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Background. AI emergence and rapid development of the related technologies is significantly changing the economy and social sphere of Russia. The influence of AI technologies on education is quite complex and multifaceted: on the one hand, artificial intelligence contributes to the processes of digital transformation, and, therefore, to increased efficiency and technological effectiveness. On the other hand, it poses a number of complex ethical problems and makes the daily work of the teacher in monitoring students more complicated. Purpose. Analysis of the capabilities of generative artificial intelligence for generating individual teaching materials (using the example of the disciplines Programming and Foreign (English) language. Materials and methods. The main research method is experimental. ChatGPT-4o and queries in English are used to generate tasks, test questions of various types, information and reference materials (synopses). To analyze the received materials, standard algorithms for reviewing teaching aids are used. The article includes a content analysis of publications on the problems of using artificial intelligence in education, educational materials presented by generative AI development companies (Google, Microsoft). Results. Current stage of development of generative artificial intelligence facilitates to use it effectively to generate standard teaching materials: tasks, texts, examples, brief inquiries. The quality of the obtained materials depends on the accuracy of the queries (AI prompt) and is heterogeneous for different disciplines. The paper presents methodological recommendations for the generation of test materials for the disciplines “Foreign Language (English)” and “Programming”, as well as institutional conditions for the safe use of the obtained materials in the educational process of the university.

Search engines must understand user queries to provide relevant search results. Search engines can enhance their understanding of user intent by employing named entity recognition (NER) to identify the entity in the query. Knowing the types of entities in the query can be the initial step in helping search engines better understand search intent. In this research, a dataset was constructed using search query history from the Statistics Indonesia (Badan Pusat Statistik, BPS) website, and NER in query modeling was employed to extract entities from search queries related to statistical datasets. The research stages included query data collection, query data preprocessing, query data labeling, NER in query modeling, and model evaluation. The conditional random field (CRF) model was employed for NER in query modeling with two scenarios: CRF with basic features and CRF with basic features plus part of speech (POS) features. The CRF model was used due to its well-known effectiveness in natural language processing (NLP), particularly for tasks like NER with sequence labeling. In this research, the basic CRF and the CRF model with POS feature achieved an F1-score of 0.9139 and 0.9110, respectively. A case study on a Linked Open Data (LOD) statistical dataset indicated that searches with synonym query expansion on entities from NER in query produced better search results than regular searches without query expansion. The model's performance incorporating additional POS tagging features did not result in a significant improvement. Therefore, it is recommended that future research will elaborate on deep learning.

Before he originated the field of philosophy for children, Matthew Lipman spent nearly twenty years teaching at Columbia University and its affiliated colleges under the tutelage of the American philosopher Justus Buchler. In those years Lipman’s scholarship focused on Buchler’s naturalist metaphysics, which was informed by Buchler’s scholarship on the philosophy of Charles Peirce. In this essay I relate Lipman’s relationship with Buchler, summarise Buchler’s theory of human judgement, and indicate key parts of that theory that influenced Lipman’s own theory of judgement and multidimensional thinking. I then discuss the pedagogical turn Buchler made with his theory, including his conception of classroom discussion as a mode of ‘community of query,’ which became the prototype for Lipman and Ann Margaret Sharp’s theory of ‘community of inquiry’ as a protocol of classroom dialogue and a paradigm of philosophical practice and democratic decision-making.

This paper discusses methods and technologies for creating user interfaces in natural language. In the context of modern technological development, user interfaces play an important role in human interaction with computer programs and devices, which determines their simplicity, efficiency and overall user experience. The idea of an NLP-based user interface is based on the use of natural language processing (NLP) algorithms, machine learning and artificial intelligence. The research discussed methods for creating such interfaces, their advantages and limitations, as well as prospects for further development. Analysis of TTI platforms such as Google Dialogflow, Microsoft Bot Framework, Wit.ai, IBM Watson Assistant and Amazon Lex, showed their important role in the development of user-friendly interfaces. The Methods and Materials section discusses existing methods for creating user interfaces in natural language, including speech recognition and synthesis systems, machine learning algorithms, as well as morphological, syntactic and semantic analysis. These approaches allow for a deeper understanding of user requests and provide more accurate answers. [ABSTRACT FROM AUTHOR]

Abstract Background Research Electronic Data CAPture (REDCap) is a web application for creating and managing online surveys and databases. Clinical data management is an essential process before performing any statistical analysis to ensure the quality and reliability of study information. Processing REDCap data in R can be complex and often benefits from automation. While there are several R packages available for specific tasks, none offer an expansive approach to data management. Results The REDCapDM is an R package for accessing and managing REDCap data. It imports data from REDCap to R using either an API connection or the files in R format exported directly from REDCap. It has several functions for data processing and transformation, and it helps to generate and manage queries to clarify or resolve discrepancies found in the data. Conclusion The REDCapDM package is a valuable tool for data scientists and clinical data managers who use REDCap and R. It assists in tasks such as importing, processing, and quality-checking data from their research studies.

The biomedical research community is motivated to share and reuse data from studies and projects by funding agencies and publishers. Effectively combining and reusing neuroimaging data from publicly available datasets, requires the capability to query across datasets in order to identify cohorts that match both neuroimaging and clinical/behavioral data criteria. Critical barriers to operationalizing such queries include, in part, the broad use of undefined study variables with limited or no annotations that make it difficult to understand the data available without significant interaction with the original authors. Using the Brain Imaging Data Structure (BIDS) to organize neuroimaging data has made querying across studies for specific image types possible at scale. However, in BIDS, beyond file naming and tightly controlled imaging directory structures, there are very few constraints on ancillary variable naming/meaning or experiment-specific metadata. In this work, we present NIDM-Terms, a set of user-friendly terminology management tools and associated software to better manage individual lab terminologies and help with annotating BIDS datasets. Using these tools to annotate BIDS data with a Neuroimaging Data Model (NIDM) semantic web representation, enables queries across datasets to identify cohorts with specific neuroimaging and clinical/behavioral measurements. This manuscript describes the overall informatics structures and demonstrates the use of tools to annotate BIDS datasets to perform integrated cross-cohort queries.

In case of natural disasters, it is vital that accurate information is delivered to disasters managers through correct communication channels at a correct time. The technological infrastructure of law enforcement can be categorized into three main areas: communication technologies, information query technologies, and geographic information systems. Coordination and the correct channeling of resources in post-disaster search and rescue operations and support services are paramount. Within this framework, communication emerges as the fundamental need. Initially, the need for emergency communication was met through the narrow band JEMUS system, then through wired and GSM infrastructures repaired; high data requirement was met through broad band systems. Following the adoption of broad band capabilities in disaster management centers, Geographic Information Systems (GIS) began to be utilized in disaster management, planning efforts, and data analysis. Safety and security services were initially operated over narrow band systems and later supported by broad band systems. Ensuring that personnel in the field can take the most appropriate actions without delay highlights that communication is the most critical service required for disaster management and coordination. The significance of instant sharing of data or high-resolution multimedia content such as photos and videos, remote access to institutional databases, access to biometric data, utilization of geographic information systems, and the use of law enforcement technologies were underscored by the earthquakes centered in Kahramanmaraş. In addition to all other preparations, supporting the decision-making processes of disaster managers and ensuring coordination to reduce loss of life and property, and to provide the best possible assistance to disaster victims through the efficient use of resources underscores the significant role of law enforcement technologies. [ABSTRACT FROM AUTHOR]

Addressing the issues of low negative sampling efficiency and tendency towards overfitting in existing dense passage retrieval (DPR) models, this paper proposed a DPR model based on query semantic characteristics (Q-DPR). Firstly, it introduced a negative sampling method based on neighbor queries for the negative sampling process. This method constructed high-quality negative samples rapidly by retrieving neighboring queries, thereby reducing the training costs. Secondly, to mitigate overfitting, it proposed a query self-supervised method based on contrastive learning. This method alleviated overfitting to training labels by establishing a self-supervised contrastive loss among queries, thereby enhancing retrieval accuracy. Q-DPR performed exceptionally well on the large-scale MSMARCO dataset for open-domain question answering, achieving a mean reciprocal rank of 0.348 and a recall rate of 0.975. Experimental results demonstrate that this model successfully reduces trai-ning overhead while also improving retrieval performance. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) includes billions of sensors and actuators (which we refer to as IoT devices) that harvest data from the physical world and send it via the Internet to IoT applications to provide smart IoT services and products. Deploying, managing, and maintaining IoT devices for the exclusive use of an individual IoT application is inefficient and involves significant costs and effort that often outweigh the benefits. On the other hand, enabling large numbers of IoT applications to share available third-party IoT devices, which are deployed and maintained independently by a variety of IoT device providers, reduces IoT application development costs, time, and effort. To achieve a positive cost/benefit ratio, there is a need to support the sharing of third-party IoT devices globally by providing effective IoT device discovery, use, and pay between IoT applications and third-party IoT devices. A solution for global IoT device sharing must be the following: (1) scalable to support a vast number of third-party IoT devices, (2) interoperable to deal with the heterogeneity of IoT devices and their data, and (3) IoT-owned, i.e., not owned by a specific individual or organization. This paper surveys existing techniques that support discovering, using, and paying for third-party IoT devices. To ensure that this survey is comprehensive, this paper presents our methodology, which is inspired by Systematic Literature Network Analysis (SLNA), combining the Systematic Literature Review (SLR) methodology with Citation Network Analysis (CNA). Finally, this paper outlines the research gaps and directions for novel research to realize global IoT device sharing. [ABSTRACT FROM AUTHOR]

Background: Research Electronic Data CAPture (REDCap) is a web application for creating and managing online surveys and databases. Clinical data management is an essential process before performing any statistical analysis to ensure the quality and reliability of study information. Processing REDCap data in R can be complex and often benefits from automation. While there are several R packages available for specific tasks, none offer an expansive approach to data management. Results: The REDCapDM is an R package for accessing and managing REDCap data. It imports data from REDCap to R using either an API connection or the files in R format exported directly from REDCap. It has several functions for data processing and transformation, and it helps to generate and manage queries to clarify or resolve discrepancies found in the data. Conclusion: The REDCapDM package is a valuable tool for data scientists and clinical data managers who use REDCap and R. It assists in tasks such as importing, processing, and quality-checking data from their research studies. [ABSTRACT FROM AUTHOR]

Semantic segmentation of remote sensing (RS) images is vital in various practical applications, including urban construction planning, natural disaster monitoring, and land resources investigation. However, RS images are captured by airplanes or satellites at high altitudes and long distances, resulting in ground objects of the same category being scattered in various corners of the image. Moreover, objects of different sizes appear simultaneously in RS images. For example, some objects occupy a large area in urban scenes, while others only have small regions. Technically, the above two universal situations pose significant challenges to the segmentation with a high quality for RS images. Based on these observations, this paper proposes a Mask2Former with an improved query (IQ2Former) for this task. The fundamental motivation behind the IQ2Former is to enhance the capability of the query of Mask2Former by exploiting the characteristics of RS images well. First, we propose the Query Scenario Module (QSM), which aims to learn and group the queries from feature maps, allowing the selection of distinct scenarios such as the urban and rural areas, building clusters, and parking lots. Second, we design the query position module (QPM), which is developed to assign the image position information to each query without increasing the number of parameters, thereby enhancing the model's sensitivity to small targets in complex scenarios. Finally, we propose the query attention module (QAM), which is constructed to leverage the characteristics of query attention to extract valuable features from the preceding queries. Being positioned between the duplicated transformer decoder layers, QAM ensures the comprehensive utilization of the supervisory information and the exploitation of those fine-grained details. Architecturally, the QSM, QPM, and QAM as well as an end-to-end model are assembled to achieve high-quality semantic segmentation. In comparison to the classical or state-of-the-art models (FCN, PSPNet, DeepLabV3+, OCRNet, UPerNet, MaskFormer, Mask2Former), IQ2Former has demonstrated exceptional performance across three publicly challenging remote-sensing image datasets, 83.59 mIoU on the Vaihingen dataset, 87.89 mIoU on Potsdam dataset, and 56.31 mIoU on LoveDA dataset. Additionally, overall accuracy, ablation experiment, and visualization segmentation results all indicate IQ2Former validity. [ABSTRACT FROM AUTHOR]

The Language Consultation Centre (LCC) of the Czech Language Institute has been offering telephone consultations for almost eighty years. During that time papers about the content of queries were published fairly regularly, but the interaction between callers and LCC employees was mostly disregarded (with a few recent exceptions). Therefore, this paper presents an analysis of the ways that the callers formulate their queries. For this purpose, I examined 102 language queries from 63 recordings of authentic phone calls to the LCC using the methodology of conversation analysis. In the queries I identified recurring components and divided them into seven categories based on their functions: thematizing knowledge deficit, defining the topic of the query, supporting or rejecting the solution, disclaiming authorship (of a "problematic" language form), justifying the query, providing additional information, and signalling transition to the next part of the utterance. This categorization proved that language queries are complex utterances that reflect the norms of the genre of telephone language consulting. In other words, the inquirers are well aware of what information they can present and how, and what they can expect from LCC employees in return. [ABSTRACT FROM AUTHOR]

Purpose of Review: There were two primary purposes to our reviews. First, to provide an update to the scientific community about the impacts of COVID-19 on musculoskeletal health. Second, was to determine the value of using a large language model, ChatGPT 4.0, in the process of writing a scientific review article. To accomplish these objectives, we originally set out to write three review articles on the topic using different methods to produce the initial drafts of the review articles. The first review article was written in the traditional manner by humans, the second was to be written exclusively using ChatGPT (AI-only or AIO), and the third approach was to input the outline and references selected by humans from approach 1 into ChatGPT, using the AI to assist in completing the writing (AI-assisted or AIA). All review articles were extensively fact-checked and edited by all co-authors leading to the final drafts of the manuscripts, which were significantly different from the initial drafts. Recent Findings: Unfortunately, during this process, it became clear that approach 2 was not feasible for a very recent topic like COVID-19 as at the time, ChatGPT 4.0 had a cutoff date of September 2021 and all articles published after this date had to be provided to ChatGPT, making approaches 2 and 3 virtually identical. Therefore, only two approaches and two review articles were written (human and AI-assisted). Here we found that the human-only approach took less time to complete than the AI-assisted approach. This was largely due to the number of hours required to fact-check and edit the AI-assisted manuscript. Of note, the AI-assisted approach resulted in inaccurate attributions of references (about 20%) and had a higher similarity index suggesting an increased risk of plagiarism. Summary: The main aim of this project was to determine whether the use of AI could improve the process of writing a scientific review article. Based on our experience, with the current state of technology, it would not be advised to solely use AI to write a scientific review article, especially on a recent topic. [ABSTRACT FROM AUTHOR]

Before he originated the field of philosophy for children, Matthew Lipman spent nearly twenty years teaching at Columbia University and its affiliated colleges under the tutelage of the American philosopher Justus Buchler. In those years Lipman's scholarship focused on Buchler's naturalist metaphysics, which was informed by Buchler's scholarship on the philosophy of Charles Peirce. In this essay I relate Lipman's relationship with Buchler, summarise Buchler's theory of human judgement, and indicate key parts of that theory that influenced Lipman's own theory of judgement and multidimensional thinking. I then discuss the pedagogical turn Buchler made with his theory, including his conception of classroom discussion as a mode of 'community of query,' which became the prototype for Lipman and Ann Margaret Sharp's theory of 'community of inquiry' as a protocol of classroom dialogue and a paradigm of philosophical practice and democratic decision-making. [ABSTRACT FROM AUTHOR]

We study exa k (n , F) , the largest number of edges in an n -vertex graph that contains exactly k copies of a given subgraph F. The case k = 0 is the Turán number ex (n , F) that is among the most studied parameters in extremal graph theory. We show that for any F and k , exa k (n , F) = (1 + o (1)) ex (n , F) and determine the exact values of exa k (n , K 3) and exa 1 (n , K r) for n large enough. We also explore a connection to the following well-known problem in search theory. We are given a graph of order n that consists of an unknown copy of F and some isolated vertices. We can ask pairs of vertices as queries, and the answer tells us whether there is an edge between those vertices. Our goal is to describe the graph using as few queries as possible. Aigner and Triesch in 1990 showed that the number of queries needed is at least n 2 − exa 1 (n , F). Among other results we show that the number of queries that were answered NO is at least n 2 − exa 1 (n , F). [ABSTRACT FROM AUTHOR]

The article discusses an efficient and automated way to import large amounts of data from Excel tables into a database. In various projects, there are tasks in which the flow of huge data, such as logs of program operations or manual operations performed at work sites, is vital for effective analysis. Purpose – development of a module for automatic import of a large amount of data from Excel format into a database. Method or methodology of work: the article discusses a method that implements automatic import of data from Excel tables into a Postgresql database. Result: developed its own unique module that is able to process huge Excel tables and import them into a Postgresql database without manual operations. Scope of the results: the data obtained, which are stored in the database, should be used to identify high-yield accounts for subsequent investment.

The increasing reliance on online communication and data storage has made protecting sensitive information a top priority for businesses and individuals. This study investigates the effectiveness of different security protocols in safeguarding databases from external threats. By comparing the performance of databases that use SSL and those that do not, this project provided valuable insights into the importance of securing data in transit and the impact of security protocols on database performance. The results of this research could be helpful for companies and individuals looking to improve the security of their databases and protect sensitive information. These research findings demonstrate that enabling SSL encryption leads to longer average execution times for database operations. This latency can be attributed to the computational workload associated with SSL's encryption and decryption processes. However, the trade-off between performance and security is essential to safeguarding data in transit.

Queries inviting recipients to reflect on the biased knowledge that hinders purchases are widely used in advertising. This study adopts processing fluency as a psychological mechanism for evaluating when and how promotional messages designed as queries about biased knowledge might impact persuasion. The results of five between-subjects experiments show that biased knowledge framed as queries raises individuals’ evaluations of the advocated product by causing consumers to experience fluency of processing (pilot study). Furthermore, individuals are more apt to increase processing, resulting in the item being perceived as more attractive, when linking a query’s regulatory focus with their self-construal (Study 1a) or by a fit between the quality/price concern highlighted in a query message and their decision-making styles (Study 1b). Finally, the query effect on a consumer’s sense of processing fluency and product evaluation is enhanced when the biased knowledge stated in a query is refuted by a negative effect claim (versus a no-effect claim) (Study 2a) or coupled with a claim addressing the disadvantages of the competing options rather than the non-negative features of the advocated option (Study 2b), in line with negativity bias. [ABSTRACT FROM AUTHOR]

Instance segmentation (IS) of remote sensing (RS) images can not only determine object location at the box-level but also provide instance masks at the pixel-level. It plays an important role in many fields, such as ocean monitoring, urban management, and resource planning. Compared with natural images, RS images usually pose many challenges, such as background clutter, significant changes in object size, and complex instance shapes. To this end, we propose a query-based RS image cascade IS network (QCIS-Net). The network mainly includes key components, such as the efficient feature extraction (EFE) module, multistage cascade task (MSCT) head, and joint loss function, which can characterize the location and visual information of instances in RS images through efficient queries. Among them, the EFE module combines global information from the Transformer architecture to solve the problem of long-term dependencies in visual space. The MSCT head uses a dynamic convolution kernel based on the query representation to focus on the region of interest, which facilitates the association between detection and segmentation tasks through a multistage structural design that benefits both tasks. The elaborately designed joint loss function and the use of the transfer-learning technique based on a well-known dataset (MS COCO) can guide the QCIS-Net in training and generating the final instance mask. Experimental results show that the well-designed components of the proposed method have a positive impact on the RS image instance segmentation task. It achieves mask average precision (AP) values of 75.2% and 73.3% on the SAR ship detection dataset (SSDD) and Northwestern Polytechnical University Very-High-Resolution dataset (NWPU-VHR-10 dataset), outperforming the other competitive models. The method proposed in this paper can enhance the practical application efficiency of RS images. [ABSTRACT FROM AUTHOR]

Semantic segmentation of remote sensing (RS) images is vital in various practical applications, including urban construction planning, natural disaster monitoring, and land resources investigation. However, RS images are captured by airplanes or satellites at high altitudes and long distances, resulting in ground objects of the same category being scattered in various corners of the image. Moreover, objects of different sizes appear simultaneously in RS images. For example, some objects occupy a large area in urban scenes, while others only have small regions. Technically, the above two universal situations pose significant challenges to the segmentation with a high quality for RS images. Based on these observations, this paper proposes a Mask2Former with an improved query (IQ2Former) for this task. The fundamental motivation behind the IQ2Former is to enhance the capability of the query of Mask2Former by exploiting the characteristics of RS images well. First, we propose the Query Scenario Module (QSM), which aims to learn and group the queries from feature maps, allowing the selection of distinct scenarios such as the urban and rural areas, building clusters, and parking lots. Second, we design the query position module (QPM), which is developed to assign the image position information to each query without increasing the number of parameters, thereby enhancing the model’s sensitivity to small targets in complex scenarios. Finally, we propose the query attention module (QAM), which is constructed to leverage the characteristics of query attention to extract valuable features from the preceding queries. Being positioned between the duplicated transformer decoder layers, QAM ensures the comprehensive utilization of the supervisory information and the exploitation of those fine-grained details. Architecturally, the QSM, QPM, and QAM as well as an end-to-end model are assembled to achieve high-quality semantic segmentation. In comparison to the classical or state-of-the-art models (FCN, PSPNet, DeepLabV3+, OCRNet, UPerNet, MaskFormer, Mask2Former), IQ2Former has demonstrated exceptional performance across three publicly challenging remote-sensing image datasets, 83.59 mIoU on the Vaihingen dataset, 87.89 mIoU on Potsdam dataset, and 56.31 mIoU on LoveDA dataset. Additionally, overall accuracy, ablation experiment, and visualization segmentation results all indicate IQ2Former validity.

The drug information services help to assist healthcare workers to address patient-specific drugrelated needs and promote rational drug use. The objective of the study is to assess the pattern of drug information queries in a tertiary care hospital from the Physician, Nurses, Pharmacists, Students, and other healthcare professionals. In one year, the drug information queries have been received, documented and retrospectively analyzed for various parameters including the professional status of the requestor, mode of receipt and reply, type and purpose of query, and reference details. Out of 588 queries received, the majority were answered by Physicians (n=468; 79.59%), followed by Pharmacists (n=55; 9.35%), Nurses (n=15; 2.55%), also Technicians (n=9; 1.53%), Students (n=21; 3.58%), Patients (n=3; 0.5%) and other health care professionals (n=17; 2.90%). The secondary resources (Micromedex/Medscape) were used majorly, followed by Textbooks (n=117; 19.89%), Internet (n=103; 17.51%), Journals (n=23; 3.9%) and others (n=5; 0.85%). The most common drugrelated query was Pharmacological drug profile (n=149; 25.34%) and including Product identification (n=132; 22.44%), Product Information (n=84; 14.28%), Adverse Drug Reactions (n=74; 12.58%), Therapeutic uses (n=55; 9.35%) and others (n=94; 15.98%). Drug Information Services has been developed to promote rational prescribing patterns among prescribers, reduce medication errors and provide better clinical outcomes. [ABSTRACT FROM AUTHOR]

With the proliferation of location-based social media, it is of great importance to provide web users and mobile users with timely and high-quality information. In this light, we study the problem of continuous spatial keyword search over a stream of spatio-temporal messages by taking spatial relevance, textual relevance, and result diversification into consideration. We define a novel continuous query named Diversified Continuous Spatial Keyword (DCSK) query. A DCSK query consists of a spatial region, a set of query keywords, a similarity threshold 휃, and the number of query results k. Given a stream of spatio-temporal messages, the DCSK query continuously receive spatio-temporal messages such that: (1) they are located inside the query region and contain at least one query keyword, and (2) the similarities between each message and its previous k messages are all mess than 휃. Compared to traditional continuous spatial keyword query, the DCSK query can provide subscribers with spatio-temporal messages of higher quality because that the DCSK query takes both spatio-temporal relevance and query result diversification into consideration. We develop a Spatio-temporal Diversified Publish/Subscribe (STD-PS) framework to process a large number of DCSK queries efficiently. We conduct extensive experiments with real-world datasets. Our experimental results confirm the capability of our proposal in terms of result diversity, efficiency, and salability. [ABSTRACT FROM AUTHOR]

Currently, on the Internet, the information about agriculture is augmenting extremely; thus, searching for precise, relevant data of various details is highly complicated. To deal with particular difficulties like lower relevancy rate, false detection of retrieval resources, poor similarity rate, unstructured data format, multivariate data, irrelevant spelling, and higher computation time, an intelligent Information Retrieval (IR) system is required. An IR Framework centered on Levenshtein Distance Weight-centric Ontology (LDW-Ontology) and Sutskever Nesterov Momentum-centred Bidirectional Encoder Representation from Transformer (SNM-BERT) methodologies is presented here to overcome the complications as mentioned earlier. Firstly, the data is pre-processed, transmuting the unstructured data into a structured format, thus mitigating the error probabilities. Then, the LDW-Crop Ontology construction is done regarding the structured data. In the methodology presented, significance, frequency, and the suggestion of word in mind are considered to build Crop ontology. In the MongoDB database, the data being constructed are amassed. Then, by utilizing SNM-BERT, the data is trained for IR regarding clustered input produced by Inter Quartile Pruning Range-centred Hierarchical Divisive Clustering (IQPR-HDC) model. The LDW is computed for the provided user query; subsequently, the similarity evaluation outcomes are obtained from the database. The experiential evaluation displays that when analogized with the prevailing methodologies, a better accuracy of 94 % for simple queries and 92% for complex queries is achieved. Along with retrieval rate with lower computation time is achieved by the proposed methodology. [ABSTRACT FROM AUTHOR]

The article discusses the issues of making effective decisions about investing in commercial accounts and currencies. In order to know how much these solutions will be beneficial in the long term, specialized information is needed: data on the dynamics of growth, decline in the value of accounts and currencies, analytical information. But there is no single source where this data will be accumulated, systematized and stored. Purpose – create a tool for collecting and storing the parameters of investment accounts for their subsequent analysis in order to make an effective investment decision. Method or methodology of work: the article discusses a method that can implement a tool for collecting specialized information of investment accounts, two tools can be used for this: Postgresql databases and Python programming language Result: a tool has been developed that performs the function of collecting and storing information on investment accounts. Scope of the results: the data obtained, which are stored in the database, should be used to identify high-yield accounts for subsequent investment.

The biomedical research community is motivated to share and reuse data from studies and projects by funding agencies and publishers. Effectively combining and reusing neuroimaging data from publicly available datasets, requires the capability to query across datasets in order to identify cohorts that match both neuroimaging and clinical/behavioral data criteria. Critical barriers to operationalizing such queries include, in part, the broad use of undefined study variables with limited or no annotations that make it difficult to understand the data available without significant interaction with the original authors. Using the Brain Imaging Data Structure (BIDS) to organize neuroimaging data has made querying across studies for specific image types possible at scale. However, in BIDS, beyond file naming and tightly controlled imaging directory structures, there are very few constraints on ancillary variable naming/meaning or experiment-specific metadata. In this work, we present NIDM-Terms, a set of user-friendly terminology management tools and associated software to better manage individual lab terminologies and help with annotating BIDS datasets. Using these tools to annotate BIDS data with a Neuroimaging Data Model (NIDM) semantic web representation, enables queries across datasets to identify cohorts with specific neuroimaging and clinical/behavioral measurements. This manuscript describes the overall informatics structures and demonstrates the use of tools to annotate BIDS datasets to perform integrated cross-cohort queries.

Sensor-IoT (Internet of Things) networks allow users, cloud, or edge servers to query and collect data from sensor-IoT devices. It is important to enable the resilience of failures in data query and collection. This paper studies the issue of functional structure to enable the mutual support among sensor-IoT devices in discovery and recovery of query losses with low cost. We especially address the problem of how to configure the sensor-IoT network with a sufficient but small number of detector-role nodes that perform the monitoring role for nearby nodes. We propose two schemes. One is random selection of detectors at each sensor-IoT node. Another scheme allows each IoT-sensor node to choose the its detectors in an order of node IDs. The evaluation results show the effectiveness in the construction of detector sets with a small number of sensors by using the proposed schemes. Moreover, we verify that the ID-sequence based scheme is more effective in detector selection to reach the target of easy construction of detector set using a small number of detectors. [ABSTRACT FROM AUTHOR]

Given the complex search tasks imposed to social multimedia retrieval systems, the generated similarity-based ranked results often represent redundant item sets, including, e.g., near-duplicates or unrepresentative samples. In this context, several real-world search tasks demand broad coverage of multiple implicit subtopics of a given query in order to properly fulfill the user need. Many works have proposed the use of result diversification for addressing such problem. As a popular approach, the diversification is achieved by grouping similar items obtained from the original ranked list. Hence, a new and diverse ranked list is constructed by iteratively selecting a representative item from each cluster. However, the definition of the number of clusters (subtopics) to be discovered is a long-lasting challenge. Moreover, most clustering optimization approaches for diversification rely on offline training for the selection of a general best configuration used for all queries at run-time. However, this is a complex task given the multiple heterogeneity associated (data, user, query, concepts, etc.) and the consequent impact on the effectiveness of retrieval algorithms. Therefore, such approaches are usually prone to overfit. Hence, in order to attenuate such problems, this work proposes a novel diverse image retrieval approach as an unsupervised query-adaptive subtopic discovery based on intrinsic clustering quality optimization. Our experimental analysis have shown significant improvements in relation to the baseline, both in terms of relevance and diversity. [ABSTRACT FROM AUTHOR]

Nowadays, it is quite common in linguistics to base research on data instead of introspection. There are countless corpora – both raw and linguistically annotated – available to us which provide essential data needed. Corpora are large in most cases, ranging from several million words to some billion words in size, clearly not suitable to investigate word by word by close reading. Basically, there are two ways to retrieve data from them: (1) through a query interface or (2) directly by automatic text processing. Here we present principles on how to soundly and effectively collect linguistic data from corpora by querying i.e. without knowledge of programming to directly manipulate the data. What is worth thinking about, which tools to use, what to do by default and how to solve problematic cases. In sum, how to obtain correct and complete data from corpora to do linguistic research. [ABSTRACT FROM AUTHOR]