Query Functions Research Articles

Deepening Interactive Processing of NLP on Application Side

Natural Language Processing (NLP) is a discipline that focuses on natural language understanding in the information interaction between human and computer, and plays an important role in the intelligent service field of Internet of Things. A complete policy transformation and generation system is developed, which can solve various functional requirements. Then, according to the functional requirements, the performance and using methods of various tools that can achieve the functional goals are analyzed. Taking IoT acquisition layer sensing devices as social network resources, an interactive IoT service framework based on natural language processing was designed, and an interactive platform of agricultural IoT for Weibo was developed, which realized the functions of data query, command setting and regular reporting between users and IoT devices in natural language. The end-to-end command control of the Internet of Things is realized, which proves the feasibility that the control strategy system of the Internet of Things can be applied to realize command control processing in the Internet of Things.

Design of Testing Device for Mechanical Property Testing System of Tai Lake Shield

According to the requirements of testing the mechanical properties of water s hield from Tai Lake the corresponding testing system was designed by adopting the id ea of open structure. The test system includes the detection device, the upper computer operation unit and the connected control unit. The testing device adopts a vertical fram e suitable for testing materials in water, USES a stepper motor to drive the lead screw t o achieve the basic tensile movement, and can achieve other testing functions through the addition of shear, friction and other expansion modules. The upper computer USES Kingview7.5 to develop the test interface corresponding to each test function, which c an carry out manual debugging and automatic test operations, and input the measurem ent data and process parameters into the MySQL database to provide real-time display and historical query functions. The main controller is realized by Arduino Mega2560 c ircuit board, which communicates with the HX711 conversion module through the sin gle bus protocol to collect the stress data of materials. By monitoring the running proc ess of stepping motor, the material deformation data is calculated. Through communic ation with Modbus RTU protocol of upper computer, operation command and test data transmission are realized.

Mining Similar Methods for Test Adaptation

Developers may choose to implement a library despite the existence of similar libraries, considering factors such as computational performance, language or platform dependency, accuracy, convenience, and completeness of an API. As a result, GitHub hosts several library projects that have overlaps in their functionalities. These overlaps have been of interest to developers from the perspective of code reuse or the preference of one implementation over the other. Through an empirical study, we explore the extent and nature of existence of these similarities in the library functions. We have further studied whether the similarity of functions across different libraries and their associated test suites can be leveraged to reveal defects in one another. We see scope for effectively using the mining of test suites from the perspective of revealing defects in a program or its documentation. Another noteworthy observation made in the study is that similar functions may exist across libraries implemented in the same language as well as in different languages. Identifying the challenges that lie in building a testing tool, we automate the entire process in <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Metallicus</small> , a test mining and recommendation tool. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Metallicus</small> returns a test suite for the given input of a query function and a template for its test suite. On a dataset of query functions taken from libraries implemented in Java or Python, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Metallicus</small> revealed 46 defects.

Reinforcement-Learning-Based Query Optimization in Differentially Private IoT Data Publishing

With the advancement of Internet of Things (IoT) and computing paradigms, massive data are collected and processed to enhance intelligent applications. However, by deliberately sending some queries, an attacker may be able to derive the sensitive information of IoT data owners. To prevent privacy leakage during IoT data query, differential privacy (DP) hides private information by introducing noise to the query results. As DP introduces randomized noise that will affect query accuracy (data utility), the tradeoff between privacy preservation and data utility is a challenge. In this article, we first propose a novel optimization framework for single query to minimize the privacy cost, while satisfying both personalized DP and customized data utility. We design a reinforcement learning-based algorithm for single query optimization framework (SQOF_RL) to solve the optimization problem efficiently. Then, we propose a SQOF_RL and SVT-based batch query optimization mechanism (S2BQOM) to answer more queries privately. The performance evaluation shows that SQOF_RL and S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> BQOM can effectively optimize single query and batch queries in terms of privacy cost, data utility, personalized privacy, and query satisfaction. Finally, the performance analysis reveals that our work can be applied to multiple linear/nonlinear query functions instead of one particular query function.

Remote monitoring system for ITER PF converter system based on EtherCAT

Abstract This paper describes ITER PF converter system architecture in details. According to its real-time and reliability characteristics, ITER PF converter system uses an industrial Ethernet fieldbus based on EtherCAT protocol with high real-time communication performance to develop its remote monitoring system. This system follows ITER design specification, it realize the real-time monitoring of thousands of analog and digital signals, and can realize remote operation of the switches in the field layer, real-time data and historical data query, event alarm and authority management functions. The system has achieved continuous and stable operation on the test platform of ITER PF converter system, meeting the requirements of high real-time performance and reliability of the remote monitoring system of ITER PF converter system.

Research on technical support SaaS platform of soil pollution control based on the whole process management

In order to improve the effect of soil pollution prevention and control, a technical-support cloud platform is designed to establish a high-level technologies, uniform standards, and regulatory tool for soil environmental investigation, risk assessment and repair work. Firstly, according to the process of soil remediation, combined with the source and pollution characteristics of soil pollutants, the main pollution components in the soil environment were classified and distributed, and then the platform was designed and developed. The system has the functions of project management, query, risk analysis and prediction, drawing, tabulation and so on. Finally, combining with the soil environmental quality standards, the effectiveness and cost indicators of the treatment methods were classified and digitized, which would greatly improve the implementation efficiency and reduce the cost of the project management.

Graph Node Strength Histogram Publication Method with Node Differential Privacy

The node differential privacy (node-DP) can be used to protect the private information of nodes and edges in the graph. In this paper, we propose an algorithm of publishing node strength histogram under node-DP, which could improve the accuracy of publishing the node strength histogram. In this algorithm, we use the Sequence Edge removal to reduce the sensitivity of query function and restrict the weight of edges to make the distribution of node strength denser. Furthermore, we use the histogram grouping algorithm Hierarchical Cluster Grouping to group the buckets to merge buckets with close values into one group. The experiments show that our algorithm maintains higher data utility than those traditional histogram publishing algorithms under the same privacy budget.

A High-Performance Data Accessing and Processing System for Campus Real-time Power Usage

With the flourishing of Internet of Things (IoT) technology, ubiquitous power data can be linked to the Internet and be analyzed for real-time monitoring requirements. Numerous power data would be accumulated to even Tera-byte level as the time goes. To approach a real-time power monitoring platform on them, an efficient and novel implementation techniques has been developed and formed to be the kernel material of this thesis. Based on the integration of multiple software subsystems in a layered manner, the proposed power-monitoring platform has been established and is composed of Ubuntu (as operating system), Hadoop (as storage subsystem), Hive (as data warehouse), and the Spark MLlib (as data analytics) from bottom to top. The generic power-data source is provided by the so-called smart meters equipped inside factories located in an enterprise practically. The data collection and storage are handled by the Hadoop subsystem and the data ingestion to Hive data warehouse is conducted by the Spark unit. On the aspect of system verification, under single-record query, these software modules: HiveQL and Impala SQL had been tested in terms of query-response efficiency. And for the performance exploration on the full-table query function. The relevant experiments have been conducted on the same software modules as well. The kernel contributions of this research work can be highlighted by two parts: the details of building an efficient real-time power-monitoring platform, and the relevant query-response efficiency for reference.

Marine Three-dimensional Simulation Animation Design Based on Computer Rapid Simulation Technology

Wang, Y., 2020. Marine three-dimensional simulation animation design based on computer rapid simulation technology. In: Li, L. and Huang, X. (eds.), Sustainable Development in Coastal Regions: A Perspective of Environment, Economy, and Technology. Journal of Coastal Research, Special Issue No. 112, pp. 386-389. Coconut Creek (Florida), ISSN 0749-0208.In order to realize the three-dimensional integrated visualization of marine scalar data with characteristics such as massive, multi-source, heterogeneous, multi-dimensional, multi-format, dynamic, etc., a three-dimensional virtual visualization architecture based on service ideas and oriented to general scalar data formats is designed, and ocean three-dimensional Visualized the prototype system, and explained its architecture and functional design in detail; using a visualization method based on geometric objects, combined with key technologies such as LOD dynamic scheduling, surface texture and fast query, realized the efficient and fast 3D ocean scalar field data Simulation animation visualization, element value query and time sequence analysis functions. This research can provide ideas and technical reference for the visualization and analysis of other marine environmental element data.

Construction of College Student Credit Information System Based on Blockchain Technology

In recent years, falsification of learning experience, academic fraud and other academic misconduct are common among college students. However, the data collection and query functions of various management systems used in domestic colleges and universities are relatively single, which is not enough to meet the needs of the society. With the application of blockchain technology in all walks of life, the decentralization, security, credibility and traceability of blockchain technology are highly matched with the construction of College Students’ integrity system. The research and development of College Students’ credit information system based on blockchain can effectively improve the credibility of the education system, and play a powerful role in promoting the application of education big data and innovation breakthrough.

CircVAR database: genome-wide archive of genetic variants for human circular RNAs

BackgroundCircular RNAs (circRNAs) play important roles in regulating gene expression through binding miRNAs and RNA binding proteins. Genetic variation of circRNAs may affect complex traits/diseases by changing their binding efficiency to target miRNAs and proteins. There is a growing demand for investigations of the functions of genetic changes using large-scale experimental evidence. However, there is no online genetic resource for circRNA genes.ResultsWe performed extensive genetic annotation of 295,526 circRNAs integrated from circBase, circNet and circRNAdb. All pre-computed genetic variants were presented at our online resource, circVAR, with data browsing and search functionality. We explored the chromosome-based distribution of circRNAs and their associated variants. We found that, based on mapping to the 1000 Genomes and ClinVAR databases, chromosome 17 has a relatively large number of circRNAs and associated common and health-related genetic variants. Following the annotation of genome wide association studies (GWAS)-based circRNA variants, we found many non-coding variants within circRNAs, suggesting novel mechanisms for common diseases reported from GWAS studies. For cancer-based somatic variants, we found that chromosome 7 has many highly complex mutations that have been overlooked in previous research.ConclusionWe used the circVAR database to collect SNPs and small insertions and deletions (INDELs) in putative circRNA regions and to identify their potential phenotypic information. To provide a reusable resource for the circRNA research community, we have published all the pre-computed genetic data concerning circRNAs and associated genes together with data query and browsing functions at http://soft.bioinfo-minzhao.org/circvar.

Transparent and Accountable Vehicular Local Advertising With Practical Blockchain Designs

Vehicular local advertising enables roadside retailers to provide timely and location-aware advertisements to on-road vehicular users to attract more in-store visits. However, the prevalence of the vehicular local advertising has raised increasing transparency and accountability concerns on the spatial keyword query process, such as why a vehicular user receives advertisements from specific spatial entities as well as the timely detection of the advertising misbehavior. In this paper, we develop a transparent and accountable vehicular local advertising system by utilizing the tamper-proof and open nature of the blockchain technology. Considering the large scale of the spatial-keyword database and the expensive computation and storage cost on the blockchain, we introduce two design strategies, digest-and-verify and divide-then-assemble . In specific, a large-scale spatial keyword database is digested and stored on the blockchain. The spatial keyword query is then conducted with modular executions of two off-chain spatial keyword query functions, the results of which are efficiently assembled and verified in an advertising smart contract. By doing so, expensive on-chain computation and storage overheads are significantly reduced at a cost of acceptable off-chain overheads. We define the security requirements of the vehicular local advertising system as Auditing Security and achieve the notion with the security analysis. We also conduct real-world experiments to show the efficiency of the blockchain-based vehicular local advertising system.

Design of 3D Model Database of Substation Equipment Based on Access Software

Building model base and sharing model effectively can accelerate the process of 3D new design and scene reconstruction, and reduce the cost of modeling has important engineering value. Based on the analysis of the functional requirements and architecture design of the three-dimensional model database of substation, this paper puts forward the storage information attributes and coding rules of the three-dimensional model database of substation typical equipment, and creates the three-dimensional model database of substation equipment based on access. The three-dimensional model database of substation includes model management and maintenance module, human-computer interaction module, which realizes the storage, storage, query and other functions of three-dimensional model of substation. The research results can provide reference for the construction of three-dimensional model base of substation equipment.

Research on Application of Three-Dimensional Design Standardization and Three-Dimensional Design Review Method for Transmission and Transformation Project

This paper systematically introduces a three-dimensional transmission and transformation engineering auxiliary design software, and analyses its function, structure and technical characteristics. The main feature of this software is that it has a relatively powerful three-dimensional auxiliary design capability, which can help designers quickly generate a three-dimensional model of power transmission and transformation projects. The three-dimensional model can not only give the project investor an overall image and visual effect of the project during the design bidding stage, but also serve as a reference for the design of the relevant drawings in the later period. At the same time, after having an accurate model, the designer can verify the correctness and reliability of the design, can directly obtain some spatial information, and can dynamically modify, adjust and verify the model as needed. The software also provides related information statistics, query, modification, retrieval and other functions, and provides corresponding drawing management tools according to drawing management specifications. (With the actual application of the system, the design level and technical capabilities of designers will be further improved to ensure that the design work can be completed with high quality and efficiency.

Development of a research platform for BEPC II accelerator fault diagnosis

BackgroundBEPC II is an electron–positron collider with a beam energy of 1.89 GeV and luminosity of 1 × 1033 cm−2 s−1. Being a complex accelerator, fault diagnostics both in an accurate manner and in time are often challenging during its operation.PurposeThe fault diagnosis research platform is capable of locating and resolving faults on time, thus can largely improve the operation of BEPCII by reducing downtime due to machine fault and consequently satisfying the demand for fault diagnosis of a complex system.MethodsMaking full use of the existing large-scale hardware foundation, abundant data resources and previous experiences, a fault diagnosis research platform was built by applying data mining, machine learning and other related algorithms to carry out specific fault analysis on the entire system.ConclusionThe fault diagnosis research platform has been developed with implemented functions of data query, statistics and analysis. Partial fault diagnoses of the three subsystems have been realized. The 4W1 power supply fault has been successfully discovered, and effective solutions have been subsequently provided.

Collusion-resistant protocols for private processing of aggregated queries in distributed databases

Private processing of database queries protects the confidentiality of sensitive data when queries are answered. It is important to design collusion-resistant protocols ensuring that privacy remains protected even when a certain number of honest-but-curious participants collude to share their knowledge in order to gain unauthorised access to sensitive information. A novel setting arises when aggregated queries need to be answered for a large distributed database, but legal requirements or commercial interests forbid making access to records in each subdatabase available to other counterparts. For example, a very large number of medical records may be stored in a distributed database, which is a union of several separate databases from different hospitals, or even from different countries. The present article introduces and investigates two protocols for collusion-resistant private processing of aggregated queries in this novel setting: Accelerated Multi-round Iterative Protocol (AMIP) and Restricted Multi-round Iterative Protocol (RMIP). We define a large collection of query functions and show that AMIP and RMIP protocols can answer all queries in this collection. Our experiments demonstrate that the AMIP protocol outperforms all other applicable algorithms, and this achievement is especially significant in terms of the communication complexity.

*-DCC: A platform to collect, annotate, and explore a large variety of sequencing experiments.

BackgroundOver the past few years the variety of experimental designs and protocols for sequencing experiments increased greatly. To ensure the wide usability of the produced data beyond an individual project, rich and systematic annotation of the underlying experiments is crucial.FindingsWe first developed an annotation structure that captures the overall experimental design as well as the relevant details of the steps from the biological sample to the library preparation, the sequencing procedure, and the sequencing and processed files. Through various design features, such as controlled vocabularies and different field requirements, we ensured a high annotation quality, comparability, and ease of annotation. The structure can be easily adapted to a large variety of species. We then implemented the annotation strategy in a user-hosted web platform with data import, query, and export functionality.ConclusionsWe present here an annotation structure and user-hosted platform for sequencing experiment data, suitable for lab-internal documentation, collaborations, and large-scale annotation efforts.

Quantitative and qualitative analyses of orthodontic-related videos on YouTube.

To investigate content of orthodontic-related videos on YouTube to improve the understanding of orthodontic patients' perceptions and treatment experiences. A systematic search was conducted on YouTube on March 20, 2018, and updated on August 4, 2019, to identify all relevant videos using search terms "orthodontic," "orthodontics," "braces," and "orthodontic braces." The data set was captured from YouTube Data API (Application Programming Interface) and stored in an Excel database using a query function written in Python. All videos captured were viewed and categorized by three independent dental investigators using thematic analysis. The top 100 videos (by view count) related to patients' treatment experience were further analyzed using discourse analysis. A total of 600 orthodontic videos were screened, and 546 were included in the study. Six main themes were identified: (1) individual review of orthodontic treatment (45.8%, n = 250), (2) entertainment (19.8%, n = 108), (3) education (18.3%, n = 100), (4) advertisements (6.6%, n = 36), (5) time lapse of orthodontic treatment (5.3%, n = 29), and (6) do-it-yourself orthodontics (4.2%, n = 23). Of the top 100 videos related to patient's individual review of treatment, patients' main focuses were on pain (24%), problems with chewing and swallowing (12%), and adhesive removal (10%). Orthodontic-related YouTube videos are diverse in nature. The most common video category was video providing an individual review of orthodontic treatment experience. Other popular video categories included entertainment, education, and advertisements. A range of do-it-yourself YouTube videos were also identified. YouTube may provide an opportunity for orthodontic professionals to disseminate health information.

Active Balancing Mechanism for Imbalanced Medical Data in Deep Learning–Based Classification Models

Imbalanced data always has a serious impact on a predictive model, and most under-sampling techniques consume more time and suffer from loss of samples containing critical information during imbalanced data processing, especially in the biomedical field. To solve these problems, we developed an active balancing mechanism (ABM) based on valuable information contained in the biomedical data. ABM adopts the Gaussian naïve Bayes method to estimate the object samples and entropy as a query function to evaluate sample information and only retains valuable samples of the majority class to achieve under-sampling. The Physikalisch Technische Bundesanstalt diagnostic electrocardiogram (ECG) database, including 5,173 normal ECG samples and 26,654 myocardial infarction ECG samples, is applied to verify the validity of ABM. At imbalance rates of 13 and 5, experimental results reveal that ABM takes 7.7 seconds and 13.2 seconds, respectively. Both results are significantly faster than five conventional under-sampling methods. In addition, at the imbalance rate of 13, ABM-based data obtained the highest accuracy of 92.23% and 97.52% using support vector machines and modified convolutional neural networks (MCNNs) with eight layers, respectively. At the imbalance rate of 5, the processed data by ABM also achieved the best accuracy of 92.31% and 98.46% based on support vector machines and MCNNs, respectively. Furthermore, ABM has better performance than two compared methods in F 1-measure, G-means, and area under the curve. Consequently, ABM could be a useful and effective approach to deal with imbalanced data in general, particularly biomedical myocardial infarction ECG datasets, and the MCNN can also achieve higher performance compared to the state of the art.

Conformal prediction based active learning by linear regression optimization

Conformal prediction uses the degree of strangeness (nonconformity) of data instances to determine the confidence values of new predictions. We propose a conformal prediction based active learning algorithm, referred to as CPAL-LR, to improve the performance of pattern classification algorithms. CPAL-LR uses a novel query function that determines the relevance of unlabeled instances through the solution of a constrained linear regression model, incorporating uncertainty, diversity, and representativeness in the optimization problem. Furthermore, we present a nonconformity measure that produces reliable confidence values. CPAL-LR is implemented in conjunction with support vector machines, sparse coding algorithms, and convolutional networks. Experiments conducted on face and object recognition databases demonstrate that CPAL-LR improves the classification performance of a variety classifiers, outperforming previously proposed active learning techniques, while producing reliable confidence values.