Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
Not a member yet
1506 research outputs found
Sort by
Empirical Analysis on OpenAPI Topic Exploration and Discovery to Support the Developer Community
Get PDFOpenAPI has become a dominant standard for documentation in the service-oriented software industry. OpenAPI is used in many analysis and reengineering approaches for RESTful service and microservice-based systems. An OpenAPI document has several components that are usually filled by humans using natural language (e.g. description of a certain functionality). Thus, subjectivity may lead to inconsistencies and ambiguities. Understanding what an API does is a challenging question. As a consequence, this issue could hinder developers from identifying the functionality of APIs, after reading all its components. Along this line, we argue that developers will be provided with supportive tools to find those APIs that better suit their needs. In this paper, we propose a step towards creating these kinds of tools by empirically analyzing a set of 2 000 OpenAPI documents with the goal of extracting the main topics of an API using three topic modeling algorithms. To address this issue, we focus on three tasks: i) determine which component of an OpenAPI document provides the most meaningful information, ii) compare three state-of-the-art topic modeling algorithms, and iii) determine the optimal number of topics to represent an API. Our findings show that the best results could be obtained from the Description component by using the Non-negative Matrix Factorization (NMF) or Latent Semantic Indexing (LSI) algorithms. To help developers find services in the OpenAPI directory, we also propose a prototype tool to explore the OpenAPI documents and analyze extracted topics to assess if the APIs meet developers needs
GTA-IDS: Game Theoretic Approach to Enhance IDS Detection in Cloud Environment
Get PDFThe Internet of Things (IoT) industry is growing with the high-quality collaboration with Cloud Computing. The data generated by the IoT devices is quite large which can be efficiently stored and processed by the cloud. Further, the scenario like COVID-19 led to an unexpected flood of IoT devices on enabling networks to facilitate online services, which increases the potential threats to the companies fighting to remain operational during the crises. Still, the problem with the IoT devices is their weak security implications because vendors prioritize other factors like energy-saving and efficiency at the cost of security. The Attacker can send malicious requests through the vulnerable IoT device to the network and exploit the cloud in various ways. So, to address this issue, a Game Theoretic Approach to enhance IDS detection (GTA-IDS) in Cloud Environment has been devised that helps the Defender system to be more efficient, accurate in decision-making and save energy. The algorithm based on relative information entropy has been developed to defend against such attacks. The Bayesian Nash Equilibrium (BNE) has been used to make the Defender's strategies and perform actions to maximize its payoffs. The model has been tested on the NSL-KDD dataset and the results have been compared to the existing techniques. The results show that despite efforts made by the Attacker, the Defender always gets a better gain and ultimately eliminates the attack.
Deep Learning Based Real-Time Facial Mask Detection and Crowd Monitoring
Get PDFDuring the Covid pandemic, the importance of wearing mask has been noted globally. Additionally, crowded human clusters facilitated the transmission of the virus, which brings up the need for new systems for monitoring such situations. To address such issues, this research proposes an object recognition visual system based on deep learning to monitor the mask-wearing in a certain space and the control of the number of people indoors as an important tool during an epidemic. This research mainly investigates two types of identification. The first is to monitor whether people entering the site wear a mask at the entrance and exit of the field, and the second is to count the number of people entering a specific area. Experimental results show that by utilising the visual sensor, it is possible to detect and identify the people who frequently enter and exit in real-time. An advanced transfer learning approach has been employed to achieve the best discrimination performance. The actual training results prove that the migration learning Mask R-CNN algorithm produced by this method and the original Mask R-CNN algorithm have increased the mAP by 3 %, reaching a mAP of 96 %. In addition, the accuracy of the random sampling and identification in actual scenes has reached 92.1 %. The developed deep learning vision system has an enhanced identification ability for the verification and analysis of actual scenes and has a great application potential
Intelligent Annotation Algorithm Based on Deep-Sea Macrobenthic Images
Get PDFIn the field of image processing, due to the need of expertise and skills in deep-sea biology and the disadvantages of high labor cost and long time consuming, it has always been a difficult task to mark the images of deep-sea benthic organisms. To solve this problem, this paper proposes a new image intelligent labeling algorithm LACP AL (Localization-Aware-Choice and Pseudo Label Active Learning) which is based on Localization-Aware Active Learning. LACP AL is an active learning framework based on Faster R-CNN, it finds the "valuable" samples from unlabeled samples by clustering algorithm for every training; it selects hard-to-identify samples for manual annotation and further optimizes the model; and it proposes an improved pseudo-labeling mechanism to expand the training set and improve the model accuracy. According to the publicly available dataset provided by 2020 China Underwater Robot Professional Contest, a series of experiments has been done to verify that our algorithm can achieve higher recognition accuracy with fewer training samples compared with the existing algorithms for Marine benthic image recognition
Regression Analysis and Modeling of Local Environmental Pollution Levels for the Electric Power Industry Needs
Get PDFReliability, longevity, and maintenance costs of electric power industry installations and equipment depend strongly on the extent to which their design reflects relevant environmental factors, such as expected levels of local environmental pollution. These factors guide the choice of specific types of components – insulators, towers, conductors, etc. – and are often estimated through complex and tedious long-term field measurements of pollution deposits. In Slovakia, such field measurements were mandated by the national standard STN 33 0405. This standard was retired in 2015 without replacement, which opened the way for developing alternative and less cumbersome methods. One such alternative is to apply artificial intelligence techniques to atmospheric pollution and other relevant data, which is already routinely monitored and collected in many countries. In this paper, we explore the strength of the relationships between the field measurements performed in various regions of Slovakia according to STN 33 0405 and atmospheric pollution data monitored and collected by the Slovak Hydrometeorological Institute (SHMÚ). The paper is focused on input attributes significance, in relation to output attributes. It represents the first phase of our long-term research aiming at the creation of reliable regression models of local pollution in order to replace the cumbersome field measurements mandated by STN 33 0405
Business Process Analysis and Simulation: An Industrial Application
Get PDFAnalysis and automation of business processes are a relevant topic in Industry 4.0. This document describes a framework called BP-M* for the analysis, restructuring and implementation of business processes, starting with the creation of a process model and ending with the implementation of the process itself on a workflow management system. The BP-M* framework has been applied to a real case study, the production of fabrics for the collection that will be distributed worldwide by an Italian woolen mill. This process was analyzed and automated, providing the company with useful information to simplify processes and support human operators
New Cyber Physical System Architecture for the Management of Driving Behavior Within the Context of Connected Vehicles
Get PDFIn this paper, we address the problem of managing driving behaviours within the context of Connected Vehicles (CVs). In contrast with the existing related solutions, we are proposing a Cyber Physical System (CPS) architecture that ultimately enables the continuous acquisition and processing of driving data and then the assessment and classification of driving performance according to a welldefined set of driving states. The transitions between these states are decided based on current and previous driving records. In addition to their use for the generation of the appropriate feedback to the driver, the driving states could be used to identify relevant data to be shared with the CVs in the vicinity. They could also be used to recommend personalized trainings to the driver based on his/her driving performance
Towards a Network of Dynamic Message Signs for Congestion Alerting
Get PDFTraffic applications such as Google Traffic and Waze have been introduced to let users know about existing congestions on real time. However, this cannot help drivers who are not using these applications or not connected to Internet. Besides, it also suggests that drivers can interact with their smart phones while driving, which is illegal in most countries. The idea of this paper is to use dynamic road signs which can collect real-time data from traffic applications and alert drivers who are heading towards congestions. A proof-of-concept of the dynamic road sign has been developed
New Hybrid Data Preprocessing Technique for Highly Imbalanced Dataset
Get PDFOne of the most challenging problems in the real-world dataset is the rising numbers of imbalanced data. The fact that the ratio of the majorities is higher than the minorities will lead to misleading results as conventional machine learning algorithms were designed on the assumption of equal class distribution. The purpose of this study is to build a hybrid data preprocessing approach to deal with the class imbalance issue by applying resampling approaches and CSL for fraud detection using a real-world dataset. The proposed hybrid approach consists of two steps in which the first step is to compare several resampling approaches to find the optimum technique with the highest performance in the validation set. While the second method used CSL with optimal weight ratio on the resampled data from the first step. The hybrid technique was found to have a positive impact of 0.987, 0.974, 0.847, 0.853 F2-measure for RF, DT, XGBOOST and LGBM, respectively. Additionally, relative to the conventional methods, it obtained the highest performance for prediction
PSO-CALBA: Particle Swarm Optimization Based Content-Aware Load Balancing Algorithm in Cloud Computing Environment
Get PDFCloud computing provides hosted services (i.e., servers, storage, bandwidth, and software) over the internet. The key benefits of cloud computing are scalability, efficiency, and cost reduction. The key challenge in cloud computing is the even distribution of workload across numerous heterogeneous servers. Several Cloud scheduling and load-balancing techniques have been proposed in the literature. These techniques include heuristic-based, meta-heuristics-based, and hybrid algorithms. However, most of the current cloud scheduling and load balancing schemes are not content-aware (i.e., they are not considering the content-type of user tasks). The literature studies show that the content type of tasks can significantly improve the balanced distribution of workload. In this paper, a novel hybrid approach named Particle Swarm Optimization based Content-Aware Load Balancing Algorithm (PSO-CALBA) is proposed. PSO-CALBA scheduling scheme combines machine learning and meta-heuristic algorithm that performs classification utilizing file content type. The SVM classifier is used to classify users' tasks into different content types like video, audio, image, and text. Particle Swarm Optimization (PSO) based meta-heuristic algorithm is used to map user's tasks on Cloud. The proposed approach has been implemented and evaluated using a renowned Cloudsim simulation kit and compared with ACOFTF and DFTF. The proposed study shows significant improvement in terms of makespan, degree of imbalance (DI)