Data Upload Research Articles

PERLINDUNGAN DATA PRIBADI DITINJAU DARI TEORI PERLINDUNGAN HUKUM DAN TEORI PERLINDUNGAN HAK ATAS PRIVASI

The emergence of Corona-virus 19 (COVID-19) requires the Indonesian government to impose social distancing provisions in the form of laws and regulations so that the Indonesian government can reduce the percentage and rate of spread of COVID-19 in the country. As a result of the social distancing policy, people depend on information technology to support their activities. In using information technology services, technology service providers require technology service users to fill in personal data information and upload personal identity documents. Technology service providers ask for approval from users that the information and documents that have been provided will only be used for the benefit of technology services, will not be used for other things without the consent of technology service users. With regard to the protection of personal data, the Indonesian government issues regulations regarding the protection of personal data. However, recently there has been a leak of people's personal data that is misused by unauthorized parties. This is contrary to one of the obligations of the Indonesian government which must ensure the protection of people's personal data.

Petrophysical analysis to determine the hydrocarbon prospectivity of sands in AA field, Niger Delta

Petrophysical analysis is a crucial process in the oil and gas industry. It entails the analysis and interpretation of well logs, fluid samples or core smaples to understand the behaviour of the embedded reservoirs in the subsurface. Three well logs from AA field were provided for this study, but two well data were finally loaded to the workstation due to absence of key well logs (such as gamma ray and density logs) from the third well. The quality control check of the data was done prior to the uploading of data. Delineation of lithologies and identification of hydrocarbon reservoirs were done; the identified reservoirs were correlated across the two wells; and the petrophysical evaluation (such as estimations of shale volume, porosity, permeability and water/hydrocarbon saturation) of the three pay zones (that is, Sand A, B and C) in AA field were done. The porosity of Sands A, B and C varied from 0.27 to 0.28, 0.24 to 0.30 and 0.27. The permeability of Sands A, B and C varied from 1012 to 1314 md, 884 to 1013 md and 692 to 892 md. Meanwhile, the hydrocarbon saturation for Sands A, B and C varied from 1 to 89%, 45 to 80% and 79 to 80%, respectively. It can be concluded that the order of hydrocarbon prospectivity of the reservoir sands correlated across Well AA-1 and Well AA-2 is Sand C > Sand B > Sand A.

A System of IoT Devices to Prevent Under-Loading Over-Loading of Cargo Vehicles

Efficient and safe cargo transportation is a cornerstone of modern logistics and the loading of cargo vehicle is a crucial aspect of this process. This project introduces a comprehensive system of IOT Devices designed to prevent both under-loading and overloading of CARGO VEHICLE.The system incorporates an Arduino UNO microcontroller, load cells for load detection, MEMS sensors to act as virtual doors for theft detection, IOT modules for sending SMS alerts and cloud data upload, GPS for location tracking and information sharing and an LCD display for data visualization.

Improving Security on Election Results Data Transmission via Cloud Using Hybrid Homomorphic Encryption

Elections in recent years have become topical issues and characterized by violence and conflicts leading to loss of lives and properties. The integrity and confidentiality of the declared collated results have been questioned by individuals and organisations with keen interest in the outcomes of every election. Different countries have adopted different Election Results Management Systems (RMS) to help present a credible, fair and transparent election results. These systems adopted are not without criticisms and suspicions. This research paper has presented various factors that need to be considered when selecting a Results Management System (RMS) for elections. A cloud-based using a Hybrid homomorphic encryption approach is proposed in managing the election results data security and transmission. The proposed scheme has demonstrated effectiveness in handling data integrity, data confidentiality, data privacy and access control. The proposed scheme presented has enhanced the security of election results data against Chosen Ciphetext Attacks (CCA) and Denial of Service (DoS) as well as other cyber related attacks. The time required for the entire election data encryption, transmission, decryption, upload time and download time has been greatly enhanced with the proposed system. The proposed system workflow algorithm, key generation algorithm, encryption algorithm and decryption have been presented in this research paper. The outcome of the research work indicates that only 0.00078 seconds is required to generate keys for about 100 users. About 0.705 seconds and 0.863 seconds is required for the encryption and decryption of a 500MB election results data. It was observed that the overall election results data transmission time is about 51.779 seconds which is less than one minute (60 seconds) for about 500MB data size of the election results data. This paper makes a case for the adoption and implementation of the proposed system since it performs better in terms of securing the election results data and transmission time in the cloud environment.

ZooTraits: An R shiny app for exploring animal trait data for ecological and evolutionary research.

Animal trait data are scattered across several datasets, making it challenging to compile and compare trait information across different groups. For plants, the TRY database has been an unwavering success for those ecologists interested in addressing how plant traits influence a wide variety of processes and patterns, but the same is not true for most animal taxonomic groups. Here, we introduce ZooTraits, a Shiny app designed to help users explore and obtain animal trait data for research in ecology and evolution. ZooTraits was developed to tackle the challenge of finding in a single site information of multiple trait datasets and facilitating access to traits by providing an easy-to-use, open-source platform. This app combines datasets centralized in the Open Trait Network, raw data from the AnimalTraits database, and trait information for animals compiled by Gonçalves-Souza etal. (2023, Ecology and Evolution 13, e10016). Importantly, the ZooTraits app can be accessed freely and provides a user-friendly interface through three functionalities that will allow users to easily visualize, compare, download, and upload trait data across the animal tree of life-ExploreTrait, FeedTrait, and GetTrait. By using ExploreTrait and GetTrait, users can explore, compare, and extract 3954 trait records from 23,394 species centralized in the Open Traits Network, and trait data for ~2000 species from the AnimalTraits database. The app summarizes trait information for numerous taxonomic groups within the Animal Kingdom, encompassing data from diverse aquatic and terrestrial ecosystems and various geographic regions worldwide. Moreover, ZooTraits enables researchers to upload trait information, serving as a hub for a continually expanding global trait database. By promoting the centralization of trait datasets and offering a platform for data sharing, ZooTraits is facilitating advancements in trait-based ecological and evolutionary studies. We hope that other trait databases will evolve to mirror the approach we have outlined here.

Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

The typical industrial Internet of Things (IIoT) network system relies on a real-time data upload for timely processing. However, the incidence of device heterogeneity, high network latency, or a malicious central server during transmission has a propensity for privacy leakage or loss of model accuracy. Federated learning comes in handy, as the edge server requires less time and enables local data processing to reduce the delay to the data upload. It allows neighboring edge nodes to share data while maintaining data privacy and confidentiality. However, this can be challenged by a network disruption making edge nodes or sensors go offline or experience an alteration in the learning process, thereby exposing the already transmitted model to a malicious server that eavesdrops on the channel, intercepts the model in transit, and gleans the information, evading the privacy of the model within the network. To mitigate this effect, this paper proposes asynchronous privacy-preservation federated learning for mobile edge networks in the IIoT ecosystem (APPFL-MEN) that incorporates the iteration model design update strategy (IMDUS) scheme, enabling the edge server to share more real-time model updates with online nodes and less data sharing with offline nodes, without exposing the privacy of the data to a malicious node or a hack. In addition, it adopts a double-weight modification strategy during communication between the edge node and the edge server or gateway for an enhanced model training process. Furthermore, it allows a convergence boosting process, resulting in a less error-prone, secured global model. The performance evaluation with numerical results shows good accuracy, efficiency, and lower bandwidth usage by APPFL-MEN while preserving model privacy compared to state-of-the-art methods.

Predict-IT - Forecasting District Heating Loads

In the realm of many thermal energy systems, and particularly within district heating networks, heat load forecasts play a pivotal role in optimizing system operation and efficient infrastructure usage. While district heating operators routinely log measurement data, its potential remains underutilized. One essential application of such data is forecasting a network’s heat load based on historical data records. Such forecasts can improve the efficient usage of plant infrastructure and facilitate predictive operational strategies. This paper introduces ”Predict-IT”, a web-based platform designed to standardize the entire forecasting pipeline, making the generation of predictions largely independent of expert knowledge. The Predict-IT platform is powered by a state-of-the-art long short-term memory (LSTM) based neural network algorithm which only requires very little inputs (measured heat load and ambient temperature) to deliver satisfying forecasting accuracy, even a couple of days ahead. The prediction algorithm is validated on two data sets from local Austrian district heating networks, showing the general applicability of the LSTM-based neural network, given an appropriate set of hyperparameters. The Predict-IT platform simplifies the process of forecasting heat loads into a few discrete steps: data upload, algorithm training, heat load forecast generation, and visualization of forecasts. The source code will be open-source, and deployment and installation will be facilitated by an easily installable Docker solution.

WiSA: Privacy-enhanced WiFi-based activity intensity recognition in smart buildings using personalized federated learning

The increasing demand for enhanced energy efficiency and occupant comfort in buildings has led to a focus on developing occupant-centric building control systems. A key aspect of this study is to obtain real-time and accurate information related to occupants' activities. This study presents WiSA, an innovative, WiFi-based framework for recognizing the intensity of occupants' activities. WiSA offers non-intrusive, personalized activity recognition while minimizing privacy risks through the use of federated learning. We developed a specialized deep learning neural network for classifying activities into different intensity levels (light, moderate, vigorous), incorporating a tailored fine-tuning strategy to address the complexities of federated learning. Our dataset, exhibiting non-identically and independently distributed (No-IID) characteristics, was derived from the activities of 15 occupants in two rooms. This dataset was instrumental in demonstrating WiSA's effectiveness. The results show that WiSA achieved an impressive 98.0% average accuracy across all clients without necessitating the upload of annotated local data. The personalized fine-tuning strategy enhanced the performance of federated learning models by an average of 18.7%, markedly increasing adaptability to No-IID data. Additionally, WiSA displayed substantial robustness in handling the resolution of CSI frames. This approach offers a privacy-conscious method for building managers to gather information on occupant activities, enabling the development of more effective building operation strategies.

Visualizing set relationships: EVenn's comprehensive approach to Venn diagrams.

Venn diagrams serve as invaluable tools for visualizing set relationships due to their ease of interpretation. Widely applied across diverse disciplines such as metabolomics, genomics, transcriptomics, and proteomics, their utility is undeniable. However, the operational complexity has been compounded by the absence of standardized data formats and the need to switch between various platforms for generating different Venn diagrams. To address these challenges, we introduce the EVenn platform, a versatile tool offering a unified interface for efficient data exploration and visualization of diverse Venn diagrams. EVenn (http://www.ehbio.com/test/venn) streamlines the data upload process with a standardized format, enhancing the capabilities for multimodule analysis. This comprehensive protocol outlines various applications of EVenn, featuring representative results of multiple Venn diagrams, data uploads in the centralized data center, and step-by-step case demonstrations. Through these functionalities, EVenn emerges as a valuable and user-friendly tool for the in-depth exploration of multiomics data.

DATA FINDING, SHARING AND DUPLICATION REMOVAL IN THE CLOUD

Deduplication involves eliminating duplicate or redundant data to reduce stored data volume, commonly used in data backup, network optimization, and storage management. However, traditional deduplication methods have limitations with encrypted data and security. The primary objective of this project is to develop new distributed deduplication systems that offer increased reliability. In these systems, data chunks are distributed across the Hadoop Distributed File System (HDFS), and a robust key management system is utilized to ensure secure deduplication with slave nodes. Instead of having multiple copies of the same content, deduplication removes redundant data by retaining only one physical copy and referring other instances to that copy. The granularity of deduplication can vary, ranging from an entire file to a data block. The MD5 and 3DES algorithms are used to enhance the deduplication process. The proposed approach in this project is the Proof of Ownership (POF) of the file. With this method, deduplication can effectively address the issues of reliability and label consistency in HDFS storage systems. The proposed system has successfully reduced the cost and time associated with uploading and downloading data, while also optimizing storage space. Key Words: Cloud computing, data storage, file checksum algorithms, computational infrastructure, duplication.

Association Between Treatment Adherence and Continuous Glucose Monitoring Outcomes in People With Diabetes Using Smart Insulin Pens in a Real-World Setting.

To evaluate the association of insulin injection adherence, smart insulin pen engagement, and glycemic control using real-world data from 16 countries from adults self-administering basal insulin degludec and bolus insulin with a smart insulin pen (NovoPen 6 or NovoPen Echo Plus) alongside continuous glucose monitoring (CGM). Data were aggregated over 14-day periods. Treatment adherence was defined according to the number of missed basal and missed bolus insulin doses and smart pen engagement according to the number of days with data uploads. Data from 3,945 adults, including 25,157 14-day periods with ≥70% CGM coverage, were analyzed. On average, 0.2 basal and 6.0 bolus insulin doses were missed over 14 days. The estimated probability of missing at least one basal insulin dose over a 14-day period was 17.6% (95% CI 16.5, 18.7). Missing one basal or bolus insulin dose per 14 days was associated with a significant decrease in percentage of time with glucose levels in range (TIR) (3.9-10.0 mmol/L), of -2.8% (95% CI -3.7, -1.8) and -1.7% (-1.8, -1.6), respectively; therefore, missing two basal or four bolus doses would decrease TIR by >5%. Smart pen engagement was associated positively with glycemic outcomes. This combined analysis of real-world smart pen and CGM data showed that missing two basal or four bolus insulin doses over a 14-day period would be associated with a clinically relevant decrease in TIR. Smart insulin pens provide valuable insights into treatment injection behaviors.

ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

CLOUD BASED DUPLICATION REMOVAL SYSTEM

Deduplication involves eliminating duplicate or redundant data to reduce stored data volume, commonly used in data backup, network optimization, and storage management. However, traditional deduplication methods have limitations with encrypted data and security. The primary objective of this project is to develop new distributed deduplication systems that offer increased reliability. In these systems, data chunks are distributed across the Hadoop Distributed File System (HDFS), and a robust key management system is utilized to ensure secure deduplication with slave nodes. Instead of having multiple copies of the same content, deduplication removes redundant data by retaining only one physical copy and referring other instances to that copy. The granularity of deduplication can vary, ranging from an entire file to a data block. The MD5 and 3DES algorithms are used to enhance the deduplication process. The proposed approach in this project is the Proof of Ownership (POF) of the file. With this method, deduplication can effectively address the issues of reliability and label consistency in HDFS storage systems. The proposed system has successfully reduced the cost and time associated with uploading and downloading data, while also optimizing storage space. Key Words: Cloud computing, data storage, file checksum algorithms, computational infrastructure, duplication.

Holomics - a user-friendly R shiny application for multi-omics data integration and analysis

An organism’s observable traits, or phenotype, result from intricate interactions among genes, proteins, metabolites and the environment. External factors, such as associated microorganisms, along with biotic and abiotic stressors, can significantly impact this complex biological system, influencing processes like growth, development and productivity. A comprehensive analysis of the entire biological system and its interactions is thus crucial to identify key components that support adaptation to stressors and to discover biomarkers applicable in breeding programs or disease diagnostics. Since the genomics era, several other ’omics’ disciplines have emerged, and recent advances in high-throughput technologies have facilitated the generation of additional omics datasets. While traditionally analyzed individually, the last decade has seen an increase in multi-omics data integration and analysis strategies aimed at achieving a holistic understanding of interactions across different biological layers. Despite these advances, the analysis of multi-omics data is still challenging due to their scale, complexity, high dimensionality and multimodality. To address these challenges, a number of analytical tools and strategies have been developed, including clustering and differential equations, which require advanced knowledge in bioinformatics and statistics. Therefore, this study recognizes the need for user-friendly tools by introducing Holomics, an accessible and easy-to-use R shiny application with multi-omics functions tailored for scientists with limited bioinformatics knowledge. Holomics provides a well-defined workflow, starting with the upload and pre-filtering of single-omics data, which are then further refined by single-omics analysis focusing on key features. Subsequently, these reduced datasets are subjected to multi-omics analyses to unveil correlations between 2-n datasets. This paper concludes with a real-world case study where microbiomics, transcriptomics and metabolomics data from previous studies that elucidate factors associated with improved sugar beet storability are integrated using Holomics. The results are discussed in the context of the biological background, underscoring the importance of multi-omics insights. This example not only highlights the versatility of Holomics in handling different types of omics data, but also validates its consistency by reproducing findings from preceding single-omics studies.

NURECON: A Novel Online System for Determining Nutrition Requirements Based on Microbial Composition.

Dietary habits have been proven to have an impact on the microbial composition and health of the human gut. Over the past decade, researchers have discovered that gut microbiota can use nutrients to produce metabolites that have major implications for human physiology. However, there is no comprehensive system that specifically focuses on identifying nutrient deficiencies based on gut microbiota, making it difficult to interpret and compare gut microbiome data in the literature. This study proposes an analytical platform, NURECON, that can predict nutrient deficiency information in individuals by comparing their metagenomic information to a reference baseline. NURECON integrates a next-generation bacterial 16S rRNA analytical pipeline (QIIME2), metabolic pathway prediction tools (PICRUSt2 and KEGG), and a food compound database (FooDB) to enable the identification of missing nutrients and provide personalized dietary suggestions. Metagenomic information from total number of 287 healthy subjects was used to establish baseline microbial composition and metabolic profiles. The uploaded data is analyzed and compared to the baseline for nutrient deficiency assessment. Visualization results include gut microbial composition, related enzymes, pathways, and nutrient abundance. NURECON is a user-friendly online platform that provides nutritional advice to support dietitians' research or menu design.

Automatic learning-based data optimization method for autonomous driving

One of the major reasons for the explosion of autonomous driving in recent years is the great development of computer vision. As the number of model parameters increases, many large autonomous driving models need to be deployed in the cloud. The increasing data on the vehicle side has put huge pressure on transmission bandwidth. In order to reduce data transmission consumption and improve the quality of uploaded data, we design a data optimization method based on reinforcement learning. Through this method, the collected data will be filtered and only those data that are valuable for model training will be uploaded, thereby saving communication resources. The results show that this method can reduce the collected data to 42.9% by implementing data optimization, while increasing the convergence speed by 46% in model training.

Destination-Oriented Data Collection and Uploading for Mobile Crowdsensing With Multiagent Deep Reinforcement Learning

Destination-Oriented Data Collection and Uploading for Mobile Crowdsensing With Multiagent Deep Reinforcement Learning

Enhancing Aotearoa, New Zealand's Free Healthline Service through Image Upload Technology.

Healthline is one of the 39 free telehealth services that Whakarongorau Aotearoa/New Zealand Telehealth Services provides to New Zealanders. In early 2021, an image upload system for viewing service user-uploaded images was implemented into the Healthline service. The aim of this research was to understand the utilisation of Healthline's image upload system by clinicians and service users in New Zealand. This is a retrospective observational study analysing Healthline image upload data over a two-year period: March 2021 through to December 2022. A total of 40,045 images were analysed, including demographics of the service users who uploaded an image: ethnicity, age group, and area of residence. The outcome or recommendation of the Healthline call was also assessed based on whether an image was included. Images uploaded accounted for 6.0% of total Healthline calls (n = 671,564). This research found that more service users were advised to go to an Emergency Department if they did not upload an image compared to service users who used the tool (13.5% vs. 7.7%), whereas a higher proportion of service users were given a lower acuity outcome if they included an image, including visiting an Urgent Care (24.0% vs. 16.9%) and GP (36.7% vs. 24.3%). Service users who did not upload an image had a higher proportion of Emergency Department outcomes than service users who did use the tool. This image upload tool has shown the potential to decrease stress on Emergency Departments around Aotearoa, New Zealand, through increased lower acuity outcomes.

Using Multiple Strategies to Improve the Accuracy of Hemodynamic Monitoring

Hemodynamic monitoring is an important part of nursing care in the intensive care unit. Recent advances in medical technology and the diversification of intensive care equipment have increased the variety of instruments used in clinical hemodynamic monitoring. Many nurses who use new hemodynamic monitors are not familiar with instrument care, resulting in patient safety incidents caused by nurses not identifying warnings of hemodynamic data change and notifying doctors to provide treatment. The accuracy of hemodynamic monitoring care in our ward of 74.0% motivated this improvement project. To improve the accuracy of hemodynamic monitoring care to 98.3%. Conduct educational training and plan professional education; establish an audit system to regularly monitor the accuracy of nursing care; provide tips to make the operation manual easier to read and understand; establish mobile learning to make learning immediate and more accessible; hold instrument operation practice sessions to improve nursing staff proficiency; monitor and upload data to the hospital information system. After the improvement project, the accuracy of hemodynamic monitoring care increased to 98.7%. The impact achieved met expectations, and the improvement project will be extended to other intensive care units in the hospital. Our nurses are now more familiar with the operation methods and the significance of monitoring values and interpretation of data. Also, when a value changes or becomes abnormal, they immediately notify the doctor for further evaluation and interventions to improve patient safety.

Spreadsheet-Based Car Engine Temperature And Compression Pressure Gauge

Measuring the engine's temperature and compression pressure in cars is a crucial part of engine maintenance and performance evaluation. The objective of this study is to develop an accurate and efficient spreadsheet-based measurement system for determining engine temperature and compression pressure in automobiles. For this investigation, data from car engines was collected using engine temperature thermometers and compression pressure gauges. The collected data is then fed into a spreadsheet designed specifically to assess and automatically calculate engine temperature and compression pressure. The results show how reliable and accurate the measurements made using this spreadsheet-based measurement technique are. This approach's main benefits are also its flexibility and ease of use, since users can rapidly adapt the spreadsheet to the particular needs of the engine being tested. With this technology in place, car owners or professionals who do engine maintenance can quickly detect potential engine problems, such as valve leakage or unexpected temperature increases. Therefore, to increase fuel efficiency and extend engine life, spreadsheets can be utilized as a tool for measuring engine performance. The goal of this project is to facilitate the work of auto repair shop owners by utilizing the Esp8266 microcontroller for the DS18b20 sensor and compression sensor. Even so, there are several difficulties, like a sluggish internet connection that delays uploading data to the LCD and spreadsheets that are less useful because of a bad internet connection.