Real-time Code Research Articles

Combining Galileo HAS and Beidou PPP-B2b with Helmert coordinate transformation method

The European Galileo High Accuracy Service (HAS) started to provide freely and openly accessible real-time precise satellite orbit, clock and code bias products to global users on January 24, 2023. Combined with the already running BeiDou PPP-B2b service, the launch of a variety of satellite-based PPP services provided more choices to users. However, different satellite-based PPP services provide services for different GNSS systems, which hamper users to make full use of multi-GNSS systems. Therefore, the combination of different satellite-based products can further improve the availability of corrections, usage of multi-GNSS observation data and positioning performance. This paper proposes to combine HAS and PPP-B2b products by the Helmert coordinate transformation method. To validate the algorithm, HAS and PPP-B2b products of day of year (DOY) 308–317 in 2023 were collected in Zhengzhou, China. First, they are evaluated in terms of correction availability, orbit and clock quality. Then the HAS and PPP-B2b products are combined by the Helmert coordinate transformation method. Two combination strategies are proposed. The first strategy is integrating BDS satellites of PPP-B2b products into HAS products (denoted as C_H), while the second strategy is integrating Galileo satellites of HAS products into PPP-B2b products (denoted as C_B). Finally, the combined strategies are evaluated with static and kinematic data. Based on the static data of 18 Multi-GNSS Experiment (MGEX) stations in China and its surrounding areas, the results show that, when separately using HAS and PPP-B2b products for PPP, the average accuracy in horizontal and vertical directions are (2.4, 2.7 cm) and (2.4, 2.0 cm), respectively. The average accuracy of C_H strategy is 2.1 and 1.7 cm, which was improved by 31.3% compared with separately using the products. Similarly, the average accuracy of C_B strategy is 2.1 and 1.9 cm, corresponding to improvements of 29.6%. When comparing the two combined strategies, it is noted that the C_B strategy converges faster. Based on the data from vehicle platform, the results show that the horizontal and vertical accuracy of the C_B strategy is 8.6 and 15.7 cm respectively. The accuracy improvement of C_B is better than that of C_H strategy, and the average accuracy is 68.4% better than that of separately using the products. The above results show that the two combined strategies can improve positioning accuracy. In addition, the improvements in accuracy and convergence speed of C_B strategy are more significant. Users are advised to use C_B strategy for the combination of HAS and PPP-B2b products, which will greatly expand the application of HAS and PPP-B2b services.

Multifunctional flexible self-supporting film electrode for wearable energy-storage sensing system

Thriving digital information technologies are propelling the elegant convergence of aesthetics and functionality in wearable electronic devices. However, developing tiny wearable systems is challenging due to the integration of bulky, cumbersome, and hazardous power supply components. To address this issue, we design a wearable energy-storage sensing system that integrates a V2O5/MXene flexible film serving as both a cathode of zinc-ion microbatteries (ZIMBs) and a piezoresistive sensor. The flexible film, free of inert binder, enhances electrode utilization for higher energy densities. Characterised by its heterostructure and interconnected porous structure, the film provides a ultra-stability (18000 cycles at 30 A g−1) and excellent rate performance (167 mAh g−1 at 50 A g−1). Beyond that, the film with high flexibility can be assembled into planar ZIMBs via vacuum filtration. The ZIMBs, capable of reliable energy output, exhibit a decent area capacity of 531.6 μAh cm−2 under dynamic bending conditions. The adaptable ZIMB-sensing system integrated into the same plane offers better coupling effects. The uninterruptible power supply from microbattery ensures the system’s independence, which can transmit Morse code in real time and detect body motions with a swift response time of just 84 ms. Our design principle provides new ideas for the independent and miniaturized development of wearable electronics.

A Framework for Real-Time AI-Driven Secure Code Analysis Integrated with DevSecOps in Cloud-Native CI/CD Pipelines

Abstract—This paper presents a novel framework for inte- grating real-time AI-driven secure code analysis into DevSecOps practices within cloud-native CI/CD pipelines. As organizations increasingly adopt cloud- native architectures and agile develop- ment methodologies, the need for robust, automated security mea- sures becomes paramount. Our proposed framework leverages advanced machine learning algorithms to perform continuous, real-time code analysis, identifying potential vulnerabilities and security risks throughout the development lifecycle. By seamlessly integrating with existing CI/CD tools and cloud platforms, our solution enables organizations to enforce security policies, detect threats, and remediate issues without compromising development velocity. We evaluate the effectiveness of our framework through a series of case studies across diverse software projects, demon- strating significant improvements in threat detection accuracy, reduced false positives, and overall security posture. Our results indicate that the proposed AI-driven approach can enhance code security by up to 40% compared to traditional static analysis tools, while maintaining the agility of modern development prac- tices. This research contributes to the evolving field of DevSecOps by offering a scalable, intelligent solution for embedding security into the heart of cloud-native software development processes. Index Terms—DevSecOps, AI-driven security, cloud- native, CI/CD pipelines, secure code analysis

Highly conductive and sensitive alginate hydrogel strain sensors fabricated using near-field electrohydrodynamic direct-writing process

Hydrogel flexible sensors have attracted considerable attention because of their wearability, biocompatibility, and precision signal transmission capability. However, the hydrogel strain sensors fabricated by conventional printing or hand-injection methods have difficulty balancing their mechanical strength and sensing characteristics, limiting the application of hydrogel strain sensors. Herein, polyvinyl alcohol and polyacrylamide were loosely crosslinked with sodium alginate through chemical cross-linking. Subsequently, MXene nanosheets were introduced for doping, the crosslinked hydrogel conductive network was constructed, and the hydrogel strain sensors were fabricated using the electrohydrodynamic (EHD) printing method. The ions in the EHD-printed hydrogel undergo directional movement under an externally enhanced electric field, causing the formation of more uniform and dense porous conductive networks inside the hydrogel, and high electrical conductivity (0.49 S m−1) is obtained. These hydrogel strain sensors have excellent mechanical properties (tensile strength: 0.17 MPa at 787 % strain), high sensitivity (gauge factor: 1.54 at 0–100 % strain), and low detection limits (1 % strain). Furthermore, demonstrations of real-time Morse code tapping information transmission, handwriting recognition during writing, and human physiological behavior monitoring demonstrations using the fabricated sensors indicate that the EHD-printed hydrogel strain sensor method has significant potential for wearable devices and human-computer interaction applications.

Improving IIoT security: Unveiling threats through advanced side-channel analysis

The widespread deployment of IIoT edge devices makes them attractive victims for malicious activities. Consequently, how to implement trustworthy operations becomes a realistic topic in embedded systems. While most current physical systems for detecting malicious activities primarily focus on identifying known intrusion codes at the block level, they ignore that even an unnoticeable injected function can result in system-wide loss of security. In this paper, we propose a framework called CNDSW built on deep-learning side-channel analysis for function-level industrial control flow integrity monitoring. By collaboratively utilizing correlation analysis and deep-learning techniques, the dual window sliding monitoring mechanism in the proposed CNDSW framework demonstrates a real-time code intrusion tracking capacity on embedded controllers with a 99% detection accuracy on average. Instead of focusing on known block-level intrusions, we experimentally show that our model is feasible to detect function-level code intrusions without knowing the potential threat type. Besides, we further explore how different configurations of the CNDSW framework can help the monitoring process with different emphases and to which extent the model can concurrently detect multiple code intrusion activities. All our experiments are conducted on 32-bit ARM Cortex-M4 and 8-bit RISC MCUs across five different control flow programs, providing a comprehensive evaluation of the framework’s capabilities.

Real Time Collaborative Code Editor

Abstract: The main goal of our project is to provide an online platform especially for group coding interviews. This platform provides a comprehensive environment for seamless communication between interviewers and candidates, with the goal of addressing the challenges faced during remote technical assessments. The creation ofa feature-rich web platform is the goal of our project. Thisincludes an adaptable rich text editor that enables coding in real time, a mechanism for instantaneous code evaluation via execution and compilation, integrated video conferencing for efficient communication, and the abilityto set up interview rooms to hold several interviews at once. We're designing a user intuitive interface using technology and strong programming frameworks. The main objectives of the platform are to ensure real time code execution and a seamless interview process. This is achieved by incorporating code compilation engines implementing editing tools and utilizing WebRTC for video conferencing. By creating a user-friendly and productive environment that benefits both interviewers and candidates, our platform seeks to expedite the remote technical interview process. This means reducing logistical obstacles, making thorough coding evaluations possible, and creating a positive interview environment.

Generative Ai in Software Development : an Overview and Evaluation of Modern Coding Tools

Generative AI has significantly transformed software development by leveraging advanced machine learning models to automate coding tasks, generate code, and enhance productivity. This paper provides an overview and evaluation of modern AI-powered coding tools, including GitHub Copilot, OpenAI Codex, DeepCode, Amazon CodeGuru, TabNine, Kite, and IntelliCode, which use large language models (LLMs) to offer real-time code suggestions, automated error detection, and intelligent code completions. Despite their benefits, these tools face challenges related to accuracy, contextual understanding, security, privacy, and ethical considerations, necessitating thorough review and testing of AI-generated code by developers. The integration of AI in coding also raises concerns about proprietary information protection and ethical implications such as job displacement. This paper explores the capabilities, applications, and limitations of current generative AI tools, highlighting their impact on software development and discussing future directions. Emphasis is placed on the need for improved model training, enhanced contextual understanding, secure AI training methods, and ethical AI usage. By addressing these challenges, the industry can maximize the potential of generative AI, creating more accurate, reliable, and ethically sound tools that support a collaborative and innovative software development environment.

Spatiotemporal diffusion metamaterials: Theories and applications

Diffusion metamaterials with artificial spatial structures have significant potential in controlling energy and mass transfer. Those static structures may lead to functionality and tunability constraints, impeding the application scope of diffusion metamaterials. Dynamic structures, adding the temporal dimension, have recently provided a new possibility for electric charge and heat diffusion regulation. This perspective introduces the fundamental theories and practical constructions of spatiotemporal diffusion metamaterials for achieving nonreciprocal, topological, or tunable properties. Compared with traditional static design, spatiotemporal modulation is promising to manipulate diffusion processes dynamically, with applications of real-time thermal coding and programming. Existing spatiotemporal diffusion explorations are primarily at macroscopic systems, and we may envision extending these results to microscale and other physical domains like thermal radiation and mass diffusion shortly.

Real-time coded measures in natural language samples capture change over time in minimally verbal autistic children.

Prior research supports the use of natural language sampling (NLS) to assess the rate of speech utterances (URate) and the rate of conversational turns (CTRate) in minimally verbal (MV) autistic children. Bypassing time-consuming transcription, previous work demonstrated the ability to derive URate and CTRate using real-time coding methods and provided support for their strong psychometric properties. (1) Unexplored is how URate and CTRate using real-time coding methods capture change over time and (2) whether specific child factors predict changes in URate and CTRate in 50 MV autistic children (40 males; M = 75.54, SD = 16.45 (age in months)). A NLS was collected at Time 1 (T1) and Time 2 (T2) (4.5 months between T1 and T2) and coding was conducted in ELAN Linguistic Annotator software using a real-time coding approach to derive URate and CTRate. Findings from paired samples Wilcoxon tests revealed a significant increase in child URate (not examiner URate) and child and examiner CTRate from T1 to T2. Child chronological age, Mullen expressive language age equivalent scores, and URate and CTRate at T1 were predictive of URate and CTRate at T2. Findings support using NLS-derived real-time coded measures of URate and CTRate to efficiently capture change over time in MV autistic children. Identifying child factors that predict changes in URate and CTRate can help in the tailoring of goals to children's individual needs and strengths.

Tailoring Full STEAM Ahead Empowerment: Creative Coding for 21st Century Girls

INTRODUCTION: This paper provides a comprehensive examination of the Creative Coding project’s impact on female empowerment.OBJECTIVES: The project addresses the gender disparity in STEAM fields by integrating coding and art for girls aged 10-15, using the Strudel tool that combines real-time live coding and music composition.METHODS: Through observation and interviews with the girls who participated and performed at the end of the Creative Coding project, but also with those who dropped out, we cast a deep look into the positive and negative parameters to tailor concrete measures for every girl empowerment - not only super achievers. We collaborated with teachers and female mentors from the Computer Science and Engineering Department as relatable role models, to support feelings of belonging and spark curiosity. Using the new user-friendly Strudel tool that supports the integration of art, music, and audio-visual elements, we aimed at an engaging learning experience. To tailor new actions and increase attrition we used a mess map for analysis of the wicked problem of dropouts. Finally, collaboration among the girls and peer learning were expected as by-product effects.RESULTS: Positive outcomes include increased interest, self-confidence, creativity, and aspirations in STEAM subjects among the girls. However, a more comprehensive and supportive environment is recognized as necessary to engage a broader group of girls in STEAM activities. The findings highlight the importance of cultivating curiosity, providing mentorship, promoting inclusivity and collaboration, and integrating creativity in education to inspire and empower girls in coding and STEAM disciplines.CONCLUSION: The paper concludes by emphasizing the potential of Creative Coding practices and the audio-visual perception of music to enhance girls’ empowerment, creativity, and interest in coding and STEAM fields.

Real-time control of NBI fast ions, current-drive and heating properties

Conventionally, neutral beam injection (NBI) in tokamaks is controlled via engineering parameters such as injection voltage and power. Recently, the high-fidelity real-time NBI code RABBIT has been coupled to the discharge control system of ASDEX Upgrade. It allows to calculate the NBI fast-ion distribution and hence the properties of NBI in real-time, making it possible to control them directly. We successfully demonstrate control of driven current, ion heating and stored fast-ion energy by modifying the injected beam power. A combined ECRH and NBI controller is also successfully tested, which is able to adjust the heating mix between ECRH and NBI to match a certain desired ion heating fraction at given total power. Further experiments have been carried out towards control of the ion heat flux (i.e. ion heating plus collisional heat transfer between ions and electrons). They show good initial success, but also leave room for future improvements as the controller runs into instabilities at too high requests.

Defendroid: Real-time Android code vulnerability detection via blockchain federated neural network with XAI

Ensuring strict adherence to security during the phases of Android app development is essential, primarily due to the prevalent issue of apps being released without adequate security measures in place. While a few automated tools are employed to reduce potential vulnerabilities during development, their effectiveness in detecting vulnerabilities may fall short. To address this, “Defendroid”, a blockchain-based federated neural network enhanced with Explainable Artificial Intelligence (XAI) is introduced in this work. Trained on the LVDAndro dataset, the vanilla neural network model achieves a 96% accuracy and 0.96 F1-Score in binary classification for vulnerability detection. Additionally, in multi-class classification, the model accurately identifies Common Weakness Enumeration (CWE) categories with a 93% accuracy and 0.91 F1-Score. In a move to foster collaboration and model improvement, the model has been deployed within a blockchain-based federated environment. This environment enables community-driven collaborative training and enhancements in partnership with other clients. The extended model demonstrates improved accuracy of 96% and F1-Score of 0.96 in both binary and multi-class classifications. The use of XAI plays a pivotal role in presenting vulnerability detection results to developers, offering prediction probabilities for each word within the code. This model has been integrated into an Application Programming Interface (API) as the backend and further incorporated into Android Studio as a plugin, facilitating real-time vulnerability detection. Notably, Defendroid exhibits high efficiency, delivering prediction probabilities for a single code line in an average processing time of a mere 300 ms. The weight-sharing transparency in the blockchain-driven federated model enhances trust and traceability, fostering community engagement while preserving source code privacy and contributing to accuracy improvement.

Learning Styles Impact Students’ Perceptions on Active Learning Methodologies: A Case Study on the Use of Live Coding and Short Programming Exercises

This research paper explores the effectiveness of live coding as an active learning methodology in teaching programming, particularly in the context of diverse learning styles. Live coding, characterized by real-time coding demonstrations by instructors, has been increasingly adopted to enhance the learning experience in programming education. It offers immediate feedback, demonstrates problem-solving in action, and allows instructors to incorporate student suggestions, making it a dynamic and engaging teaching tool. However, its effectiveness varies among students with different learning preferences. This study investigates the impact of various learning style dimensions, as defined by the Felder–Silverman model, on the effectiveness of live coding in an introductory object-oriented programming course. The study was conducted at Aalborg University, Denmark, with students from the BSc Software program. It aims to provide empirical evidence on how different learning style dimensions influence student preferences and the effectiveness of live coding, offering insights to educators for tailoring active learning methodologies in programming courses to diverse learner needs.

A flood knowledge-constrained large language model interactable with GIS: enhancing public risk perception of floods

Public’s rational flood mitigation behaviors depend on accurate perception of flood risks. The use of natural language for flood risk perception is an effective approach, and it is critical to ensure the accuracy and comprehensibility of the flood information provided by the system in natural language dialogues. This study presents a framework for large language model (LLM) that is constrained by flood knowledge and can interact with geographic information system (GIS), aimed at enhancing the public’s perception of flood risks. We tested the performance of LLM within this framework and the results demonstrate that LLM can generate accurate information about floods under the constraints of entities and relationships in the knowledge graph, and interact with GIS to produce personalized knowledge through real-time coding. Furthermore, we conducted flood risk perception experiments on users with different cognitive levels. The results indicate that using natural language dialogue can narrow the differences brought about by cognitive levels, allowing the public to equally access knowledge related to flood events.

Understanding Real-time Collaborative Programming: a Study of Visual Studio Live Share

Real-time collaborative programming (RCP) entails developers working simultaneously, regardless of their geographic locations. RCP differs from traditional asynchronous online programming methods, such as Git or SVN, where developers work independently and update the codebase at separate times. Although various real-time code collaboration tools (e.g., Visual Studio Live Share , Code with Me , and Replit ) have kept emerging in recent years, none of the existing studies explicitly focus on a deep understanding of the processes or experiences associated with RCP. To this end, we combine interviews and an email survey with the users of Visual Studio Live Share , aiming to understand (i) the scenarios, (ii) the requirements, (ii) and the challenges when developers participate in RCP. We find that developers participate in RCP in 18 different scenarios belonging to six categories, e.g., pair programming , group debugging , and code review . However, existing users’ attitudes toward the usefulness of the current RCP tools in these scenarios were significantly more negative than the expectations of potential users. As for the requirements, the most critical category is live editing , followed by the need for sharing terminals to enable hosts and guests to run commands and see the results, as well as focusing and following , which involves “following” the host’s edit location and “focusing” the guests’ attention on the host with a notification. Under these categories, we identify 17 requirements, but most of them are not well supported by current tools. In terms of challenges, we identify 19 challenges belonging to seven categories. The most severe category of challenges is lagging followed by permissions and conflicts . The above findings indicate that the current RCP tools and even collaborative environment need to be improved greatly and urgently. Based on these findings, we discuss the recommendations for different stakeholders, including practitioners, tool designers, and researchers.

Effectiveness of mRNA Booster Vaccine Against Coronavirus Disease 2019 Infection and Severe Outcomes Among Persons With and Without Immune Dysfunction: A Retrospective Cohort Study of National Electronic Medical Record Data in the United States.

Real-world evidence of coronavirus disease 2019 (COVID-19) messenger RNA (mRNA) booster effectiveness among patients with immune dysfunction are limited. We included data from patients in the United States National COVID Cohort Collaborative (N3C) who completed ≥2 doses of mRNA vaccination between 10 December 2020 and 27 May 2022. Immune dysfunction conditions included human immunodeficiency virus infection, solid organ or bone marrow transplant, autoimmune diseases, and cancer. We defined incident COVID-19 BTI as positive results from laboratory tests or diagnostic codes 14 days after at least 2 doses of mRNA vaccination; and severe COVID-19 BTI as hospitalization, invasive cardiopulmonary support, and/or death. We used propensity scores to match boosted versus nonboosted patients and evaluated hazards of incident and severe COVID-19 BTI using Cox regression after matching. Among patients without immune dysfunction, the relative effectiveness of booster (3 doses) after 6 months from the primary (2 doses) vaccination against BTI ranged from 69% to 81% during the Delta-predominant period and from 33% to 39% during the Omicron-predominant period. Relative effectiveness against BTI was lower among patients with immune dysfunction but remained statistically significant in both periods. Boosted patients had lower risk of COVID-19-related hospitalization (hazard ratios [HR] ranged from 0.5 [95% confidence interval {CI}, .48-.53] to 0.63 [95% CI, .56-.70]), invasive cardiopulmonary support, or death (HRs ranged from 0.46 [95% CI, .41-.52] to 0.63 [95% CI, .50-.79]) during both periods. Booster vaccines remain effective against severe COVID-19 BTI throughout the Delta- and Omicron-predominant periods, regardless of patients' immune status.

Enhancing Computer Science Education: The Benefits of Real-Time Collaborative Code Editors in Schools and Universities.

Enhancing Computer Science Education: The Benefits of Real-Time Collaborative Code Editors in Schools and Universities.

Analysis and Evaluation of the Contestant’s Progress in Real-time Coding Contests

This paper presents a model for analyzing contestant progress in real-time coding contests, emphasizing the critical need for effective measures in assessing code similarity and plagiarism cases. Current coding contest platforms often need more robust procedures to identify and address these issues, compromising the integrity of the evaluation process. To tackle these challenges, we propose a novel system that leverages advanced techniques to analyze code and collective behavior, providing a holistic evaluation of submissions. The system enhances performance assessment accuracy and maintains fairness and credibility in real-time coding contests. The findings and insights from this study shed light on the importance of integrating sophisticated mechanisms to ensure the authenticity of code submissions and uphold the competitive nature of coding competitions.

Real-Time Collaboration in Code Development: A Comprehensive Review of Code Jett

CodeJett is a pioneering collaborative real-time code editor that serves as a beacon of innovation in the field of software development. With the increasing prevalence of remote work and distributed development teams, the need for tools that facilitate seamless collaboration among developers is paramount. CodeJett rises to this challenge by offering an array of features, including real-time synchronization, syntax highlighting, version control integration, and a built-in chat system. This paper provides a comprehensive overview of CodeJett, highlighting its significance and the evolving landscape of collaborative development tools. In a world where software development transcends borders and time zones, CodeJett acts as a unifying force, enabling developers to work together harmoniously on coding projects. The real-time nature of the tool eliminates the need for manual synchronization, enhancing productivity and reducing the likelihood of errors. CodeJett's versatility extends to its support for multiple programming languages, making it accessible to a broad spectrum of developers. This abstract underscore the role of CodeJett in facilitating collaboration, real-time development, and efficient communication within the software development community. The paper also explores the technical challenges faced in the development of CodeJett and offers insights into its future potential as a transformative tool in an ever-changing software development landscape. With keywords such as collaboration, real-time synchronization, syntax highlighting, and version control, this abstract encapsulates the essence of CodeJett's importance and its enduring impact on the field of software development.

The real-time TDDFT code “Quantum Dissipative Dynamics” on a GPU

We present the second release of the real-time time-dependent density functional theory code “Quantum Dissipative Dynamics” (QDD). It augments the first version [1] by a parallelization on a GPU coded with CUDA fortran. The extension focuses on the dynamical part only because this is the most time consuming part when applying the QDD code. The performance of the new GPU implementation as compared to OpenMP parallelization has been tested and checked on a couple of small sodium clusters and small covalent molecules. OpenMP parallelization allows a speed-up by one order of magnitude in average, as compared to a sequential computation. The use of a GPU permits a gain of an additional order of magnitude. The performance gain outweighs even the larger energy consumption of a GPU. The impressive speed-up opens the door for more demanding applications, not affordable before.