existing research on the distance measure of Z-number is rare, and most of the research works cannot reflect well the advantages of Z-information and the characteristics of Z-number. Given this research gap, this paper simultaneously considered the randomness and fuzziness of Z-number and defined the comprehensive weighted distance measure of Z-number. To extend classic VlseKriterijum-ska Optimizacija I Kompromisno Resenje (VIKOR) method to the Z-information environment, we suggested the Z-VIKOR method based on the proposed distance measure. This method is convenient and effective for the direct computation of Z-numbers. We also provided an example of multicriteria decision-making for selecting regional circular economy development plan to illustrate the feasibility and validity of our proposed method. We then verified the applicability and superiority of our method through comparative analyses with other existing methods.

Constructing and Displaying the Trajectory Path on the Aircraft On-board Cockpit Multifunction Displays

Background/Objectives: development of algorithms for flight plan data transformation into the projected trajectory of the aircraft based on the unification of used trajectories in the horizontal plane to reduce the consumed computing resources. Methods: In order to display the flight route, specialized software is used in the on-board systems enabling to convert a flight plan data set into a predictable trajectory of the aircraft path for its subsequent visualization. Findings: The authors have defined, developed and presented the principles and algorithms for computation of route trajectories ensuring the building of calculated flight path of the aircraft to be displayed on the on-board displays. The route displaying is carried out by consistent plotting of the typical trajectories that constitute the route path and in their turn consist of a set of graphic primitives such as line and arc. The database to be displayed is based on the flight plan, made in accordance with the Jeppesen database or similar, after bringing it to the formats of three typical defined trajectories. Applications/Improvements: The algorithms of the flight plan data conversion into a projected trajectory in the horizontal plane unify the process of information generation for displaying on the on-board multifunction displays and optimize a set of computation data to control the motion along a trajectory.

Using big data for evidence based governance in child welfare

Numerous approaches are available for improving governance of the child welfare system, all of which require longitudinal data reporting on child welfare clients. A substantial amount of agency administrative information – big data – can be transformed into knowledge for policy and management actions through a rigorous information generation process. Important properties of the information generation process are that it must generate accurate, timely information while protecting the confidentiality of the clients. In addition, it must be extensible to serve an ever-changing policy and technology environment. Knowledge discovery and data mining (KDD), aka data science, is a method developed in the private sector to mine consumer data and can be used in public settings to support evidence based governance. KDD consists of a rigorous 5-step process that includes a Web-based end-user interface. The relationship between KDD and governance is a continuous feedback cycle that enables ongoing development of new information and knowledge as stakeholders identify emerging needs. In this paper, we synthesis the different frameworks for utilizing big data for public governance, introduce the KDD process, describe the nature of big data in child welfare, and then present an updated KDD architecture that can support these frameworks to utilize big data for governance. We also demonstrate the role KDD plays in child welfare management through 2 case studies. We conclude with a discussion on implications for agency–university partnerships and research-to-practice.

General Information Product Theory in Economics Science

The information, including the knowledge in the science, business and society, is being generated, transmitted, received and analyzed by the humans in the various countries for many centuries. The generated information can be structured, coded, stored and retrieved, representing a most valuable asset in possession by the acting economic agents in the modern economies of the scales and scopes in the information societies in an information age. The authors introduce a notion on the general information product (GIP) in the macroeconomics, thoughtfully defining the GIP(t) as a ratio of the total generated information stream to the selected finite time period (GIP(t)=total generated information/time) in the macroeconomics for the first time. Authors consider the GIP(t) as a main parameter, which evaluates the performance of the economies of the scales and scopes from the macroeconomics perspective. Authors assume that the multiple possible origins of the fluctuations of GIP(t) and the accurate characterization of GIP(t) can be made in agreement with the Ledenyov theory on the GIP(t) in the macroeconomics. Authors believe that the Ledenyov indicator GIP(t) instead of the Kuznets indicator GDP(t), can be successfully used to accurately measure the state/performance of any economy in the time domain. Authors think that the GIP(t) is a discrete-time digital signal (the Ledenyov digital wave with the Markov information), but it is not the continuous-time signals (the continuous waves), because of the discrete-time digital nature of information generation process in the developed/developing economies. The article considers the empirical theoretical approaches and reveals the possible practical technical limitations in relation to the modeling of the new types of the discrete-time digital signals generators for the Ledenyov digital waves generation in the economies of the scales and scopes at the time of globalization.

NETWORK CULTURE - INTEGRAL PART OF NEW VALUES OF CIVIL SOCIETY

New technologies not only improve working conditions or communication, they are also bringing new values to the society. This article discusses the concept of «network culture», which is now perceived by society as an integral part of values that can only exist in a civil society. We can research ( find) this kind of society in modern time period in Russia. The article analyzes the meaning of communication, how to use it, development processes in network media. Nowadays network culture and its influence on society are almost in all spheres of state and society, as well as changes in perception of information and content generation process. Development of the Internet and Internet technologies largely set the tone for the development of popular culture and allows you to store or to influence the national culture. Internet press today is and example which shows us, how this kind of tool can be used to influence on citizens. DOI: http://dx.doi.org/10.12731/2218-7405-2014-3-6

Big Data, Selection Bias, and the Statistical Patterns of Mortality in Conflict

The notion of “big data” implies very specific technical assumptions. The tools that have made big data immensely powerful in the private sector depend on having all (or nearly all) of the possible data. In our experience, these technical assumptions are rarely met with data about the policy and social world. This paper explores how information is generated about killings in conflict, and how the process of information generation shapes the statistical patterns in the observed data. Using case studies from Syria and Iraq, we highlight the ways in which bias in the observed data could mislead policy. The paper closes with recommendations about the use of data and analysis in the development of policy.

Stationary distribution of self-organized states and biological information generation

Self-organization, where spontaneous orderings occur under driven conditions, is one of the hallmarks of biological systems. We consider a statistical mechanical treatment of the biased distribution of such organized states, which become favored as a result of their catalytic activity under chemical driving forces. A generalization of the equilibrium canonical distribution describes the stationary state, which can be used to model shifts in conformational ensembles sampled by an enzyme in working conditions. The basic idea is applied to the process of biological information generation from random sequences of heteropolymers, where unfavorable Shannon entropy is overcome by the catalytic activities of selected genes. The ordering process is demonstrated with the genetic distance to a genotype with high catalytic activity as an order parameter. The resulting free energy can have multiple minima, corresponding to disordered and organized phases with first-order transitions between them.

Bookreview of principles of data integration

Bookreview of principles of data integration

적응적 탐색 영역을 이용한 보조 정보 생성의 고속화 방법

분산 비디오 압축(DVC, Distributed Video Coding) 기술은 부호화기의 움직임 추정과 같은 복잡한 과정을 복호화기로 이동시킴으로써 경량화 비디오 부호화를 가능하게 하는 새로운 방법으로 주목 받고 있다. 한편, DVC 복호화기에서는 움직임 추정과 보상 과정뿐만 아니라 채널 복호 과정도 수행해야 하기 때문에 복호화기의 복잡도는 크게 증가하게 된다. 이에 복호화기 복잡도의 가장 많은 비중을 차지하는 채널 복호 과정을 고속화하기 위한 다양한 방법이 제안되고 있다. 하지만 다양한 고속화 방법에 의해 채널 복호 과정이 고속화될수록 채널 복호화 이외의 과정들에 대한 복잡도가 부각되는데, 일례로 DVC 복호화기에서 낮은 비율을 차지하던 보조 정보생성 과정의 복잡도가 상대적으로 증가된다. 따라서 본 논문에서는 보조 정보 생성 과정에 적응적 탐색 영역 방법을 적용하여 DVC 복호 과정을 고속화할 수 있는 알고리즘을 제안한다. 제안된 적응적 탐색 영역을 이용하는 방법을 적용할 경우 고정된 탐색 영역을 적용한 종래의 방법에 비해 평균적으로 보조 정보 생성 시간의 63% 정도가 절감 가능하며, 저하되는 율 왜곡 성능은 약 0.17%(BDBR) 정도로 상당히 낮다. In Distributed Video Coding(DVC), a low complexity encoder can be realized by shifting complex processes of encoder such as motion estimation to decoder. Since not only motion estimation/compensation processes but also channel decoding process needs to be performed at DVC decoder, the complexity of a decoder is significantly increased in consequence. Therefore, various fast channel decoding methods are proposed for the most computationally complex part, which is the channel decoding process in DVC decoding. As the channel decoding process becomes faster using various methods, however, the complexity of the other processes are relatively highlighted. For instance, the complexity of side information generation process in the DVC decoder is relatively increased. In this paper, therefore, a fast method for the DVC decoding is proposed by using adaptive search range method in side information generation process. Experimental results show that the proposed method achieves time saving of about 63% in side information generation process, while its rate distortion performance is degraded only by about 0.17% in BDBR.

A literature survey of value creation through relationship banking

Relationship banking has emerged as an important mechanism through which banks, especially the smaller ones, overcome problems associated with information asymmetry. Relationship banking also creates value for the borrowing firm through a number of channels such as more chances of loan availability, lower interest rate, and less collateral requirements. In this paper, we provide an overview of the recent literature on relationship banking and its impact on firm value. We also discuss different dimensions of relationship banking to highlight how the role of relationship banking changes in information generation process under different circumstances. Finally, we identify potential areas for future research in this area. Key words: Banking relationship, firm value, multiple banking relationships, length of relationship, deregulation and technological change.

A Wyner-Ziv Video Transcoder

Wyner-Ziv (WZ) coding of video utilizes simple encoders and highly complex decoders. A transcoder from a WZ codec to a traditional codec can potentially increase the range of applications for WZ codecs. We present a transcoder scheme from the most popular WZ codec architecture to a differential pulse code modulation/discrete cosine transform codec. As a proof of concept, we implemented this transcoder using a simple pixel-domain WZ codec and the standard H.263+. The transcoder design aims at reducing complexity as a large amount of computation is saved by reusing the motion estimation, calculated at the side information generation process, and the l-frame streams. New approaches are used to generate side information and to map motion vectors for the transcoder. Results are presented to demonstrate the transcoder performance.

Towards an Information Model of Consistency Maintenance in Distributed Interactive Applications

A novel framework to model and explore predictive contract mechanisms in distributed interactive applications (DIAs) using information theory is proposed. In our model, the entity state update scheme is modelled as an information generation, encoding, and reconstruction process. Such a perspective facilitates a quantitative measurement of state fidelity loss as a result of the distribution protocol. Results from an experimental study on a first-person shooter game are used to illustrate the utility of this measurement process. We contend that our proposed model is a starting point to reframe and analyse consistency maintenance in DIAs as a problem in distributed interactive media compression.

Information-directed routing in ad hoc sensor networks

In a sensor network, data routing is tightly coupled to the needs of a sensing task, and hence the application semantics. This paper introduces the novel idea of information-directed routing, in which routing is formulated as a joint optimization of data transport and information aggregation. The routing objective is to minimize communication cost, while maximizing information gain, differing from routing considerations for more general ad hoc networks. The paper uses the concrete problem of locating and tracking possibly moving signal sources as an example of information generation process, and considers two common information extraction patterns in a sensor network: routing a user query from an arbitrary entry node to the vicinity of signal sources and back, or to a prespecified exit node, maximizing information accumulated along the path. We derive information constraints from realistic signal models, and present several routing algorithms that find near-optimal solutions for the joint optimization problem. Simulation results have demonstrated that information-directed routing is a significant improvement over a previously reported greedy algorithm, as measured by sensing quality such as localization and tracking accuracy and communication quality such as success rate in routing around sensor holes.