Cognitive informatics in image semantics description, identification and automatic pattern understanding

The high-speed and constants changes in information technology and business needs to drive the development and evolution of Cognitive Information Systems (CIS). There are limited items of research on the general model related to the analysis and design of CIS. This paper attempts to create a joint design scheme, where tries to joining the various models for CISs and Understanding-based management systems (UBMSS). The examined components substantiate elements of CIS analysis and design; however, they weren’t delineated as modeling elements and not depicted as enabling instruments that are needed to create a consistent and integrated system. The vital ingredients for modeling as follows: semi-structured documents, business processes, constituents of knowledge management, the enterprise, and the information architecture, together with the self-directing software components—Artificial Intelligence (AI)—that yield functions. For CIS modeling, the constituents, which were mentioned previously, were combined into a unified framework. That framework follows the object-oriented paradigm and architecture approach. The research aims to describe and present a framework that covers an overarching model and assists our understanding of the properties of CIS and UBMSS, permitting the formulation of a practical development method for Cognitive Information and Cognitive Management Systems (CMSs).

Data management in cognitive financial systems

This publication presents cognitive systems designed for analysing economic data. Such systems have been created as the next step in the development of classical DSS systems (Decision Support Systems), which are currently the most widespread tools providing computer support for economic decision-making. The increasing complexity of decision-making processes in business combined with increasing demands that managers put on IT tools supporting management cause DSS systems to evolve into intelligent information systems. This publication defines a new category of systems - UBMSS (Understanding Based Management Support Systems) which conduct in-depth analyses of data using on an apparatus for linguistic and meaning-based interpretation and reasoning. This type of interpretation and reasoning is inherent in the human way of perceiving the world. This is why the authors of this publication have striven to perfect the scope and depth of computer interpretation of economic information based on human processes of cognitive data analysis. As a result, they have created UBMSS systems for the automatic analysis and interpretation of economic data. The essence of the proposed approach to the cognitive analysis of economic data is the use of the apparatus for the linguistic description of data and for semantic analysis. This type of analysis is based on expectations generated automatically by a system which collects resources of expert knowledge, taking into account the information which can significantly characterise the analysed data. In this publication, the processes of classical data description and analysis are extended to include cognitive processes as well as reasoning and forecasting mechanisms. As a result of the analyses shown, we will present a new class of UBMSS cognitive economic information systems which automatically perform a semantic analysis of business data.

This paper presents some advances techniques for the strategic data sharing and splitting used for encryption and division of secret data, and enterprise management as well as modern approaches for management of divided information. The presented data sharing and splitting techniques will concern cryptographic algorithms as well as data sharing procedures based on cognitive data analysis techniques. Secret information sharing techniques aim to secure strategic information or secrets against disclosure to unauthorized persons. This paper presents algorithms and techniques allowing for visual information division and sharing on the basis of cognitive data analysis systems. The development of computer techniques for classified information splitting should also be useful in the process of shared information distribution and management. Cognitive systems for the semantic analysis of strategic economic and financial data will also be used to conceal information about the condition of the enterprise/organization.

This chapter proposes innovative features of future industrial organizations in order to provide them with the capabilities to manage high levels of environmental complexity in the 21st century. For such a purpose the author introduces the concept of Computational Organization Management Networks (COMN), which represents new organizations whose principles of operation are based on the concepts of Hierarchic Cognitive Systems (HCS) along with those of Telecommunications Management Networks (TMN). Structured with functional layers and cognitive roles that range from technical and managerial to institutional levels of analysis, and also equipped with operational, managerial and strategic processes, the concept of Computational Organization Management Networks (COMN) plays an important part in the developments of future organizations where cognitive machines and Cognitive Information Systems (CIS) are prominent actors of governance, automation and control of the whole enterprise. It is in such a context that the new organization COMN will provide customers and the whole environment with innovations such as immersiveness for the production of services and goods that are most customer-centric.

This paper presents some important aspects of cognitive informatics operated by cognitive processes in the human mind and implemented to new generation IT systems. This paper presents especially a selected class of cognitive categorization systems called UBIAS (Understanding Based Image Analysis Systems ). The UBIAS systems are especially dedicated to support analysis of data recorded in the form of images for example medical images. Cognitive categorization systems operate by executing a particular type of human thought, cognitive and analysis processes which take place in the human mind and which ultimately lead to making an in-depth description of the analysis and interpreting reasoning process. The most important element in this analysis and reasoning process is that it occurs both in the human cognitive process and in the system's information process that conducts the in-depth interpretation and analysis of data.

This paper aims to introduce three types of understanding models from the perspective of human cognitive systems and machine information processing. The method steps are as follows: 1. Obtain a complete all equal formal understanding model (A) by constructing a twin Turing machine between numbers and numbers. 2. Obtain an approximately equal intelligent understanding model (B) by constructing a twin Turing machine between numbers and symbols. 3. Obtain a similar socialized understanding model (C) by constructing a twin Turing machine between numbers and characters, which is characterized by: the model A to B and then C gradually converge. As a result, it was found that the machine formal information processing and the human content information processing are opposite in convergence. It is clear that the combination of the three formalized understanding models and the bilingual model of interpretative translation is the key to formal understanding, intelligent understanding and social understanding. Based on them, ambiguity, misunderstanding and understanding are all well understood. The significance is that it proves that the three types of understanding models and the two sets of convergence modes can effectively determine the formal understanding process. Furthermore, it is clear that the ways of human and computer are combined completely which is better than pure humans or simple machines. That can be applied to cognitive systems and information processing perfectly. And its application is in the combination of human-machine-specific personalized ability training and standardized knowledge learning and management, especially based on the targeted reuse of subject knowledge centers.

In the paper will be presented a new way of intelligent cognitive systems and pattern analysis using cognitive categorization. Such an understanding will be based on the linguistic and cognitive mechanisms of pattern recognition and classification. The goal is making computer analysis of the meaning for some selected classes of medical patterns. The approach presented will show the possibilities of automatic and intelligent disease detection and its classification based on cognitive resonance processes. Cognitive categorisation systems operate by executing a particular type of thought, cognitive and reasoning processes which take place in the human mind and which ultimately lead to making an in-depth description of the analysis and reasoning process.

This study presents an end-to-end narrative research approach that is particularly useful for describing, undertaking, and understanding process-oriented field research. The approach is informed by scholars in the fields of cognitive psychology, organization theory, and information systems. We situate the end-to-end approach within an emerging body of IS narrative research and argue that its adoption by qualitative researchers can provide multiple insights about computer-mediated change initiatives. By exploiting a narrative research strategy at all stages of the empirical work, researchers are better equipped to see the interconnectivity of stories and to follow the change process as it unfolds. The narrative of emergence and the narrative of repair are introduced as two useful concepts for the collection, handling, and analysis of empirical data.

Sensitivity of the lane change test as a measure of in-vehicle system demand

We live in a world of limitless information. With technology advancing at an astonishingly fast pace, we are challenged to adapt to robotics and automated systems that threaten to replace us. Both at home and at work, an endless range of devices and Information Technology (IT) systems place demands upon our attention that human beings have never experienced before, but are our brains capable of processing it all? In this important new book, an in-depth view is taken of IT's under-studied dark side and its dire consequences on individuals, organizations, and society. With theoretical underpinnings from the fields of cognitive psychology, management, and information systems, the idea of brain overload is defined and explored, from its impact on our decision-making and memory to how we may cope with the resultant 'technostress'. Discussing the negative consequences of technology on work substitution, technologically induced work-family conflicts, and organizational design as well as the initiatives set up to combat these, the authors go on to propose measurement approaches for capturing the entangled aspects of IT-related overload. Concluding on an upbeat note, the book's final chapter explores emerging technologies that can illuminate our world when mindfully managed. Designed to better equip humans for dealing with new technologies, supported by case studies, and also exploring the idea of 'IT addiction', the book concludes by asking how IT processes may aid rather than hinder our cognitive functioning. This is essential reading for anyone interested in how we function in the digital age.

A Cognitive Revolution in Management (CRM) has been brought about through Stafford Beer's work in management cybernetics. Explains the philosophical undertone of CRM as well as cybernetic features associated with it. The work on varietal aspects of management systems by Ansoff has also contributed to CRM, which needs to be backed by a cognitive information system, and a Cybernetic Theory of Management.

In this publication will be proposed a new algorithm for secure information sharing using personal or biometric information. In classic cryptographic threshold schemes used for secret splitting or sharing there aren’t any connection between generated shares and particular participants of communication protocol. Sometimes it may be worth to generate personalized shadows, which allow not only recovery the original information or secret data, but also to identify who exactly took part in the secret reconstruction procedure. In the paper will be described the way of extraction some personal or biometric characteristics using cognitive information systems, as well as the algorithm of application such personal information for shadow generation in threshold schemes. Such new approach may play a great role in intelligent distributed computing or secure urban life.

Service robots will gradually be present in residences interacting with human beings in unstructured environments. Their acceptance is conditioned to the evolution of research in the area of social robotics, in particular using cognitive systems. One factor that compromises the rapid evolution of these studies is the difficulty in modeling cognitive systems due to the volume and complexity of information produced by a chaotic world full of sensory information. In addition, the validation of results with the use of real environments involving buildings, and people presents a high cost of installation and maintenance. This article offers two strategies to speed up this process. The first involves the definition of OntCog ontology that models the senses captured by the agent robotic sensors. This modeling facilitates the reproduction of experiments associated with cognitive models and the comparison among different implementations. The second is associated with the development of Robot House Simulator, which provides an environment where a robot and a human character can interact socially with increasing levels of cognitive processing. An unprecedented feature of this simulator is to provide information about all the senses of the robot, actually only the sense of vision or touch has been considered in the existing robotic simulators.

Language learning is an intuitive statistical learning problem…. Research in cognitive psychology demonstrates that such implicit tallying is the raw basis of human pattern recognition, categorization, and rationalcognition. (Ellis, 2007: 80–81) Chapters 2 and 3 provide an outline of how reading comprehension arises out of the efficient combination of lower-level and higher-level processes. The present chapter explores more general cognitive concepts that intersect with reading comprehension. These concepts help explain the effectiveness of various components of reading abilities as they work in combination. This chapter helps explain how fluency and automaticity emerge as incremental outcomes of the implicit learning system, why extensive exposure to print is a crucial goal for reading development (to provide continual implicit learning opportunities and incremental growth of reading abilities), and how reading, and learning from texts, is supported by attentional processing, inferencing, and background knowledge of many types. These concepts and underlying cognitive processing systems are not solely involved in reading-comprehension processing; rather, they are key to all aspects of learning (including all aspects of language learning). Each of the following concepts and cognitive systems is critical for understanding the central role of cognition in reading comprehension: Implicit and explicit learning Frequency, associative learning, co-occurrence, and emergence Attention, noticing, and consciousness Inferencing The role of context in L2 reading The role of background knowledge in L2 reading The importance of these cognitive concepts for reading cannot be overestimated. They constitute the foundations of learning theory for all cognitive and educational psychology. They provide the basis not only for how reading comprehension works, but also for how it develops.