Multiagent systems have become popular over the last few years for building complex, adaptive systems in a distributed, heterogeneous setting. Multiagent systems tend to be more robust and, in many cases, more efficient than single monolithic applications. However, unpredictable application environments make multiagent systems susceptible to individual failures that can significantly reduce its ability to accomplish its overall goal. The problem is that multiagent systems are typically designed to work within a limited set of configurations. Even when the system possesses the resources and computational power to accomplish its goal, it may be constrained by its own structure and knowledge of its member's capabilities. To overcome these problems, we are developing a framework that allows the system to design its own organization at runtime. This paper presents a key component of that framework, a metamodel for multiagent organizations named the Organization Model for Adaptive Computational Systems. This model defines the requisite knowledge of a system's organizational structure and capabilities that will allow it to reorganize at runtime and enable it to achieve its goals effectively in the face of a changing environment and its agent's capabilities.Keywords: adaptation, organizations, metamodel, self-organization IntroductionSystems are becoming more complex, in part due to increased customer requirements and the expectation that applications should be seamlessly integrated with other existing, often distributed applications and systems. In addition, there is an increasing demand for these complex systems to exhibit some type of intelligence as well. No longer is it "good enough" to be able to access systems across the internet, but customers require that their systems know how to access data and systems, even in the face of unexpected events or failures.The goal of our research is to develop a framework for constructing complex, distributed systems that can autonomously adapt to their environment. Multiagent systems have become popular over the last few years for providing the basic notions that are applicable to this problem. A multiagent Scott A. DeLoach, Walamitien Oyenan & Eric T. Matson. A Capabilities Based Model for Artificial Organizations. Journal of Autonomous Agents and Multiagent Systems. Volume 16, no. 1, February 2008, pp. 13-56. DOI: 10.1007 (note: this text is identifiable to the journal, however, the format is notThe original publication is available at www. springerlink.com.) system uses groups of self-directed agents working together to achieve a common goal. Such multiagent systems are widely proposed as replacements for sophisticated, complex, and expensive stand-alone systems for similar applications. Multiagent systems tend to be more robust and, in many cases, more efficient (due to their ability to perform parallel actions) than single monolithic applications. In addition, the individual agents tend to be simpler to build, as they are built from a single agent's perspective...

With the increasing number of drones, the danger of their illegal use has become relevant. This has necessitated the creation of automatic drone protection systems. One of the important tasks solved by these systems is the reliable detection of drones near guarded objects. This problem can be solved using various methods. From the point of view of the price–quality ratio, the use of video cameras for a drone detection is of great interest. However, drone detection using visual information is hampered by the large similarity of drones to other objects, such as birds or airplanes. In addition, drones can reach very high speeds, so detection should be done in real time. This paper addresses the problem of real-time drone detection with high accuracy. We divided the drone detection task into two separate tasks: the detection of moving objects and the classification of the detected object into drone, bird, and background. The moving object detection is based on background subtraction, while classification is performed using a convolutional neural network (CNN). The experimental results showed that the proposed approach can achieve an accuracy comparable to existing approaches at high processing speed. We also concluded that the main limitation of our detector is the dependence of its performance on the presence of a moving background.

This paper introduced a near real-time acoustic unmanned aerial vehicle detection system with multiple listening nodes using machine learning models. An audio dataset was

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