Platform Independence Research Articles

Enhancing Germplasm Collection with the NBPGR-PDS App

This research explores the intersection of Java programming and plant genetic resources (PGR) management by focusing on the development and implementation of the NBPGR-PDS (National Bureau of Plant Genetic Resources Passport Data Sheet) app. By utilizing Java in mobile development, the NBPGR-PDS app addresses challenges in real-time data collection, security, and usability during field explorations for plant germplasm. This paper investigates how Java’s core features—such as platform independence, multithreading, and security—support scientific research in PGR, ensuring reliable data management and enhancing germplasm collection missions. The study concludes with recommendations for improving digital tools and future research directions in this domain.

The Semantic Function of Modern Chinese “Negation + X” Modal Words Based on Communication Technology and Big Data Corpus

The semantic function of modern Chinese “negation + X” modal words based on communication technology and big data corpus has gained wide attention. As the basis of SOA architecture, Web services provide the key resources for worldwide information transfer and information sharing with their characteristics of loose coupling, platform independence, and data exchange without additional support from third-party hardware and software. However, along with the popularization and improvement of Web service technology, the number and types of Web services in the Internet are also increasing massively, and there are a large number of Web services with various functions, quality and granularity. Therefore, how to quickly and accurately discover Web services that satisfy users’ query requests from a large and complex set of services has become a critical problem to be solved in the current Web service discovery research. Based on the real corpus, this paper analyzes the similarities and differences in the semantic functions of modern Chinese “negation + X” modal words by combining lexicalization and grammatization, cognitive linguistics, systemic functional grammar and other related theories. The experimental results demonstrate that the model is designed for automatic annotation of semantic word classes, and the annotation algorithm based on the hidden horse model, combined with the Viterbi algorithm based on dynamic programming, achieves a correct rate of 94.3% in the closed test and 89.1% in the open test despite the small size of the training corpus and severe data sparsity, and the model fitting effect meets the dynamic expectations.

Java Development across Industries: Transforming Healthcare, Retail, and Agriculture

Java, a versatile and widely-adopted programming language, is transforming critical industries such as healthcare, retail, and agriculture. This article explores how Java developers leverage the language's robustness, platform independence, and extensive ecosystem to create innovative solutions that address industry-specific challenges. In healthcare, Java's security features and scalability enable the development of secure and interoperable Electronic Health Record systems. Retail benefits from Java's cross-platform compatibility and performance in building omnichannel platforms that integrate online and offline experiences. In agriculture, Java's IoT capabilities and big data integration revolutionize farming practices through precision agriculture and data-driven decision-making. Case studies in each industry demonstrate Java's significant impact on key metrics such as patient data accessibility, online-to-offline conversions, and crop yields. As these sectors continue to evolve, Java's adaptability positions it as a key driver of future advancements.

Speech production and perception data collection in R: A tutorial for web-based methods using speechcollectr.

This tutorial is designed for speech scientists familiar with the R programming language who wish to construct experiment interfaces in R. We begin by discussing some of the benefits of building experiment interfaces in R-including R's existing tools for speech data analysis, platform independence, suitability for web-based testing, and the fact that R is open source. We explain basic concepts of reactive programming in R, and we apply these principles by detailing the development of two sample experiments. The first of these experiments comprises a speech production task in which participants are asked to read words with different emotions. The second sample experiment involves a speech perception task, in which participants listen to recorded speech and identify the emotion the talker expressed with forced-choice questions and confidence ratings. Throughout this tutorial, we introduce the new R package speechcollectr, which provides functions uniquely suited to web-based speech data collection. The package streamlines the code required for speech experiments by providing functions for common tasks like documenting participant consent, collecting participant demographic information, recording audio, checking the adequacy of a participant's microphone or headphones, and presenting audio stimuli. Finally, we describe some of the difficulties of remote speech data collection, along with the solutions we have incorporated into speechcollectr to meet these challenges.

Agile, continuous building energy modeling and simulation

Digital twins have emerged as highly valuable tools for model-based planning, simulation and optimization over the last couple of years, thereby demonstrating considerable potential for application within the construction industry. The introduction of building information modeling (BIM) has effectively established a standardized approach to representing building models. However, in practice, many of these models currently exhibit limitations as to their quality, specifically concerning the level of detail they encompass. In addition, BIM models too often are locked inside a specific vendor’s tool which readily implies a lack of platform independence, or interoperability, which, however, is essential for facilitating single and regressive, i.e., after a design change, model-based building performance simulations. Model-based engineering has effectively addressed comparable challenges within the domain of software engineering over the past decades by facilitating the integration and interoperability of models from various origins, by capitalizing on model-based tool integration. Prompted by these advantages, this study introduces a model-based tool environment that addresses the aforesaid challenges concerning BIM model quality and interoperability. Taking advantage of our proposed model-based tool environment, we implement an agile, continuous planning process for regressive, model-based building performance simulations, thereby enhancing building energy efficiency planning.

When the dragons defeat the knight: Basilisk an architectural pattern for platform and language independent development

In this work, we introduce Basilisk, a high-level architectural pattern designed to facilitate interoperability among various languages, platforms, and ecosystems. The pursuit of language-independent software development is highly desirable, enabling developers to utilize existing software products with most programming languages. Achieving platform independence is equally advantageous, allowing code deployment on different platforms effortlessly. While the development community has often aimed for either language or platform independence, Basilisk aims to combine both into a single product. To realize this dual objective, Basilisk employs two fundamental components. The first is a transpilation infrastructure used to render software products language-independent. The second is an abstraction layer over platforms, enabling the creation of platform-independent software products. To illustrate Basilisk’s potential, we introduce Hydra, a one-to-many, declarative transpilation infrastructure. Hydra has been utilized to develop transpilers from HydraKernel (source language) to various target languages, including D, C++, C#, Scala, Ruby, Hy, and Python. Additionally, we instantiate the abstraction layer in Wyvern, a low-level embedded domain-specific language for GPU programming, supporting any Vulkan-compatible GPU. With the Hydra transpilation infrastructure, Wyvern becomes available for D, C++, C#, Scala, Ruby, Hy, and Python. We evaluate Basilisk through the instantiation of Hydra and Wyvern, writing five algorithms from the Rodinia suite for the seven available languages, totaling 35 benchmarks. These benchmarks are executed on four different hardware platforms.

Implementation of a Universal Framework Using Design Patterns for Application Development on Microcontrollers.

This article focuses on the area of software development for microcontrollers and details the implementation of modern programming practices and principles in embedded systems and IoT applications. This article explains how we implemented previously unimplemented principles and applied design patterns for quality software design on microcontrollers, which are currently only used for developing applications on the higher layers of the IoT reference model. A custom modular framework for microcontrollers is presented, based on applying SOLID principles and adapting design patterns specific to the microcontrollers' application development needs. The implemented framework enables independent communication between modules and flexible integration of hardware components. It is designed with platform independence in mind, contributing to its wide adaptability and ease of use in diverse development environments. By applying these technological approaches, we can create applications that are not only testable and extensible in terms of application logic but also allow for easy adaptation to changes in these hardware resources. Utilizing these capabilities represents an innovative approach to development for microcontrollers that fundamentally improves the long-term sustainability and scalability of applications.

Intercomparisons of Tracker v1.1 and four other ocean particle-tracking software packages in the Regional Ocean Modeling System

Abstract. Particle tracking is widely utilized to study transport features in a range of physical, chemical, and biological processes in oceanography. In this study, a new offline particle-tracking package, Tracker v1.1, is introduced, and its performance is evaluated in comparison to an online Eulerian dye, one online particle-tracking software package, and three offline particle-tracking software packages in a small, high-resolution model domain and a large coarser model domain. It was found that both particle and dye approaches give similar results across different model resolutions and domains when they were tracking the same water mass, as indicated by similar mean advection pathways and spatial distributions of dye and particles. The flexibility of offline particle tracking and its similarity against online dye and online particle tracking make it a useful tool to complement existing ocean circulation models. The new Tracker was shown to be a reliable particle-tracking package to complement the Regional Ocean Modeling System (ROMS) with the advantages of platform independence and speed improvements, especially in large model domains achieved by the nearest-neighbor search algorithm. Lastly, trade-offs of computational efficiency, modifiability, and ease of use that can influence the choice of which package to use are explored. The main value of the present study is that the different particle and dye tracking codes were all run on the same model output or within the model that generated the output. This allows some measure of intercomparison between the different tracking schemes, and we conclude that all choices that make each tracking package unique do not necessarily lead to very different results.

Boosting the visibility of services in microservice architecture

Monolithic software architectures are no longer sufficient for the highly complex software-intensive systems, which modern society depends on. Service Oriented Architecture (SOA) surpassed monolithic architecture due to its reusability, platform independency, ease of maintenance, and scalability. Recent SOA implementations made use of cloud-native architectural approaches such as microservice architecture, which has resulted in a new challenge: the discovery difficulties of services. One way to dynamically discover and route traffic to service instances is to use a service discovery tool to locate the Internet Protocol (IP) address and port number of a microservice. In the event that replicated microservice instances are found to provide the same function, it is crucial to select the right microservice that provides the best overall experience for the end-user. Parameters including success rate, efficiency, delay time, and response time play a vital role in establishing a microservice’s Quality of Service (QoS). These assessments can be performed by means of a live health-check service, or, alternatively, by making a prediction of the current state of affairs with the application of machine learning-based approaches. In this research, we evaluate the performance of several classification algorithms for estimating the quality of microservices using the QWS dataset containing traffic data of 2505 microservices. Our research also analyzed the boosting algorithms, namely Gradient Boost, XGBoost, LightGBM, and CatBoost to improve the overall performance. We utilized parameter optimization techniques, namely Grid Search, Random Search, Bayes Search, Halvin Grid Search, and Halvin Random Search to fine-tune the hyperparameters of our classifier models. Experimental results demonstrated that the CatBoost algorithm achieved the highest level of accuracy (90.42%) in predicting microservice quality.

Fog computing out of the box: Dynamic deployment of fog service containers with TOSCA

AbstractThe conventional cloud‐centric Internet of Things (IoT) application fails to meet the latency requirement of time‐critical applications. The idea of edge and fog computing arrived to distribute workloads across the fog devices located in the local area. However, achieving seamless interoperability, platform independence, and automatic deployment of services becomes the major challenge over heterogeneous fog devices. This paper proposes an integrated and standards‐based fog computing federation framework, FogDEFT, that adapts OASIS–Topology and Orchestration Specification for Cloud Applications (TOSCA) for service deployment in fog. The framework standardizes the distributed application design with TOSCA Service Template to deploy Docker Containers in Swarm mode and manages interoperability over heterogeneous fog devices. The framework uses a lightweight TOSCA compliant orchestrator to dynamically deploy various fog applications (user‐developed services) on the fly.

Robust PDF Watermarking against Print-Scan Attack.

Portable document format (PDF) files are widely used in file transmission, exchange, and circulation because of their platform independence, small size, good browsing quality, and the ability to place hyperlinks. However, their security issues are also more thorny. It is common to distribute printed PDF files to different groups and individuals after printing. However, most PDF watermarking algorithms currently cannot resist print-scan attacks, making it difficult to apply them in leak tracing of both paper and scanned versions of PDF documents. To tackle this issue, we propose an invisible digital watermarking technology based on modifying the edge pixels of text strokes to hide information in PDFs, which achieves high robustness to print-scan attacks. Moreover, it cannot be detected by human perception systems. This method focuses on the representation of text by embedding watermarks by changing the features of the text to ensure that changes in these features can be reflected in the scanned PDF after printing. We first segment each text line into two sub-blocks, then select the row of pixels with the most black pixels, and flip the edge pixels closest to this row. This method requires the participation of original PDF documents in detection. The experimental results show that all peak signal-to-noise ratio (PSNR) values of our proposed method exceed 32 dB, which indicates satisfactory invisibility. Meanwhile, this method can extract the hidden information with 100% accuracy under the JPEG compression attack, and has high robustness against noise attacks and print-scan attacks. In the case of no attacks, the watermark can be recovered without any loss. In terms of practical applications, our method can be applied in the practical leak tracing of official paper documents after distribution.

DAFNI: a computational platform to support infrastructure systems research

Research into the engineering of infrastructure systems is increasingly data intensive. Researchers build computational models to explore scenarios such as investigating the merits of infrastructure plans, analysing historical data to inform system operations or assessing the impacts of infrastructure on the environment. Models are more complex, at higher resolution and with larger coverage. Researchers also require a ‘multi-systems’ approach to explore interactions between systems, such as energy and water with urban development, and across scales, from buildings and streets to regions or nations. Consequently, researchers need enhanced computational resources to support cross-institutional collaboration and sharing at scale. The Data and Analytics Facility for National Infrastructure (DAFNI) is an emerging computational platform for infrastructure systems research. It provides high-throughput compute resources so larger data sets can be used, with a data repository to upload data and share these with collaborators. Users’ models can also be uploaded and executed using modern containerisation techniques, giving platform independence, scaling and sharing. Further, models can be combined into workflows, supporting multi-systems modelling and generating visualisations to present results. DAFNI forms a central resource accessible to all infrastructure systems researchers in the UK, supporting collaboration and providing a legacy, keeping data and models available beyond the lifetime of a project.

Improved Multisignature Scheme for Authenticity of Digital Document in Digital Forensics Using Edward-Curve Digital Signature Algorithm

At the moment, digital documents are just as important as paper documents. As a result, authenticity is essential, especially in legal situations and digital forensics. As technology advances, these digital signature algorithms become weaker, necessitating the development of digital authentication schemes capable of withstanding current security threats. This study proposed a scheme based on an asymmetric key cryptosystem and the user’s biometric credentials to generate keys for digital signatures. A single document can be signed by multiple signatories at the same time under this scheme. The primary goal of this article is to create a safe and cost-effective multiignature scheme. To create keys for document signing and verification, the Edwards-curve Digital Signature Algorithm (EdDSA), especially Ed25519, is employed. The Edwards-curve Digital Signature Algorithm is used with blockchain technology to sign crypto wallets. The Python implementation of a scheme that enables platform independence. We performed performance, security, and comparative analysis to ensure maximum usability. The article’s main findings are that the Ed25519 algorithm can be used in blockchain.

Analysis of the Information System Integration of University Fusion Culture Communication Based on Network Video Automatic Generation Technology

Web services are self-describing and self-contained application modules, which have the advantages of platform independence and programming language independence. Service-oriented architecture is to use the above characteristics of Web services. Its innovation lies in solving the difficulty of manual production of network teaching resources, which is time-consuming, troublesome, long-term, and high-level requirements for teachers. Using this solution, any teacher can record the entire teaching situation and automatically generate two network teaching resources while teaching, which is easy to accumulate into a teaching resource library. The teaching process can be broadcast live on the Internet in real time (including video, audio, computer pictures, etc.). Based on the classic CNN-LSTM structure, the model adds a subject-verb-object classifier to the output of the LSTM network. Subject-verb-object information is the skeleton information of a sentence.

АППАРАТНАЯ ЗАЩИТА UEFI-ПРОШИВКИ И NVRAM-ПАМЯТИ ЭВМ С ПОМОЩЬЮ РЕЗИДЕНТНОГО КОМПОНЕНТА БЕЗОПАСНОСТИ

The issue of protecting the firmware and the memory area for storing variables (NVRAM - Non Volatile Random Access Memory) of the UEFI (Unified Extensible Firmware Interface) system are discussed in the paper. The research methodology is a deduction. The problem of trusted computer loading, in particular, the proprietarity of the UEFI stage, is relevant in the field of computer security. For an introduction to the context and subject field, the components and environment of the UEFI system, attack vectors on the system, the consequences of successful attacks for the user and built-in security tools are briefly described. The advantages and disadvantages of using two memory areas with different access modes as a way to protect critical UEFI system data are considered. As a memory area with a configurable access policy, it is proposed to use the hardware implementation of the resident security component (HRSC). Finally, the functionality of the HRSC and the applicability of this solution for ensuring the security of the UEFI system are considered. As a result, the justification of the applicability of the HRSC as a tool for differentiating access to critical parts of UEFI firmware and NVRAM was obtained. In addition, the advantages of using the HRSC as a memory area with a configurable access policy are identified. In particular those are the ease of implementation, variability of access differentiation and platform independence from the model and architecture of a computer with UEFI.

Problems and Opportunities of Distance Learning in The Context of The Pandemic: Case of One University in Kazakhstan

Distance education systems are being actively studied and developed in higher educational institutions of Kazakhstan, and have already passed the path from the distance learning system to Intelligent flexible learning model based on Web technologies. The advantages of online learning are classroom and platform independence. The goal of various intelligent learning systems is to use knowledge about the field of study, the learner, and learning strategies to provide flexible, individualized study and training. Many Kazakhstani universities were engaged in distance learning as part of the implementation of the second higher education. Obtaining a second higher education in Kazakhstan refers to studying in higher education programs after completing studies at the first higher education in the form of a bachelor's or specialist's degree. By obtaining a second higher education, you can get a degree or qualification in another educational program. The purpose of this empirical research is to study the attitudes of students regarding distance learning and to identify problems in the fulfillment of distance learning in an emergency transition. The study uses a quantitative research method based on a one-dimensional data analysis using descriptive statistics. In order to assess the level of distance learning organization process, the institutional study was initiated at the Kazakh National Women’s Teacher Training University and conducted by the Research Institute for Social and Gender Studies. Based on this study recommendations were developed to improve the educational, methodological, social and educational work of the university, including the use of library resources in the conditions of distance learning. The study was conducted during June - July 2020. The study involved 677 students from all faculties of the university, including bachelor and master degree students, doctoral students. The study was prompted by: the need to assess the quality and level of organization of distance learning at the university; the need to identify problems (issues) of educational and social adaptation of the first-year students; the need to establish the degree of satisfaction of students with the distance learning process. The survey also allows to determine: the most effective forms of distance learning; study the performance indicators of the services provided by the university; to study the indicators of adaptation of students, master degree and doctoral students (satisfaction with the results; deficit experienced by students); evaluate the effectiveness of the work of deans, teachers and advisors from among teachers from the point of view of students. This research identifies the problems in the transition from traditional to distance learning and difficulties that arise when students receive high-quality professional education. Recommendations are developed to optimize the work of university structures aimed at improving the efficiency of the institutes’ work and psychological services. The results obtained will make it possible to make managerial decisions based on information that considers the opinion of consumers of educational services.

A description of the first open-source community release of MISTRA-v9.0: a 0D/1D atmospheric boundary layer chemistry model

Abstract. We present MISTRA-v9.0, a one-dimensional (1D) and box (0D) atmospheric chemistry model. The model includes a detailed particle description with regards to the microphysics, gas–particle interactions, and liquid-phase chemistry within particles. Version 9.0 (v9.0) is the first release of MISTRA as an open-source community model. A major review of the code has been performed along with this public version release to improve the user friendliness and platform independence of the model. The purpose of this public release is to maximise the benefit of MISTRA to the community by making the model freely available and easier to use and develop. This paper presents a thorough description of the model characteristics and components. We show some examples of simulations reproducing previous studies with MISTRA, finding that v9.0 is consistent with previous versions.

Web approach to creating VR content during lessons in the VR quantum of the mini technopark "Quantorium"

The article proposes methodological support for classes in the VR quantum of the mini technopark "Quantorium" for creating VR content using web technologies. The description of practical works allows students to learn how to create virtual worlds step by step by generating code in the simplest text editor Notepad. Creating a virtual world is like creating web pages in HTML. Platform independence and the availability of software for creating virtual reality make it possible to use such a web-based approach both at school in the mini technopark classes and when students work independently at home. This approach is ideal for distance learning. Conducting classes in a VR quantum based on the proposed methodology will help students develop the skills to create VR content for virtual reality helmets and the ability to apply information technologies to solve creative problems in the information society.

Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning.

Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are either vulnerable to web defacement attacks or created and managed by hackers such as fraudulent and phishing websites. Detecting malicious websites is essential to prevent the spreading of malware and protect end-users from being victims. However, most existing solutions rely on extracting features from the website’s content which can be harmful to the detection machines themselves and subject to obfuscations. Detecting malicious Uniform Resource Locators (URLs) is safer and more efficient than content analysis. However, the detection of malicious URLs is still not well addressed due to insufficient features and inaccurate classification. This study aims at improving the detection accuracy of malicious URL detection by designing and developing a cyber threat intelligence-based malicious URL detection model using two-stage ensemble learning. The cyber threat intelligence-based features are extracted from web searches to improve detection accuracy. Cybersecurity analysts and users reports around the globe can provide important information regarding malicious websites. Therefore, cyber threat intelligence-based (CTI) features extracted from Google searches and Whois websites are used to improve detection performance. The study also proposed a two-stage ensemble learning model that combines the random forest (RF) algorithm for preclassification with multilayer perceptron (MLP) for final decision making. The trained MLP classifier has replaced the majority voting scheme of the three trained random forest classifiers for decision making. The probabilistic output of the weak classifiers of the random forest was aggregated and used as input for the MLP classifier for adequate classification. Results show that the extracted CTI-based features with the two-stage classification outperform other studies’ detection models. The proposed CTI-based detection model achieved a 7.8% accuracy improvement and 6.7% reduction in false-positive rates compared with the traditional URL-based model.

Quantum service-oriented computing: current landscape and challenges

The development that quantum computing technologies are achieving is beginning to attract the interest of companies that could potentially be users of quantum software. Thus, it is perfectly feasible that during the next few years hybrid systems will start to appear integrating both the classical software systems of companies and new quantum ones providing solutions to problems that still remain unmanageable today. A natural way to support such integration is Service-Oriented Computing. While conceptually the invocation of a quantum software service is similar to that of a classical one, technically there are many differences and technological limitations, which refer to platform independence, decoupling, scalability, etc. To highlight these differences and the difficulties to develop quality quantum services, this paper takes a well-known problem to which a quantum solution can be provided, integer factorization, making use of the Amazon Braket quantum service platform. The exercise of trying to provide the factorization as a quantum service is carried out following the best practices, design patterns and standards existing in the implementation of classical services. This case study is used to highlight the rough edges and limitations that arise in the integration of classical-quantum hybrid systems using service-oriented computing. The conclusion of the study allows us to point out directions in which to focus research efforts in order to achieve effective quantum service-oriented computing.