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This paper introduces a systematic classification of the facial nerve grading system using a comprehensive methodology using a pioneering Multi-Path Heterogeneous Neural Network (MPHNN) method designed for the accurate classification of exercise. It integrates four distinct Convolutional Neural Networks (CNNs) and Custom Feedforward Neural Networks (CFNNs) to enhance the precision of the classification. The CNNs are specifically tailored to scrutinize changes in the coordinates of facial landmarks over time, enabling the capture of both spatial information and temporal patterns in facial expressions during exercise. The CFNNs incorporate patient-specific variables and exercise statistics, including factors such as their surgical history, the type of exercise, its duration, and synthetic features like cumulative movement for each landmark. By leveraging this comprehensive framework, the proposed method offers a nuanced representation of the patient's exercise performance, thereby facilitating more precise outcomes of a classification. • Multi-Path Heterogeneous Neural Network method designed for accurate classification of exercises. • Convolutional Neural Networks and Custom Feedforward Neural Networks to enhance classification precision. • Facilitates more precise classification outcomes. [ABSTRACT FROM AUTHOR]

This paper explores the problem of processing the immense volume of measurement data arising during network traffic monitoring. Due to the everincreasing demands of current networks, observing accurate information about every single flow is virtually infeasible. In many cases the existing methods for the reduction of flow records are still not sufficient enough. Since the accurate knowledge of flows termed as "heavy-hitters" suffices to fulfill most of the monitoring purposes, we decided to aggregate the flow records pertaining to non-heavy-hitters. However, due to the ever-changing nature of traffic, their identification is a challenge. To overcome this challenge, our proposed approach - the adaptive aggregation of flow records - automatically adjusts its operation to the actual traffic load and to the monitoring requirements. Preliminary experiments in existing network topologies showed that adaptive aggregation efficiently reduces the number of flow records, while a significant proportion of traffic details is preserved. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is the underlying platform for the application of dynamic spectrum access (DSA) networks. Although the auction theory and spectrum trading mechanisms have been discussed in the CR related works, their joint techno-economic impact on the effciency of distributed CR networks has not been researched yet. In this paper we assume heterogeneous primary channels with network availability statistics unknown to each secondary user (SU) terminal. In order to detect the idle primary user (PU) network channels, the SU terminals trigger regularly the spectrum sensing mechanism and make the cooperative decision regarding the channel status at the fusion center. The imperfections of the spectrum mechanism create the possibility of the channel collision, resulting in the existence of the risk (in terms of user collision) in the network. The spectrum trading within SU network is governed by the application of the sealed-bid rst-price auction, which takes into account the channel valuation as well as the statistical probability of the risk existence. In order to maximize the long-term payoff, the SU terminals take an advantage of the reinforcement comparison strategy. The results demonstrate that in the investigated model, total revenue and total payoff of the SU operator (auctioneer) and SU terminals (bidders) are characterized by the existence of the global optimum, thus there exists the optimal sensing time guaranteeing the optimum economic factors for both SU operator and SU terminals. [ABSTRACT FROM AUTHOR]

In the last decade, the processing of the high dimensional data became inevitable task in many areas of research and daily life. Feature selection (FS), as part of the data processing methodology, is an important step in knowledge discovery. This paper proposes nine variation of two-step feature selection approach with filter FS employed in the first step and exhaustive search in the second step. The performance of the proposed methods is comparatively analysed from the stability and predictive performance point of view. As the obtained results indicate the choice of the filter FS in the first stage has strong inuence on the resulting stability. Here, the choice of univariate Pearson correlation coefficient based FS method appears to provide the most stable results. [ABSTRACT FROM AUTHOR]

Computer system security is the fastest developing segment in information technology. The conventional approach to system security is mostly aimed at protecting the system, while current trends are focusing on more aggressive forms of protection against potential attackers and intruders. One of the forms of protection is also the application of advanced technology based on the principle of baits {honeypots. Honeypots are specialized devices aimed at slowing down or diverting the attention of attackers from the critical system resources to allow future examination of the methods and tools used by the attackers. Currently, most honeypots are being configured and managed statically. This paper deals with the design of a sophisticated hybrid honeypot and its properties having in mind enhancing computer system security. The architecture of a sophisticated hybrid honeypot is represented by a single device capable of adapting to a constantly changing environment by using active and passive scanning techniques, which mitigate the disadvantages of low-interaction and high-interaction honeypots. The low-interaction honeypot serves as a proxy for multiple IP addresses and filters out traffic beyond concern, while the high-interaction honeypot provides an optimum level of interaction. The proposed architecture employing the prototype of a hybrid honeypot featuring autonomous operation should represent a security mechanism minimizing the disadvantages of intrusion detection systems and can be used as a solution to increase the security of a distributed computer system rapidly, both autonomously and in real-time. [ABSTRACT FROM AUTHOR]