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Data flow-based strategies that seek to improve the understanding of A.I.-based results are examined here by carefully curating and monitoring the flow of data into, for example, artificial neural networks and random forest supervised models. While these models possess structures and related fitting procedures that are highly complex, careful restriction of the data being utilized by these models can provide insight into how they interpret data structures and associated variables sets and how they are affected by differing levels of variation in the data. The goal is improving our understanding of A.I.-based supervised modeling-based results and their stability across different data sources. Some guidelines are suggested for such first-stage adjustments and related data issues. [ABSTRACT FROM AUTHOR]

To enhance the effective detection of abnormal points in complex network data flow, perform multi-dimensional dynamic detection, and establish a more stable and reliable data flow abnormal detection method, a continuous abnormal point detection method for complex network data flow based on C-LSTM is proposed. The features of continuous outliers in complex network data streams are extracted, and a data anomaly detection model is established according to the features. The input features of continuous outliers in complex network data streams are qualitatively and quantitatively transformed into multi-scale anomalies, and the outlier detection based on C-LSTM is realized. The experimental results show that the maximum sensitivity of the proposed method reaches 42%, and the average routing overhead is less than 24 Mb. Regardless of the data in any scenario, the detection accuracy is higher than 0.92, the recall is higher than 0.81, and the F1 value is higher than 0.62. Although there may be some misjudgments or omissions due to noise, the overall detection performance is good. [ABSTRACT FROM AUTHOR]

To address the issue of large data flow of 5G channel for differential protection of distribution lines， a 5G-based scheme for differential protection for distribution lines considering data flow control is proposed. This scheme optimizes transmission strategy， streamlines data formats， and boosts message efficiency by focusing on the transmission frequency， message content， and message types of differential protection data. Furthermore， it enhances the data synchronization scheme for differential protection and refines the logic for saturation discrimination of current transformer （CT）， significantly reducing data flow without compromising the performance of differential protection. Through experiments， the scheme’s viability for practical applications has been confirmed， offering valuable insights for the real-world deployment of 5G-enabled differential protection for distribution networks.

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Summary In response to the difficulties faced by the open data movement, the Chinese government has prioritized promoting the opening of high-value data sets to the public. However, the policy is promoted slowly in practice, and the basic problems are not studied enough in theory. Most of the existing literature focuses on open data subjects and open data platforms. and there is a lack of existing literature on data sets and their value recognition. This paper uses comparative research literature analysis and other methods to carry out research. Firstly, the paper analyzes the difficulties faced by the open data movement of governments around the world points out that the cause is the technical, institutional and organizational constraints, and puts forward the necessity and feasibility of opening high value data sets. Secondly, based on the process sorting and mechanism comparison of opening high-value data sets around the world, it is pointed out that the United States and the European Union have reached a consensus on basic positioning, promotion system and governance means. but there are also differences in goal orientation and regulation degree. Thirdly, the paper systematically combs the relevant practical exploration of European and American countries, extracts the existing experience, and analyzes the current challenges. Finally, in view of China's promotion of the priority opening of high-value data sets. it is suggested that based on the practical basis of data opening, the concept of data opening should be optimized. an open mechanism should be systematically established. and open norms should be clarified in a timely manner, so as to form a comprehensive, systematic and clear institutional system. Compared with previous literatures, this paper expands on the two aspects of experience extraction and countermeasures. First, the experience of European and American countries in exploring open high-value data set standards is summarized into the establishment of typed content standards. three-star formal standards and multi- agent participation procedure standards. Second, it puts forward the overall strategy of prioritizing the opening of high-value data sets in China including optimizing the opening concept and strengthening the market-oriented concept consciousness, the establishment of subjective and objective standard identification mechanism. policy and law reference system adjustment mechanism. data suppliers and data demand communication feedback mechanism. It also includes open specifications that clarify the concept of high-value data sets, content boundaries, and open order. This study systematically summarized the practical experience of opening high-value data sets outside the region. extracted the basic operation mechanism, analyzed the challenges faced by the current work of opening high-value data sets, expanded the existing theoretical research on data opening, and provided a foundation for comprehensive and in-depth research on open high-value data sets in the future. On this basis, the policy suggestions are helpful for the further development of China's practice of opening high-value data sets. [ABSTRACT FROM AUTHOR]

ADITYA-L1 is India's first dedicated mission to observe the Sun and its atmosphere from a halo orbit around L1 point. Visible emission line coronagraph (VELC) is the primary payload on board Aditya-L1 to observe the Sun's corona. VELC is designed as an internally occulted reflective coronagraph to meet the observational requirements of wide wavelength band and the field of view close to the solar limb (1.05 R Sun ). Images of the solar corona in continuum and spectra in three emission lines 5303 Å [Fe xiv], 7892 Å [Fe xi] and 10747 Å [Fe xiii] obtained with high cadence are to be analyzed using software algorithms automatically. A reasonable part of these observations will be made in a synoptic mode in which the data are processed through a standard pipeline and the resulting products will be made available for public use. The procedure involves the calibration of instrument and detectors, converting the images into the FITS format, correcting the images and spectra for the instrumental effects, and aligning the images (in terms of position angles and image scales) etc. Then, develop image processing algorithms to detect the occurrence of energetic events using continuum images. Also derive physical parameters, such as temperature and velocity structure of solar corona using emission line observations. Here, we describe the calibration of detectors and the development of software algorithms to detect the occurrence of CMEs and analyze the spectroscopic data. [ABSTRACT FROM AUTHOR]

With the recent advancements in high-performance computing, convolutional neural networks (CNNs) have achieved remarkable success in various vision tasks. However, along with improvements in model accuracy, the size and computational complexity of the models have significantly increased with the increasing number of parameters. Although graphics processing unit (GPU) platforms equipped with high-performance memory and specialized in parallel processing are commonly used for CNN processing, the significant power consumption presents challenges in their utilization on edge devices. To address these issues, research is underway to design CNN models using field-programmable gate arrays (FPGAs) as accelerators. FPGAs provide a high level of flexibility, allowing efficient optimization of convolution operations, which account for a significant portion of the CNN computations. Additionally, FPGAs are known for their low power consumption compared to GPUs, making them a promising energy-efficient platform. In this paper, we review and summarize various approaches and techniques related to the design of FPGA-based CNN accelerators. Specifically, to comprehensively study CNN accelerators, we investigate the advantages and disadvantages of various methods for optimizing CNN accelerators and previously designed efficient accelerator architectures. We expect this paper to serve as an important guideline for future hardware research in artificial intelligence. [ABSTRACT FROM AUTHOR]

Purpose. Improving the process of information transfer for critical infrastructure sectors and enterprises through new approaches to real-time tracking of goods, services, and equipment, ensuring secure and transparent data integration and auditing of data flows in information systems using blockchain technologies. Methodology. This research moves away from traditional centralized data management systems based on SQL and no-SQL databases by implementing a decentralized, immutable system built on blockchain technology. This uses the principles of the Merle tree in a digital ledger within blockchain technology to verify data integrity and smart contracts to automate key data flow processes. By tracking goods and equipment through supply chains on the blockchain, this approach ensures product authenticity, provenance, and transparency in real time. In addition, it creates a secure and transparent audit trail for all data in the system compared to conventional centralized data management systems based on SQL and no-SQL databases. Findings. The developed blockchain-based approach improves data security, transparency, automation, and trust in managing data flows. Compared to traditional systems, it offers unique advantages such as immutability, decentralized management, and improved traceability. But while offering numerous advantages, blockchain also faces some limitations in terms of scalability and system complexity. Originality. Digital ledger and blockchain methods have been further developed in the context of designing information systems and data flow management systems based on blockchain algorithms in the context of Industry 4.0. This allows increasing data security, transparency, automation, and trust in data flow management. Practical value. The proposed approach is used to design information and data flow management systems based on blockchain algorithms. This improves the quality of data flow management in industrial enterprises and critical infrastructure, as well as supply chains. [ABSTRACT FROM AUTHOR]

The pursuit of environmental sustainability and resource conservation has underscored the growing significance of the circular economy paradigm across industries. This transformation is particularly pertinent within the automotive industry, necessitating a profound restructuring of the value chain, with a specific focus on end-of-life considerations. Significant potential for circular economy arises from the thorough examination of end-of-life vehicles and processes, involving strategies such as component reuse, material recycling and primary resource reduction. Providing process-, product-, component-, and material-specific information at the end-of-life of automotive vehicles can improve decision-making regarding circular strategies. It also demands for new digital solutions. This paper addresses an integrated approach of data flow analysis and Life Cycle Assessment (LCA) to enable a comprehensive monitoring of the environmental impact of these processes. First, the use case of end-of-life dismantling processes is presented - including pyrotechnics neutralization, vehicle dehydration, disassembly of reuse components as well as vehicle compaction. Secondly, the data demand respective information and material flows is depicted. As a main result, the LCA data flow model specifically tailored to the end-of-life vehicle dismantling processes is described. This model serves as a fundamental basis for analyzing the environmental impact of end-of-life processes combined with the underlying data flow. [ABSTRACT FROM AUTHOR]

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Investigations in the field of spiking neural networks (SNNs) encompass diverse, yet overlapping, scientific disciplines. Examples range from purely neuroscientific investigations, researches on computational aspects of neuroscience, or applicative-oriented studies aiming to improve SNNs performance or to develop artificial hardware counterparts. However, the simulation of SNNs is a complex task that can not be adequately addressed with a single platform applicable to all scenarios. The optimization of a simulation environment to meet specific metrics often entails compromises in other aspects. This computational challenge has led to an apparent dichotomy of approaches, with model-driven algorithms dedicated to the detailed simulation of biological networks, and data-driven algorithms designed for efficient processing of large input datasets. Nevertheless, material scientists, device physicists, and neuromorphic engineers who develop new technologies for spiking neuromorphic hardware solutions would find benefit in a simulation environment that borrows aspects from both approaches, thus facilitating modeling, analysis, and training of prospective SNN systems. This manuscript explores the numerical challenges deriving from the simulation of spiking neural networks, and introduces SHIP, Spiking (neural network) Hardware In PyTorch, a numerical tool that supports the investigation and/or validation of materials, devices, small circuit blocks within SNN architectures. SHIP facilitates the algorithmic definition of the models for the components of a network, the monitoring of states and output of the modeled systems, and the training of the synaptic weights of the network, by way of user-defined unsupervised learning rules or supervised training techniques derived from conventional machine learning. SHIP offers a valuable tool for researchers and developers in the field of hardware-based spiking neural networks, enabling efficient simulation and validation of novel technologies. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid growth of Internet-related services, the traditional software-defined network architecture has gradually failed to adapt to user demands and services. This paper proposes an ant colony algorithm (ACO)-based data flow control policy optimization scheme specifically designed for software-defined networks (SDNs). It has been found that the traditional ACO algorithm is prone to overfitting during the optimization process of data flow control policies for SDN, and a pheromone updating strategy has been introduced to optimize this phenomenon. After solving this phenomenon, the optimization scheme of data flow control policy for software-defined networks based on the ACO algorithm will be formally formulated, and simulation experiments will be used to confirm the effectiveness of the optimization scheme in this paper. The results show that this paper’s algorithm has a higher priority than the control algorithm in terms of four evaluation metrics: average link throughput, link utilization, average round-trip delay, and data packet loss rate. This study enables the optimization of data flow control strategies under software-defined network architecture and also improves the utilization of network data flow to bring about a better network experience.

Investigations in the field of spiking neural networks (SNNs) encompass diverse, yet overlapping, scientific disciplines. Examples range from purely neuroscientific investigations, researches on computational aspects of neuroscience, or applicative-oriented studies aiming to improve SNNs performance or to develop artificial hardware counterparts. However, the simulation of SNNs is a complex task that can not be adequately addressed with a single platform applicable to all scenarios. The optimization of a simulation environment to meet specific metrics often entails compromises in other aspects. This computational challenge has led to an apparent dichotomy of approaches, with model-driven algorithms dedicated to the detailed simulation of biological networks, and data-driven algorithms designed for efficient processing of large input datasets. Nevertheless, material scientists, device physicists, and neuromorphic engineers who develop new technologies for spiking neuromorphic hardware solutions would find benefit in a simulation environment that borrows aspects from both approaches, thus facilitating modeling, analysis, and training of prospective SNN systems. This manuscript explores the numerical challenges deriving from the simulation of spiking neural networks, and introduces SHIP, Spiking (neural network) Hardware In PyTorch, a numerical tool that supports the investigation and/or validation of materials, devices, small circuit blocks within SNN architectures. SHIP facilitates the algorithmic definition of the models for the components of a network, the monitoring of states and output of the modeled systems, and the training of the synaptic weights of the network, by way of user-defined unsupervised learning rules or supervised training techniques derived from conventional machine learning. SHIP offers a valuable tool for researchers and developers in the field of hardware-based spiking neural networks, enabling efficient simulation and validation of novel technologies.

Copyright of Law / Teise is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This Replicating Computational Report (RCR) describes (a) our datAFLow fuzzer and (b) how to replicate the results in "datAFLow: Toward a Data-Flow-Guided Fuzzer." Our primary artifact is the datAFLow fuzzer. Unlike traditional coverage-guided greybox fuzzers-which use control-flow coverage to drive program exploration-datAFLow uses data-flow coverage to drive exploration. This is achieved through a set of LLVM-based analyses and transformations. In addition to datAFLow, we also provide a set of tools, scripts, and patches for (a) statically analyzing data flows in a target program, (b) compiling a target program with the datAFLow instrumentation, (c) evaluating datAFLow on the Magma benchmark suite, and (d) evaluating datAFLow on the DDFuzz dataset. datAFLow is available at https://github.com/HexHive/datAFLow. [ABSTRACT FROM AUTHOR]

The influence of Internet marketing has grown so much that producers must now reconfigure their businesses from offline operation to online presence simply to meet user expectations. Thus, the development of an intelligent information system for product promotion online is quite relevant. It may lead to automatized selection of competing products and advertising content, a subsequent increase in the effectiveness of advertisements, and a decrease in costs for Internet ad placements. The paper presents the approach for creating an intelligent information system for product promotion in online spaces that makes it possible to reduce advertising costs. A methodology is based on outcomes of own previous studies as well as the flow nature and semantics of data streams. The framework of the proposed intelligent system includes the four key procedures and functions: intelligent formation of keywords for advertising content based on feedback, intelligent formation of product catalogs of online stores, generation of advertising content, and generation of improved advertising content and its targeting generation of text based on keywords. An experimental study confirmed that the effectiveness of posts on social media increased by at least 125%, while the price decreased by 87%. [ABSTRACT FROM AUTHOR]

Abstract This article presents a taxonomy and represents a repository of open problems in computing for numerically and logically intensive problems in a number of disciplines that have to synergize for the best performance of simulation-based feasibility studies on nature-oriented engineering in general and civil engineering in particular. Topics include but are not limited to: Nature-based construction, genomics supporting nature-based construction, earthquake engineering, and other types of geophysical disaster prevention activities, as well as the studies of processes and materials of interest for the above. In all these fields, problems are discussed that generate huge amounts of Big Data and are characterized with mathematically highly complex Iterative Algorithms. In the domain of applications, it has been stressed that problems could be made less computationally demanding if the number of computing iterations is made smaller (with the help of Artificial Intelligence or Conditional Algorithms), or if each computing iteration is made shorter in time (with the help of Data Filtration and Data Quantization). In the domain of computing, it has been stressed that computing could be made more powerful if the implementation technology is changed (Si, GaAs, etc.…), or if the computing paradigm is changed (Control Flow, Data Flow, etc.…).

To model the data and functions in various computer science applications, the researcher uses a Data Flow Diagram (DFD). DFD has been constructed using [open-source software tools that provide users with different shapes and environments. However, the existing approaches require substantial human effort, the validity of the generated output is still a loophole, and they have never gained traction in practice. Our research objective is to develop a semi-automated tool for drawing complex Data Flow Diagrams in the shortest time according to the specified features of the intended system. We developed a Natural Language Interface (NLI) that allows the user to compose a query and identify the system functionality and constraints for the composition of DFD. Natural Language Processing (NLP) techniques are applied to scrapped data to extract the keywords and develop a data repository. Also, we developed rule-based algorithms to map user queries onto respective token shapes to draw the required functionality into appropriate levels of DFD. For verification, output DFDs were converted into conceptual digraphs using adjacency and permutation matrices to evaluate isomorphism. The empirical results reflect that the DFDs generated by the system are correct, complete, and significant.

Runtime systems can significantly reduce the cognitive complexity of scientific applications, narrowing the gap between systems engineering and domain science in HPC. One of the most important angles in this is automating data migration in a cluster. Traditional approaches require the application developer to model communication explicitly, for example through MPI primitives. Celerity, a runtime system for accelerator clusters heavily inspired by the SYCL programming model, instead provides a purely declarative approach focused around access patterns. In addition to eliminating the need for explicit data transfer operations, it provides a basis for efficient and dynamic scheduling at runtime. However, it is currently only suitable for accessing array-like data from runtime-controlled tasks, while real programs often need to interact with opaque data local to each host, such as handles or database connections, and also need a defined way of transporting data into and out of the virtualised buffers of the runtime. In this paper, we introduce a graph-based approach and declarative API for expressing side-effect dependencies between tasks and moving data from the runtime context to the application space. [ABSTRACT FROM AUTHOR]