Causal motifs and existence of endogenous cascades in directed networks with application to company defaults

Motivated by the problem of detection of cascades of defaults in economy, we developed a detection framework for an endogenous spreading based on causal motifs we define in this paper. We assume that the change of state of a vertex can be triggered either by an endogenous (related to the network) or an exogenous (unrelated to the network) event, that the underlying network is directed and that times when vertices changed their states are available. After simulating default cascades driven by different stochastic processes on different synthetic networks, we show that some of the smallest causal motifs can robustly detect endogenous spreading events. Finally, we apply the method to the data of defaults of Croatian companies and observe the time window in which an endogenous cascade was likely happening.

Stitching together: Ethical dimensions and innovative approaches to participatory textile making

This article, written by the coordinators of the Stitching Together network, introduces a diverse range of case studies that critically discuss participatory textile making activities, complementing a first collection of case studies that was provided in the previous volume of this journal. Drawing on a recent network event and the case studies included in this issue, the article outlines a number of ethical dimensions that arise in participatory textile making activities: first, the challenge of inclusivity; second, the vulnerabilities that arise when space is made for shared learning; third, the issue of communication between facilitators, participants and partners in collaborative projects; and fourth, the ways in which projects and participants are (re)presented in research findings. The theme of innovation is also discussed, with a focus on the participant experience. Looking to the future, the need for further collaborative interrogation of the complex questions raised through participatory textile work is highlighted. A good practice document, created with the input of network members, is highlighted as a potentially useful foundation for continued critical discussion.

Architectural Features of Neural Network Forecasting Software Systems with Continuous Training

In recent years, a method of neural network event forecasting has been developed, based on the use of a pair of recurrent neural networks with controlled elements. This method allows you to make predictions without interrupting training. However, for its full use, a reasonable software implementation is necessary. This study considers the problem of searching for a software architecture that implements the method of neural network forecasting with continuous learning. Offers an improved prediction method that significantly reduces the required amount of memory. A procedure for accelerated calculation of the weights of neural network synapses has been developed. To assess the effectiveness of the proposed architectural solutions, a comparative analysis of various variants of software implementations was conducted. In systems developed with the proposed innovations, the requirements for memory and computing resources are much lower than in software implementations of the prototype method. For example, the amount of memory required has decreased by an average of 15 times, and system initialization has taken 16 times less time. At the same time, the strategy of maximum memory saving in such systems proved to be unproductive compared to the combined approach. Based on the obtained comparison results, recommendations are given for the use and choice of architectures depending on the specific tasks facing the end user, and the hardware and software environment in which the forecasting systems are supposed to operate.

Neural network event forecasting for robots with continuous training

Introduction: High hopes for a significant expansion of human capabilities in various fields of activity are pinned on the creation and use of highly intelligent robots. To achieve this level of robot intelligence, it is necessary to successfully solve the problems of predicting the external environment and the state of the robots themselves. Solutions based on recurrent neural networks with controlled elements are promising neural network forecasting systems. Purpose: Search for appropriate neural network structures for predicting events. Development of approaches to controlling the associative call of information from a neural network memory. Methods: Computer simulation of recurrent neural networks with controlled elements and various structures of layers. Results: An improved method of neural network event forecasting with continuous robot training has been developed. This method allows you to predict events on either long or short samples of time series. In order to improve the forecasting accuracy, new rules have been proposed for controlling the associative call of information from the neural network memory. A software system has been developed which implements the proposed method and supports the emulation of neural networks with various layer structures. The possibilities of recurrent neural networks with linear or spiral layer structures are analyzed using the example of urban traffic flow forecasting. The gain of the proposed method in comparison with the ARIMA model for the MAPE indicator is from 4.1 to 7.4%. Among the studied neural network structures, the spiral structures have shown the highest accuracy, and linear structures have shown the lowest accuracy. Practical relevance: The results of the study can be used to improve the accuracy of event forecasting for intelligent robots.

Redundancy Management and Host Intrusion Detection in WSN

Intrusion detection system (IDS) is a software application that gives the facility to monitor the traffic of network, event, or activities on networks and finds if any malicious operation occurs. Hackers use different types of attacks to capture the information and use brute force attacks to match the authenticated key with the key, which the hacker has in its stable. When there is a match, the hacker gets the authenticated key through which he can connect with the hotspot or AP. IDS finds invalid or any other misbehavior in the system. The protocol will take care of it; protocol checks the MAC address of the device which wishes to connect with the hotspot or AP, and if any device repeatedly enters a wrong password, the protocol will gives a pop up on the administrator system. The objective of this chapter is to provide information about the protocol that behaves like IDS and is pre-implemented in the routers, which gives the alert to the administrator if any intruder tries to connect with the hotspot or AP (access point) with the rapid wrong key.