AUTOMATED DEPLOYMENT OF A SOFTWARE ENVIRONMENT FOR MICROSERVICES IN A RAPIDLY CHANGING TECHNOLOGY STACK

The article is devoted to the development and substantiation of practical recommendations regarding the formation of a mechanism for deploying a software environment for creating and executing microservices in a rapidly changing technological stack. The subject of the research is the basics of building a system for automated deployment of a software environment for the development and execution of microservices. The purpose of the article is to develop and substantiate practical recommendations for the formation of a mechanism for deploying a software environment for creating and executing microservices in a rapidly changing technological stack. The task of the work: to determine the necessary elements of the deployment mechanism of the software environment and provide an analysis of the functional load for each of them, set specific tasks that must be solved when building each of them, propose and justify the choice of tools for their solution. In the course of the study, the methods of system analysis were used to decompose a complex system into elements and each element into functional components. As of the study, it was established that such a mechanism should consist of the following elements: a universal server initialization a result subsystem for any technological stack and a software environment deployment subsystem for developing or executing an application of a certain type on a certain technological stack. Each element is described in detail, its functional load is shown and its role in the overall system is substantiated. It is shown that such a standardized approach to the deployment of the development and runtime environment allows, among other things, to solve the problem of operating microservices in a tested environment. Conclusions. Practical recommendations for the formation of a mechanism for deploying a software environment for creating and executing microservices in a rapidly changing technological stack have been developed and substantiated. This mechanism is automated. It shows its flexibility and versatility in relation to programming languages and other features of the software environment. It is pointed out that when implemented in the shell language, bash does not need any third-party applications for its work. The economic benefit of using the proposed mechanism is shown. The ways of its improvement are shown.

A Review on BCI Emotions Classification for EEG Signals Using Deep Learning

Emotion awareness is one of the most important subjects in the field of affective computing. Using nonverbal behavioral methods such as recognition of facial expression, verbal behavioral method, recognition of speech emotion, or physiological signals-based methods such as recognition of emotions based on electroencephalogram (EEG) can predict human emotion. However, it is notable that data obtained from either nonverbal or verbal behaviors are indirect emotional signals suggesting brain activity. Unlike the nonverbal or verbal actions, EEG signals are reported directly from the human brain cortex and thus may be more effective in representing the inner emotional states of the brain. Consequently, when used to measure human emotion, the use of EEG data can be more accurate than data on behavior. For this reason, the identification of human emotion from EEG signals has become a very important research subject in current emotional brain-computer interfaces (BCIs) aimed at inferring human emotional states based on the EEG signals recorded. In this paper, a hybrid deep learning approach has proposed using CNN and a long short-term memory (LSTM) algorithm is investigated for the purpose of automatic classification of epileptic disease from EEG signals. The signals have been processed by CNN for feature extraction from runtime environment while LSTM has used for classification of entire data. Finally, system demonstrates each EEG data file as normal or epileptic disease. In this research to describes a state of art for effective epileptic disease detection prediction and classification using hybrid deep learning algorithms. This research demonstrates a collaboration of CNN and LSTM for entire classification of EEG signals in numerous existing systems.

Application of Pre-Trained Deep Learning Models for Clinical ECGs

Automatic electrocardiogram (ECG) analysis has been one of the very early use cases for computer assisted diagnosis (CAD). Most ECG devices provide some level of automatic ECG analysis. In the recent years, Deep Learning (DL) is increasingly used for this task, with the first models that claim to perform better than human physicians. In this manuscript, a pilot study is conducted to evaluate the added value of such a DL model to existing built-in analysis with respect to clinical relevance. 29 12-lead ECGs have been analyzed with a published DL model and results are compared to build-in analysis and clinical diagnosis. We could not reproduce the results of the test data exactly, presumably due to a different runtime environment. However, the errors were in the order of rounding errors and did not affect the final classification. The excellent performance in detection of left bundle branch block and atrial fibrillation that was reported in the publication could be reproduced. The DL method and the built-in method performed similarly good for the chosen cases regarding clinical relevance. While benefit of the DL method for research can be attested and usage in training can be envisioned, evaluation of added value in clinical practice would require a more comprehensive study with further and more complex cases.

FastAER Det: Fast Aerial Embedded Real-Time Detection

Automated detection of objects in aerial imagery is the basis for many applications, such as search and rescue operations, activity monitoring or mapping. However, in many cases it is beneficial to employ a detector on-board of the aerial platform in order to avoid latencies, make basic decisions within the platform and save transmission bandwidth. In this work, we address the task of designing such an on-board aerial object detector, which meets certain requirements in accuracy, inference speed and power consumption. For this, we first outline a generally applicable design process for such on-board methods and then follow this process to develop our own set of models for the task. Specifically, we first optimize a baseline model with regards to accuracy while not increasing runtime. We then propose a fast detection head to significantly improve runtime at little cost in accuracy. Finally, we discuss several aspects to consider during deployment and in the runtime environment. Our resulting four models that operate at 15, 30, 60 and 90 FPS on an embedded Jetson AGX device are published for future benchmarking and comparison by the community.

Deep Understanding of Runtime Configuration Intention

The runtime environment and workload of software are constantly changing, requiring users to make appropriate adjustments to accommodate these changes. The runtime configuration, however, as the interface for users to manipulate software behavior often requires domain-specific knowledge to understand. This usually results in users spending a considerable amount of time wading through document and user manuals trying to understand the runtime configuration. In this paper, we study the possibility of understanding the intention of runtime configuration options through their documents, even sometimes it is difficult for users to understand. Based on these studies, we classify the runtime configuration option’s intention into six categories. Accordingly, we design runtime Configuration Intention Classifier (CIC), a supervised approach based on CNN to classify the runtime configuration option’s intention according to its document. CIC integrates the features of runtime configuration names and descriptions according to different levels of granularity and predicts the intention of runtime configuration options accordingly. Extensive experiments show that our approach can achieve an accuracy of 85.6% and outperform nine comparative approaches by up to 16.6% over the dataset we customized.

Concepts and Models of Environment in Self-Adaptive Systems: A Systematic Literature Review

The runtime environment is an important concern for self-adaptive systems (SASs). Although researchers have proposed many approaches for developing SASs that address the issue of uncertain runtime environments, the understanding of these environments varies depending on the objectives, perspectives, and assumptions of the research. Thus, the current understanding of the environment in SAS development is ambiguous and abstract. To make this understanding more concrete, we describe the landscape in this area through a systematic literature review (SLR). We examined 128 primary studies and 14 unique environment models. We investigated concepts of the environment depicted in the primary studies and the proposed environment models based on their ability to aid in understanding. This illustrates the characteristics of the SAS environment, the associated emerging environmental uncertainties, and what is expressed in the existing environment models. This paper makes explicit the implicit understanding about the environment made by the SAS research community and organizes and visualizes them.

Novel, User-friendly Experimental and Analysis Strategies for Fast Voltammetry: 1. The Analysis Kid for FSCV

Fast-scan cyclic voltammetry at carbon fiber microelectrodes measures low concentrations of analytes in biological systems. There are ongoing efforts to simplify FSCV analysis and several custom platforms are available for filtering and multi-modal analysis of FSCV signals but there is no single, easily accessible platform that has capacity for all these features. Here we present The Analysis Kid: a free, open-source cloud application that does not require a specialized runtime environment and is easily accessible via common browsers. We show that a user-friendly interface can analyze multi-platform file formats to provide multimodal visualization of FSCV color plots with digital background subtraction. We highlight key features that allow interactive calibration and parametric analysis via peak finding algorithms to automatically detect the maximum amplitude, area under the curve and clearance rate of the signal. Finally, The Analysis Kid enables semi-automatic fitting of data with Michaelis Menten kinetics with single or dual reuptake models. The Analysis Kid can be freely accessed at https://analysis-kid.herokuapp.com/. The web application code is found, under an MIT license, at https://github.com/sermeor/The-Analysis-Kid.

New cloud-based tool to Dynamically Manage Urban Resilience: the Fresnel Platform for Greater Paris

<p>As cities are put under greater pressure from the threat of impacts of climate change, in particular the risk of heavier rainfall and flooding, there is a growing need to establish a hierarchical form of resilience in which critical infrastructures can become sustainable. The main difficulty is that geophysics and urban dynamics are strongly nonlinear with an associated, extreme variability over a wide range of space-time scales.</p><p>The polarimetric X-band radar at the ENPC’s campus (East of Paris) introduced a paradigm change in the prospects of environmental monitoring in Ile-de France. The radar is operated since May 2015 and has several characteristics that makes it of central importance for the environmental monitoring of the region.</p><p>Based on the radar data and other scientific mesurement tools, the platform for greater Paris was developped in participative co-creation, and in scientific collaboration with the world leader industrial in water management. As the need for data accessibility, a fast and reliable infrastructure were major requirements from the scientific community, the platform was build as a cloud-based solution. It provides scientific weather specialists, as well as water manager,  a fast and steady platform accessible from their web browser on desktop and mobile displays.</p><p>It was developped using free and open sources librairies, it is rooted on an integrated suite of modular components based on an asynchronous event-driven JavaScript runtime environment. It includes a comprehensive and (real-time) accessible database and also provides tools to analyse historical data on different time and geographic scales around the greater Paris.</p><p>The Fresnel SaaS (Sofware as a Service) cloud-based platform is an example of nowadays IT tools to dynamically enhance urban resilience. Developments are still in progress, in constant request and feedback loops from the scientific and professional world.</p>