Flexible Data Research Articles

Smart Transaction System using MERN Stack

Efficient and secure transaction systems are critical to the success of enterprises in the quickly changing world of today. There is a growing need for new solutions since traditional methods of conducting transactions are becoming antiquated and laborious. The goal of this paper is to demonstrate a comprehensive and up-to-date solution for transaction management: The Smart Transaction System (STS), which was developed utilizing the MERN stack (MongoDB, Express.js, React.js, and Node.js). Simplifying the transaction process while maintaining security, dependability, and scalability is the goal of the STS. Both administrators and users can easily do a variety of transaction-related operations with its user-friendly interface. The solution leverages the capabilities of the MERN stack, using Node.js for backend development, Express.js for server-side logic, React.js for creating dynamic and responsive user interfaces, and a NoSQL database for flexible data storage. Keywords: Smart Transaction System, MERN Stack, MongoDB, Express.js, React.js, Node.js, Security, Efficiency, Real-time Tracking

Online Bayesian optimization of vagus nerve stimulation.

Objective.In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs, modifying their function. Stimulation devices capable of triggering exogenous neural signals using electrical waveforms require a complex and multi-dimensional parameter space to control such waveforms. Determining the best combination of parameters (waveform optimization or dosing) for treating a particular patient's illness is therefore challenging. Comprehensive parameter searching for an optimal stimulation effect is often infeasible in a clinical setting due to the size of the parameter space. Restricting this space, however, may lead to suboptimal therapeutic results, reduced responder rates, and adverse effects.Approach. As an alternative to a full parameter search, we present a flexible machine learning, data acquisition, and processing framework for optimizing neural stimulation parameters, requiring as few steps as possible using Bayesian optimization. This optimization builds a model of the neural and physiological responses to stimulations, enabling it to optimize stimulation parameters and provide estimates of the accuracy of the response model. The vagus nerve (VN) innervates, among other thoracic and visceral organs, the heart, thus controlling heart rate (HR), making it an ideal candidate for demonstrating the effectiveness of our approach.Main results.The efficacy of our optimization approach was first evaluated on simulated neural responses, then applied to VN stimulation intraoperatively in porcine subjects. Optimization converged quickly on parameters achieving target HRs and optimizing neural B-fiber activations despite high intersubject variability.Significance.An optimized stimulation waveform was achieved in real time with far fewer stimulations than required by alternative optimization strategies, thus minimizing exposure to side effects. Uncertainty estimates helped avoiding stimulations outside a safe range. Our approach shows that a complex set of neural stimulation parameters can be optimized in real-time for a patient to achieve a personalized precision dosing.

Ancient Chinese Character Recognition with Improved Swin-Transformer and Flexible Data Enhancement Strategies.

The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, research on the overall recognition of ancient Chinese characters remains somewhat lacking. To tackle this issue, we pioneered the construction of a large-scale image dataset comprising 9233 distinct ancient Chinese characters sourced from images obtained through archaeological excavations. We propose the first model for recognizing the common ancient Chinese characters. This model consists of four stages with Linear Embedding and Swin-Transformer blocks, each supplemented by a CoT Block to enhance local feature extraction. We also advocate for an enhancement strategy, which involves two steps: firstly, conducting adaptive data enhancement on the original data, and secondly, randomly resampling the data. The experimental results, with a top-one accuracy of 87.25% and a top-five accuracy of 95.81%, demonstrate that our proposed method achieves remarkable performance. Furthermore, through the visualizing of model attention, it can be observed that the proposed model, trained on a large number of images, is able to capture the morphological characteristics of ancient Chinese characters to a certain extent.

Comparison of change-based and shape-based data fusion methods in fine-resolution land surface phenology monitoring with Landsat and Sentinel-2 data

Fine-resolution land surface phenology (LSP) is urgently required for applications on agriculture management and vegetation-climate interaction, especially over heterogeneous areas, such as agricultural lands and fragmented forests. The critical challenge of fine-resolution LSP monitoring is how to reconstruct the spatiotemporal continuous vegetation index time series. To solve this problem, various data fusion methods have been devised; however, the comprehensive inter-comparison is lacking across different spatial heterogeneity, data quality, and vegetation types. We divide these methods into two main categories: the change-based methods fusing satellite observations with different spatiotemporal resolutions, and the shape-based methods fusing prior knowledge of shape models and satellite observations. We selected four methods to rebuilt two-band enhanced vegetation index (EVI2) series based on the harmonized Landsat and Sentinel-2 (HLS) data, including two change-based methods, namely the Spatial and temporal Adaptive Reflectance Fusion Model (STARFM), the Flexible Spatiotemporal DAta Fusion (FSDAF), and two shape-based methods, namely the Multiple-year Weighting Shape-Matching (MWSM), and the Spatiotemporal Shape-Matching Model (SSMM). Four phenological transition dates were extracted, evaluated with PhenoCam observations and the 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) phenology product. The 30 m transition dates show more spatial details and reveal more apparent intra-class and inter-class phenology variation compared with 500 m product. The four transition dates of SSMM and FSDAF (R2>0.74, MAD<15 days) show better agreement with PhenoCam-derived dates. The performance difference between fusion methods over various application scenarios are then analyzed. Fusion results are more robust when temporal frequency is higher than 15 observations per year. The shape-based methods are less sensitive to temporal sampling irregularity than change-based methods. Both change-based methods and shape-based methods cannot perform well when the region is heterogeneous. Among different vegetation types, SSMM-like methods have the highest overall accuracy. The findings in this paper can provide references for regional and global fine-resolution phenology monitoring.

Abstract 3531: PRESSNET: Patient stratification and biomarker discovery using multi-modal knowledge graph framework

Abstract Background: Multiomics data is critical to obtain a near comprehensive picture of disease progression and drug response. In addition, the generation of response and survival biomarkers, and the segmentation of patients into subtypes with distinct, actionable ‘omic signatures and survival trajectories, is vital for personalised medicine research and successful trial design. However, as the volume and diversity of data increases, so too does the challenge of effective multiomic data integration. Knowledge graphs (KGs) can capture heterogeneous data and relationships between entities in a flexible and scalable data structure, making them suitable for this domain. We have developed PRESSnet, a framework for building Patient KGs and analysing clinical data for novel patient stratification hypotheses and clinical biomarker discovery. Methods: PRESSnet is an end-to-end framework that turns raw patient data into graph-derived insights. Firstly, the user chooses which modality files to include, what assumptions to make about data processing, and which graph algorithms to use. PRESSnet then automatedly creates a patient KG of the input data, where nodes represent patients and their associated features. In addition, biomedical prior knowledge, for example in the form of gene-pathway or gene-gene relationship data, is also integrated with the graphs. Insights are generated from the KG via community detection, graph embedding generation, and graph neural networks; these generate hypotheses for novel patient subtypes or biomarkers for clinical outcomes. As graph algorithms capture interrelationships between nodes, PRESSnet offers biomarkers that are composite, i.e. that can contain features from multiple ‘omics and clinical features. Results: We applied PRESSnet to the MSK 20221 cohort of IO-treated LUAD patients, and it uncovered prognostic composite biomarkers that stratified biomarker-positive patients from the whole cohort with a p value &amp;lt; 0.001 (95% CI) for OS, including known markers of poor prognosis such as STK11, RBM10, KRAS and KEAP1 mutations, and high neutrophil/lymphocyte ratio. We also generated embeddings of patients in the cohort and predicted survived/deceased status with an AUC of 0.82, outperforming published state-of-the-art. Conclusions: We have successfully developed a generalisable framework for generating insights from patient data using state-of-the art knowledge graph data science. PRESSnet can generate novel stratification and biomarker hypotheses that can potentially inform the next generation of IO targets and clinical biomarkers. Footnotes1 Vanguri et al., “Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer”, Nat Cancer. 2022 Citation Format: Krishna C. Bulusu, Jake Cohen-Setton, Ioannis Kagiampakis, Miguel Goncalves, Gavin Edwards, Sanddhya Jayabalan, Shruti Shikare, Kelvin Tsang, Ben Sidders, Etai Jacob. PRESSNET: Patient stratification and biomarker discovery using multi-modal knowledge graph framework [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3531.

Evaluation of Slope Stability on Dam Using Ground-based Interferometric Radar

Dams are man-made structures built to manage water resources efficiently and prepare for natural disasters such as droughts and floods. It requires careful and continuous inspection to prevent its failure. Research reported to assess dam stability using terrestrial surveys such as ground penetration radar, electrical resistivity tomography, and remote sensing methods such as space-borne synthetic aperture radar (SAR). Differential interferometric SAR (DInSAR) calculates the phase difference between two consecutive images acquired at separate times and has been widely utilized to detect surface displacement from volcanoes, earthquakes, and ground subsidence. However, space-borne InSAR applications have limitations in acquiring flexible data for specific dates or regions due to the revisit cycle of the orbital configuration and the fixed acquisition geometry. In this feasibility study, the slope stability of the dam was evaluated using the Gamma Portable Radar Interferometer-II (GPRI-II) which has the advantage of overcoming the limitation of satellite observations. The GPRI-II is a ground-based real aperture radar that operates in the Ku-band wavelength (~1.7 cm), providing convenient portability and installation for high spatial and temporal resolution. A total of 20 GPRI-II datasets were acquired for 22 minutes on June 7, 2023, at a dam in Jeollanam-do for the DInSAR application. The displacement calculation revealed an average displacement of approximately -0.36 mm at a randomly selected point, which is negligible. The average displacement of -0.17 mm was observed for the entire dam. Our results suggest that ground-based radar interferometry could assess the dam slope stability.

IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods

IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB.

Exploiting Hidden Information Leakages in Backward Privacy for Dynamic Searchable Symmetric Encryption

Dynamic searchable symmetric encryption (DSSE) enables searches over encrypted data as well as data dynamics such as flexible data addition and deletion operations. A major security concern in DSSE is how to preserve forward and backward privacy, which are typically achieved by removing the linkability between the newly added data and previous queries, and between the deleted data and future queries, respectively. After information leakage types were formally defined for different levels of backward privacy (i.e., Type-I, II, III), many backward private DSSE schemes have been constructed under the definitions. However, we observed that the backward privacy can be violated by leveraging additional secondary leakage, which is typically leaked in specific constructions of schemes in spite of their theoretical guarantees. In this paper, in order to understand the security gap between the theoretical definitions and practical constructions, we conduct an in-depth analysis of the root cause for the secondary leakage, and demonstrate how it can be abused to violate Type-II backward privacy (e.g., the exposure of the deletion history) of DSSE constructions in practice. We then propose a novel Type-II backward private DSSE scheme based on Intel SGX, which is resilient to the secondary leakage abuse attack. According to the comparative analysis of our scheme with the state-of-the-art SGX-based DSSE schemes, Bunker-B (EuroSec’19) and SGX-SE1 (ACNS’20), our scheme shows higher efficiency in terms of the search latency with a negligible utility loss under the same security level (cf. Bunker-B) while showing similar efficiency with a higher security level (cf. SGX-SE1). Finally, we formally prove that our scheme guarantees Type-II backward privacy.

A Conceptual Framework for Immersive Acoustic Auralisation: Investigating the Key Attributes

In architectural acoustics, the assessment of sound propagation in interior and/or environmental conditions has now become progressively more prominent throughout the past few decades, as a response to the development of advanced prediction tools. Within the adaptation of virtual reality (VR) systems, it is necessary to considerably expedite the prediction and simulation software as well as to enable flexible and responsive data analysis during simulation and 3D audio sensory projection. To generate ideal immersion in a simulated virtual environment, the generated stimulus across all senses should therefore be coherent. Accordingly, in the domain of acoustic in virtual reality, the system sound simulation must be constructed efficiently in order to convey the auditory stimuli to the user in an appropriate manner. This necessitates the implementation of virtual reality system as an advanced prediction tool that can accurately anticipate and replicate realistic audio experiences. Therefore, this study explores the realm of acoustic virtual reality (AVR) through a critical review with the purpose of elucidating design attributes and determining factors in generating immersive acoustic VR experiences. In light of these findings, the aim of this paper is to develop a comprehensive conceptual framework that will serve as a beneficial guide and road map for future researchers and developers in the field.

Secure and flexible authorized data sharing for smart grid

Secure and flexible authorized data sharing for smart grid

Exploiting Data Locality to Improve Performance of Heterogeneous Server Clusters

We consider load balancing in large-scale heterogeneous server systems in the presence of data locality that imposes constraints on which tasks can be assigned to which servers. The constraints are naturally captured by a bipartite graph between the servers and the dispatchers handling assignments of various arrival flows. When a task arrives, the corresponding dispatcher assigns it to a server with the shortest queue among [Formula: see text] randomly selected servers obeying these constraints. Server processing speeds are heterogeneous, and they depend on the server type. For a broad class of bipartite graphs, we characterize the limit of the appropriately scaled occupancy process, both on the process level and in steady state, as the system size becomes large. Using such a characterization, we show that imposing data locality constraints can significantly improve the performance of heterogeneous systems. This is in stark contrast to either heterogeneous servers in a full flexible system or data locality constraints in systems with homogeneous servers, both of which have been observed to degrade the system performance. Extensive numerical experiments corroborate the theoretical results. Funding: This work was partially supported by the National Science Foundation [CCF. 07/2021–06/2024].

New ridge parameter estimators for the zero-inflated Conway Maxwell Poisson ridge regression model

One of the flexible count data models for dealing with over and under-dispersion with extra zeroes is the zero-inflated Conway–Maxwell Poisson (ZICOMP). The ZICOMP regression coefficients are generally estimated using the maximum likelihood estimator (MLE). In the ZICOMP regression model, when the explanatory variables are correlated, the MLE does not give efficient results. To overcome the effect of multicollinearitymode in the ZICOPM regression, we proposed the ridge regression estimator. To evaluate the performance of the estimator, we use mean squared error (MSE) as the performance evaluation criteria. A theoretical comparison of the ridge estimator with MLE is made to show the superiority of the estimator. The proposed estimator is evaluated with the help of a simulation study and a real application. The results of the simulation study and real application show the superiority of the proposed estimator because it produces a smaller MSE as compared to the MLE.

Towards flexible data stream collaboration: Federated Learning in Kafka-ML

Federated learning is applied in scenarios where organisations lack sufficient data volume for modelling their business logic and cannot share their data with external parties. Moreover, Industry 4.0 and IoT scenarios generate massive data streams, which normally are fed to ML/AI solutions for model training and prediction. However, in most cases, ML/AI frameworks are not prepared to work with these streaming pipelines. In this paper, we present an asynchronous federated learning solution based on the Kafka-ML data stream framework, which is able to combine federated learning and data stream capabilities within ML/AI applications. While most federated learning approaches are tailored to a specific ML model or a use case, the solution provided adapts itself to the availability of both data and ML models, achieving a flexible and dynamic federated learning solution. To validate its performance, an evaluation of the federated learning solution is carried out on different scenarios in a multi-node state-of-the-art infrastructure. Results show that this framework can work with multiple federated clients, being the resulting accuracy dependent on the amount of data and the behaviour of clients during training.

Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging.

Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.

OpenStreetMap for multi-faceted climate risk assessments

Natural hazards pose significant risks to human lives, infrastructure, and ecosystems. Understanding risks along all these dimensions is critical for effective adaptation planning and risk management. However, climate risk assessments mostly focus on population, economic asset values, and road or building infrastructure, because publicly available data on more diverse exposures are scarce. The increasing availability of crowd-sourced geospatial data, notably from OpenStreetMap, opens up a novel means for assessing climate risk to a large range of physical assets. To this end, we present a stand-alone, lightweight, and highly flexible Python-based OpenStreetMap data extraction tool: OSM-flex. To demonstrate the potential and limitations of OpenStreetMap data for risk assessments, we couple OSM-flex to the open-source natural hazard risk assessment platform CLIMADA and compute winter storm risk and event impacts from winter storm Lothar across Switzerland to forests, UNESCO heritage sites, railways, healthcare facilities, and airports. Contrasting spatial patterns of risks on such less conventional exposure layers with more traditional risk metrics (asset damages and affected population) reveals that risk hot-spots are inhomogeneously and distinctly distributed. For instance, impacts on forestry are mostly expected in Western Switzerland in the Jura mountain chain, whereas economic asset damages are concentrated in the urbanized regions around Basel and Zurich and certain train lines may be most often affected in Central Switzerland and alpine valleys. This study aims to highlight the importance of conducting multi-faceted and high-resolution climate risk assessments and provides researchers, practitioners, and decision-makers with potential open-source software tools and data suggestions for doing so.

ARCHITECTURE AND IMPLEMENTATION OF THE CALORIFY MOBILE APPLICATION BASED ON THE UNITY ENGINE

In today's world, mobile applications for various industries are taking up more and more space in the field of software. Mobile applications for maintaining and monitoring a healthy lifestyle are especially important. This work is devoted to the continuation of the software development of the Calorify mobile application, and in particular the server part. The server part of the Calorify mobile application is implemented on the basis of the Unity engine.in the C# programming language. C# is an object-oriented, strongly typed, high-level programming language developed and maintained by Microsoft. In general, the architecture of Calorify is built on the basis of a component-oriented approach. This ensures a clear separation of functionality and ease of scaling. All major aspects of the application, including augmented reality AR, flexible user interface and data management, are implemented through modular components that are responsible for specific tasks. The project structure in Unity is organized in the form of scenes that contain different parts of the application, such as the main menu and AR functionality. Such an architecture allows integrating technologies for working with cloud services and local databases, providing effective data management and optimal interaction with the user. The particularity of this Calorify mobile application is the use of mathematical, scientifically based analytical formulas to calculate the number of calories and water consumed during the day. Mobile applicatioon Calorify has an appropriate number of databases, most of which are local and created using the SQLite DBMS in Unity. Remote databases in the project include the Firebase database and the Image Target Vuforia database. Mobile application Calorify uses the tools provided by the Firebase platform as a basis for authorization. The advantages of such a solution are a satisfactory range and quality of user authentication services and access to remote databases. Firebase Realtime Database is used for user data manipulation. Therefore, after authorization, all entered data, except for the password, is stored on the remote Firebase server.

Event and data persistency models for the LHCb Real Time Analysis System

Starting in 2022, the upgraded LHCb detector is collecting data with a pure software trigger. In its first stage, reducing the rate from 30MHz to about 1MHz, GPUs are used to reconstruct and trigger on B and D meson topologies and high-pT objects in the event. In its second stage, a CPU farm is used to reconstruct the full event and perform candidate selections, which are persisted for offline use with an output rate of about 10GB/s. Fast data processing, flexible and custom-designed data structures tailored for SIMD architectures and efficient storage of the intermediate data at various steps of the processing pipeline onto persistent media, e.g. tapes is essential to guarantee the full physics program of LHCb. We present the event model and data persistency developments for the trigger of LHCb in Run 3. Particular emphasis is given to the novel software-design aspects with respect to the Run 1+2 data taking, the performance improvements which can be achieved and the experience of restructuring a major part of the reconstruction software in a large HEP experiment.

VideoPro: A Visual Analytics Approach for Interactive Video Programming.

Constructing supervised machine learning models for real-world video analysis require substantial labeled data, which is costly to acquire due to scarce domain expertise and laborious manual inspection. While data programming shows promise in generating labeled data at scale with user-defined labeling functions, the high dimensional and complex temporal information in videos poses additional challenges for effectively composing and evaluating labeling functions. In this paper, we propose VideoPro, a visual analytics approach to support flexible and scalable video data programming for model steering with reduced human effort. We first extract human-understandable events from videos using computer vision techniques and treat them as atomic components of labeling functions. We further propose a two-stage template mining algorithm that characterizes the sequential patterns of these events to serve as labeling function templates for efficient data labeling. The visual interface of VideoPro facilitates multifaceted exploration, examination, and application of the labeling templates, allowing for effective programming of video data at scale. Moreover, users can monitor the impact of programming on model performance and make informed adjustments during the iterative programming process. We demonstrate the efficiency and effectiveness of our approach with two case studies and expert interviews.

Smartphone-based undergraduate research projects in an introductory mechanics course

At the University of Göttingen, we implemented undergraduate research projects into a first-year mechanics course for physics majors and teacher-training students. Our primary goal was to foster students’ affective factors and higher-order thinking skills in a self-directed, crosslinking, inquiry-based learning setting. A total of 160 students were organized into 40 small groups which worked on one of six open-ended experimental tasks, utilizing smartphone sensors for flexible first-hand data collection outside laboratories. The tasks originate from the Erasmus+ project DigiPhysLab and were significantly modified and opened to be used for undergraduate research projects. In this manuscript, we present the underlying rationales behind this program, outline the core concepts behind the developed experimental tasks, and explain the actual implementation. Additionally, we offer insights into the assessment process for the project work, including the evaluation of scientific posters and responses to eight reflection questions. To facilitate this, we have employed two rubrics to ensure a comprehensive evaluation process.

Efficient User-Centric Privacy-Friendly and Flexible Wearable Data Aggregation and Sharing

Efficient User-Centric Privacy-Friendly and Flexible Wearable Data Aggregation and Sharing