Number Of Information Exchanges Research Articles

Combined formаtion of a cryptographic key using synchronized artificial neural networks

А combined method for forming a cryptographic key is proposed in the article. The proposed combined formation consists of two stages: the formation of partially coinciding binary sequences using synchronized artificial neural networks and the elimination of mismatched bits by open comparison of the parities of bit pairs. In this paper, possible vulnerabilities of the basic method of forming a cryptographic key using synchronized artificial neural networks are considered, their danger is assessed, and a correction of the method is proposed to ensure the required confidentiality of the generated shared secret. At the first stage, a deferred brute-force attack is considered. To neutralize this attack, it is proposed to use the convolution function of the results of several independent synchronizations. As a convolution function, the bitwise addition modulo 2 of the vectors of the weights of the networks is used. Due to the correction of the first stage of the basic algorithm, the amount of deferred search exponentially increases, and frequency analysis of binary sequences also becomes ineffective. At the second stage, an attack based on the knowledge of pair parities is considered, taking into account the proposed method for correcting the first stage. The analysis of the influence of network parameters on the process of eliminating the bit mismatch at the second stage is carried out. Statistical modeling of this analysis has been performed. The results obtained showed that the cryptanalyst could not uniquely distinguish the values of the remaining bits. The proposed combined method makes it possible to increase the confidentiality of the generated shared secret and significantly reduce the number of information exchanges in comparison with the Neural key generation technology.

An Improved Triggering Updating Method of Interest Message with Adaptive Threshold Determination for Directed Diffusion Routing Protocol

Although some progress has been made in studying the triggering updating methods of interest message for reducing node energy consumption in directed diffusion routing protocols, they do not consider the anisotropy of sensing areas of nodes and the requirement of diversity of interest message exchange rates within real‐time performance, which makes these studies unable to accurately adapt to the characteristics of interest message update. In this work, we proposed an improved triggering updating method (ITUM) consisted of adaptive threshold determination based on the analytic hierarchy process and update judgement by similarity comparison of interest messages. Meanwhile, the network model and the sensing model are presented to describe actual network scenarios. We analyze the impact of critical parameters and evaluate the performance of ITUM with several interest message updating methods from the aspect of lowering the number of information exchanges. The simulation results prove that ITUM can improve the adaptability to scene changes while decreasing the number of information exchanges compared with the existing methods. Furthermore, it is shown that ITUM is a highly effective solution for determining the triggering updating conditions of various information exchange rates in directed diffusion routing protocol.

Study on Consensus of the Forth-Order Discrete-Time Multiagent System in Directed Networks

In this paper, we are concerned with the consensus of the forth-order discrete-time multiagent system in directed networks. First, by defining a group of new variables, the multiagent system is converted to the corresponding reduced order system. Based on the stability of the reduced order system, we discuss the consensus of the original multiagent system. Then, two necessary and sufficient conditions for the consensus of the forth-order discrete-time multiagent system are established by using the related knowledge of algebraic graph theory and matrix theory. Compared with the existing results, the obtained conditions of this paper are more extensive. In order to reduce the number of information exchange between agents, we use event-triggered mechanism to deal with the consensus of the multiagent system. Furthermore, Zeno behavior of multiagent system will not make an appearance due to the reasonable design of the triggering function. A numerical example is finally given to demonstrate the effectiveness of theoretical results.

Quantitative Evaluation of Efficacy of Intraoperative Examination Monitor for Awake Surgery

When brain tumors are located near the language area, a test to assess language function is required. During the test, it is practical to display combined information obtained from all the equipment so that the surgeon can confirm the patient's response to the tasks. We developed the intraoperative examination monitor for awake surgery (IEMAS) mainly to combine all information so that the language function test could be performed efficiently. The IEMAS has proved to be useful in clinical settings; however, no quantitative evaluation has been performed. This study aimed to demonstrate the clinical usefulness of the IEMAS through comparison of cases withand without IEMAS use in language function test simulation. The language function test simulator was created to eliminate any uncertain factors, such as symptoms, which vary among patients. Neurosurgeons participated in the test, and the usefulness of the IEMAS was investigated. We analyzed test duration and number of information exchanges between surgeon and examiner. Total test duration with IEMAS use was significantly shorter than without IEMAS use (116.1 ± 23.1 seconds vs. 147.8 ± 48.7 seconds; P < 0.02). The number of information exchanges between surgeon and examiner was significantly lower with IEMAS use than without IEMAS use (0.2 ± 0.6 times vs. 16.1 ± 15.6 times; P < 0.02). We compared cases with and without IEMAS use. Total test duration decreased with IEMAS use, and number of information exchanges was reduced, thus demonstrating the usefulness of the IEMAS.

For whom will the Bayesian agents vote?

Within an agent-based model where moral classifications are socially learned, we ask if a population of agents behaves in a way that may be compared with conservative or liberal positions in the real political spectrum. We assume that agents first experience a formative period, in which they adjust their learning style acting as supervised Bayesian adaptive learners. The formative phase is followed by a period of social influence by reinforcement learning. By comparing data generated by the agents with data from a sample of 15000 Moral Foundation questionnaires we found the following. 1. The number of information exchanges in the formative phase correlates positively with statistics identifying liberals in the social influence phase. This is consistent with recent evidence that connects the dopamine receptor D4-7R gene, political orientation and early age social clique size. 2. The learning algorithms that result from the formative phase vary in the way they treat novelty and corroborative information with more conservative-like agents treating it more equally than liberal-like agents. This is consistent with the correlation between political affiliation and the Openness personality trait reported in the literature. 3. Under the increase of a model parameter interpreted as an external pressure, the statistics of liberal agents resemble more those of conservative agents, consistent with reports on the consequences of external threats on measures of conservatism. We also show that in the social influence phase liberal-like agents readapt much faster than conservative-like agents when subjected to changes on the relevant set of moral issues. This suggests a verifiable dynamical criterium for attaching liberal or conservative labels to groups.

A STUDY OF THE IMPACT OF ACTIVITY OVERLAPPING ON DEVELOPMENT LEAD-TIME IN DESIGN CHAIN

ABSTRACT Outsourcing more of the design and production than before, manufacturers increasingly take on the role of systems integrators and collaborate with “design chain” partners to bring a new product to market. The purpose of this paper is to provide a preliminary study that applies the concept of activity overlapping to accelerate design chain activities in order to improve the time to market. An activity overlapping model adapted from the literature is used to determine the optimal overlapping time and the number of information exchanges required to minimize the development lead-time between two dependent activities. Two parametric functions are proposed to model information evolution and iteration sensitivity for upstream and downstream activities. The impacts of activity overlapping on development lead-time can be thoroughly analyzed for different combinations of evolutions and sensitivities. Several strategies for managing design chain to improve the time to market are also discussed.

The impact of graphic contextual information and instruction on the conversational behaviours of a person with severe aphasia

Backgrounds: Individuals with severe expressive aphasia often have difficulty sharing adequate amounts of specific information to sustain topical conversations. Aims: This single subject experiment investigated whether graphic representations of topics increased conversational duration, number of information exchanges, proportion of participant initiations, and percentage of successful communication exchanges during dyadic conversations involving a communicator with severe, nonfluent aphasia (SD). Methods & Procedures: SD conversed with two partners about personal and current events in “no treatment” and “graphic topic-setter” conditions. He also participated in an additional “instruction” condition with Partner 2. Conversations were videotaped, transcribed, and coded for the dependent variables. Results were averaged by condition, graphed, and analysed for statistical significance using randomisation testing. Outcomes & Results: Despite variability in conversational parameters across sessions, mean data from each condition revealed that graphic topic setters increased the average duration of interactions and mean number of communication exchanges per topic across both partners. Proportion of initiations increased significantly with graphic context for Partner 1 but not Partner 2; this effect was more pronounced for current event topics than personal events. Information transmission was significantly more successful when graphic context was present with both partners, but differences were more noticeable with Partner 1. Conclusions: Participants appeared better able to co-construct conversations when graphic topic setters were available to supplement the natural communication signals of a communicator with severe aphasia. Clinical implementation issues are discussed.

Knowledge and information communication in organizations: an analysis of core, support and improvement processes

This paper analyzes how knowledge and information are communicated in different types of business processes. Twenty-two business process instances obtained from three organizations were studied and these were split into three types: core, support and improvement processes. The study led to three main research findings. The first was that when core, support and improvement processes were considered separately, the proportion of knowledge exchanges was considerably higher in improvement processes than in core and support processes. The second was that when all processes were considered together, most of the knowledge exchanges happened in improvement processes. The third was that the number of information exchanges in the processes was strongly correlated to the number of process functions and to the number of knowledge exchanges. Implications are drawn regarding organizational cognition and competitiveness. © 1998 John Wiley & Sons Ltd and Cornwallis Emmanuel Ltd.