Query Method Research Articles

In-IDE Code Generation from Natural Language: Promise and Challenges

A great part of software development involves conceptualizing or communicating the underlying procedures and logic that needs to be expressed in programs. One major difficulty of programming is turning concept into code , especially when dealing with the APIs of unfamiliar libraries. Recently, there has been a proliferation of machine learning methods for code generation and retrieval from natural language queries , but these have primarily been evaluated purely based on retrieval accuracy or overlap of generated code with developer-written code, and the actual effect of these methods on the developer workflow is surprisingly unattested. In this article, we perform the first comprehensive investigation of the promise and challenges of using such technology inside the PyCharm IDE, asking, “At the current state of technology does it improve developer productivity or accuracy, how does it affect the developer experience, and what are the remaining gaps and challenges?” To facilitate the study, we first develop a plugin for the PyCharm IDE that implements a hybrid of code generation and code retrieval functionality, and we orchestrate virtual environments to enable collection of many user events (e.g., web browsing, keystrokes, fine-grained code edits). We ask developers with various backgrounds to complete 7 varieties of 14 Python programming tasks ranging from basic file manipulation to machine learning or data visualization, with or without the help of the plugin. While qualitative surveys of developer experience are largely positive, quantitative results with regards to increased productivity, code quality, or program correctness are inconclusive. Further analysis identifies several pain points that could improve the effectiveness of future machine learning-based code generation/retrieval developer assistants and demonstrates when developers prefer code generation over code retrieval and vice versa. We release all data and software to pave the road for future empirical studies on this topic, as well as development of better code generation models.

The interactive effects of sexual harassment and psychological capital on victims’ burnout: evidence from the post-#MeToo movement era

PurposeIn the post-#MeToo movement era, heightened awareness regarding harassment at workplace has forced corporations to consider gender-based harassment as a serious issue. This study aims to theorise and test psychological capital as a resource reservoir to cope with burnout experienced in the form of emotional exhaustion, depersonalisation and reduced personal accomplishments as results of gender-based harassment at workplace.Design/methodology/approachMulti-wave data were collected from 304 female employees working in project-based organisations in the information technology industry in Pakistan.FindingsThe analysis shows contradictory findings about the prevalence of sexual harassment when a behavioural measurement approach was used against a direct query method. The results also show that gender harassment leads to burnout causing emotional exhaustion, depersonalisation and reduced personal accomplishment among the victims. However, psychological capital helps victims cope with these adverse effects of gender harassment.Practical implicationsManagers need to boost the psychological capital of female workers and devise effective policies to combat gender harassment in the workplace. Enforcement of legislation regarding harassment in the workplace should be ensured, which will also indicate seriousness towards the achievement of the United Nations’ Sustainable Development Goal No. 5, that is, gender equality.Originality/valueTo the best of the authors’ knowledge, this study is the first to present psychological capital as a resource reservoir to combat the negative effects of gender harassment and has been conducted in less studied non-Western work settings.

Ocena jakości usług w hotelach czterogwiazdkowych w Lublinie z zastosowaniem metody analizy kontekstowej

The aim of the article is the quality assessment of service at five four-star hotels in Lublin in the light of reviews left on booking.com. Query and contextual analysis methods were used during the research. Of the hotels analysed, the highest quantitative score (9.25) was given to the Atelia Banquet & Catering Centre hotel, while the highest qualitative score (taking into account guests’ opinions) was given to Focus Hotel Premium Lublin (9.08). In terms of qualitative assessment in relation to the assumed areas, it appears that the intangible elements (especially the variety and taste of meals, cleanliness in the hotel and in the room) received the most comments, but the tangible elements (exterior appearance of the building, interior design/aesthetics, location) received the highest remarks. The least feedback was in the areas of “price-quality ratio” and “comfort of stay”. A comparison of the ratings of the four-star hotels analysed with those of the three-star hotels and hostels shows that in all of them, cleanliness (in the hotel and in the room), the location of the facilities and price-quality ratio were important to guests. Thus, in relation to the emotional model of customer satisfaction, clean hotels, their aesthetic interiors, their good location, varied and tasty meals, friendly staff, and good price-quality ratio can all have a positive impact on the perception of Lublin’s tourist space.

Active recursive Bayesian inference using Rényi information measures

• An active recursive inference method based on Rényi information measures. • Posterior probability changes can enhance both query optimization and stopping criterion in the RBI process. • Using Rényi entropy and α -divergence unifies the multi-objective RBI problem. • The proposed unified framework enhances both speed and accuracy, in the presence of an adversarial prior. Recursive Bayesian inference (RBI) framework provides optimal Bayesian latent variable estimates in real-time settings with streaming noisy observations. Active RBI attempts to effectively select queries that lead to more informative observations to reduce uncertainty until the process is stopped at a certain confidence level. However, the mismatch between querying objective and stopping criterion creates the conundrum of improving the performance of one objective at the expense of deteriorating the other. Moreover, conventional active querying methods stagger in the presence of misleading prior information. Inspired by information theoretic approaches, we propose an active RBI framework where query and stopping criterion are jointly selected through a unified objective based on Rényi information measures. The proposed unified formulation enables us to jointly enhance speed and accuracy in the RBI process. We theoretically demonstrate that the proposed objective encourages exploration in the presence of misleading prior. Furthermore, we motivate our framework by proving a geometrical representation for active querying and decision making on a probability simplex. We provide empirical and experimental studies on two applications including restaurant recommendation and brain-computer interface (BCI) typing systems and demonstrate that our method outperforms comparable approaches by improving both speed and accuracy.

Real‐Time Microstructure Rendering with MIP‐Mapped Normal Map Samples

AbstractNormal map‐based microstructure rendering can generate both glint and scratch appearance accurately. However, the extra high‐resolution normal map that defines every microfacet normal may incur high storage and computation costs. We present an example‐based real‐time rendering method for arbitrary microstructure materials, which significantly reduces the required storage space. Our method takes a small‐size normal map sample as input. We implicitly synthesize a high‐resolution normal map from the normal map sample and construct MIP‐mapped 4D position‐normal Gaussian lobes. Based on the above MIP‐mapped 4D lobes and a LUT (lookup table) data structure for the synthesized high‐resolution normal map, an efficient Gaussian query method is presented to evaluate ‐NDFs (position‐normal distribution functions) for shading. We can render complex scenes with glint and scratch surfaces in real time (30 fps) with a full high‐definition resolution, and the space required for each microstructure material is decreased to 30 MB.

Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning

Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) resolutions using deep neural networks (DNNs). While DNNs can learn complex representations that prove challenging for kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within a GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions using range-separated hybrid density functional theory (DFT), obtaining a 107 speedup relative to naive all-atom QC. By treating the predicted electronic properties as random Gaussian Processes, DKL incorporates CG mapping degeneracy by learning the distribution of electronic energies as a function of CG configuration. DKL-ECG accurately reproduces molecular orbital energies from range-separated DFT while facilitating efficient training via active learning using the uncertainties provided by DKL. We show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, all explored query methods exhibit comparable performance for the examined system. We attribute this result to the significant overlap of the feature space and output property distributions across multiple temperatures.

Integrity Assurance Method of Multi-Keyword Query for Encrypted Outsourced Data

As the data scale exceeds the petabyte level, more and more users outsource their local databases to third-party service providers to save local storage costs and avoid cumbersome local data management. However, because third-party service providers are not fully trusted, they may leak outsourced data and bring security risks to the personal privacy of data users. The service provider may also return incorrect or incomplete query results to the data user. Therefore, in this paper, we propose a Lightweight and Verifiable Multi-keyword Query Scheme for the integrity verification of multi-keyword query of outsourced data. This scheme supports multi-keyword query integrity verification and does not require a third-party auditor, combining a hash-based accumulator that provides correctness verification and a cuckoo filter that provides integrity verification to form a provably secure composite verification structure, and at the same time, it uses controllable Paillier encryption to support data update operations. This scheme strikes a balance between the cost of verification structure generation, verification object generation, and proof verification cost to enable data users to perform secure multi-keyword query while efficiently verifying the integrity of query results. The security proof and the performance analysis show that the proposed scheme is secure and efficient for practical deployment.

Bloom Filter with Noisy Coding Framework for Multi-Set Membership Testing

This paper is on designing a compact data structure for multi-set membership testing that allows fast set querying. Multi-set membership testing is a fundamental operation for computing systems. Most existing schemes for multi-set membership testing are built upon Bloom filter and fall short in either storage space cost or query speed. To address this issue, we propose Noisy Bloom Filter (NBF), Error Corrected Noisy Bloom Filter (NBF-E), and Data-driven Noisy Bloom Filter (NBF-D) in this paper. We optimize their misclassification and false positive rates by theoretical analysis and present criteria for selection between NBF, NBF-E, and NBF-D. The key novelty of the three schemes is to store set ID information in a compact but noisy way that allows fast recording and querying and use a denoising method for querying. Especially, NBF-E incorporates asymmetric error-correcting coding techniques into NBF, and NBF-D encodes set ID based on their cardinality. To evaluate NBF, NBF-E, and NBF-D in comparison with the prior art, we conducted experiments using real-world network traces. The results show that NBF, NBF-E, and NBF-D significantly advance the state-of-the-art on multi-set membership testing.

3DPro: Querying Complex Three-Dimensional Data with Progressive Compression and Refinement.

Large-scale three-dimensional spatial data has gained increasing attention with the development of self-driving, mineral exploration, CAD, and human atlases. Such 3D objects are often represented with a polygonal model at high resolution to preserve accuracy. This poses major challenges for 3D data management and spatial queries due to the massive amounts of 3D objects, e.g., trillions of 3D cells, and the high complexity of 3D geometric computation. Traditional spatial querying methods in the Filter-Refine paradigm have a major focus on indexing-based filtering using approximations like minimal bounding boxes and largely neglect the heavy computation in the refinement step at the intra-geometry level, which often dominates the cost of query processing. In this paper, we introduce 3DPro, a system that supports efficient spatial queries for complex 3D objects. 3DPro uses progressive compression of 3D objects preserving multiple levels of details, which significantly reduces the size of the objects and has the data fit into memory. Through a novel Filter-Progressive-Refine paradigm, 3DPro can have query results returned early whenever possible to minimize decompression and geometric computations of 3D objects in higher resolution representations. Our experiments demonstrate that 3DPro out-performs the state-of-the-art 3D data processing techniques by up to an order of magnitude for typical spatial queries.

An Accurate Matching Query Method of Natural Language Knowledge Graph Based on Hierarchical Graph Topological Sequence

In recent years, although the application of knowledge graph in natural language processing has made some progress, there are still some key problems to be solved, especially the matching query problem in natural language knowledge graph. Since the basic data model of knowledge graph is graph, the matching query problem in natural language knowledge graph is usually transformed into graph matching query problem. However, at present, the traditional graph matching technology applied in knowledge graph consumes too much time and has low query efficiency, which cannot meet the needs of users for large-scale natural language knowledge graph query. Based on the full analysis of the defects of the traditional graph matching technology applied in the knowledge graph, according to the characteristics of the natural language knowledge graph, in order to improve the query efficiency, we propose an accurate matching query method of graph hierarchical topological sequence based on the graph model of knowledge graph. Through experiment analysis, compared with the traditional graph model matching algorithm applied in knowledge graph query, this method can quickly filter the unqualified knowledge graph candidate sets, effectively reduce the number of knowledge graph candidate sets, and make it have more advantages in matching efficiency and time performance. In addition, compared with two algorithms of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GIndex</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FG-Index</i> , this method has better performance in index construction time, average size of candidate set and average running time of online update.

A Gitbash-Generative Model for an Enhanced Query Processing In a Distributed Database System

This research study is directly focused on an enhanced query processing system using GitBash- Deep Generative Model, it is use to improve the performance of big data. Due to Poor query execution plan and absence of an improved aggregate query method which impose threats to qualities of good query processing in distributed databases which also cause memory pressure in a database, inflated central processing unit (CPU) and an overall reduction in concurrency. Also the volume of data use in any organization is progressively increasing in it seize, thereby resulting or demand a large storage location or space system. The availability of intended software that will confront the problem of storage contents and execution plan for query processing is a vital challenge facing big data in a processing system, which can prompt to information loss and missing record of vital document in an enterprises. The study will boost solution and addresses the problem of latencies, inability to transform and store queries, and poor query processing plans for future improvement of distributed databases, using an unsupervised learning approach such as deep generative model. The adopted methodology is dynamic system development method (DSDM). php programming language and mongoDB database is adopted for implementation. The datasets used for the proposed system was generated from e-library server/data.json (query) and is inserted into MongoDB database json format. The datasets are all trained and recognized by the Deep Generative algorithm through the Git Bash server. The proposed system result shows highly increase in the efficiency of management of big data and improved query processing. General Term Query Processing System Keywords Big Data, Deep Generative Model, Query Processing System, Information Management, Gitbash.

CellMeSH: probabilistic cell-type identification using indexed literature.

MotivationSingle-cell RNA sequencing (scRNA-seq) is widely used for analyzing gene expression in multi-cellular systems and provides unprecedented access to cellular heterogeneity. scRNA-seq experiments aim to identify and quantify all cell types present in a sample. Measured single-cell transcriptomes are grouped by similarity and the resulting clusters are mapped to cell types based on cluster-specific gene expression patterns. While the process of generating clusters has become largely automated, annotation remains a laborious ad hoc effort that requires expert biological knowledge.ResultsHere, we introduce CellMeSH—a new automated approach to identifying cell types for clusters based on prior literature. CellMeSH combines a database of gene–cell-type associations with a probabilistic method for database querying. The database is constructed by automatically linking gene and cell-type information from millions of publications using existing indexed literature resources. Compared to manually constructed databases, CellMeSH is more comprehensive and is easily updated with new data. The probabilistic query method enables reliable information retrieval even though the gene–cell-type associations extracted from the literature are noisy. CellMeSH is also able to optionally utilize prior knowledge about tissues or cells for further annotation improvement. CellMeSH achieves top-one and top-three accuracies on a number of mouse and human datasets that are consistently better than existing approaches.Availability and implementationWeb server at https://uncurl.cs.washington.edu/db_query and API at https://github.com/shunfumao/cellmesh.Supplementary information Supplementary data are available at Bioinformatics online.

Application of an artificial intelligence algorithm model of memory retrieval and roaming in sorting Chinese medicinal materials

In order to capture autobiographical memory, inspired by the development of human intelligence, a computational AM model for autobiographical memory is proposed in this paper, which is a three-layer network structure, in which the bottom layer encodes the event-specific knowledge comprising 5W1H, and provides retrieval clues to the middle layer, encodes the related events, and the top layer encodes the event set. According to the bottom-up memory search process, the corresponding events and event sets can be identified in the middle layer and the top layer respectively; At the same time, AM model can simulate human memory roaming through the process of rule-based memory retrieval. The computational AM model proposed in this paper not only has robust and flexible memory retrieval, but also has better response performance to noisy memory retrieval cues than the commonly used memory retrieval model based on keyword query method, and can also imitate the roaming phenomenon in memory.

An Indexing Method of Continuous Spatiotemporal Queries for Stream Data Processing Rules of Detected Target Objects.

Real-time performance is important in rule-based continuous spatiotemporal query processing for risk analysis and decision making of target objects collected by sensors of combat vessels. The existing Rete algorithm, which creates a compiled node link structure for executing rules, is known to be the best. However, when a large number of rules are to be processed and the stream data to be performed are large, the Rete technique has an overhead of searching for rules to be bound. This paper proposes a hashing indexing technique for Rete nodes to the overhead of searching for spatiotemporal condition rules that must be bound when rules are expressed in a node link structure. A performance comparison evaluation experiment was conducted with Drool, which implemented the Rete method, and the method that implemented the hash index method presented in this paper. For performance measurement, processing time was measured for the change in the number of rules, the change in the number of objects, and the distribution of objects. The hash index method presented in this paper improved performance by at least 18% compared to Drool.

Growth and Overall Health of Patients with SLC13A5 Citrate Transporter Disorder.

We were interested in elucidating the non-neurologic health of patients with autosomal recessive SLC13A5 Citrate Transporter (NaCT) Disorder. Multiple variants have been reported that cause a loss of transporter activity, resulting in significant neurologic impairment, including seizures, as well as motor and cognitive dysfunction. Additionally, most patients lack tooth enamel (amelogenesis imperfecta). However, patients have not had their overall health and growth described in detail. Here we characterized the non-neurologic health of 15 patients with medical records uploaded to Ciitizen, a cloud-based patient medical records portal. Ciitizen used a query method for data extraction. Overall, the patients’ records suggested a moderate number of gastrointestinal issues related to feeding, reflux, vomiting and weight gain and a diverse number of respiratory complaints. Other organ systems had single or no abnormal diagnoses, including liver, renal and cardiac. Growth parameters were mostly in the normal range during early life, with a trend toward slower growth in the few adolescent patients with data available. The gastrointestinal and pulmonary issues may at least partially be explained by the severity of the neurologic disorder. More data are needed to clarify if growth is impacted during adolescence and if adult patients develop or are protected from non-neurologic disorders.

Global Research Trends in Pediatric Trauma From 1968 to 2021: A Bibliometric Analysis.

Introduction: Every year, millions of children die from preventable causes worldwide. According to World Health Organization, injuries are the leading cause of disability and death among all age groups below 60 years.Aim: This study aimed to evaluate the global research outcomes and trends, and some key bibliometric indicators in pediatric trauma.Methods: A descriptive bibliometric analysis study was designed. On June 14, 2021, an electronic search was performed in the Web of Science Core Collection database using the potential searching keywords “Pediatric AND Trauma” in the title field without any limitations. The search was performed using the Boolean search query method. The data were downloaded in plaintext and comma-separated values format. The required graphs were generated using OriginPro 2018. Furthermore, the data were transferred to HistCite™ software for bibliometric analysis. In addition, the obtained data were plotted for network visualization mapping using VOSviewer software version 1.6.15 for windows.Results: A total of 2,269 documents were included in the final analysis. The included documents were authored by 7,894 authors and published in 395 research and academic journals, mainly in the English language (n = 2,222). The main document types were articles (n = 1,276, citations = 18,244), and meeting abstracts (n = 331, citations = 19). Pediatric (n = 2,269) and trauma (n = 2,257) were the most widely used keywords. The most productive year was 2019 (n = 184, citations = 527). The most prolific author was Upperman JS (n = 29, citations = 202). The most attractive journals in pediatric trauma research were The Journal of Trauma and Acute Care Surgery (n = 290, citations = 5,199) and the Journal of Pediatric Surgery (n = 256, citations = 5,088). The most active institute was the University of California System (n = 110). The most dominant country was the United States of America (USA) (n = 1,620, citations = 22,983). The USA and Canada had the highest total link strength, 103 and 70, respectively.Conclusion: This study provides a comprehensive overview of research output in pediatric trauma. The USA continues to dominate scientific research and funding in pediatric trauma. Findings of the current study will help the researchers and clinicians to understand the recent achievements and research frontiers. Collaborative research initiative needs to be established between institutions in developed and developing countries and among researchers.

A blockchain index structure based on subchain query

Blockchain technology has the characteristics of decentralization and tamper resistance, which can store data safely and reduce the cost of trust effectively. However, the existing blockchain system has weak performance in data management, and only supports traversal queries with transaction hashes as keywords. The query method based on the account transaction trace chain (ATTC) improves the query efficiency of historical transactions of the account. However, the efficiency of querying accounts with longer transaction chains has not been effectively improved. Given the inefficiency and single method of the ATTC index in the query, we propose a subchain-based account transaction chain (SCATC) index structure. First, the account transaction chain is divided into subchains, and the last block of each subchain is connected by a hash pointer. The block-by-block query mode in ATTC is converted to the subchain-by-subchain query mode, which shortens the query path. Multiple transactions of the same account in the same block are merged and stored, which simplifies the construction cost of the index and saves storage resources. then, the construction algorithm and query algorithm is given for the SCATC index structure. Simulation analysis shows that the SCATC index structure significantly improves query efficiency.

A secure and efficient range query method for two‐layer wireless sensor networks

Traditional range query methods of work still have shortcomings in node energy consumption and privacy security, so a two-layer secure and efficient range query method for wireless sensor networks is proposed. In the data storage stage, the sensing node obtains the data ciphertext and timestamp by the Advanced Encryption Standard encryption algorithm, receives the new encryption constraint chain by the reverse 0–1 encoding method and Hash-based Message Authentication Code encryption algorithm, and sends the chain to the storage node. In the query response phase, the storage node responds to the request of the base station and sends the data that meet the query requirements. After receiving the data, the base station verifies the consistency with the new encryption constraint chain and timestamp. During the experiment, the energy consumption is analysed from three aspects: the number of data collected in the period, the data length of the sensing node and the partition factor of the encryption constraint chain. The results show that this method has low energy consumption and can maintain the consistency of data.

A Reconstruction Attack Scheme on Secure Outsourced Spatial Dataset in Vehicular Ad-Hoc Networks

In the cloud-based vehicular ad-hoc network (VANET), massive vehicle information is stored on the cloud, and a large amount of data query, calculation, monitoring, and management are carried out at all times. The secure spatial query methods in VANET allow authorized users to convert the original spatial query to encrypted spatial query, which is called query token and will be processed in ciphertext mode by the service provider. Thus, the service provider learns which encrypted records are returned as the result of a query, which is defined as the access pattern. Since only the correct query results that match the query tokens are returned, the service provider can observe which encrypted data are accessed and returned to the client when a query is launched clearly, and it leads to the leakage of data access pattern. In this paper, a reconstruction attack scheme is proposed, which utilizes the access patterns in the secure query processes, and then it reconstructs the index of outsourced spatial data that are collected from the vehicles. The proposed scheme proves the security threats in the VANET. Extensive experiments on real-world datasets demonstrate that our attack scheme can achieve quite a high reconstruction rate.

English Audio Language Retrieval Based on Adaptive Speech-Adjusting Algorithm

For the purpose of language retrieval for English listening, this paper designs and implements a cross-language information retrieval system for English listening. Different implementation methods of cross-language information retrieval, query, and translation are analyzed. The system adopts cross-language information retrieval technology based on bilingual dictionaries. According to the cross-language retrieval system of the existing bilingual dictionaries and monolingual dictionaries, based on the design and implementation of the fuzzy search dictionary lookup mechanism, the existing dictionary lookup mechanism is constructed and analyzed. Aiming at the problem of translation ambiguity in information retrieval systems based on bilingual dictionaries, a disambiguities elimination algorithm based on cooccurrence technology is proposed. In continuous speech, the speed of different speakers in different contexts is very different. Deviation from normal speech speed often leads to recognition errors, which makes recognition performance decline. Considering that the influence of speech speed on the length of speech units increases or decreases synchronously, and there is a strong correlation between the lengths of adjacent speech units, an adaptive speech speed algorithm is proposed based on the framework of implicit Markov model based on the information of the length of speech units. Experiments on number string and large vocabulary continuous speech recognition show that the algorithm is effective.