Processing Of Input Data Research Articles

BCUB – a large-sample ungauged basin attribute dataset for British Columbia, Canada

Abstract. The British Columbia Ungauged Basin (BCUB) dataset is an open-source, extensible dataset of attributes describing terrain, soil, land cover, and climate indices of over 1.2 million ungauged catchments in British Columbia, Canada, including trans-boundary regions. The attributes included in the dataset are similar to those found in the large-sample hydrology literature for their association with hydrological processes. The BCUB database is intended to support water resources research and practice, namely monitoring network analysis studies, or hydrological modelling where basin characterization is used for model calibration. The dataset and the complete workflow to collect and process input data, to derive stream networks, and to delineate sub-basins and extract attributes, are available under a Creative Commons BY 4.0 license. The DOI link for the BCUB dataset is https://doi.org/10.5683/SP3/JNKZVT (Kovacek and Weijs, 2023).

Effectiveness of the Integrated Administrative Information System (PATEN) in Public Service

Problems related to population administration and civil registration, such as identity falsification and duplicate data due to lack of accurate details of population data documents, the process of obtaining a Population Identity Card and issuing a civil registration certificate still takes a long time because the data input process is still done manually. In this regard, the Garut Regency government, in this case the Tarogong Kaler District, is trying to collect valid population data so that it can develop mapping to overcome this problem. Currently there is an information system that makes it easier to provide services for creating and issuing resident document certificates, namely by implementing a website-based integrated administration service system or PATENT. This research aims to analyze Effectiveness of the Integrated Administrative Information System (PATEN) in Public Service. This research uses a qualitative descriptive type of research with four informants. Data collection techniques start from direct observation, interviews, and documentation. The data analysis techniques used are data collection, data reduction, data presentation and drawing conclusions and verification. The results of the research show that the community can enjoy the quality of services provided in Tarogong Kaler District. This can be seen from several things. The reliability dimension can be seen from the district operators who are reliable in providing services to the community. The responsiveness dimension related to the ability to respond is said to be good if the PATENT system has immediate disruption. can be overcome by not reducing services to the community. The obstacles are that the availability of human resources is still insufficient, as well as the need for socialization to the community about understanding the system.

The Lawfulness of Using Inventions for Generative AI Training : A Case Study of a US Lawsuit against OpenAI and Perplexity AI

Copyright protection in Indonesia is governed by Law No. 28 of 2014 (Law 28/2014), encompassing moral and economic rights. This law imposes limitations on the use of works for education, law enforcement, or technological development as long as such use does not harm the legitimate interests of the rightholder. The development of generative AI (GAI) poses challenges in determining the legality of using copyrighted works for GAI training. This study examines copyright regulations concerning GAI through normative, conceptual, and comparative legal approaches, including case studies on lawsuits against OpenAI and Perplexity AI. The findings indicate that the legality of using copyrighted works depends on the data input process and output (responses). Data scraping is considered an economic right of the rightholder, classified as reproduction under Article 9 of Law 28/2014. If such acts are conducted without the rightholder’s consent and for commercial purposes, they are deemed unlawful under Indonesia’s current copyright law. GAI outputs may also infringe copyright if: (1) the source is not cited, violating Article 7 on copyright management information; (2) substantial portions of the work are reproduced, violating the rightholder's economic rights under Article 9; or (3) the work is distorted in a way that harms the rightholder’s honor, infringing on moral rights under Article 5. To accommodate AI development, specific regulations integrating AI transparency principles outlined in SE Kominfo 9/2023 are required. These regulations could include obligations for AI companies to release summaries of training datasets, include Uni EropaLAs that define the responsibilities of AI developers and users, and provide disclaimers regarding AI's limitations. Regarding the fulfillment of rightholders’ economic rights, a non-exclusive blanket license through Collective Management Organizations (CMOs) as stipulated in Permenkumham 15/2024 is necessary. These regulations should be synchronized with related policies to establish legal certainty that adapts to technological advancements.

Implementation of Principal Decision Making Based on Management Information System on the Quality of Education at Batu Bolong State Elementary School, Central Lombok Regency

This study aims to determine: 1) Principal Decision Making Planning based on Management Information System on Education Quality 2) Implementation of Principal Decision Making based on Management Information System on Education Quality. 3) Supporting and Inhibiting Factors. The research method used is a qualitative approach with a case study research type. Data collection is carried out using interview, observation, and documentation techniques. As well as data analysis techniques used through three procedures, including data reduction, data presentation, and drawing conclusions. The data that has been obtained is then checked for data validity using triangulation techniques. The findings and analysis of the study show that 1) Principal Decision Making Planning based on Management Information System on Education Quality is carried out by forming a team, planning results and implementation strategies for MIS, building infrastructure and meetings with teachers. 2) In general, the implementation of Principal Decision Making based on Management Information System on Education Quality with data input, data authentication, data processing, data presentation and decision-making process based on MIS data. 3) Supporting and inhibiting factors, supporting factors are, the addition of adequate supporting facilities and infrastructure. Meanwhile, inhibiting factors are students or parents who do not yet have the devices to access the management information system.

A Parallel Framework for Fast Charge/Discharge Scheduling of Battery Storage Systems in Microgrids

Fast charge/discharge scheduling of battery storage systems is essential in microgrids to effectively balance variable renewable energy sources, meet fluctuating demand, and maintain grid stability. To achieve this, parallel processing is employed, allowing batteries to respond instantly to dynamic conditions. By managing the complexity, high data volume, and rapid decision-making requirements in real time, parallel processing ensures that the microgrid operates with stability, efficiency, and safety. With the application of deep reinforcement learning (DRL) in scheduling algorithm design, the demand for computational power has further increased significantly. To address this challenge, we propose a Ray-based parallel framework to accelerate the development of fast charge/discharge scheduling for battery storage systems in microgrids. We demonstrate how to implement a real-world scheduling problem in the framework. We focused on minimizing power losses and reducing the ramping rate of net loads by leveraging the Asynchronous Advantage Actor Critic (A3C) algorithms and the features of the Ray cluster for real-time decision making. Multiple instances of OpenDSS were executed concurrently, with each instance simulating a distinct environment and efficiently processing input data. Additionally, Numba CUDA was utilized to facilitate GPU acceleration of shared memory, significantly enhancing the performance of the computationally intensive reward function in A3C. The proposed framework enhanced scheduling performance, enabling efficient energy management in complex, dynamic microgrid environments.

Measuring-polynomial processing of input data of a computer system

Purpose of research. The purpose of this study is to solve the problem of restoring the external load on the rack– and-beam structural system and to assess the impact on the accuracy of solving the problem of the error of noisy deflections – constructive input data of the computing system.Methods. The main scientific methods used in this study are methods of modeling and identification of boundary conditions, the grid method of regularization of solving inverse incorrect problems. Measurement reduction and approximation methods, methods for evaluating the quality of input data processing, regularization and approximation algorithms using the Lebesgue grid function, and numerical methods are also used.Results. The main result of this work is two theorems about the external load on a rack-and-beam structural system. The existence and uniqueness of the solution is proved. Also, the results are the formulas of Lagrange multipliers in linear Lagrangian approximation and the optimal plan of coordinates of the nodes of the approximation grid for the equation of deflections of the beam of the fourth and fifth degree with Chebyshev alternance. An assessment of the quality of the approximation of the external load on the rack-and-beam structure by the values of the target parameters was carried out.Conclusion. This article proposes a method for restoring the external load on a rack-and-beam structure using the results of solving the inverse Cauchy problem for the equation of deflections of a beam with minimizing the influence of the error of noisy input data.

Sistem Informasi Arus Kas Dengan Metode Cash Basis Pada TK Mekarsari Sidokumpul Patean Kendal

Mekarsari Kindergarten is a private school that was founded in 2015 with C accreditation and has one classroom located in Cipluk Sidokumpul Hamlet, Patean Kendal District, Central Java. As for the information system for receipts and expenditures in educational institutions, which still uses a manual system, in the data input process until the report is still written in recording financial transactions, especially in cash receipts and expenditures, by recording cash receipts and disbursements on receipts, then the information is summarized and recorded in the cash ledger. This makes the cycle spend most of its time in presenting financial reports, data discrepancies and large levels of errors can occur due to the large number of transactions that have been carried out, and double or double recording occurs if recorded manually. In addition, the speed of data input to report information may be late if needed at any time. This research aims to design a cash flow information system using the cash basis method, which makes it easier to record cash receipts and disbursements at Kindergarten Mekarsari Sidokumpul Patean Kendal using a computerized system. Building an effective and efficient information system in presenting financial reports. And minimize errors and deficiencies in input because every money received is immediately recorded into the system.

Perancangan Sistem Informasi Pendaftaran Rawat Inap pada Layanan Kesehatan Rumah Sakit Umum Daerah Siti Fatimah

The purpose of setting up SIMRS is to improve efficiency, effectiveness, professionalism, performance, and access and services in hospitals. In the preparation of this paper, data was collected through observation methods, interviews, literature studies, and the application of software development models using the waterfall method. The result is the design of a web-based patient registration information system using Laravel and MySQL as a database. Hospitals face several problems, including data redundancy in the input process and less than optimal data presentation, making it difficult to manipulate, change, and delete data. The author hopes that the development of a new information system can simplify and speed up the data input process. The information system designed is expected to make it easier for patients to register online as well as assist admins in the patient registration process, making it easier to make registration reports.

Deep Learning-Based Reservoir Simulation Proxy Models

The behavior of oil reservoirs, characterized by geophysical, geochemical, and geological properties, can be understood through the simulation of computational models, involving the construction of meshes of finite volume elements with equations derived from fundamental principles. However, the execution of multiple simulations for activities such as optimization and uncertainty assessment results in substantial computational costs.To overcome this challenge, proxy models are proposed, aiming to replace reservoir simulators with adequate precision. This work proposes the implementation of data-based proxies for reservoir simulators using Artificial Neural Networks (ANNs). This approach utilizes time series of well controls as inputs, generating responses for Bottom Hole Pressures (BHPs) and/or flow rates.In recent years, proxy models based on neural networks have been applied to obtain predictions of flows and/or pressures in reservoirs. For example, Recurrent Neural Networks (RNNs), specialized in handling sequential data, were used by [1] to predict water flows in the Xiluodu hydroelectric reservoir in China. Convolutional Neural Networks (CNNs), specialized in pattern recognition in images and videos, were also employed by [2] to predict pressures and flow rates of injector and producer wells, respectively.In the scope of this study, distinct neural network architectures were evaluated to predict outputs of a synthetic two-phase model with partial faults. Given the adoption of mixed controls, where producer wells are controlled by BHP and injector wells by flow rate, the use of Multihead Neural Networks [3] was also investigated. This approach allows differentiated processing of input data, contributing to more robust and efficient learning.For each considered architecture, the impact of the number of timesteps in the samples and their size on the accuracy of predictions was analyzed. The results indicate that parallel hybrid architectures exhibit the best performance, forming a complementary mutual network where each architecture contributes with different learning approaches. Additionally, a higher number of samples contributes to reducing result dispersion, while an increase in the number of timesteps does not show a significant contribution to reducing mean error.We gratefully acknowledge the support provided by PETROBRAS, ANP, FINEP, PRH, EMBRAPII, FACEPE, CNPq, and CAPES, which has been instrumental in the successful execution of this work.

Recommendation system for financial decision-making using Artificial intelligence

The rapid expansion of artificial intelligence (AI) in consumer markets presents challenges, particularly in how cognitive biases influence financial decision-making. These biases can lead to irrational spending, raising ethical concerns about AI’s role in such applications. This research explores how AI can enhance decision-making effectiveness and support consumers in making more rational financial choices. The focus is on developing an intelligent financial management system that applies modern AI algorithms to analyze financial behavior, detect anomalies, and offer personalized recommendations. The article considers a system for generating personalized financial recommendations based on large language models, which uses transaction history, predicted costs, and anomaly information to generate individual advice. Techniques include using Isolation Forest for identifying atypical financial actions and a combination of ARIMA and LSTM models for budget forecasting. The research also considers integrating these models with large language models (LLMs) to generate personalized recommendations. The methodological part of the work includes an analysis of existing models and their areas of application, defining data types and structures for processing, developing a system that integrates the available models, and testing it. The process of generating recommendations is described, which includes the stages of processing input data, forming context, generating recommendations and evaluating them taking into account user characteristics, such as risk level, financial goals and preferences. The generated recommendations are aimed at optimizing the user's financial behavior and can be adapted to different income levels. Special attention is paid to the ethical aspects of the system, which include ensuring confidentiality, fairness and transparency, as well as the importance of supporting user autonomy in making financial decisions. The system promotes responsible financial behavior by helping to avoid impulsive spending and increasing financial awareness without manipulation or imposing specific decisions.

Method for Helicopter Turboshaft Engines Controlling Energy Characteristics Through Regulating Free Turbine Rotor Speed and Fuel Consumption Based on Neural Networks

This research is devoted to the development of a method for helicopter turboshaft engine energy characteristics control by regulating the free turbine rotor speed and fuel consumption using neural network technologies. A mathematical model was created that links the main rotor and free turbine rotor speed parameters, based on which a relation with the engine output power was established. In this research, a differential equation was obtained that links fuel consumption, output power, and rotor speed, which makes it possible to monitor engine dynamics in various operating modes. A fuel consumption controller was developed based on a neuro-fuzzy network that processes input data, including the desired and current rotor speed, which allows real-time adjustments to improve the operational efficiency. In the research, based on the flight data analysis obtained during the Mi-8MTV helicopter with a TV3-117 turboshaft engine flight test, improved signal processing quality was obtained due to time sampling and adaptive quantisation methods (this is confirmed by assessing the homogeneity and representativeness of the training and test datasets). A comparative analysis of the developed and traditional controllers showed that the neuro-fuzzy network use reduces the transient fuel consumption process time by 8.92% while increasing the accuracy and F1 score by 18.28% and 21.32%, respectively.

Parametric identification of coefficients for a model of fatigue stiffness degradation of a composite material

The problem of finding the fatigue characteristics of a composite material based on test results is considered. The results of endurance tests of unidirectional polymer composite materials with different initial stiffness, breaking stress and working cycle stress were used as the initial data. As a mathematical model of stiffness degradation, a nonlinear ordinary differential equation with five unknown parameters is used, reflecting characteristic changes in the properties of the material. It is required to find such parameter values that the solution of the differential equation should describe the available test results with sufficient accuracy. The solution procedure is reduced to the problem of optimizing the objective function, the value of which characterizes the achieved accuracy. As optimization methods, a method simulating the behavior of a flock of moths and a method of sequential reduction of the search set were used. A step-by-step algorithm for finding unknown model parameters is proposed, and numerical results of processing input data containing information on changing the elasticity modulus of the composite material in the course of applying load cycles are presented.

SecureEI: Proactive intellectual property protection of AI models for edge intelligence

Deploying AI models on edge computing platforms enhances real-time performance, reduces network dependency, and ensures data privacy on terminal devices. However, these advantages come with increased risks of model leakage and misuse due to the vulnerability of edge environments to physical and cyber attacks compared to cloud-based solutions. To mitigate these risks, we propose SecureEI, a proactive intellectual property protection method for AI models that leverages model splitting and data poisoning techniques. SecureEI divides the model into two components: DeviceNet, which processes input data into protected license data, and EdgeNet, which operates on the license data to perform the intended tasks. This method ensures that only the transformed license data yields high model accuracy, while original data remains unrecognizable, even under fine-tuning attacks. We further employ targeted training strategies and weight adjustments to enhance the model’s resistance to potential attacks that aim to restore its recognition capabilities for original data. Evaluations on MNIST, Cifar10, and FaceScrub datasets demonstrate that SecureEI not only maintains high model accuracy on license data but also significantly bolsters defense against fine-tuning attacks, outperforming existing state-of-the-art techniques in safeguarding AI intellectual property on edge platforms.

PLC Honeypots: Enhancing Interaction-Level Assessment

The motivation for this work arose when noticing that definitions of honeypots’ interaction level are mainly based on the information technology environment and do not reflect operational technology even if several honeypot projects approach this field. Within operational technology, programmable logic controllers (PLCs) have a main role, resulting in several honeypot researchers choosing to mimic this device at a certain interaction level. However, searching for an interaction level definition that approaches PLCs results in few studies. In this context, this work aims to explore how to adapt the information technology definition of the interaction level in order to encompass PLCs and their specific features. The method chosen to obtain inputs was a literature review where, in attempting to keep the connection with information technology, the features were based in terms of honey system, honey service, and honey token. The findings of this review provide a means to translate these terms when developing a PLC honeypot for a desired interaction level, resulting in a metrics proposal for low and high interaction. Summarizing the proposed metrics, the system of a PLC can be considered as the vendor specific firmware, its unique device banner, and a realistic network topology. For services, a PLC honeypot reflects the tasks performed by the real device, thus resulting in industrial communication protocols, network management protocols, appropriate response times, code-related interactions, dynamic input and output data processing, physical process simulation, and web interface. Lastly, a PLC honey token can be approached with the PLC program file, MIB file, and software license, among other elements. Based on these metrics, researchers can better evaluate how to design a programmable logic controller honeypot or select tools that match their target interaction level.

Collaborating across government agencies to combine linkage processes and algorithms to create an Australian National Linkage Spine (NLS) to facilitate cross-jurisdictional linkage

The Australian Bureau of Statistics (ABS) and the Australian Institute of Health and Welfare (AIHW) are co-technical lead agencies for developing the National Disability Data Asset (NDDA) which will bring together data across jurisdictions to build a more complete picture of the lives of people with disability. ABS and AIHW are collaboratively building a National Linkage Spine (NLS), a high-quality high-coverage population dataset to be shared under new legislation to facilitate cross-jurisdictional linkage and build the NDDA. ABS and AIHW each have extensive data integration experience, however our linkage methods have evolved differently in response to the needs of our respective researcher client bases and the datasets we work with. An ABS-AIHW cross-agency team was established to align terminology and share data processing methods, linkage approaches, and locally built linkage tools. The team collaborated on the design of an NLS that combines the best aspects of each of our approaches. The NLS build methodology delivers aligned processing of input data and applies a combination of ABS’s deterministic linkage algorithm and AIHW’s probabilistic linkage and machine learning algorithms. The NLS was peer reviewed by its future jurisdictional users and is considered fit for purpose for delivering the NDDA. The open sharing of knowledge and tools has significantly benefited both agencies and the broader NDDA project. Takeaways from this project extend beyond it, with improved data process approaches being considered by each agency in other work, and a growing common agreement of linkage best practice.

A review of memristive reservoir computing for temporal data processing and sensing

AbstractReservoir computing (RC) is a promising paradigm for machine learning that uses a fixed, randomly generated network, known as the reservoir, to process input data. A memristor with fading memory and nonlinearity characteristics was adopted as a physical reservoir to implement the hardware RC system. This article reviews the device requirements for effective memristive reservoir implementation and methods for obtaining higher‐dimensional reservoirs for improving RC system performance. In addition, recent in‐sensor RC system studies, which use a memristor that the resistance is changed by an optical signal to realize an energy‐efficient machine vision, are discussed. Finally, the limitations that the memristive and in‐sensor RC systems encounter when attempting to improve performance further are discussed, and future directions that may overcome these challenges are suggested.

ANALYSIS AND DESIGN OF DIGITAL APPLICATION SYSTEMS FOR EMPLOYEE DATA MANAGEMENT IN XYZ STORE

As technology and information systems develop in the digital era, managing employee data has become very important for organizations and companies. An efficient and accurate employee data input process is the first step in managing human resources optimally. XYZ Shop is a shop located in the Banten area, which sells retail goods to end consumers, usually on a small scale and at home. The shop provides a variety of products, ranging from clothing, food, electronic goods, to household equipment. However, the XYZ store still faces challenges in managing employee data, such as limited time and human energy in carrying out this process because it is still manual. To overcome this problem, an employee data input program was designed to be an effective solution. With this program, the employee data management process can become more efficient, accurate and organized. The author used three techniques for data collection activities, namely observation, interviews and literature study. Development of an application program system for managing employee data at XYZ store using the PHP programming language and MySql database. The application system built is web-based, so it can be accessed easily anywhere and anytime. This provides immediate benefits in day-to-day operational efficiency, allowing human resources to focus on more strategic tasks. With the ability to manage employee data efficiently, stores can easily adapt to company growth, including branch expansion and increased employment

A Single-Pixel Event Photoactive Device for Real-Time, In-Sensor Spatiotemporal Optical Information Processing.

The increasing demand for energy-efficient, sophisticated optical sensing technologies in various applications, from machine vision to optical communication, highlights the necessity for innovations in spatiotemporal information sensing and processing at a nearly single-pixel scale. Traditional methods, including multi-pixel photodetector arrays and event-based camera systems, often fail to provide rapid, real-time detection and processing of dynamic events within the sensor. This shortcoming is particularly notable in handling high-dimensional spatiotemporal data, where the dependency on sequential data input and external processing tools leads to latency, reduced throughput, and heightened energy consumption, thereby impeding real-time parallel data processing capabilities. Here, a carrier-selective, single-pixel, position-sensitive planar photoactive device that integrates spatiotemporal event sensing with inherent short-term memory capabilities is introduced. The proof-of-concept single-pixel event photoactive device enables in-sensor spatiotemporal parallel optical information processing, efficiently managing multibit (>4 bit) data simultaneously and facilitating ultrafast (≈0.4µs) recognition of input patterns with low energy consumption (25 femtojoules). Additionally, by adjusting the operating speed from continuous to pulsed light illumination, the sensor array can detect trajectories and absolute position of events, offering in-sensor optical flow detection. This single-pixel event photodetector marks significant advancement toward developing compact, energy-efficient, ultrafast sensors suitable for a wide range of in sensor-based photonic applications.

A Review of Transformer Networks in MRI Image Classification

The urgent need to improve the efficiency and precision of analyzing images in the field of magnetic resonance imaging (MRI) has led to the rise of transformer networks, as a groundbreaking solution. This review delves deeply into how transformer networksre used in classifying MRI images summarizing studies to showcase the progress made and challenges faced in this evolving area. With their abilities transformers excel at analyzing MRI images by highlighting both details and functional complexities while effectively capturing both global and local nuances using their advanced handling of long distance connections. Additionally their adaptability allows for processing of input data in sizes along with the capability to break down processing and analysis tasks. By providing researchers with an insight into transformers and their crucial role in enhancing medical imaging methods this research aims to lay the groundwork, for advancements in this critical field.

On the Analysis of Coverage Feedback in a Fuzzing Proprietary System

Coverage feedback is one of the key mechanisms for improving the effectiveness of fuzzers by measuring and comparing the executed code regions while processing input data. In general, such guidance should always improve the performance of fuzzers to better find unexplored code regions. However, proprietary systems with uncommon I/O interfaces (e.g., UAV system, IoT devices, satellite firmware) require extensive engineering/porting efforts to apply coverage feedback support in developing their fuzzing platform. In this paper, we evaluate the detailed efficacy of coverage feedback in fuzzing based on 44 real-world bugs we found using OSS-Fuzz. Our analysis uncovered when and how code coverage information can be helpful, and our experiment demonstrates that although coverage guidance is always helpful to some extent, its effectiveness depends on various external factors. Therefore, such factors should be carefully considered for optimizing the cost and efficiency in designing the fuzzing architecture of proprietary systems.