Oil palm is an important economic crop that is widely cultivated, especially in Southeast Asia. Thailand is one of the world’s largest producers and exporters of palm oil. Efficient management of oil palm plantations is crucial for increasing yields and minimizing agricultural losses. This study aimed to develop a smart oil palm plantation and production management system. This system utilizes Internet of Things (IoT) technology and an integrated supervised machine learning model utilizing regression analysis to monitor and control agricultural equipment within the plantation. MySQL database was used for management of sensor data. Python (version 3.9.6) programming and Google Map API were used for data analysis, spatial analysis and data visualization suite in the system. The results showed that the data from the sensors are displayed in real-time, allowing plantation managers to monitor conditions remotely and make informed adjustments as needed. The system also includes data analysis and data visualization tools for decision-making regarding production management. The model attained an accuracy of over 95%, which reflects its reliability in performing the specified prediction task. The system serves as a support tool for automating soil quality monitoring, fertilization, and field maintenance in oil palm plantations. This enhances productivity, reduces operational costs, and improves yield planning.

Creating music software relies fundamentally on elements such as programming languages, reasoning paradigms, libraries, APIs, and frameworks, which together shape both the technical architecture and creative potential of musical composition. This work presents the design and implementation of a multi-paradigm environment that enables building software for comprehensive sound manipulation, while addressing specific demands of automatic music composition. The proposed environment integrates imperative, object-oriented, functional, and logic programming, bringing together structural control, expressiveness, and declarative reasoning within a unified platform. Entirely based on free software, it employs open-source tools to ensure flexibility, transparency, and community-driven evolution. Java acts as the structural backbone, organizing the system’s architecture; Scala offers functional abstractions for concise musical transformations; and Prolog provides logical inference and knowledge representation for modeling musical structures and rules. In addition, integration with APIs such as Java Sound and jMusic allows tasks like score generation, harmonic analysis, and synthesis of musical phrases and chords. This setup supports automating the creation of musical excerpts and encoding compositional knowledge, fostering algorithmic creativity. The resulting environment offers an effective, extensible, and accessible solution for computer music research, teaching, and creative experimentation. By leveraging open-source technologies, it promotes interdisciplinary collaboration and continuous innovation in algorithmic composition, ultimately contributing to advancing knowledge representation and automatic music generation.

Abstract Large Language Models (LLMs) have been adopted increasingly in oncology, for example, in structuring data from clinical notes, inferring diagnoses from free text or imaging data, and anonymizing of data. Due to the rapid development pace of LLMs, best practices for conducting and reporting oncological research in these applications have yet to be fully established.We queried PubMed for oncology-related LLM research with the last cutoff set at Dec 31st 2024. We investigated 179 papers. Of these, 131 were removed due to omission criteria, and 48 were structured and reported here. Inclusion criteria were oncology-related research and full research articles. Structured fields included date of submission, acceptance, and publishing, the granularity of model reporting (model family, model snapshot), reporting of key LLM model parameters, availability of source code and data, and programming language and API details. We noted an almost exponential growth of LLM-related publications in oncology, with a relatively short time from authors’ submission to publicly available publication (median 3.7 months, IQR 2.5-5.9 months). Interestingly, despite the relatively short processing time, in 25% of cases, the exact model essential to the publication had been deprecated by the model service providers or a newer version was available at the time of publishing. 35.4% of published research relied solely on a graphical user-interface (GUI) of LLMs such as ChatGPT, while 37.5% reported programmatically API-use, with Python as the most common language. While most publications either fully or partially reported the utilized prompts (75%), only 22.9% reported the exact key model parameters, such as temperature. Even when the temperature parameter was available, 45.4% of these publications used a temperature value larger than 0, resulting in more stochastic answers. Source code was made publicly available in 18.7% of publications that reported using a programming language such as Python or R. While practically all publications (97.9%) reported the used model families such as GPT-4o, Claude 3.5 Sonnet or Llama 3-70B, only 27% reported the exact model snapshot usage such as GPT-4o with snapshot options available for May 13th, August 6th or November 20th in 2024. We exemplify and report shortcomings of recent LLM adoption in oncological research. To alleviate these issues, we propose a checklist to improve reproducibility, transparency, and longevity of LLM research directed at researchers and journals. We propose the following preliminary checklist: exact reporting of model snapshot and model parameter bound to a specific snapshot instead of latest release, API usage instead of GUI chatbots, temperature-parameter equal to 0, assessment of variability across runs, session restarts to avoid biases, and caution in researching models that are bound to be deprecated due to the short turn-around time in LLMs. Additionally, rigorous prompt engineering and especially few-shot learning show potential in optimizing interactions with LLMs, also in oncology. Citation Format: Tolou Shadbahr, Antti S. Rannikko, Tuomas Mirtti, Teemu D. Laajala. Current oncological large language model research lacks reproducibility, transparency, and long term support [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B021.

This study describes an advanced recognition system embedded in an Android smart parking software application for Makassar City. The system augments the recognition of the parking space and the navigation as well as the payment of the parking fee using a Tesseract OCR module in conjunction with YOLO object detection. The ability of Tesseract OCR to recognize parking spaces, road signs, and vehicle registration plates in real time improves the accuracy of availability updates and assists drivers in finding parking spaces quickly. The application was developed using multiple programming languages, Android Studio, and API integrations for real-time data updates and payment transactions. Iterative testing, including black-box evaluations, ensures cross-device reliability, functionality, and ease of use. Experimental results demonstrated the system's effectiveness in optimizing parking resource utilization. Additionally, user feedback mechanisms refined the app for an evolving user experience. In conclusion, the YOLO-Tesseract recognition system represents a robust solution to urban parking problems. Its core model to improve on-street parking management can be scaled to smart city models worldwide.

ABSTRACTContextReactive Programming (RP) provides powerful abstractions for managing asynchronous and event‐driven behaviors, but its APIs are often perceived as complex and difficult to use, particularly due to their reliance on functional programming concepts.ObjectiveThis study investigates the usability of two prominent JavaScript RP libraries, RxJS and Bacon.js, to identify common usability issues and suggest improvements.MethodA mixed‐method evaluation was conducted. First, objective structural metrics were applied to assess the libraries' design. Then, a user‐centered study was performed involving programming tasks, a post‐task questionnaire based on the Cognitive Dimensions of Notation (CDN) framework, and follow‐up interviews.ResultsBoth libraries exhibited moderate usability. Metrics revealed strengths in reusability and parameter design, while user studies highlighted challenges in learnability, error handling, and documentation. RxJS generally performed better in metrics, while Bacon.js had stronger task completion rates.ConclusionThe findings suggest that despite their popularity, RP APIs can still hinder adoption due to usability issues. Improvements such as enhanced documentation, guided error‐handling strategies, and reduced operator complexity can significantly improve developer experience.

The age of pervasive computing has initiated a boom in the development of adaptive context-aware learning environments (ACALEs), i.e., systems that are capable of detecting a learner’s context and providing adaptive learning services based on this context. Many of the existing educational systems were developed as standalone applications for specific or a small range of adaptive educational scenarios. It would be extremely helpful for developers and educators to have a unified framework that provides an infrastructure for the development of ACALEs. In this study, we propose Lightlore—an adaptation framework that enables the development of different types of ACELEs for a wide range of learning scenarios in formal and informal settings. We first used scenario-based design (SBD) as the design methodology for creating a conceptual model of Lightlore. Educational scenarios were adopted from the results of a previous literature review. We then developed a proof-of-concept implementation of Lightlore, with a hypermedia system for learning data structures that uses the adaptation service of Lightlore. This implementation is essentially an adaptation infrastructure and a programming API for creating new (or transforming existing) adaptive and context-aware educational services. It exploits the experience API (xAPI), a modern e-learning standard and learning record store, thus making coupling with existing learning environments easier. We expect that diverse types of users will benefit from using Lightlore, such as learners, educators, learning environment developers, and researchers on educational technologies.

The object of research is neural networks, the VKontakte platform, the Telegram messenger, the Python programming language and its libraries, and a block diagram of a computer system model. The subject of the study is a computer technology for detecting extremist content in text form and specific groups containing it on the VKontakte social network. The authors consider in detail the structural scheme of the computer system model, the functional modules included in it, and illustrate their interaction. The paper uses a pre-trained model designed for processing the Russian language, and provides conditions for ensuring high accuracy of recognition of illegal content without signs of retraining. The paper presents the results of checking the test data confirming the operability of the computer system. The proposed prototype of the computer system ensures its integration with the Telegram messenger, which increases usability and facilitates the process of generating queries and reports. The novelty of the research lies in the creation of a prototype of a computer system for searching and detecting extremist messages on the VKontakte social network using the Python programming language and the VKontakte API programming interface (VK API). The basis of the prototype computer system is a neural network that works with the Тгаnsformers and Тоrch. A special feature of the computer system is the ability to analyze messages on a social network and subject them to binary classification for the content or non-content of illegal information in messages. The main conclusions of the study show the efficiency of the system, the simplicity and convenience of its use, the possibility of detecting illegal text content. A distinctive feature of the prototype is the ability to detect illegal content presented using slang expressions.

Heterogeneous nodes composed of a multicore CPU and accelerators are today's norm in highperformance computing (HPC) platforms due to their superior performance and energy efficiency. Tools such as OpenCL and hybrid combinations such as OpenMP plus OpenACC are used for developing portable parallel programs for such nodes. However, these tools have some drawbacks, including a lack of compiler support for nested parallelism, performance portability, automatic heterogeneous workload distribution, userfriendly thread placement, and processor affinity essential to the portable performance of hybrid programs executing on such nodes. In this paper, we propose OpenH, a novel programming model and library API for developing portable parallel programs on heterogeneous hybrid servers composed of a multicore CPU and one or more different types of accelerators. OpenH integrates Pthreads, OpenMP, and OpenACC seamlessly to facilitate the development of hybrid parallel programs. An OpenH hybrid parallel program starts as a single main thread, creating a group of Pthreads called hosting Pthreads. A hosting Pthread then leads the execution of a software component of the program, either an OpenMP multithreaded component running on the CPU cores or an OpenACC (or OpenMP) component running on one of the accelerators of the server. The OpenH library provides API functions that allow programmers to get the configuration of the executing environment and bind the hosting Pthreads (and hence the execution of components) of the program to the CPU cores of the hybrid server to get the best performance. We illustrate the OpenH programming model and library API using two hybrid parallel applications based on matrix multiplication and 2D fast Fourier transform for the most general case of a hybrid hyperthreaded server comprising p computing devices. Finally, we demonstrate the practical performance and energy consumption of OpenH for the hybrid parallel matrix multiplication application on a server comprising an Intel Icelake multicore CPU and two Nvidia A40 GPUs.

IllustrisTNG and the corresponding programming API function are used in this paper to analyze the relationship between different values and parameters (e.g., mass, density and SFR) and metallicity, FMR and visualize the essential relations such as relationship of metallicities and galaxies’ star formation rates (SFRs), gas fraction in galaxies with different redshifts and the stellar mass versus gas fraction at a given redshift. Based on the analysis, a path to a deeper understanding of the MZR properties and evolution is now paved by various results and conclusions. Based on the IllustrisTNG simulation, a major test of galaxy feedback models is the MZR, which simulates metal distribution and evolution. Based on IllustrisTNG simulations, MZR at redshifts 0 - 2 is generally in agreement with observations. A fundamental metallicity relation is supported by the model used and the conclusions presented in this article. In addition, this paper also focuses on the analysis of star formation rate associated with different values and parameters like redshifts with suitable example galaxies. For the relationship between the gas fraction and stellar mass, different z was fitted according to the relationship that Mgas/M* is proportional to M*γ, and for z=0, γ = −0.32. The fitting results agrees with the conclusion drawn, which states that as redshift increases, the relationship of gas fraction and stellar mass steepens.

Inequity technology development in Indonesia had a bad impact on micro and small local stores all over Indonesia. This condition made Emporia Digital Raya acquire micro and small local stores as digital store partners to boost up their income. In a way to fasten the process, Emporia Digital Raya developed a mobile application to help and fasten the Acquirer (Emporia Digital Raya call it Area Leader) process in acquisition. This journal explains how this application is developed in the form of a mobile application based on Area Leader needs like acquisition, supervise store performance, and store visit report. This research was conducted with a quantitative method. The data collection is done by survey and literature study. Mobile application development is carried out using Java, Codeigniter 2 as the Application Programming Interface or API, PostgreSQL as database server, and Apache as the server. The study shows an overall result that this application is useful in helping Area Leaders to acquire and supervise their stores. This application is also considered user friendly. The results of application testing have been confirmed to pass the quantitative test using a survey method with 100 Area Leader from all over Indonesia.

This study aims to examine the extent to which native-speakerism (NS) is embedded in the comments responding to a YouTube video entitled “Salah Kursus Bahasa Inggris?- Seleb English” by a YouTuber named Sacha Stevenson. The researchers believe that native-speakerism, an ideology that creates a power imbalance favoring native speakers of English over nonnative speakers of English, is present in the video, in which the content creator scrutinizes Indonesian people’s English accents. However, this paper focuses on the comments responding to the video, since they can show to what extent NS is adopted by EFL speakers/learners in Indonesia. The data was retrieved using YouTube Data API and Python programming language, sorted using Microsoft Excel, and categorized by applying three reading steps into four levels which spans from 0 (no indication of native speakerism) to 3 (strong indication of native speakerism). The findings are presented descriptively and analyzed within the theoretical framework on NS. The results suggest that NS is present in the observed comments on all four levels, which suggests how deeply embedded the NS ideology within the community of Indonesian English language learners and speakers. Suggestions on how to counter this ideology are presented in conclusion.

Software-Defined Networking (SDN) significantly simplifies programming, reconfiguring, and optimizing network devices, such as switches and routers. The de facto standard for programming SDN devices is the P4 language. However, the flexibility and power of P4, and SDN more generally, gives rise to important risks. As a number of incidents at major cloud providers have shown, errors in SDN programs can compromise the availability of networks, leaving them in a non-functional state. The focus of this paper are errors in control-plane programs that interact with P4-enabled network devices via the standardized P4Runtime API. For clients of the P4Runtime API it is easy to make mistakes that may lead to catastrophic failures, despite the use of Google’s Protocol Buffers as an interface definition language. This paper proposes P4R-Type, a novel verified P4Runtime API for Scala that performs static checks for P4 control plane operations, ruling out mismatches between P4 tables, allowed actions, and action parameters. As a formal foundation of P4R-Type, we present the F P4R calculus and its typing system, which ensure that well-typed programs never get stuck by issuing invalid P4Runtime operations. We evaluate the safety and flexibility of P4R-Type with 3 case studies. To the best of our knowledge, this is the first work that formalises P4Runtime control plane applications, and a typing discipline ensuring the correctness of P4Runtime operations.

Abstract High performance computing (HPC) clusters are increasingly handling workloads where working data sets cannot be easily partitioned or are too large to fit into local node memory. In order to enable HPC workloads to access memory external to the node, HPE has defined a programming API (OpenFAM) for developing applications that use large‐scale disaggregated memory. In this paper we describe an open‐source reference implementation of OpenFAM that can be used on scale‐up machines, traditional HPC clusters, as well as emerging disaggregated memory architectures. We demonstrate the efficiency of the implementation using micro‐benchmarks on InfiniBand and Slingshot‐based clusters.

BackgroundThe widespread use of social media has made it easier for patients to access cancer information. However, a large amount of misinformation and harmful information that could negatively impact patients’ decision-making is also disseminated on social media platforms.ObjectiveWe aimed to determine the actual amount of misinformation and harmful information as well as trends in the dissemination of cancer-related information on Twitter, a representative social media platform. Our findings can support decision-making among Japanese patients with cancer.MethodsUsing the Twitter app programming interface, we extracted tweets containing the term “cancer” in Japanese that were posted between August and September of 2022. The eligibility criteria were the cancer-related tweets with the following information: (1) reference to the occurrence or prognosis of cancer, (2) recommendation or nonrecommendation of actions, (3) reference to the course of cancer treatment or adverse events, (4) results of cancer research, and (5) other cancer-related knowledge and information. Finally, we selected the top 100 tweets with the highest number of “likes.” For each tweet, 2 independent reviewers evaluated whether the information was factual or misinformation, and whether it was harmful or safe with the reasons for the decisions on the misinformation and harmful tweets. Additionally, we examined the frequency of information dissemination using the number of retweets for the top 100 tweets and investigated trends in the dissemination of information.ResultsThe extracted tweets totaled 69,875. Of the top 100 cancer-related tweets with the most “likes” that met the eligibility criteria, 44 (44%) contained misinformation, 31 (31%) contained harmful information, and 30 (30%) contained both misinformation and harmful information. Misinformation was described as Unproven (29/94, 40.4%), Disproven (19/94, 20.2%), Inappropriate application (4/94, 4.3%), Strength of evidence mischaracterized (14/94, 14.9%), Misleading (18/94, 18%), and Other misinformation (1/94, 1.1%). Harmful action was described as Harmful action (9/59, 15.2%), Harmful inaction (43/59, 72.9%), Harmful interactions (3/59, 5.1%), Economic harm (3/59, 5.1%), and Other harmful information (1/59, 1.7%). Harmful information was liked more often than safe information (median 95, IQR 43-1919 vs 75.0 IQR 43-10,747; P=.04). The median number of retweets for the leading 100 tweets was 13.5 (IQR 0-2197). Misinformation was retweeted significantly more often than factual information (median 29.0, IQR 0-502 vs 7.5, IQR 0-2197; P=.01); harmful information was also retweeted significantly more often than safe information (median 35.0, IQR 0-502 vs 8.0, IQR 0-2197; P=.002).ConclusionsIt is evident that there is a prevalence of misinformation and harmful information related to cancer on Twitter in Japan and it is crucial to increase health literacy and awareness regarding this issue. Furthermore, we believe that it is important for government agencies and health care professionals to continue providing accurate medical information to support patients and their families in making informed decisions.

Contemporarily, one of the most significant current trends in the studies of black holes is the black hole mergers. In this paper, we would present prescriptions for star formation and stellar evolution of black hole, and make comparisons in features of the black holes before and after the merger. Based on Illustris TNG, we collect our data to investigate Galaxy and black hole mergers and utilize the corresponding programming API function on it to visualize the results. Specifically, the relationship between galaxy mass and gas size will be demonstrated with the help of diagrams in part one. Besides, the general relationship between the characteristics of the black holes before the merger and the characteristics of the black hole after the merger will be discussed. The variance and standard division of the data will be calculated to confirm the relationship. These results explore the basic relationship between galaxies and the similarity between black holes before and after the merger, which shed light on further understanding of the corresponding processes.

Abstract We present Jay, a software framework for offloading applications in hybrid edge clouds. Jay provides an API, services, and tools that enable mobile application developers to implement, instrument, and evaluate offloading applications using configurable cloud topologies, offloading strategies, and job types. We start by presenting Jay's job model and the concrete architecture of the framework. We then present the programming API with several examples of customization. Then, we turn to the description of the internal implementation of Jay instances and their components. Finally, we describe the Jay Workbench, a tool that allows the setup, execution, and reproduction of experiments with networks of hosts with different resource capabilities organized with specific topologies. The complete source code for the framework and workbench is provided in a GitHub repository.

Currently, the urgent task is developing the methods and tools for increasing Smart Parking software system security. The purpose of this study is conducting analysis of requirements for Smart Parking System software security in order to find the bottlenecks and parts of the software that are most vulnerable to external threats and develop the methods and tools for increasing their security. The paper proposes the method of increasing Smart Parking software system security based on integrating the middleware in Smart Parking System software architecture. The proposed method takes into account all the criteria for Smart Parking System software security, i.e. parameters of safe access to the database, client program security, server security and API security and provides a complex solution for increasing the safety of Smart Parking software system. The proposed method differs from the known ones in that it allows taking into account all the criteria for increasing the Smart Parking System software security in complex using security middleware.

Tensor Cores have been an important unit to accelerate Fused Matrix Multiplication Accumulation (MMA) in all NVIDIA GPUs since Volta Architecture. To program Tensor Cores, users have to use either legacy wmma APIs or current mma APIs. Legacy wmma APIs are more easy-to-use but can only exploit limited features and power of Tensor Cores. Specifically, wmma APIs support fewer operand shapes and can not leverage the new sparse matrix multiplication feature of the newest Ampere Tensor Cores. However, the performance of current programming interface has not been well explored. Furthermore, the computation numeric behaviors of low-precision floating points (TF32, BF16, and FP16) supported by the newest Ampere Tensor Cores are also mysterious. In this paper, we explore the throughput and latency of current programming APIs. We also intuitively study the numeric behaviors of Tensor Cores MMA and profile the intermediate operations including multiplication, addition of inner product, and accumulation. All codes used in this work can be found in https://github.com/sunlex0717/DissectingTensorCores.

The work proposes an approach to programming parallel architectures without using parallel programming interfaces (APIs) and parallel directives for various numerical simulation and computer graphics applications. The primary goal of this approach is to resolve the fundamental conflict between cross-platform compatibility and hardware acceleration when developing high-performance programs. This conflict is resolved through the automation of the development process: algorithmic descriptions in C++, free from any specific parallel constructs, are automatically translated into the implementation of the same algorithm, and a realization is generated on an existing parallel architecture programming API (C++ translates to SPIR-V for GPUs, and vector instructions for CPUs). Furthermore, if specific hardware acceleration is required, the programmer can replace individual parts of the generated code by substituting kernels and virtual functions in the generated code. Such substitution allows for code regeneration without losing usermade changes tailored to a particular hardware platform. The developed system operates as a white box, allowing the programmer to read and debug the generated code as if it were written manually. This enables us to easily distinguish translator errors from user errors and, moreover, does not create dependencies on the developed system in projects. The generated code can always be manually rewritten when necessary. The application of the developed technology is considered using the example of one of the most complex and inconvenient problems for GPUs — parallel algorithms for constructing trees.

Hardware specialization is a well-known means to significantly improve the performance and energy efficiency of various application domains. Modern computing systems consist of multiple specialized processing devices which need to collaborate with each other to execute common tasks. New heterogeneous programming abstractions have been created to program heterogeneous systems. Even though many of these abstractions are open vendor-independent standards, cross-vendor interoperability between different implementations is limited since the vendors typically do not have commercial motivations to invest in it. Therefore, getting good performance from vendor-independent heterogeneous programming standards has proven difficult for systems with multiple different device types. In order to help unify the field of heterogeneous programming APIs for platforms with hardware accelerators from multiple vendors, we propose a new software abstraction for hardware-accelerated tasks based on the open OpenCL programming standard. In the proposed abstraction, we rely on the built-in kernel feature of the OpenCL specification to define a portability layer that stores enough information for automated accelerator utilization. This enables the portability of high-level applications to a diverse set of accelerator devices with minimal programmer effort. The abstraction enables a layered software architecture that provides for an efficient combination of application phases to a single asynchronous application description from multiple domain-specific input languages. As proofs of the abstraction layer serving its purpose for the layers above and below it, we show how a domain-specific input description ONNX can be implemented on top of this portability abstraction, and how it also allows driving fixed function and FPGA-based hardware accelerators below in the hardware-specific backend. We also provide an example implementation of the abstraction to show that the abstraction layer does not seem to incur significant execution time overhead.