Parallel Data Research Articles

Revolutionizing SIEM Security: An Innovative Correlation Engine Design for Multi-Layered Attack Detection.

Advances in connectivity, communication, computation, and algorithms are driving a revolution that will bring economic and social benefits through smart technologies of the Industry 4.0 era. At the same time, attackers are targeting this expanded cyberspace to exploit it. Therefore, many cyberattacks are reported each year at an increasing rate. Traditional security devices such as firewalls, intrusion detection systems (IDSs), intrusion prevention systems (IPSs), anti-viruses, and the like, often cannot detect sophisticated cyberattacks. The security information and event management (SIEM) system has proven to be a very effective security tool for detecting and mitigating such cyberattacks. A SIEM system provides a holistic view of the security status of a corporate network by analyzing log data from various network devices. The correlation engine is the most important module of the SIEM system. In this study, we propose the optimized correlator (OC), a novel correlation engine that replaces the traditional regex matching sub-module with a novel high-performance multiple regex matching library called "Hyperscan" for parallel log data scanning to improve the performance of the SIEM system. Log files of 102 MB, 256 MB, 512 MB, and 1024 MB, generated from log data received from various devices in the network, are input into the OC and simple event correlator (SEC) for applying correlation rules. The results indicate that OC is 21 times faster than SEC in real-time response and 2.5 times more efficient in execution time. Furthermore, OC can detect multi-layered attacks successfully.

Big Data Analytics Model with Deep Learning Architecture to Evaluate Live Dance Ecology Through the Internet

Dance ecology, a burgeoning field at the intersection of dance, technology, and environmental studies, relies on real-time data analysis for understanding and optimizing dance performances. This paper proposed a novel Parallel Edge Big Data Analytics (PEBDA) framework, designed to efficiently process and analyze dance movement data in real time. The proposed PEBDA model uses parallel processing in the edge computing model for the analysis of the live dance ecology. Through the parallel processing of the edge model in the network big data analytics is implemented for the estimation of the multiple nodes in the network. The PEBDA model estimates the nodes across multiple environments for the examination of the ecology in the live dance. Finally, through parallel processing classification is performed with the deep learning model for the classification of live dance ecology in the computing platform. The proposed PEBDA framework, assesses classification accuracy, precision, recall, and F1-score. The simulation analysis expressed that Node 8 consistently outperforms others, achieving exceptional accuracy and precision levels above 0.97. These findings highlight the potential of edge computing in revolutionizing dance ecology analysis, enabling enhanced real-time monitoring, decision-making, and optimization of dance performances.

High-quality implementation for a continuous-in-time financial API in C#

In recent years, there has been a rising interest in potentially complex software and financial industries with applications in many engineering fields. With this rise comes a host of developing a usable and consistent Application Programming Interface (API). Prioritize designing and building the software ensures to enrich the platform and emphasize inventorying APIs. In this paper, we proposed a high-quality API to implement the continuous-in-time financial model. The existing discrete framework cannot be evaluated at any time period, involving drawbacks in operating the data structures. Then, the continuous framework is implemented based on the measure theory paradigm. Our proposal uses mathematical modeling, which consists of some objects as measures and fields. It is suitable to develop this API in C# to provide the requirement quality in programming language professionally. This also integrates demands, codes, and verification in the system development life cycle. The advantages are aimed at increasing the structuring and readability. The presented work provides an overview of the design, implementation, testing, and delivery aspects of the API, highlighting the importance of architecture, testing, and numerical choices. The article gives an overview of the API by describing the implementation concerning the data structures and algorithms. These algorithms are based on using the Task Parallel Library (TPL) that makes the API easier and more fruitful for data parallel to benefit from the advantages provided by the .NET Framework.

ProteinFlow: An advanced framework for feature engineering in protein data analysis.

In the burgeoning field of proteins, the effective analysis of intricate protein data remains a formidable challenge, necessitating advanced computational tools for data processing, feature extraction, and interpretation. This study introduces ProteinFlow, an innovative framework designed to revolutionize feature engineering in protein data analysis. ProteinFlow stands out by offering enhanced efficiency in data collection and preprocessing, along with advanced capabilities in feature extraction, directly addressing the complexities inherent in multidimensional protein data sets. Through a comparative analysis, ProteinFlow demonstrated a significant improvement over traditional methods, notably reducing data preprocessing time and expanding the scope of biologically significant features identified. The framework's parallel data processing strategy and advanced algorithms ensure not only rapid data handling but also the extraction of comprehensive, meaningful insights from protein sequences, structures, and interactions. Furthermore, ProteinFlow exhibits remarkable scalability, adeptly managing large-scale data sets without compromising performance, a crucial attribute in the era of big data.

Enhancing Human Activity Recognition through Integrated Multimodal Analysis: A Focus on RGB Imaging, Skeletal Tracking, and Pose Estimation.

Human activity recognition (HAR) is pivotal in advancing applications ranging from healthcare monitoring to interactive gaming. Traditional HAR systems, primarily relying on single data sources, face limitations in capturing the full spectrum of human activities. This study introduces a comprehensive approach to HAR by integrating two critical modalities: RGB imaging and advanced pose estimation features. Our methodology leverages the strengths of each modality to overcome the drawbacks of unimodal systems, providing a richer and more accurate representation of activities. We propose a two-stream network that processes skeletal and RGB data in parallel, enhanced by pose estimation techniques for refined feature extraction. The integration of these modalities is facilitated through advanced fusion algorithms, significantly improving recognition accuracy. Extensive experiments conducted on the UTD multimodal human action dataset (UTD MHAD) demonstrate that the proposed approach exceeds the performance of existing state-of-the-art algorithms, yielding improved outcomes. This study not only sets a new benchmark for HAR systems but also highlights the importance of feature engineering in capturing the complexity of human movements and the integration of optimal features. Our findings pave the way for more sophisticated, reliable, and applicable HAR systems in real-world scenarios.

Remaining useful life prediction of milling cutters based on long-term data sequence and parallel fully convolutional feature learning

Remaining useful life prediction of milling cutters based on long-term data sequence and parallel fully convolutional feature learning

Validation of an IPE checklist developed to guide the design and evaluation of interprofessional learning activities

Despite Interprofessional Education (IPE) being integrated in most healthcare curricula with proven outcomes, barriers to implementation remain, including inconsistency in the quality of IPE activities and lack of high-quality instruments to evaluate the merit of planned and existing IPE activities. To mitigate these barriers, an interprofessional, multi-institutional team gathered data on the utility of an IPE checklist designed to assist educators evaluate the quality, value, and relevance of interprofessional resources and educational materials. A convergent, mixed method design was used to collect and analyze quantitative and qualitative data in parallel. Findings demonstrate the IPE checklist has real-world application and substantive evidence for evaluating IPE activities regardless of experience and expertise and the practical issues encountered. In conclusion, the IPE checklist can contribute to the evaluation of the quality of IPE activities and enhance the education of trainees who will ultimately deliver care to patients and populations.

O-117 Advances in PGT diagnostics - technologies beyond copy number

Abstract Genetic abnormalities are well recognised as the main factor contributing to the high rate of infertility, pregnancy loss and congenital abnormalities in humans. A prime example of this can be seen from recent studies showing that embryos identified as uniformly aneuploid following PGT-A have little to no reproductive potential. However, embryos shown to have no gross chromosomal abnormalities after PGT are not a guarantee of healthy pregnancy and many other undetected genetic issues are likely to influence embryo competence. Developments in both sample preparation and methods for DNA analysis are driving ever-increasing data yield from PGT platforms, permitting not only DNA quantitation, but also allowing qualitative evaluation of genotyping data in parallel. This opens the door to the possibility of overcoming some of the previous limitations of PGT, leading to a more comprehensive assessment of the genetic health and reproductive potential of embryos. How to use this increasing amount of information, and if it can tangibly improve test accuracy and clinical outcomes, remains open to debate. There is, for instance, no consensus on how to interrogate genotyping data for better distinction of clinically relevant aneuploidy as well as genome wide (ploidy) abnormalities and exogenous DNA contamination, which could impact the correct reporting of PGT results. There is also no consensus on the potential benefit of genotyping data to be used for the accurate distinction of meiotic recombination by linkage analysis, nor for fingerprinting of embryos to identify rare, but catastrophic, lab error events as a routine part of treatment. Finally, there is no clear path on how to utilise these improvements in embryo genotyping, to detect sub-chromosomal de novo genetic abnormalities that could impact embryo viability beyond the scope of current testing for chromosomal abnormalities alone. The aim of this presentation is therefore to focus on the current state of the ART and address the current and potential future uses of genotyping in PGT. Topics covered will include identification of biopsy sample contamination to determine the truth from the noise, through to how we can rescue embryos by utilising PGT as a molecular fertilisation check and how fingerprinting of embryo can be an invaluable tool in the clinic. The origin of aneuploidy will be explored and how genotyping is reshaping our understanding of aneuploidy landscape of the preimplantation embryo. From a more basic science perspective, the potential of genotyping to identify meiotic recombination will be discussed and how this fundamental element of human reproduction could be an important biomarker for reproductive competence. Lastly future perspectives on how the evolution of PGT technologies can bring us closer to the accurate detection of de novo genomic changes will be explored and how this is taking PGT-A beyond just aneuploidy detection. It is no secret that PGT for embryo selection continues to be a controversial area of reproductive medicine, and the field of PGT-A especially continues to show disquieting inconsistency in data analysis and reporting. This new era of genotyping brings with it information that can be used as a standardization tool helping to focus and facilitate improvements in the field of PGT for better and more consistent clinical reporting and outcomes.

Effect of neural firing pattern on NbOx/Al2O3 memristor-based reservoir computing system

The implementation of reservoir computing using resistive random-access memory as a physical reservoir has attracted attention due to its low training cost and high energy efficiency during parallel data processing. In this work, a NbOx/Al2O3-based memristor device was fabricated through a sputter and atomic layer deposition process to realize reservoir computing. The proposed device exhibits favorable resistive switching properties (>103 cycle endurance) and demonstrates short-term memory characteristics with current decay. Utilizing the controllability of the resistance state and its variability during cycle repetition, electrical pulses are applied to investigate the synapse-emulating properties of the device. The results showcase the functions of potentiation and depression, the coexistence of short-term and long-term plasticity, excitatory post-synaptic current, and spike-rate dependent plasticity. Building upon the functionalities of an artificial synapse, pulse spikes are categorized into three distinct neural firing patterns (normal, adapt, and boost) to implement 4-bit reservoir computing, enabling a significant distinction between “0” and “1.”

A snapshot of parallelism in distributed deep learning training

The accelerated development of applications related to artificial intelligence has generated the creation of increasingly complex neural network models with enormous amounts of parameters, currently reaching up to trillions of parameters. Therefore, it makes your training almost impossible without the parallelization of training. Parallelism applied with different approaches is the mechanism that has been used to solve the problem of training on a large scale. This paper presents a glimpse of the state of the art related to parallelism in deep learning training from multiple points of view. The topics of pipeline parallelism, hybrid parallelism, mixture-of-experts and auto-parallelism are addressed in this study, which currently play a leading role in scientific research related to this area. Finally, we develop a series of experiments with data parallelism and model parallelism. The objective is that the reader can observe the performance of two types of parallelism and understand more clearly the approach of each one.

Identification of Influential Nodes in Social Network: Big Data - Hadoop

Software development and associated data is the most critical factor these days. Currently, people are living in an internet world where data and related artifacts are major sets of information these days. The data is correlated with real-world data. The analysis of large datasets was done as part of the experimental analysis. The dataset for online social media like Facebook and Twitter was taken for the identification of influential nodes. The analysis of the dataset provides an overview and observation of the dataset for Facebook or Twitter. Here, in the current activity, an overview of cloud computing and big data technologies are discussed along with effective methods and approaches to resolve the problem statement. Particularly, big data technologies such as Hadoop provided by Apache for processing and analysis of Gigabyte(GB) or petabyte(PB) scale datasets are discussed for processing data in distributed and parallel data fashion. Here, the processing of large datasets is done by big data technology by implementing Apache Hadoop in online social media.

Synaptic plasticity and associative learning in IGZO-based synaptic transistor

The increasing demand for efficient data access in high-performance computing systems has emphasized the limitations of the traditional von Neumann architecture, particularly due to the von Neumann bottleneck. This bottleneck arises from the significant power and time consumption required for data transfer between the central processing units and memory, impeding overall system efficiency. Neuromorphic computing offers a promising alternative. Neuromorphic systems emulate biological neurons and synapses, enabling parallel data processing and potentially overcoming the limitations of traditional computing architectures. This study focuses on synaptic transistors with In–Ga–Zn-O (IGZO) channels and TaOx charge trapping layers (Ox-CTL) to enhance neuromorphic computing capabilities. The devices were fabricated using reactive sputtering and atomic layer deposition, followed by comprehensive structural and compositional analyses. Experimental results demonstrated significant hysteresis, high on/off ratios, and multibit conductance states, suggestive of effective charge trapping and detrapping mechanisms. Evaluation through potentiation, depression, and excitatory postsynaptic current (EPSC) measurements, along with simulations using the Modified National Institute of Standards and Technology (MNIST) database, revealed improved pattern recognition accuracy. Additionally, associative learning experiments modeled after Pavlov’s dog conditioning emphasized the device's capability for both long- and short-term memory retention. These findings suggest that IGZO-based synaptic transistors are promising candidates for next-generation neuromorphic computing systems, offering enhanced data processing efficiency and adaptability.

РЕАЛИЗАЦИЯ ПАРАЛЛЕЛЬНЫХ ВЫЧИСЛЕНИЙ НА ОСНОВЕ ПРОГРАММНО-АППАРАТНОГО СТЕКА CUDA

This paper describes in detail the problems of implementing parallel computing based on the CUDA hardware and software stack. It provides a comprehensive analysis of the key aspects of using CUDA for parallel programming and data processing. The paper examines in detail the structure of the CUDA architecture, describes the programming model and the optimization process

Understanding family-level decision-making when seeking access to acute surgical care for children: Protocol for a cross-sectional mixed methods study.

There is limited understanding of how social determinants of health (SDOH) impact family decision-making when seeking surgical care for children. Our objectives of this study are to identify key family experiences that contribute to decision-making when accessing surgical care for children, to confirm if family experiences impact delays in care, and to describe differences in family experiences across populations (race, ethnicity, socioeconomic status, rurality). We will use a prospective, cross-sectional, mixed methods design to examine family experiences during access to care for children with appendicitis. Participants will include 242 parents of consecutive children (0-17 years) with acute appendicitis over a 15-month period at two academic health systems in North Carolina and Virginia. We will collect demographic and clinical data. Parents will be administered the Adult Responses to Children's Symptoms survey (ARCS), the child and parental forms of the Adverse Childhood Experiences (ACE) survey, the Accountable Health Communities Health-Related Social Needs Screening Tool, and Single Item Literacy Screener. Parallel ARCS data will be collected from child participants (8-17 years). We will use nested concurrent, purposive sampling to select a subset of families for semi-structured interviews. Qualitative data will be analyzed using thematic analysis and integrated with quantitative data to identify emerging themes that inform a conceptual model of family-level decision-making during access to surgical care. Multivariate linear regression will be used to determine association between the appendicitis perforation rate and ARCS responses (primary outcome). Secondary outcomes include comparison of health literacy, ACEs, and SDOH, clinical outcomes, and family experiences across populations. We expect to identify key family experiences when accessing care for appendicitis which may impact outcomes and differ across populations. Increased understanding of how SDOH and family experiences influence family decision-making may inform novel strategies to mitigate surgical disparities in children.

Data oversampling and feature selection for class imbalanced datasets

Introduction: Significant advancements and modifications have been implemented in data classification (DC) in the past few decades. Due to their infinite quantity and imbalance, data becomes challenging for classification. The biggest concern in DM (Data Mining) is Class Imbalance (CI). To avoid these issues in recent work proposed map reduce based data parallelization of class imbalanced datasets. Methods: A novel Over Sampling (OS) technique called Minority Oversampling in Kernel Canonical Correlation Adaptive Subspaces (MOKCCAS) has been suggested with the objective to minimize data loss throughout (FSP) Feature Space Projections. This technique takes advantage of the constant Feature Extraction (FE) capability of a version of the ASSOM (Adaptive Subspace Self-Organizing Maps) that is derived from Kernel Canonical Correlation Analysis (KCCA). And in classification, Feature Selection (FS) plays an important role because the acquired dataset might contain large volume of samples, utilizing all features of samples from the dataset for classification will decrease the classifier performance. And then data parallelization will be done by using map reduce framework to solve this computation requirement problem.Result: Then proposes a feature selection model using Mutated whale optimization (MWO) methods and produces features and reduces the time consumption. Finally proposed class balancing model will be tested using uniform distribution based enhanced adaptive neuro fuzzy inference system (UDANFIS). Test outcomes validate the efficiency of the suggested technique by precision, recall, accuracy and Error Rate (ER).Conclusion: The study subsequently suggests a novel OS approach called MOKCCAS to lessen the loss of data throughout feature space projection.

Establishing a Generic Geographic Information Collection Platform for Heterogeneous Data

Geographic information collection platforms are widely used for acquiring geographic information. However, existing geographic information collection platforms have limited adaptability and configurability, negatively affecting their usability. They do not support complete field collection workflows or capture data with complex nested structures. To address these limitations, this paper proposes a generic geographic information collection platform based on a comprehensive XML schema definition and a corresponding XML toolkit. This platform includes professional and non-professional versions of collection software, as well as a management system. Users can configure controls and define nested tables within this platform to collect heterogeneous and complex nested data. Moreover, the platform supports functions such as task assignment, local deployment servers, multitasking parallelism, and summary statistics of heterogeneous data, ensuring complete workflow support for field data collection. The platform has been applied in agriculture, forestry, and related fields. This paper uses the agricultural industry structure survey as a case study. Practical applications and our case study show that this platform can reduce software development costs, lower user knowledge prerequisites, and fulfill 95% of geographic information collection scenarios.

An energy-efficient 32-bit bit-parallel superconducting SFQ specialized processor

As the demand for energy efficiency rises, researchers are increasingly prioritizing the quest for energy-efficient chip design. Superconducting SFQ circuit technology has garnered attention due to its ultra-high speed and low power consumption characteristics. In this paper, we propose a layout method called Maximum Operating Frequency Constraint (MOFC) for SFQ circuit design. Using this method, we demonstrated a 32-bit bit-parallel string-matching processor fabricated based on SIMIT-Nb03P technology, which holds practical value. The MOFC method focuses on achieving high bit-width processor design within constrained area cost in SFQ circuits, contributing to less energy consumption. To the best of our knowledge, this represents the first demonstrated instance of a superconducting SFQ chip achieving successful internal 32-bit data parallel processing. Our chip has been fabricated and tested, revealing not only its capability for 32-bit bit-parallel processing at a high speed of 12 GHz but also its achievement of an energy efficiency ratio of up to 251 GOPS/W.

Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review.

Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. To date however, no review of these techniques and their application(s) within the domain of white blood cell (WBC) classification in blood smear images has been undertaken, representing a notable knowledge gap with respect to model selection and comparison. Accordingly, the current study sought to comprehensively identify, explore and contrast ML and DL methods for classifying WBCs. Following development and implementation of a formalized review protocol, a cohort of 136 primary studies published between January 2006 and May 2023 were identified from the global literature, with the most widely used techniques and best-performing WBC classification methods subsequently ascertained. Studies derived from 26 countries, with highest numbers from high-income countries including the United States (n = 32) and The Netherlands (n = 26). While WBC classification was originally rooted in conventional ML, there has been a notable shift toward the use of DL, and particularly convolutional neural networks (CNN), with 54.4% of identified studies (n = 74) including the use of CNNs, and particularly in concurrence with larger datasets and bespoke features e.g., parallel data pre-processing, feature selection, and extraction. While some conventional ML models achieved up to 99% accuracy, accuracy was shown to decrease in concurrence with decreasing dataset size. Deep learning models exhibited improved performance for more extensive datasets and exhibited higher levels of accuracy in concurrence with increasingly large datasets. Availability of appropriate datasets remains a primary challenge, potentially resolvable using data augmentation techniques. Moreover, medical training of computer science researchers is recommended to improve current understanding of leucocyte structure and subsequent selection of appropriate classification models. Likewise, it is critical that future health professionals be made aware of the power, efficacy, precision and applicability of computer science, soft computing and artificial intelligence contributions to medicine, and particularly in areas like medical imaging.

Residual Temporal Convolutional Network With Dual Attention Mechanism for Multilead-Time Interpretable Runoff Forecasting.

As a pivotal subfield within the domain of time series forecasting, runoff forecasting plays a crucial role in water resource management and scheduling. Recent advancements in the application of artificial neural networks (ANNs) and attention mechanisms have markedly enhanced the accuracy of runoff forecasting models. This article introduces an innovative hybrid model, ResTCN-DAM, which synergizes the strengths of deep residual network (ResNet), temporal convolutional networks (TCNs), and dual attention mechanisms (DAMs). The proposed ResTCN-DAM is designed to leverage the unique attributes of these three modules: TCN has outstanding capability to process time series data in parallel. By combining with modified ResNet, multiple TCN layers can be densely stacked to capture more hidden information in the temporal dimension. DAM module adeptly captures the interdependencies within both temporal and feature dimensions, adeptly accentuating relevant time steps/features while diminishing less significant ones with minimal computational cost. Furthermore, the snapshot ensemble method is able to obtain the effect of training multiple models through one single training process, which ensures the accuracy and robustness of the forecasts. The deep integration and collaborative cooperation of these modules comprehensively enhance the model's forecasting capability from various perspectives. Ablation studies conducted validate the efficacy of each module, and through multiple sets of comparative experiments, it is shown that the proposed ResTCN-DAM has exceptional and consistent performance across varying lead times. We also employ visualization techniques to display heatmaps of the model's weights, thereby enhancing the interpretability of the model. When compared with the prevailing neural network-based runoff forecasting models, ResTCN-DAM exhibits state-of-the-art accuracy, temporal robustness, and interpretability, positioning it at the forefront of contemporary research.

Thorough Characterization and Analysis of Large Transformer Model Training At-Scale

Large transformer models have recently achieved great success across various domains. With a growing number of model parameters, a large transformer model training today typically involves model sharding, data parallelism, and model parallelism. Thus, the throughput of large-scale model training depends heavily on the network bandwidth since a combination of model sharding and multiple parallelism strategies incurs various costs. However, prior characterizations of transformer models on high-bandwidth DGX machines that use TFLOPS as a metric may not reflect the performance of a system with lower bandwidth. Furthermore, data and model parallelism reveal significantly distinct training profiles on different system bandwidths at scale and, thus, need a thorough study. In this paper, we provide a bottom-up breakdown of training throughput into compute and communication time, and quantitatively analyze their respective influences on overall end-to-end training scaling. Our evaluation involves an in-depth exploration of data parallelism, scaling up to 512 GPUs with limited bandwidth, and examines three model sharding strategies among six model sizes. We also evaluate three combinations of model parallelism on both high and low bandwidth supercomputing systems. Overall, our work provides a broader perspective on large-scale transformer model training, and our analysis and evaluation yield practical insights for predicting training scaling, shaping the future development of supercomputing system design.