Data Processing Mode Research Articles

Privacy and personal data risk governance for generative artificial intelligence: A Chinese perspective

The rapid development of generative artificial intelligence (AI) has attracted global attention and posed challenges to existing data governance frameworks. The increased technical complexity and expanded scale of data usage not only make it more difficult to regulate AI but also present challenges for the current legal system. This article, which takes ChatGPT's training data and working principles as a starting point, examines specific privacy risks, data leakage risks, and personal data risks posed by generative AI. It also analyzes the latest practices in privacy and personal data protection in China. This article finds that while China's governance on privacy and personal data protection takes a macro-micro integration approach and a private-and-public law integration approach, there are shortcomings in the legal system. Given that the current personal data protection system centered on individual control is unsuitable for the modes of data processing by generative AI, and that private law is insufficient in safeguarding data privacy, urgent institutional innovation is needed to achieve the objective of “trustworthy AI.”

A network security protection scheme for tax system based on elliptic curve cryptography

With the rapid development of modern information technology and network technology, the construction of tax informatization plays an important role in improving the level of tax management and work efficiency. However, in the centralized data processing mode, the establishment of large data center and the efficient, stable, and secure connection of server bandwidth have become key challenges. Moreover, the openness and interconnectivity of the tax system network pose various network security threats, seriously threatening the integrity and security of data. For the above issues, this article proposes a tax system network security protection scheme based on elliptic curve cryptography. This scheme combines hash function and elliptic curve cryptography to achieve user anonymity and secure generation of session keys, effectively ensuring the secure transmission of confidential information. The security and effectiveness of the scheme are verified through informal and formal security analysis based on security proof, BAN logic and AVISPA tool. And a comprehensive evaluation of the scheme is conducted, and the results show that the scheme significantly reduced the costs while providing security features.

Non-targeted mass spectrometry and feature-based molecular networking for determination of branched fatty acid esters of hydroxy fatty acids in milk.

Branched fatty acid esters of hydroxy fatty acids (FAHFAs) represent trace lipids with significant natural biological functions. While exogenous FAHFAs have been extensively studied, research on FAHFAs in milk remains limited, constraining our grasp of their nutritional roles. This study introduces a non-targeted mass spectrometry approach combined with chemical networking of spectral fragmentation patterns to uncover FAHFAs. Through meticulous sample handling and comparisons of various data acquisition and processing modes, we validate the method's superiority, identifying twice as many FAHFAs compared to alternative techniques. This validated method was then applied to different milk samples, revealing 45 chemical signals associated with known and potential FAHFAs, alongside findings of 66 ceramide/hexosylceramide (Cer/HexCer), 48 phosphatidyl ethanolamine/lyso phosphatidyl ethanolamine (PE/LPE), 21 phosphatidylcholine/lysophosphatidylcholine (PC/LPC), 16 phosphatidylinositol (PI), 7 phosphatidylserine (PS), and 11 sphingomyelin (SM) compounds. This study expands our understanding of the FAHFA family in milk and provides a fast and convenient method for identifying FAHFAs.

Optical fiber-based open source low cost portable spectrometer system

This article explores the development of a small, compact fiber-based spectrometer system designed to overcome the limitations of standard spectrometers, such as the high cost and restricted accessibility.Operated by a Raspberry Pi, the fiber-based spectrometer system uses the increased computing power to provide versatile modes of operation and powerful data processing, while maintaining a small size. Specifically crafted for basic chemistry and biology lab setups, where fibers allow measurements in different conditions, and customization enables fluorescence, light scattering, and absorption measurements.The system is adaptable and versatile, offering ease of modification and adaptation for a broad range of applications.

CoGTEx: Unscaled system-level coexpression estimation from GTEx data forecast novel functional gene partners.

Coexpression estimations are helpful for analysis of pathways, cofactors, regulators, targets, and human health and disease. Ideally, coexpression estimations should consider as many diverse cell types as possible and consider that available data is not uniform across tissues. Importantly, the coexpression estimations accessible today are performed on a "tissue level", which is based on cell type standardized formulations. Little or no attention is paid to overall gene expression levels. The tissue-level estimation assumes that variance expression levels are more important than mean expression levels. Here, we challenge this assumption by estimating a coexpression calculation at the "system level", which is estimated without standardization by tissue, and show that it provides valuable information. We made available a resource to view, download, and analyze both, tissue- and system-level coexpression estimations from GTEx human data. GTEx v8 expression data was globally normalized, batch-processed, and filtered. Then, PCA, clustering, and tSNE stringent procedures were applied to generate 42 distinct and curated tissue clusters. Coexpression was estimated from these 42 tissue clusters computing the correlation of 33,445 genes by sampling 70 samples per tissue cluster to avoid tissue overrepresentation. This process was repeated 20 times, extracting the minimum value provided as a robust estimation. Three metrics were calculated (Pearson, Spearman, and G-statistic) in two data processing modes, at the system-level (TPM scale) and tissue levels (z-score scale). We first validate our tissue-level estimations compared with other databases. Then, by specific analyses in several examples and literature validations of predictions, we show that system-level coexpression estimation differs from tissue-level estimations and that both contain valuable information reflected in biological pathways. We also show that coexpression estimations are associated to transcriptional regulation. Finally, we present CoGTEx, a valuable resource for viewing and analyzing coexpressed genes in human adult tissues from GTEx v8 data. We introduce our web resource to list, view and explore the coexpressed genes from GTEx data. We conclude that system-level coexpression is a novel and interesting coexpression metric capable of generating plausible predictions and biological hypotheses; and that CoGTEx is a valuable resource to view, compare, and download system- and tissue- level coexpression estimations from GTEx data. The web resource is available at http://bioinformatics.mx/cogtex.

Design of Accounting Information Big Data Analysis Platform Based on Cloud Computing

The advent of the era of big data provides unprecedented opportunities for the transformation and innovation of corporate accounting, but also poses severe challenges to its development. Taking Company M as an example, this paper analyzes the accounting transformation under the current big data environment, and raises many new issues, such as the security of accounting information and the authenticity of information, and the transformation of accounting information data processing mode. At the same time, enterprises are also facing many opportunities, such as: the impact on accounting confirmation, enterprise financial forecasting, decision-making and cost control, and the management of enterprise accounting electronic documents in the cloud computing environment and big data environment. This paper intends to study the accounting information system under the environment of cloud computing and big data, aiming to improve the security and reliability of enterprise accounting information. This article constructed an accounting information big data analysis platform. Through experimental testing, it was found that before using the platform, the cost of hardware investment and system maintenance in 2019 was 19,803 yuan, while the cost after using the platform was 13,256 yuan. Studies have shown that using big data processing technology to extract useful information from huge financial and accounting databases can help enhance the core competitiveness of enterprises.

Research on the Personal Data Portability Based on the Perspective of Property Rights Theory in the Context of Enterprise Digital Transformation

This study examines the right to data portability under the industrial economics theory, and addresses the problems of ambiguous boundaries, inconsistent formats, and data sharing. Based on the relational transactions in China, this study analyses the right to data portability from three aspects: transaction boundaries, transaction frequency, and exclusive nature. From the viewpoint of transaction uncertainty, it is necessary to clarify the content of transaction data and define the property rights boundary. From the view of transaction frequency, more detailed regulation of data processing mode is needed to reduce transaction costs in the future. The data processing mode needs to find a balance between security and rapidness. The data processing mode should imitate distributed data transfer protocol or decentralized recommendation system. From the perspective of asset exclusivity, data sharing should be classified for different users. The relationship between user privacy and data sharing should be properly balanced to prevent a “one-size-fits-all” data management model. This study applies property rights theory to address the issue of data portability, focuses on the economic benefits brought by data portability, and expands the perspective of the data portability’s nature. Based on relational transactions in China, this study enriches the research of personal data protection in developing countries. In addition, the results provide a theoretical basis for government management, help define the boundary of digital assets, promote the digital transformation of cities, and facilitate sustainable economic development.

Research on the Personal Data Portability Based on the Perspective of Property Rights Theory in the Context of Enterprise Digital Transformation

This study examines the right to data portability under the industrial economics theory, and addresses the problems of ambiguous boundaries, inconsistent formats, and data sharing. Based on the relational transactions in China, this study analyses the right to data portability from three aspects: transaction boundaries, transaction frequency, and exclusive nature. From the viewpoint of transaction uncertainty, it is necessary to clarify the content of transaction data and define the property rights boundary. From the view of transaction frequency, more detailed regulation of data processing mode is needed to reduce transaction costs in the future. The data processing mode needs to find a balance between security and rapidness. The data processing mode should imitate distributed data transfer protocol or decentralized recommendation system. From the perspective of asset exclusivity, data sharing should be classified for different users. The relationship between user privacy and data sharing should be properly balanced to prevent a "one-size-fits-all" data management model. This study applies property rights theory to address the issue of data portability, focuses on the economic benefits brought by data portability, and expands the perspective of the data portability’s nature. Based on relational transactions in China, this study enriches the research of personal data protection in developing countries. In addition, the results provide a theoretical basis for government management, help define the boundary of digital assets, promote the digital transformation of cities, and facilitate sustainable economic development.

Investigation of Data Pre-Processing Algorithms for Power Curve Modeling of Wind Turbines Based on ECC

Data pre-processing is the first step of using SCADA data to study the performance of wind turbines. However, there is a lack of knowledge of how to obtain more effective data pre-processing algorithms. This paper fully explores multiple data pre-processing algorithms for power curve modeling. A three-stage data processing mode is proposed, namely, preliminary data filtering and compensation (Stage I), secondary data filtering (Stage II), and single-valued processing (Stage Ⅲ). Different data processing algorithms are selected at different stages and are finally merged into nine data processing algorithms. A novel evaluation method based on energy characteristic consistency (ECC) is proposed to evaluate the reliability of various algorithms. The influence of sliding mode and benchmark of Binning on data processing has been fully investigated through indicators. Four wind turbines are selected to verify the advantages and disadvantages of the nine data processing methods. The result shows that at the same wind speed, the rotational speed and power values obtained by MLE (maximum likelihood estimation) are relatively high among the three single-valued methods. Among the three outlier filtering methods, the power value obtained by KDE (kernel density estimation) is relatively large. In general, KDE-LSM (least square method) has good performance in general. The sum of four evaluating index values obtained by KDE-LSM from four wind turbines is the smallest.

A real-time data acquisition method of industrial production line based on OPC technology

In view of the low timeliness and accuracy of traditional production line data acquisition methods, this study proposed a real-time data acquisition method of industrial production line based on OPC technology. Considering the heterogeneity of industrial production line data sources, a data transmission scheme is generated based on OPC technology and multi-point communication with Modbus protocol after data preprocessing by piecewise linear interpolation method. Then, the improved decision tree algorithm is used to design the data processing mode. Finally, the current state of industrial production line data is judged by comparing the threshold value, and the real-time data collection of industrial production line is completed after the collection interval is determined. Experimental results show that the acquisition accuracy of this method is up to 96.8%, the maximum acquisition time is only 12.5 s, and the highest packet loss rate is only 1.89%, indicating that this method improves the acquisition quality.

Design of IoT Gateway for Crop Growth Environmental Monitoring Based on Edge-Computing Technology.

With the extensive use of the Internet of Things (IoT) in agriculture, the number of terminals are also grow rapidly. This will increase the network traffic and computing pressure of the centralized server. The centralized data processing mode used in traditional agriculture cannot meet the needs of the Internet of everything era. This paper designs a gateway based on edge-computing technology for monitoring crop growth environment. It uses virtualized container technology to package long-range wide-area network (LoRaWAN) server, pest identification, and environmental information data fusion functions into images. It forms integrated operation mode of multiple function in agriculture. The gateway applies message-oriented middleware to standardize and customize the data transmission among functional modules, clouds, and edges. Through simulation and field test, the designed gateway can achieve the functions of each module at the same time, the resource utilization, and the transmission quality are stable. The edge-computing gateway has the advantages of low cost, low latency, and low power consumption which has practical significance.

Hybrid optical measurement system for evaluation of precision two-dimensional planar stages

This paper presents an optical measurement system, which is mainly composed of two hybrid sensor modules (HSMs), for evaluation of precision two-dimensional (2D) planar stages. The HSM is designed to integrate two different types of sensors within a single sensor module, so that it can be operated as a profile interferometer or an angle sensor by simply switching the data-processing mode. This enables us to implement efficient on-site evaluation of straightness errors of moving mirrors of a two-axis laser interferometer, and multi-axis parasitic motion errors of a 2D planar stage without reconfiguration of the evaluation setup. To estimate the performance of a prototype measurement system, an exemplary 2D stage was experimentally evaluated, and the straightness and angular motion errors were measured with expanded uncertainty (k = 2) of 40 nm and 0.14″, respectively. Reliability of the measurement system was also verified by comparison with reference instruments.

Error Modeling and Anomaly Detection of Smart Electricity Meter Using TSVD+L Method

Using remote data analysis to estimate smart electricity meters (SMs) and detect SMs&#x2019; anomaly, has aroused considerable discussion in power industry, because its lower cost and higher efficiency compared to traditional field calibration. However, the trouble of lacking topology and parameter of the distribution grid and the ill-posed problem in the SMs&#x2019; error estimation and anomaly detection (AD) are not well resolved in most energy conservation theorem-based SMs AD methods. This paper presents a sorted Top-N anomaly detection mechanism to generate a list of suspicious anomalous SMs. The error estimation model (EEM) only using SMs electricity consumption data is investigated. The truncated singular value decomposition regularization with L-curve optimization (TSVD+L) method is proposed to address the model&#x2019;s ill-posedness. Three data processing modes, namely one-pot mode, segmentation mode and sliding window technique (SWT), are suggested to obtain multiple calculation results for SMs error comprehensive evaluation. The top <i>N</i>% SMs in error sequence is proposed for onsite calibration instead of full inspection. The effectiveness and practicality of the proposed method are verified through both simulation case and practical distribution network application. The results show that the proposed method has higher accuracy in SMs anomaly detection, compared with the ordinary least squares (OLS) method, recursive least squares (RLS) method, and Tikhonov regularization (Tik) method.

Improvement of Multi-GNSS Precision and Success Rate Using Realistic Stochastic Model of Observations

With the advancement of multi-constellation and multi-frequency global navigation satellite systems (GNSSs), more observations are available for high precision positioning applications. Although there is a lot of progress in the GNSS world, achieving realistic precision of the solution (neither too optimistic nor too pessimistic) is still an open problem. Weighting among different GNSS systems requires a realistic stochastic model for all observations to achieve the best linear unbiased estimation (BLUE) of unknown parameters in multi-GNSS data processing mode. In addition, the correct integer ambiguity resolution (IAR) becomes crucial in shortening the Time-To-Fix (TTF) in RTK, especially in challenging environmental conditions. In general, it is required to estimate various variances for observation types, consider the correlation between different observables, and compensate for the satellite elevation dependence of the observable precision. Quality control of GNSS signals, such as GPS, GLONASS, Galileo, and BeiDou can be performed by processing a zero or short baseline double difference pseudorange and carrier phase observations using the least-squares variance component estimation (LS-VCE). The efficacy of this method is investigated using real multi-GNSS data sets collected by the Trimble NETR9, SEPT POLARX5, and LEICA GR30 receivers. The results show that the standard deviation of observations depends on the system and the observable type in which a particular receiver could have the best performance. We also note that the estimated variances and correlations among different observations are also dependent on the receiver type. It is because the approaches utilized for the recovery techniques differ from one type of receiver to another kind. The reliability of IAR will improve if a realistic stochastic model is applied in single or multi-GNSS data processing. According to the results, for the data sets considered, a realistic stochastic model can increase the computed empirical success rate to 100% in multi-GNSS as well as a single system. As mentioned previously, the realistic precision of the solution can be achieved with a realistic stochastic model. However, using the estimated stochastic model, in fact, leads to better precision and accuracy for the estimated baseline components, up to 39% in multi-GNSS.

Auto-qPCR; a python-based web app for automated and reproducible analysis of qPCR data

Quantifying changes in DNA and RNA levels is essential in numerous molecular biology protocols. Quantitative real time PCR (qPCR) techniques have evolved to become commonplace, however, data analysis includes many time-consuming and cumbersome steps, which can lead to mistakes and misinterpretation of data. To address these bottlenecks, we have developed an open-source Python software to automate processing of result spreadsheets from qPCR machines, employing calculations usually performed manually. Auto-qPCR is a tool that saves time when computing qPCR data, helping to ensure reproducibility of qPCR experiment analyses. Our web-based app (https://auto-q-pcr.com/) is easy to use and does not require programming knowledge or software installation. Using Auto-qPCR, we provide examples of data treatment, display and statistical analyses for four different data processing modes within one program: (1) DNA quantification to identify genomic deletion or duplication events; (2) assessment of gene expression levels using an absolute model, and relative quantification (3) with or (4) without a reference sample. Our open access Auto-qPCR software saves the time of manual data analysis and provides a more systematic workflow, minimizing the risk of errors. Our program constitutes a new tool that can be incorporated into bioinformatic and molecular biology pipelines in clinical and research labs.

Service-Oriented Real-Time Smart Job Shop Symmetric CPS Based on Edge Computing

Symmetry is one of the most important notions in the digital twins-driven manufacturing cyber–physical system (CPS). Real-time acquisition of production data and rapid response to changes in the external environment are the keys to ensuring the symmetry of the CPS. In the service-oriented production process, in order to solve the problem of the service response delay of the production nodes in a smart job shop, a CPS based on mobile edge computing (MEC) middleware is proposed. First, the CPS and MEC for a service-oriented production process are analyzed. Secondly, based on MEC middleware, a CPS architecture model of a smart job shop is established. Then, the implementation of MEC middleware and application layer function modules are introduced in detail. By designing an MEC middleware model and embedding function modules such as data cache management, redundant data filtering, and data preprocessing, the ability of data processing is sunk from the data center to the data source. Based on that, the network performances, such as network bandwidth, packet loss rate, and delay, are improved. Finally, an experiment platform of the smart job shop is used to verify different data processing modes by comparing the network performance data such as bandwidth, packet loss rate, and response delay.

Research on the Application of Intelligence Teaching Mode of Superstar Platform in the Teaching of Experimental Design and Data Processing

Experimental Design and Data Processing is an important core professional basic course for food science majors. This course is theoretical and practical, and there are many formulas, abstract contents and difficult to understand, and there are some problems in the teaching process, such as students' poor interest in learning, insufficient mastery of what they have learned, and inability to combine theory with practice organically. Through analyzing the existing problems, this paper puts forward some reform measures for the teaching mode of experimental design and data processing by using the intelligent teaching of Superstar platform.

Evaluation of different extractions for the metabolite identification of malachite green in brook trout and shrimp

Applications of mass spectrometry-based metabolomics in food science have developed fast in the last decade. Sample preparation and data processing are critical in non-target/metabolomic workflows but there is currently no standardized protocol for the development of these methods. The impact of data processing parameters or the inclusion of a different matrix is not often taken into account during the selection of an extraction. Thus, this study aimed to investigate the impact of different extractions, e.g., QuEChERS, and data processing on the determination of malachite green metabolites in two different organisms, brook trout and shrimp. The results obtained confirm the need for a harmonized approach for the validation of non-target workflows, as depending on the comparison criteria, the matrix, the mode of ionization or data processing, a different extraction could be chosen. This study also identified for the first time des-methylated leucomalachite green as another metabolite in the two organisms.

A new reconstruction algorithm based on temporal neural network and its application in power quality disturbance data

The traditional reconstruction algorithms based on p-norm, limited by their reconstruction model and data processing mode, are prone to reconstruction failure or long reconstruction time. In order to break through the limitations, this paper proposes a reconstruction algorithm based on the temporal neural network (TCN). A new reconstruction model based on TCN is first established, which does not need sparse representation and has large-scale parallel processing. Next, a TCN with a fully connected layer and symmetrical zero-padding operation is designed to meet the reconstruction requirements, including non-causality and length-inconsistency. Moreover, the proposed algorithm is constructed and applied to power quality disturbance (PQD) data. Experimental results show that the proposed algorithm can implement the reconstruction task, demonstrating better reconstruction accuracy and less reconstruction time than OMP, ROMP, CoSaMP, and SP. Therefore, the proposed algorithm is more attractive when dictionary design is complicated, or real-time reconstruction is required.

S2p: Provenance Research for Stream Processing System

The main purpose of our provenance research for DSP (distributed stream processing) systems is to analyze abnormal results. Provenance for these systems is not nontrivial because of the ephemerality of stream data and instant data processing mode in modern DSP systems. Challenges include but are not limited to an optimization solution for avoiding excessive runtime overhead, reducing provenance-related data storage, and providing it in an easy-to-use fashion. Without any prior knowledge about which kinds of data may finally lead to the abnormal, we have to track all transformations in detail, which potentially causes hard system burden. This paper proposes s2p (Stream Process Provenance), which mainly consists of online provenance and offline provenance, to provide fine- and coarse-grained provenance in different precision. We base our design of s2p on the fact that, for a mature online DSP system, the abnormal results are rare, and the results that require a detailed analysis are even rarer. We also consider state transition in our provenance explanation. We implement s2p on Apache Flink named as s2p-flink and conduct three experiments to evaluate its scalability, efficiency, and overhead from end-to-end cost, throughput, and space overhead. Our evaluation shows that s2p-flink incurs a 13% to 32% cost overhead, 11% to 24% decline in throughput, and few additional space costs in the online provenance phase. Experiments also demonstrates the s2p-flink can scale well. A case study is presented to demonstrate the feasibility of the whole s2p solution.