Rapid Application Deployment Research Articles

Automated Diagnosis of Knee Osteoarthritis Using ResNet101 on a DEEP:PHI: Leveraging a No-Code AI Platform for Efficient and Accurate Medical Image Analysis.

Background: Knee osteoarthritis (OA) is a prevalent degenerative joint disease significantly impacting global health. Early and accurate diagnosis is crucial for effective management, but traditional methods often rely on subjective assessments. This study evaluates the efficacy of a deep learning model implemented through a no-code AI platform for diagnosing and grading knee OA from plain radiographs. Methods: We utilized the Osteoarthritis Initiative (OAI) dataset, comprising knee X-ray data from 1526 patients. The data were split into training (47.0%), validation (26.5%), and test (26.5%) sets. We employed a ResNet101 model on the DEEP:PHI no-code AI platform for image analysis. The model was trained to classify knee OA into five grades (0-4) based on the Kellgren-Lawrence scale. Results: Our AI model demonstrated high accuracy in distinguishing between different OA grades, with particular strength in early-stage detection. The model achieved optimal performance at 20 epochs, suggesting efficient learning dynamics. Grad-CAM visualizations were used to enhance the interpretability of the model's decision-making process. Conclusions: This study demonstrates the potential of AI, implemented through a no-code platform, to accurately diagnose and grade knee OA from radiographs. The use of a no-code AI platform such as DEEP:PHI represents a step towards democratizing AI in healthcare, enabling the rapid development and deployment of sophisticated medical AI applications without extensive coding expertise. This approach could significantly enhance the early detection and management of knee OA, potentially improving patient outcomes and streamlining clinical workflows.

ПРАКТИЧНІ РЕКОМЕНДАЦІЇ ЩОДО ВИКОРИСТАННЯ GENERATIVE AI В БІЗНЕСІ

Generative Artificial Intelligence (GenAI) is transforming the economic landscape by introducing innovative solutions that significantly enhance business efficiency, drive innovation, and create competitive advantages. This article delves into the economic implications and practical applications of GenAI across various business domains, including marketing, customer support, product design, and data analysis. By leveraging advanced AI models, companies can automate routine tasks, generate personalized content, and optimize operations, leading to substantial economic benefits. The implementation of GenAI necessitates a systematic approach, starting with business concept validation and progressing through stages of technical solution identification, proof of technology, and project planning. These stages ensure that AI solutions are economically viable, effective, and aligned with business objectives. Businesses can adopt different strategies to integrate GenAI, ranging from the rapid deployment of third-party applications to the development of customized in-house models. Each approach offers unique economic benefits, balancing customization and control with implementation time and value. This article provides practical recommendations for strategy of implementing Generative AI, emphasizing the importance of careful economic analysis, stakeholder engagement, and continuous improvement. By adopting a structured approach and selecting the appropriate integration strategy, businesses can harness the transformative power of GenAI. This enables them to innovate and thrive in an increasingly competitive economic environment, positioning themselves as leaders in the digital age.

Quality control in manufacturing – review and challenges on robotic applications

ABSTRACT Quality control methods and techniques have been investigated in different fields of manufacturing during the last decades. The introduction of robots in manufacturing processes has created a rapid deployment of robotic applications, leading to an increased research interest in the aspect of quality control in such industrial environments. This paper summarizes and presents a review of recent research and progress on quality control technologies, focusing on robot-based production systems. The role of different parameters affecting the control of quality in robotic tasks is also discussed, incorporating the impact of operator support systems in human robot collaborative environments. Research gaps and implications of quality control in robotic applications are also described, and a future outlook of research in the particular field is provided.

Multi-Time-Scale Shapelet-Based Feature Extraction for Non-Intrusive Load Monitoring

The performance of non-intrusive load monitoring (NILM) system is closely related to the uniqueness and comprehensiveness of the load features extracted from the equipment. However, existing feature extraction methods for NILM are difficult to extract interpretable intuitive features representing the uniqueness of load, and lack of comprehensive attention to the features on different time scales. Therefore, this paper proposes a multi-time-scale shapelet based feature extraction framework. First, shapelet, the most unique segment of a sequence, which can distinguish it from other sequences is introduced as a feature and multi-time-scale current shapelets are proposed to extract comprehensive characteristics of electric appliances and offer intuitive interpretability. Meanwhile, a shapelet transform is constructed, so that the essence of shapelet based NILM is transformed from sequence matching to a general classification problem. Next, an ensemble bagging classifier is proposed on multi-time scales to effectively balance the classification errors on each time scale and enhance the performance of NILM system. In addition, this framework can support small sample training to facilitate rapid deployment of NILM applications at the initial practical stage. The results show that this framework can effectively focus on the uniqueness of load on different time scales, provide interpretability for NILM, and achieve good classification effects.

Розробка платформи для автоматизації тестування веб-додатків

In the modern fast-paced world of web development, automated testing of web applications has become an integral part of the software development process. This article provides an overview of a platform for automating web application testing, which simplifies the process of creating, executing, and analyzing automated tests. The main components of the platform, their functional capabilities, and advantages are discussed. One of the key components of this platform is a module for interacting with the web application, which provides a convenient way to control the browser and perform actions on web pages, such as navigation, form filling, clicking on elements, and more. This enables automated verification of various aspects of web application functionality and correctness. Another important component is the reporting module, which ensures the collection and storage of test results. It allows for the creation of detailed reports on executed tests, identified errors, and other useful information. This module helps understand the state of the software, facilitates the detection of issues, and simplifies error reproduction. The platform also includes a module for creating test data, which can be useful for generating random data, loading test datasets from databases or external files. This allows developers to create various testing scenarios and execute them on different data sets. One of the key advantages of this platform is the ability to configure testing parameters for different projects and environments. Developers can set different configurations, such as browser types, versions, extensions, and utilities used, for each individual project. This allows for customization of testing according to specific needs and ensures maximum compatibility. The platform also provides additional utilities to facilitate the testing process, such as integration with code repositories, version control systems, build automation, and deployment tools. This contributes to fast and efficient test execution, simplifies their integration into the development process, and improves overall software quality. Overall, this web application testing automation platform provides developers with convenient tools that simplify the testing process, ensure high-quality software, and facilitate the rapid deployment of web applications.

A Novel Implementation Framework of Digital Twins for Intelligent Manufacturing Based on Container Technology and Cloud Manufacturing Services

Many core technologies of Industry 4.0 have gained substantial advancement in recent years. Digital Twin (DT) has become the key technology and tool for manufacturing industries to realize intelligent cyber-physical integration and digital transformation by leveraging these technologies. Although there have been many DT-related works, there is no standard definition, unified framework, and implementation approach of DT until now. Widely developing DTs for the manufacturing industry is still challenging. Thus, this paper proposes a novel implementation framework of digital twins for intelligent manufacturing, denoted as IF-DTiM, which possesses several distinct merits to distinguish itself from previous works. First, IF-DTiM fully utilizes new-generation container technology so that DT-related applications and services can be packaged in a self-contained way, rapidly deployed, and robustly operated with the capabilities of failover, autoscaling, and load balancing. Second, it leverages existing intelligent cloud manufacturing services to realize the intelligence for DT externally in a scalable and plug-and-play manner instead of using traditional approaches to embed intelligence in DT. Third, IF-DTiM contains Product DT for products, Equipment DT (i.e., EQ DT) for equipment, and Process DT for production lines, which can generically fulfill the demands and scenarios to achieve intelligent manufacturing for various manufacturing industries. Testing results show that IF-DTiM can achieve remarkable performance in rapid deployment and real-time data exchanges of DT-related applications. Finally, we develop an example DTiM system for CNC machining based on IF-DTiM to demonstrate its efficacy and applicability in facilitating the manufacturing industry to build their DT systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Developing Digital Twin (DT) systems to realize intelligent manufacturing is challenging. The proposed IF-DTiM (Implementation Framework of Digital Twins for Intelligent Manufacturing) provides a novel container-technology and cloud-manufacturing-service-based systematic methodology for building DTiM. In this paper, we present the system architecture and several operational scenarios (e.g., how to create and use DTs) of IF-DTiM, together with the design of its core functional mechanisms (e.g., rapid deployment scheme for DT, real-time data exchange for DT, DT interface pattern, and general workflow architecture for DT). Also, an example DTiM system for CNC machining based on IF-DTiM is presented to facilitate the practitioners to adopt the designs and niches in IF-DTiM to build their desired DTiM systems.

IndustEdge: A Time-Sensitive Networking Enabled Edge-Cloud Collaborative Intelligent Platform for Smart Industry

An edge-cloud collaborative intelligent (ECCI) platform is of great significance for the agile development and rapid deployment of ECCI applications, which are essential for realizing smart industry in the era of Industry 4.0. However, the existing platforms lack considering the high real-time latency demand of industrial operations, which severely hinders the development of smart industry and may even lead to severe industrial accidents. To effectively reduce the response latency of industrial applications, in this article, we propose an ECCI platform IndustEdge. It takes time-sensitive networking as the deterministic transport for the link layer, and provides an extensible ECCI orchestration component to reduce the system level latency. Furthermore, IndustEdge has an ECCI algorithm library for different collaborative modes and provides the complete life cycle management for ECCI applications. We implement platforms for both the real-world prototype and emulated-world emulation, and conduct two case studies to evaluate the effectiveness of IndustEdge.

FlockAI: A Testing Suite for ML-Driven Drone Applications

Machine Learning (ML) is now becoming a key driver empowering the next generation of drone technology and extending its reach to applications never envisioned before. Examples include precision agriculture, crowd detection, and even aerial supply transportation. Testing drone projects before actual deployment is usually performed via robotic simulators. However, extending testing to include the assessment of on-board ML algorithms is a daunting task. ML practitioners are now required to dedicate vast amounts of time for the development and configuration of the benchmarking infrastructure through a mixture of use-cases coded over the simulator to evaluate various key performance indicators. These indicators extend well beyond the accuracy of the ML algorithm and must capture drone-relevant data including flight performance, resource utilization, communication overhead and energy consumption. As most ML practitioners are not accustomed with all these demanding requirements, the evaluation of ML-driven drone applications can lead to sub-optimal, costly, and error-prone deployments. In this article we introduce FlockAI, an open and modular by design framework supporting ML practitioners with the rapid deployment and repeatable testing of ML-driven drone applications over the Webots simulator. To show the wide applicability of rapid testing with FlockAI, we introduce a proof-of-concept use-case encompassing different scenarios, ML algorithms and KPIs for pinpointing crowded areas in an urban environment.

Efficient algorithms for decision making and coverage deployment of connected multi-low-altitude platforms

Unmanned aerial vehicles (UAVs) have gained significantly in popularity in recent years, and they are coupled with the emerging Internet-of-Things, 5G, and mobile edge computing. Their application domains will continue to expand. UAVs can deliver various IoT-driven applications (e.g., video surveillance UAVs-based applications) anytime and anywhere. Indeed, the rapid deployment of such applications in different environments (e.g., urban or wildlife surveillance/sensing environments) is one of the critical factors to support the emergence of new services and applications (e.g., 5G/B5G UAVs base station, civil and commercial IoT applications). However, several research challenges need to be addressed before the latter can be deployed in the real world. In this paper, we propose a novel approach that enables efficient coverage using a set of UAVs. We define two multi-objective sub-problems. The first sub-problem allows selecting a minimum number of appropriate UAVs for deployment as Low-Altitude-Platforms (LAP) over an area of interest from an extensive collection of options. We model this sub-problem as multi-criteria decision-making (MCDM) problem considering various UAV features and user constraints and solve it using a novel multi-criteria selection algorithm named SAGA. The second sub-problem is a coverage optimization problem that enables controlling UAVs’ hover locations and use gimbal-mounted rotating cameras or multi-lens cameras to increase the coverage accordingly. We propose a strategy aiming at optimizing hovering locations of UAVs and then rotating their cameras. The process comprises two techniques to solve the latter sub-problem: (1) an improved preference-guided genetic algorithm named NSPGGA; (2) a hybrid heuristic DCXGA built upon three algorithmic techniques: divide-and-conquer, greedy search, and exhaustive search. We carried out comparative analyses with seven MCDM methods and ten multi-objective evolutionary/swarm intelligence algorithms. The proposed algorithms outperformed the benchmarking techniques and showed remarkable results, e.g., the selection algorithm SAGA exhibits a high success rate, accuracy, and consistency. The preference-guided genetic algorithm NSPGGA achieves better efficiency, and it is four times faster than the famed NSGA-II. Finally, the hybrid heuristic DCXGA allows having more significant imaging coverages with few camera rotations. The aforementioned vital results are validated through diverse and intensive simulation scenarios.

Collaborative Business Process Fault Resolution in the Services Cloud.

The emergence of cloud and edge computing has enabled rapid development and deployment of Internet-centric distributed applications. There are many platforms and tools that can facilitate users to develop distributed business process (BP) applications by composing relevant service components in a plug and play manner. However, there is no guarantee that a BP application developed in this way is fault-free. In this paper, we formalize the problem of collaborative BP fault resolution which aims to utilize information from existing fault-free BPs that use similar services to resolve faults in a user developed BP. We present an approach based on association analysis of pairwise transformations between a faulty BP and existing BPs to identify the smallest possible set of transformations to resolve the fault(s) in the user developed BP. An extensive experimental evaluation over both synthetically generated faulty BPs and real BPs developed by users shows the effectiveness of our approach.

Architecture of the application for searching parking in the cit

Problem. Application development is a rather complicated process, as designing a reliable architecture plays a crucial role. The simplicity and efficiency of the process of developing and maintaining the program depends on how correctly the software architecture is selected because in the process of designing the architecture, the internal properties of the software are determined and details of its external properties are performed based on the formulated business requirements. Purpose. The development of an application for finding parking spaces is very relevant and is confirmed by the great need for this service in large cities, especially with an increase in traffic, which proportionally increases with the population of large cities. The aim of the work is to develop an application architecture that can be used as a web or mobile application depending on the needs of the user. The application will help users find and use the free parking space. Methodology. For development of the system, the following tools were used: Spring Core, Spring Cloud, Netflix OSS, Hibernate, Liquibase, Mongo DB, MySQL, and Docker. During the design process, a unified modeling language UML was used. Results. To solve this problem, we analyzed the existing architectural solutions, their advantages, disadvantages, and selected microservice architecture as the most suitable for developing an application for finding parking in the city of Kharkiv. An external configuration support system has been designed. The system for search, interaction of services, the system of containerization and rapid deployment of applications were configured. A user authentication service has been implemented, an architectural solution model has been developed, and the database structure for the application has been decomposed. Scientific novelty. Based on the results obtained in the research process architectural decisions were made to develop an application that is designed to be used in Kharkiv. Practical significance. The whole range of architectural decisions will be used as the basis for the further development of the application, which will be able to provide a user with information about available parking places, their locations, infrastructure characteristics, etc. in the Kharkiv city.

A Multi-Site NFV Testbed for Experimentation With SUAV-Based 5G Vertical Services

With the advent of 5G technologies, vertical markets have been placed at the forefront, as fundamental drivers and adopters of technical developments and new business models. Small Unmanned Aerial Vehicles (SUAVs) are gaining traction in multiple vertical sectors, as key assets to generate, process, and distribute relevant information for the provision of value-added services. However, the enormous potential of SUAVs to support a flexible, rapid, and cost-effective deployment of vertical applications is still to be exploited. In this paper, we leverage our prior work on Network Functions Virtualization (NFV) and SUAVs to design and build a multi-site experimentation testbed based on open-source technologies. The goal of this testbed is to explore synergies among NFV, SUAVs, and vertical services, following a practical approach primarily governed by experimentation. To verify our testbed design, we realized a reference use case where a number of SUAVs, cloud infrastructures, and communication protocols are used to provide a multi-site vertical service. Our experimentation results suggest the potential of NFV and SUAVs to flexibly support vertical services. The lessons learned have served to identify missing elements in our NFV platform, as well as challenging aspects for potential improvement. These include the development of specific mechanisms to limit processing load and delays of service deployment operations.

Full-Duplex-Based Control Channel Establishment for Cognitive Internet of Things

The Internet of Things (IoT) is a novel network that transforms our future by interconnecting everything. With the rapid deployment of IoT devices and applications, a large amount of data is introduced into the network. Hence, we may suffer spectrum scarcity issue due to the massive data. Cognitive radio (CR) is a potential solution for addressing this issue, which can improve spectrum efficiency by dynamic spectrum access. The IoT device with CR capability has become a new research interest. In this article, we propose a full-duplex-based control channel establishment (FDCCE) scheme for cognitive IoT (CIoT). In the FDCCE scheme, CR-based IoT devices (CRDs) can establish control channels on both licensed spectrum and unlicensed spectrum without a central entity. By combining the licensed spectrum and the unlicensed spectrum, the control channel between CRDs can be established successfully even though there is no common available licensed channel between them. Moreover, CRDs with FD radios can sense and transmit data on the same channel simultaneously. Hence, the interference with licensed primary users can be effectively avoided while the time for dedicated spectrum sensing can be saved. Rendezvous on the same channel is the necessary process for the control channel establishment between CRDs. Performance analysis demonstrates the upper bound of the maximum time to rendezvous, channel loading, and degree of rendezvous for the FDCCE scheme. Simulation results show the superiority of the FDCCE scheme in terms of time to rendezvous.

A platform for FPGA virtualization in clouds and data centers

Though Field-Programmable Gate Arrays (FPGAs) have achieved significant performance gains for many application domains, implementing applications on FPGAs still remains a non-trivial task. This is especially true for FPGAs in a Data Center or a cloud. In this work a new platform for virtualizing FPGAs has been developed. It allows the rapid deployment (or porting) of applications on cloud-based or data-center attached FPGAs. A complete platform prototype has been implemented to evaluate the area, speed, and overhead of the proposed platform. The proposed platform provides a general abstract interface to any user's design (not domain specific) and supports dynamic partial reconfiguration (so designs can be added to an FPGA that have other applications running) at comparable overhead to other notable platforms. Experimental results using a streamed application in a cloud-like environment, showed that the proposed platform is a very viable computing option (in terms of throughput) for suitable applications compared to conventional server-based or virtual-machine based SW implementations.

GenApp: Extensible tool for rapid generation of web and native GUI applications

GenApp is a general tool for rapid deployment of applications in an extensible set of target languages. GenApp builds fully functioning science gateways and standalone GUI applications from collections of definition files and libraries of code fragments. Existing and new GenApp capabilities can be modified and implemented by using special defined GenApp macros. We demonstrate how this strategy works by integration of the OAuth2 user credential management into GenApp framework. Among the main GenApp features are the minimal technical expertise requirement for the end user and an open-end design ensuring sustainability of generated applications. Because of the conceptual simplicity of use, GenApp is ideally suited to scientists who are not professional developers, to disseminate their theoretical and experimental expertise as embodied in their code to their communities by rapidly deploying advanced applications.

An overview on Cloud Security and Proposed Solutions

Cloud computing has revolutionized the world of computing in the last few years. The benefits such as reduced costs, rapid application deployment, and elastic resources, have made many organizations to utilize cloud resources or host much of their data in the cloud. Recent studies shown that more than 70 percent of the world’s businesses now operate some of their operations in the cloud. But the security of the data stored in cloud is the major concern. This paper gives a vogue idea about the cloud security threads and its proposed solutions.

Safety message generation rate adaptation in LTE-based vehicular networks

Long Term Evolution (LTE) appears to be a practical and economical alternative to IEEE 802.11p for the rapid deployment of vehicular safety applications. Vehicles periodically broadcast messages, aka beacons, that contain their location and speed. Ideally, vehicles should have accurate knowledge of the locations of surrounding vehicles, and, if possible, have a same level of precision regardless of their speed.The contributions of this paper are twofold. First, we propose an efficient solution for adapting the generation rate of vehicles’ safety messages so that each of them experiences the same level of location precision. This fairness is attained using an analytical model, based on a queueing model that approximates the level of precision for each vehicle based on their motion speed and their generation rate of safety messages. Second, we present a solution for dynamically discovering the minimum number of resources, i.e. PRBs, that should be allocated by the LTE so as to meet a certain level of location precision for all vehicles. Our numerical results show the effectiveness of our two proposed solutions.

Toward Cyborg PPGIS: exploring socio-technical requirements for the use of web-based PPGIS in two municipal planning cases, Stockholm region, Sweden

Web-based Public Participation Geographic Information Systems (PPGIS) are increasingly used for surveying place values and informing municipal planning in contexts of urban densification. However, research is lagging behind the rapid deployment of PPGIS applications. Some of the main opportunities and challenges for the uptake and implementation of web-based PPGIS are derived from a literature review and two case studies dealing with municipal planning for urban densification in the Stockholm region, Sweden. A simple clustering analysis identified three interconnected themes that together determine the performance of PPGIS: (i) tool design and affordances; (ii) organisational capacity; and (iii) governance. The results of the case studies augment existing literature regarding the connections between the different socio-technical dimensions for the design, implementation and evaluation of PPGIS applications in municipal planning. A cyborg approach to PPGIS is then proposed to improve the theoretical basis for addressing these dimensions together.

APPLICATION MIGRATION EFFORT IN THE CLOUD

Over the last years, the utilization of cloud resources has been steadily rising and an increasing number of enterprises are moving applications to the cloud. A leading trend is the adoption of Platform as a Service to support rapid application deployment. By providing a managed environment, cloud platforms take away a lot of complex configuration effort required to build scalable applications. However, application migrations to and between clouds cost development effort and open up new risks of vendor lock-in. This is problematic because frequent migrations may be necessary in the dynamic and fast changing cloud market. So far, the effort of application migration in PaaS environments and typical issues experienced in this task are hardly understood. To improve this situation, we present a cloud-to-cloud migration of a real-world application to seven representative cloud platforms. In this case study, we analyze the feasibility of the migrations in terms of portability and the effort of the migrations. We present a Docker-based deployment system that provides the ability of isolated and reproducible measurements of deployments to platform vendors, thus enabling the comparison of platforms for a particular application. Using this system, the study identifies key problems during migrations and quantifies these differences by distinctive metrics.

Performance Evaluation of IPv4 and IPv6 over MPLS using OPNET

Over the last two decades, we have witnessed a rapid deployment of real-time applications on the Internet and many research works focus on Quality of Service (QoS), in particular using IPv4 (Internet Protocol Version 4). The inevitable exhaustion of the remaining IPv4 address pool has become progressively evident and as a result the evolution of Internet Protocol (IP) continues leading to the deployment of IPv6 QoS. Today, there is limited experience in the deployment of QoS for IPv6 traffic in MPLS backbone networks in conjunction with DiffServ (Differentiated Services) support. DiffServ itself does not have the ability to control the traffic flow whereas MPLS Traffic Engineering (TE) can control the traffic and can set up end-to-end routing path before data can be forwarded. Multi-Protocol Label Switching (MPLS) plays a key role in next generation networks by delivering QoS and traffic engineering features which is helpful in managing traffic when some links or paths are under and/or over utilized This paper presents a QoS performance study of some applications such as voice, video conferencing, mail and web over DiffServ with MPLS in IPv4/IPv6 networks using Optimized Network Engineering Tool (OPNET). The effectiveness of DiffServ and MPLS integration in IPv4/IPv6 network is illustrated and analyzed.