Single-dimensional Data Research Articles

VulTR: Software vulnerability detection model based on multi-layer key feature enhancement

Software vulnerabilities pose a huge threat to current network security, which continues to lead to data leaks and system damage. In order to effectively identify and patch these vulnerabilities, researchers have proposed automated detection methods based on deep learning. However, most of the existing methods only rely on single-dimensional data representation and fail to fully explore the composite characteristics of the code. Among them, the sequence embedding method fails to effectively capture the structural characteristics of the code, while the graph embedding method focuses more on the global characteristics of the overall graph structure and is still insufficient in optimizing the representation of nodes. In view of this, this paper constructs the VulTR model, which incorporates an importance assessment mechanism to strengthen the key syntax levels of the source code (from lexical elements to nodes and graph-level structures), significantly improving the importance of key vulnerability features in classification decisions. At the same time, a relationship connection diagram is constructed to describe the spatial characteristics of the correlations between functions. Experimentally verified, VulTR's F1 scores on both synthetic and real data sets exceed those of the compared models (VulDeePecker, SySeVR, Devign, VulCNN, IVDetect, and mVulPreter).

Open-circuit fault diagnosis method of inverter in wind turbine yaw system based on GADF image coding

Considering the concealment of inverter open-circuit faults in yaw system of wind turbines, a method for diagnosing open-circuit fault of yaw system inverter is proposed based on Gramian Angular Difference Field (GADF) image coding in this article. Firstly, the current vector phase in the vicinity of relatively high yaw speed is collected as monitoring data. Secondly, considering the insufficient fault feature extraction ability of Convolutional Neural Network (CNN) structures with single dimensional data input during model training, the one-dimensional current vector phase is encoded into two-dimensional image by GADF image coding and an effective CNN fault diagnosis model is obtained with fewer fault samples. Finally, by comparing with actual monitoring data of wind turbine, the effectiveness of yaw system simulation model is verified. The proposed method can achieve identification and localization of open-circuit fault for single and two power devices, which is found to have better anti-noise interference effect and the results are not affected by yaw angle and mechanical torque. It is approved that the proposed method is effective with a simulation on the RT-LAB platform.

Enabling privacy-preserving medication analysis in distributed EHR systems

The digitalization of the medical field has resulted in a vast amount of medical data stored in Electronic Health Record (EHR) systems. Among the various valuable applications of EHRs, medication efficacy analysis stands out as a powerful tool for drug development and enhancing patient treatment. This analysis relies on multi-dimensional medical data, encompassing various factors. Currently, medical research is increasingly inclined towards inter-institutional sharing of medical data to enhance the accuracy and reliability of analysis results. However, due to the sensitivity of personal medical data, privacy and security considerations must be taken into account when conducting medical analyses across institutions. In this paper, we present a novel privacy-preserving scheme for distributed medication efficacy analysis. Our work allows for the analysis of multi-dimensional medical data by leveraging the private set intersection protocol and the Paillier cryptosystem. To validate the feasibility of our scheme, we implement it using some real medical datasets. Experimental results demonstrate that our approach offers enhanced security and lower computational and communication overhead compared to the original approach, which solely processes single-dimensional data at a time.

Yellow River Water and Sand Monitoring Data Model based on SARIMAX Rediction

With the evolution of history, the problems faced by the Yellow River Basin have been an important research topic in hydrology. In this paper, for the problems in the monitoring of water and sand in the Yellow River, a seasonal time series model of the monitoring data is established based on the time series prediction model, and the CUSUM algorithm is used to test the model. First, the data are processed to calculate the spearman correlation for each pair of categories. Then, for analyzing the description of single dimensional data, data descriptive analysis was used. Next, the data were forecasted using a seasonal time series forecasting model. Finally, the seasonal time series prediction model was used to predict water fluxes and solved using double interpolation to obtain the predicted effect plots for the next ten years. The proposed model is close to the reality, can reasonably solve the proposed problem, and is characterized by high practicality and high efficiency of the algorithm.

Research on information visualization for digital media design methodology based on big data technology

Abstract Information visualization is one of the important contents of digital media design, and they have a mutually complementary relationship. This paper combines the realistic background of the big data era and makes a more comprehensive introduction to the theoretical study of data visualization, including visual perception, visualization functions, judgment indexes, and principles. Secondly, by collecting the time series data, anomaly detection is carried out in the pre-processing stage, while the ring test is divided into a short-term ring and a long-term ring because the time series carries the property of time succession. Finally, anomaly detection is performed by its statistical features, which include distribution, moving average, exponential smoothing, standard deviation, and mean, whose values are equivalent to observing single-dimensional data when the sample data distribution is used as a perspective. The results show that through the comparative analysis of data visualization and traditional mathematical statistics, the correlation coefficients of information visualization for digital media design are between 0.8003 and 0.8129, while the traditional statistical methods for digital media design are only between 0.5038 and 0.5523. The information visualization proposed in this paper is better for digital media design to convey the art expressed by data and deeper mining analysis of spatiotemporal data.

Comparison of LSM indexing techniques for storing spatial data

In the pre-big data era, many traditional databases supported spatial queries via spatial indexes. However, modern applications are seeing a rapid increase of the volume and ingestion rate of spatial data. Log-structured Merge (LSM) tree is used by many big data systems as their storage structure in order to support write-intensive large-volume workloads, which are usually only optimized for single-dimensional data. Research has studied how spatial indexes can be supported on LSM systems, but focused mainly on the local index organization, that is, how data is organized inside a single LSM component. This paper studies various aspects of LSM spatial indexing, including spatial merge policies, which determine when and how spatial components are merged. Three stack-based and one leveled merge policies have been studied, which have been implemented on a common big data system Apache AsterixDB. The write and read performance on various workloads is evaluated, and our findings and recommendations are discussed. A key finding is that Leveled policies underperform other stack-based merge policies for most types of spatial workloads.

A method for rolling bearing fault diagnosis based on GSC-MDRNN with multi-dimensional input

The traditional fault diagnosis methods for rolling bearings through neural networks mostly use data sources collected by a single sensor and use single-dimensional data input, leading to fault features in bearings not be completely extracted. Moreover, traditional convolution often uses single-size convolution kernels, which are insufficient for fault feature extraction. In response to these problems, the global shortcut connection (GSC)-multichannel deep ResNet network model is proposed. First, a new residual structure, the GSC, is proposed to fuse two-dimensional and one-dimensional signal features. Second, involution is introduced into the field of fault diagnosis to address the problem of insufficient network feature extraction caused by using single-size convolution kernels. In addition, a convolutional block attention module can adaptively assign the weight of each channel feature to achieve adaptive channel fusion. The verification was performed on the four-category and eight-category data sets collected in the laboratory, and the results show that this method has a high fault recognition rate.

A novel computational framework for integrating multidimensional data to enhance accuracy in predicting the prognosis of colorectal cancer

AbstractAccurate prognosis prediction is the key to achieving precision treatment and guiding the selection of adjuvant chemotherapy in high‐risk stage II/III colorectal cancer (CRC) patients. Here we developed a novel machine learning method, the random non‐negative matrix factorization (RNMF) algorithm, which outperformed traditional non‐negative matrix factorization in terms of computational speed, accuracy, and robustness in simulated data sets. Moreover, based on multidimensional data from CRC patients from The Cancer Genome Atlas database and DNA methylation data from those from Sun Yat‐sen University cancer center, we further demonstrated the excellent performance of a novel prognostic computational framework based on the RNMF (PCF_RNMF), which is capable of integrating multidimensional training while allowing survival prediction when single dimensional data for validation is provided. This novel algorithm has great potential to mitigate the challenge of integrating various types of data in public databases with clinically available single‐dimensional data to allow cost‐effective survival prediction for CRC patients in clinical practice.

MDOPE: Efficient multi-dimensional data order preserving encryption scheme

Nowadays, tremendous information technology industries resort to cloud servers to store data with an outsourcing approach to extend their storage and computation power. This, however, also leads to privacy and security issues of unprotected data against curious cloud servers. The most common solution currently is to encrypt the data before uploading it. The problem is that encrypted data is out of control and users also cannot transfer the searching job to cloud serves anymore. Specifically, the order preserving encryption (OPE) provides an efficient solution to the order of plaintexts. Existing OPE schemes focus on single-dimensional data, and fail to effectively process multi-dimensional data. In this paper, we propose a multi-dimensional data order preserving encryption scheme MDOPE allowing fine-grained multi-dimensional range queries. Our scheme constructs query indexes for each dimension of the data based on the order preserving encryption network. In particular, the proposed scheme ensures that no external entity, including the cloud server, can obtain additional information other than the order of ciphertexts during the whole query process.

Deep Learning Neural Network Model for Tunnel Ground Surface Settlement Prediction Based on Sensor Data

Monitoring and prediction of ground settlement during tunnel construction are of great significance to ensure the safe and reliable operation of urban tunnel systems. Data-driven techniques combining artificial intelligence (AI) and sensor networks are popular methods in the field, which have several advantages, including high prediction accuracy, efficiency, and low cost. Deep learning, as one of the advanced techniques in AI, is demanded for the tunnel settlement forecasting problem. However, deep neural networks often require a large amount of training data. Due to the tunnel construction, the available training data samples are limited, and the data are univariate (i.e., containing only the settlement data). In response to the above problems, this research proposes a deep learning model that only requires limited number of training data for short-period prediction of the tunnel surface settlement. In the proposed complete ensemble empirical mode decomposition with adaptive noise long short term memory (CEEMDAN-LSTM model), single-dimensional data is divided into multidimensional data by CEEMDAN through the complete ensemble empirical mode decomposition. Each component is then predicted by a LSTM neural network and superimposed for obtaining the final prediction result. Experimental results show that, compared with existing machine learning techniques and algorithms, this deep learning method has higher prediction accuracy and acceptable computational efficiency. In the case of small samples, this method can significantly improve the accuracy of time series forecasting.

A Privacy-preserving Multi-dimensional Data Aggregation Scheme with Forward Security in Smart Grid

Smart grid is a new generation of power system on the strength of rapid-speed bidirectional communication network. In smart grid, the electricity data from users needs to be collected to realize efficient energy management, which may reveal their privacy. In the past period of time, data aggregation protocols have been extensively studied to solve this problem. However, these protocols treat users’ electricity data as single-dimensional data, which is inconsistent with reality. Moreover, key leakage is also a serious security threat in the smart grid. Therefore, we propose a multi-dimensional data aggregation protocol with forward security. In this protocol, a cube data structure is designed to represent the multi-dimensional electricity consumption data of users in a region over multiple time periods, realizing the dual aggregation of electricity data both in time and space. In addition, security analysis shows that the proposed protocol satisfies multiple security properties and performance analysis shows that our protocol is more superior in the smart grid environment.

Chemometrics for environmental monitoring: a review.

Environmental monitoring is necessary to ensure the overall health and conservation of an ecosystem. However, ecosystems (e.g. air, water, soil), are complex, involving numerous processes (both native and external), inputs, contaminants, and living organisms. As such, monitoring an environmental system is not a trivial task. The data obtained from natural systems is often multifaceted and convoluted, as a multitude of inputs can be intertwined within the matrix of the information obtained as part of a study. This means that trends and important results can be easily overlooked by conventional and single dimensional data analysis protocols. Recently, chemometric methods have emerged as a powerful method for maximizing the details contained within a chemical data set. Specifically, chemometrics refers to the use of mathematical and statistical analysis methods to evaluate chemical data, beyond univariant analysis. This type of analysis can provide a quantitative description of environmental measurements, while also having the capacity to reveal previously overlooked trends in data sets. Applying chemometrics to environmental data allows us to identify and describe the inter-relationship of environmental drivers, sources of contamination, and their potential impact upon the environment. This review aims to provide a detailed understanding of chemometric techniques, how they are currently used in environmental monitoring, and how these techniques can be used to improve current practices. An enhanced ability to monitor environmental conditions and to predict trends would be greatly beneficial to government and research agencies in their ability to develop environmental policies and analytical procedures.

A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data.

Breast cancer is a highly aggressive type of cancer with very low median survival. Accurate prognosis prediction of breast cancer can spare a significant number of patients from receiving unnecessary adjuvant systemic treatment and its related expensive medical costs. Previous work relies mostly on selected gene expression data to create a predictive model. The emergence of deep learning methods and multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of breast cancer and therefore can improve diagnosis, treatment and prevention. In this study, we propose a Multimodal Deep Neural Network by integrating Multi-dimensional Data (MDNNMD) for the prognosis prediction of breast cancer. The novelty of the method lies in the design of our method's architecture and the fusion of multi-dimensional data. The comprehensive performance evaluation results show that the proposed method achieves a better performance than the prediction methods with single-dimensional data and other existing approaches. The source code implemented by TensorFlow 1.0 deep learning library can be downloaded from the Github: https://github.com/USTC-HIlab/MDNNMD.

Multi-dimensional Aggregation Recommendation Algorithm Based on Average Prediction

Traditional collaborative filtering recommendation algorithm uses single dimensional data to calculate the similarity between users or items, ignoring the user’s preference, thus affect the recommendation accuracy. To this end, an averaging forecasting based multi-dimensional aggregation recommendation algorithm was proposed in this paper, which constructs the relationship aggregation function by user’s total score and dimension scores firstly, then apply the aggregation function to the initial multi-dimensional score that calculated by the modified averaging forecasting algorithm. The experiment result shows that compared with the previous collaborative filtering based recommendation algorithm, it has higher recommendation accuracy.

A Heterogeneous Network Based Method for Identifying GBM-Related Genes by Integrating Multi-Dimensional Data.

The emergence of multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of human diseases and therefore improving diagnosis, treatment, and prevention. In this study, we proposed a heterogeneous network based method by integrating multi-dimensional data (HNMD) to identify GBM-related genes. The novelty of the method lies in that the multi-dimensional data of GBM from TCGA dataset that provide comprehensive information of genes, are combined with protein-protein interactions to construct a weighted heterogeneous network, which reflects both the general and disease-specific relationships between genes. In addition, a propagation algorithm with resistance is introduced to precisely score and rank GBM-related genes. The results of comprehensive performance evaluation show that the proposed method significantly outperforms the network based methods with single-dimensional data and other existing approaches. Subsequent analysis of the top ranked genes suggests they may be functionally implicated in GBM, which further corroborates the superiority of the proposed method. The source code and the results of HNMD can be downloaded from the following URL: http://bioinformatics.ustc.edu.cn/hnmd/ .

Muxstep: an open-source C ++ multiplex HMM library for making inferences on multiple data types

With the development of experimental methods and technology, we are able to reliably gain access to data in larger quantities, dimensions and types. This has great potential for the improvement of machine learning (as the learning algorithms have access to a larger space of information). However, conventional machine learning approaches used thus far on single-dimensional data inputs are unlikely to be expressive enough to accurately model the problem in higher dimensions; in fact, it should generally be most suitable to represent our underlying models as some form of complex networksng;nsio with nontrivial topological features. As the first step in establishing such a trend, we present MUXSTEP: , an open-source library utilising multiplex networks for the purposes of binary classification on multiple data types. The library is designed to be used out-of-the-box for developing models based on the multiplex network framework, as well as easily modifiable to suit problem modelling needs that may differ significantly from the default approach described. The full source code is available on GitHub: https://github.com/PetarV-/muxstep petar.velickovic@cl.cam.ac.uk Supplementary data are available at Bioinformatics online.

Building a Data Mart Using Single Dimensional Data Store Architecture with Student Subject

Building a Data Mart Using Single Dimensional Data Store Architecture with Student Subject

A Clinically Applicable Approach for Detecting Spontaneous Action Potential Spikes in Amyotrophic Lateral Sclerosis With a Linear Electrode Array

Examination of spontaneous muscle activity is an important part of the routine electromyogram (EMG) in assessing neuromuscular diseases. The EMG is specifically valuable as a diagnostic test in supporting the diagnosis of amyotrophic lateral sclerosis. High-density surface EMG is a relatively new technique that has until now been used in research but has the potential for clinical application. This study presents a simple high-density surface EMG method for automatic detection of spontaneous action potentials from surface electrode array recordings of patients with amyotrophic lateral sclerosis. To reduce computational complexity while maintaining useful information from the electrode array recording, the multichannel high-density surface EMG was transferred to single-dimensional data by calculating the maximum difference across all channels of the electrode array. A spike detection threshold was then set in the single-dimensional domain to identify the firing times of each spontaneous action potential spike, whereas a spike extraction threshold was used to define the onset and offset of the spontaneous spikes. These data were used to extract the spontaneous spike waveforms from the electrode array EMG. A database of detected spontaneous spikes was thus obtained, including their waveforms, on all channels along with their corresponding firing times. This newly developed method makes use of the information from different channels of the electrode array EMG recording. It also has the primary feature of being simple and fast in implementation, with convenient parameter adjustment and user-computer interaction. Hence, it has good possibilities for clinical application.

An improved penetrometer technique for determining bale density

Bulk density plays an important role in silage quality. To assess the bulk density within round bales, penetrometers in conjunction with non-dimensional data analysis (statistical analysis) and single-dimensional data analysis (penetration resistance curve analysis) are commonly used, but this method provides insufficient information in terms of site-specific interpretation. The paper extends the conventional method with two-dimensional data analysis (map-based analysis) of four round bales, packed by a fixed chamber baler or a variable chamber baler. The results from the experiment demonstrated that the improved methods could provide more insight into the packing quality of bales, based on a combination of various dimensional data analyses.

Structured, Qualitative Comparison

This article defines qualitative data as representations of human acts and utterances, conventionally analysed in the form of long, coherent texts. The purpose of qualitative research is to interpret the actors' understandings and intentions. On the background of this definition, I make a distinction between variable-coding and theme-coding and between code-oriented and content-oriented analysis. Variable-coding of the text-content should be avoided, since this implies reducing multi-dimensional qualitative data to single-dimensional data. However, variable-coding of background-information can be used together with theme-coding of the text-content without corrupting the qualitative data. I call this approach ‘structured, qualitative comparison’. In that way it is possible to retain the many levels of meaning of qualitative data throughout the research-process, while focusing on the importance of macro-variables or scope-conditions, which indicate the potential area of validity of the findings. By examples from a comparison of Norwegian and German factories, I demonstrate the usefulness of such a content-oriented analysis-style for qualitative research, as well as for combining qualitative and quantitative data. In this way, the basic characteristics of qualitative data are preserved throughout the research-process.