Query Content Research Articles

From traditional teaching to flipped classroom: Impact on learning in engineering degrees

Computer engineering students should develop competences related to the contents of databases design and SQL queries. For this purpose, the recommendations on the convenience of changing the traditional teaching methodology to the flipped classroom are followed. In this article we present a quantitative study in which we compare the potential for the development of engineering students' competences in the design and use of databases of the flipped classroom methodology and the traditional teaching methodology. The results obtained in the evaluation of the subject in two different courses are compared. In the first course, traditional teaching methodology was used. In the second one, flipped classroom was used when its implementation had already been tried and tested. The results of the content blocks of database design and SQL queries are compared, according to the type of learning promoted. In this article we show evidence that the implementation of the flipped classroom teaching methodology provides different results depending on the learning promoted and on the specific contents in the subject of relational databases. The benefits of implementing the flipped classroom depend on the content and type of learning promoted. We have found evidence that they improve theoretical learning outcomes for database design and in the resolution of non-reproductive activities in the SQL queries block. No conclusive benefits are reported for students’ competency development for either of the two content blocks.

Are Virtual Assistants Trustworthy for Medicare Information: An Examination of Accuracy and Reliability.

Advances in artificial intelligence (AI)-based virtual assistants provide a potential opportunity for older adults to use this technology in the context of health information-seeking. Meta-analysis on trust in AI shows that users are influenced by the accuracy and reliability of the AI trustee. We evaluated these dimensions for responses to Medicare queries. During the summer of 2023, we assessed the accuracy and reliability of Alexa, Google Assistant, Bard, and ChatGPT-4 on Medicare terminology and general content from a large, standardized question set. We compared the accuracy of these AI systems to that of a large representative sample of Medicare beneficiaries who were queried twenty years prior. Alexa and Google Assistant were found to be highly inaccurate when compared to beneficiaries' mean accuracy of 68.4% on terminology queries and 53.0% on general Medicare content. Bard and ChatGPT-4 answered Medicare terminology queries perfectly and performed much better on general Medicare content queries (Bard = 96.3%, ChatGPT-4 = 92.6%) than the average Medicare beneficiary. About one month to a month-and-a-half later, we found that Bard and Alexa's accuracy stayed the same, whereas ChatGPT-4's performance nominally decreased, and Google Assistant's performance nominally increased. LLM-based assistants generate trustworthy information in response to carefully phrased queries about Medicare, in contrast to Alexa and Google Assistant. Further studies will be needed to determine what factors beyond accuracy and reliability influence the adoption and use of such technology for Medicare decision-making.

Simple Conditional Spatial Query Mask Deformable Detection Transformer: A Detection Approach for Multi-Style Strokes of Chinese Characters

In the Chinese character writing task performed by robotic arms, the stroke category and position information should be extracted through object detection. Detection algorithms based on predefined anchor frames have difficulty resolving the differences among the many different styles of Chinese character strokes. Deformable detection transformer (deformable DETR) algorithms without predefined anchor frames result in some invalid sampling points with no contribution to the feature update of the current reference point due to the random sampling of sampling points in the deformable attention module. These processes cause a reduction in the speed of the vector learning stroke features in the detection head. In view of this problem, a new detection method for multi-style strokes of Chinese characters, called the simple conditional spatial query mask deformable DETR (SCSQ-MDD), is proposed in this paper. Firstly, a mask prediction layer is jointly determined using the shallow feature map of the Chinese character image and the query vector of the transformer encoder, which is used to filter the points with actual contributions and resample the points without contributions to address the randomness of the correlation calculation among the reference points. Secondly, by separating the content query and spatial query of the transformer decoder, the dependence of the prediction task on the content embedding is relaxed. Finally, the detection model without predefined anchor frames based on the SCSQ-MDD is constructed. Experiments are conducted using a multi-style Chinese character stroke dataset to evaluate the performance of the SCSQ-MDD. The mean average precision (mAP) value is improved by 3.8% and the mean average recall (mAR) value is improved by 1.1% compared with the deformable DETR in the testing stage, illustrating the effectiveness of the proposed method.

GIG economy and sustainable development: bibliometric analysis and identification of future research directions

Purpose: The aim of this article is to identify the most common fields of study undertaken by researchers in scientific papers covering simultaneously the gig economy and sustainable development. Design/methodology/approach: The research study used a structured literature review method. The structured bibliometric query Q1 allowed for the exploration of the Scopus database. As a result, 48 scientific studies from the period 2017-2023 were selected for analysis, which simultaneously addressed the issues of the gig economy and sustainable development. Findings: The analyses carried out showed that the issue of sustainable development is addressed in the context of the gig economy. This is a relatively new research area, as shown, for example, by the generated database of studies used for the bibliometric analyses conducted. The issues of sharing economy and decent work are important areas addressed in the context of the gig economy. It was noticed that there was a lack of author keywords covering 'green' issues. Research limitations/implications: Research results based on other databases (e.g. Web of Science) may lead to different results. It should be noted that the different bibliometric databases do not overlap, which is related to the indexing of scientific journals or publications in the databases. Nonetheless, a universal query was used in the study, which can be used in the various bibliometric databases once it has been adapted to their respective query form. In addition, the various databases are constantly being supplemented with new scientific papers, which also depends on the publishing process. Hence, the content of the Q1 query can in future be used for comparison purposes with the results obtained, or to compare different bibliometric databases with each other. Originality/value: In the discussion that followed, attention was drawn to the issues of green jobs and green self-employment. In the authors' opinion, such research directions, although not yet visible in the authors' keywords, will be developed soon in the context of studies covering the issues of the gig economy and sustainable development considering the green transformation of the economy taking place. This article is addressed to all those who are interested in the issues of the gig economy and sustainable development. Keywords: gig economy, green jobs, green labor market, green self-employment, sustainable development. Category of the paper: Literature review.

GLPS: A Geohash-Based Location Privacy Protection Scheme.

With the development of mobile applications, location-based services (LBSs) have been incorporated into people's daily lives and created huge commercial revenues. However, when using these services, people also face the risk of personal privacy breaches due to the release of location and query content. Many existing location privacy protection schemes with centralized architectures assume that anonymous servers are secure and trustworthy. This assumption is difficult to guarantee in real applications. To solve the problem of relying on the security and trustworthiness of anonymous servers, we propose a Geohash-based location privacy protection scheme for snapshot queries. It is named GLPS. On the user side, GLPS uses Geohash encoding technology to convert the user's location coordinates into a string code representing a rectangular geographic area. GLPS uses the code as the privacy location to send check-ins and queries to the anonymous server and to avoid the anonymous server gaining the user's exact location. On the anonymous server side, the scheme takes advantage of Geohash codes' geospatial gridding capabilities and GL-Tree's effective location retrieval performance to generate a k-anonymous query set based on user-defined minimum and maximum hidden cells, making it harder for adversaries to pinpoint the user's location. We experimentally tested the performance of GLPS and compared it with three schemes: Casper, GCasper, and DLS. The experimental results and analyses demonstrate that GLPS has a good performance and privacy protection capability, which resolves the reliance on the security and trustworthiness of anonymous servers. It also resists attacks involving background knowledge, regional centers, homogenization, distribution density, and identity association.

SQAB: Specific query anchor boxes for human–object interaction detection

Human-Object Interaction (HOI) detection is the cornerstone of advanced visual understanding, aiming to identify relationships and interactions among different objects in images. Transformer-based previous methods commonly utilize traditional query embeddings to predict HOI, but this approach suffers from slow training convergence. Although recent research defines HOI queries as reference points, their semantic information remains ambiguous, ignoring object scale differences. To address these issues, we propose to use anchor boxes as queries for HOI detection for the first time, which can significantly accelerate the convergence speed. Furthermore, in order to enable anchor boxes to focus on HOI features efficiently, we designed an end-to-end Specific Query Anchor Boxes (SQAB) network. Our method includes a Hierarchical Detection Branch (HDB) and an Interaction Refinement Branch (IRB). Firstly, HDB uses specific query anchor boxes for prediction on multi-scale feature maps and uses relation content queries to associate contextual information. In addition, IRB utilizes multi-scale body part masks to guide the model to focus on key interaction regions effectively between humans and objects, improving the performance of interaction categories. Experimental results show that SQAB performs superior to the baseline, only requiring 25 epochs of the training cycles on the widely used HOI benchmark datasets (V-COCO, HICO-DET, and HOI-A). On the HICO-DET and HOI-A datasets, mean average precision(mAP) increased by approximately 5.99 % and 3.02%, respectively. On the V-COCO dataset, SQAB increases mAP by up to 10.57%.

Cross-Modal Interaction Network for Video Moment Retrieval

The video moment retrieval task aims to fetch a target moment in an untrimmed video, which best matches the semantics of a sentence query. Existing methods mainly focus on utilizing two separate modules: one learns intra-modal relations to understand video and query contents, and the other explores inter-modal interactions to build a semantic bridge between video and language. However, intra-modal relations information can be easily overlooked when capturing inter-modal interactions. In fact, intra-modal relations and inter-modal interactions can be learned simultaneously within a unified module to make video and sentence guide each other. Towards this end, we propose a Cross-Modal Interaction Network (CMIN) for video moment retrieval by jointly exploring the intra-modal relations and inter-modal interactions between video frames and query words. In CMIN, a query-guided channel attention module is designed to suppress query-irrelevant visual features and enhance crucial contents; then a cross-attention module simultaneously considers intra-modal relations within each modality and fine-grained inter-modal interactions between frames and words, to enhance the semantic relevance between video and sentence query. Compared to the state-of-the-art methods, the experiments on two public datasets (Charades-STA and TACoS) demonstrate the superiority of our method.

Co-attention enabled content-based image retrieval

Content-based image retrieval (CBIR) aims to provide the most similar images to a given query. Feature extraction plays an essential role in retrieval performance within a CBIR pipeline. Current CBIR studies would either uniformly extract feature information from the input image and use it directly or employ some trainable spatial weighting module which is then used for similarity comparison between pairs of query and candidate matching images. These spatial weighting modules are normally query non-sensitive and only based on the knowledge learned during the training stage. They may focus towards incorrect regions, especially when the target image is not salient or is surrounded by distractors. This paper proposes an efficient query sensitive co-attention11“Co-attention” in this paper refers to spatial attention conditioned on the query content. mechanism for large-scale CBIR tasks. In order to reduce the extra computation cost required by the query sensitivity to the co-attention mechanism, the proposed method employs clustering of the selected local features. Experimental results indicate that the co-attention maps can provide the best retrieval results on benchmark datasets under challenging situations, such as having completely different image acquisition conditions between the query and its match image.

Grid-guided localization network based on the spatial attention mechanism for synthetic aperture radar ship detection

Synthetic aperture radar (SAR) has become an important part of ship detection due to its all-day all-weather characteristics. Ship monitoring is important for coastal traffic control and territorial safety. Ship detection requires high accuracy, but the speed of ship detection is also an important parameter in making rapid decisions, such as those in maritime rescue and military strategy. However, fast networks tend to be less accurate. To address this problem, we propose an efficient localization method based on a spatial attention mechanism. This mechanism uses grid-guided localization instead of a frame regression mechanism, which makes the network faster but also lowers the accuracy to a level insufficient for direct application to SAR image target detection. A tested and selected spatial attention mechanism improves the detection accuracy while guaranteeing its speed. It is shown that four factors affect the design of the spatial attention mechanism, namely query and key content, query content and relative position, key content, and relative position. These factors are ordered, and the group with the best precision is added to the proposed network. The proposed network can achieve a good detection effect for sparsely distributed targets in SAR images, and the proposed algorithm can achieve AP50 ∶ 75, AP50, AP75, and APS values of 71.3%, 96.9%, 86.6%, and 72.1%, respectively, on the SAR ship detection dataset.

An Efficient Pending Interest Table Content Search In NDN through Stable Bloom Filter

AbstractNamed Data Networking (NDN) has gained importance in today’s era due to a paradigm shift in the Internet usage pattern which revolves around the content rather than the respective host addresses. Three important data structures in NDN are Content Store (CS), Pending Interest Table (PIT) and Forwarding Information Base (FIB). The search time of PIT is quite high since its size grows with the addition of new content names, and the Interest packets which are not served by CS are searched in millions of existing entries in the PIT. Hence, lookup time can be improved if, instead of checking all the available entries, initial scanning is done to determine whether the required content name exists in the PIT or not. In this paper, we propose a Stable Bloom Filter (SBF) based PIT called S-PIT, to minimize the PIT search time by identifying the existence of query content through SBF. The various experiments performed show that S-PIT outperforms existing data structures in terms of memory consumption, content insertion time, average search time and false positive rate.

Post-COVID ethics of work as a perspective for the development of self-employment in Russia

The article discusses the process of transferring the financial and economic activities of most self-employed entrepreneurs to the digital space, which was forced by the conditions of the remote work format during the COVID-19 pandemic, as well as the established legislative framework during the period of self-isolation. The analysis of the current legislative framework, which is included in the article, makes it possible to identify general trends in removing barriers to engage a larger group of entrepreneurs in a new format of working, as well as immersing the main financial instruments in the Internet. On the basis of empirical data obtained in the course of quantitative (data analytics of queries in Yandex.Direct) and qualitative sociological research (content analysis of financial applications) the features of registration and registration procedures in the digital space were formed, the online platforms of the Federal Tax Service and the largest banking organizations for servicing financial and economic activities for the self-employed were analyzed, including financial instruments. A number of main aggregators and marketplaces are distinguished by industries of the activity sphere of the self-employed according to the criterion of popularity in Internet search engines. The specificity of web analytics of the dynamics and content of search queries allows us to draw conclusions about the most pressing topics, both for the self-employed and for those interested in this topic for the purpose of subsequent registration and use of financial instruments on the Internet. Based on these requests, there is a surge of interest in self-employed entrepreneurship, the criteria for registering self-employment during the pandemic and post-pandemic, an active study of the specifics of tax liabilities and financial and economic activities, typical characteristics of search behavior and priority online platforms in the Yandex.Direct system have been formed.

ALPS: Achieving accuracy-aware location privacy service via assisted regions

Location search services (LSSs) provide convenience and enhanced functionality of ubiquitous computing, but they lead to a new risk of private location data leakage. The existing approaches generally use obfuscation to protect location privacy by perturbing the user’s location. However, this strategy inevitably degrades the accuracy of LSSs and affects the quality of service. In this paper, we propose an achieving accuracy-aware location privacy service via assisted regions, named ALPS, to protect user location privacy while ensuring the accuracy of services. Specifically, we devise two novel mechanisms (assisted regions mechanism and query obfuscation mechanism) to protect the user’s location privacy and ensure the accuracy of LSSs based on trilateration. The assisted regions mechanism prevents the attackers from getting the exact location of the user by protecting the user’s location within the overlapping region formed by the assisted regions. The query obfuscation mechanism obscures the user’s query content to prevent the attacker from predicting the user’s location based on the query content; the distance between the user and the query contents is calculated locally based on trilateration and the user’s location to ensure the accuracy of the LSSs. Theoretical analysis and experimental evaluation demonstrate that our scheme can protect location privacy without compromising the accuracy of LSSs.

Scan-free verifiable public-key searchable encryption supporting efficient user updates in distributed systems

Searchable encryption (SE) is a promising technology that provides search functions while preserving information privacy. A small number of SE schemes can be applied to a distributed system. In such systems, multiple owners provide data that is encrypted by their secret keys to the system for retrieval by users. In this setup, however, users need to scan a searchable index for retrievals, which leads to linear search complexity. Moreover, updates of users (e.g., revoking an expired user from the system) generally incur substantial overhead to re-build the index. Accordingly, we propose a scan-free verifiable public-key searchable encryption scheme with an efficient user update function in a distributed system (i.e., in a multi-owner multi-user model). First, owners self-select secret keys to encrypt keywords. A master secret key is designed to support keyword searches among these keys. Additionally, a searchable index with an implicit relationship between the same keyword is proposed. There is no need to scan the index for keyword retrieval, which achieves sub-linear search complexity. It is also important that the updates of owners do not exert an influence on users and the other owners in our proposed scheme. As the key revocation mechanism, the master secret key is split into more partial secret keys that are distributed to users. Accordingly, the system only needs to re-split and re-distribute the key rather than re-building the index for updates of users. When making queries, not only are the content of queries and the master secret key concealed, but collusion between some users and the cloud server is resisted. The proposed scheme also supports an efficient result verification mechanism. The security and utility of our scheme were confirmed thru analysis and evaluation.

База данных селевых явлений. Концепция и структура информационного наполнения

As part of the development of improving the methods of integrated monitoring of mudslides, a software shell of the electronic information resource «Mudflow Phenomena Database» (DB) was created. The developed database covers the issues of organizing the accounting of data on mudflow events based on the materials of complex and operational (space, ground route, etc.) monitoring of mudflow basins, as well as archival data for each of them. The purpose of this database is to provide an up-to-date information basis for assessing the current spatial and temporal changes in monitoring objects of mudflow basins, based on the results of analytical study of the replenished data. The information basis consists of information that allows us to assess the conditions of occurrence of mudflow processes in the region, their type, genesis, volume of the solid component of the mudflow, frequency, intensity, basic morphometric indicators of mudflow basins, etc. The analysis of the materials presented in the database makes it possible to identify the scale, danger, main features and patterns of the spread and development of mudflow activity. The database of mudslides, depending on the structure, content and form of search queries, can be compiled for different hierarchical levels of geographical, territorial and administrative division of the region and mudflow basins. The first part of the article describes the structure of the input information, justifies the choice of parameters to be taken into account.

Design an ontology-based model for intelligent querying system in mathematics education

Distance learning can be facilitated through e-learning, and students can enhance their understanding of course materials through the use of an intelligent querying system that enables self-learning. In this paper, an ontology-based method for organizing the knowledge of mathematics in high-school is presented. Ontology Rela-Ops model, which is a knowledge model for representing the knowledge of relations and operators, is combined with the knowledge component of functions. Based on the combining model, a structure of knowledge graph is proposed to represent relations of intellectual contents. An intelligent querying system is designed for the domain of high-school mathematics using these structures. This searching system can query contents of the course and extracts other knowledge related to querying results. The experimental results show that the proposed method is useful and effective to apply in the practice.

Using clarification questions to improve software developers’ Web search

Context: Recent research indicates that Web queries written by software developers are not very successful in retrieving relevant results, performing measurably worse compared to general purpose Web queries. Most approaches up to this point have addressed this problem with software engineering-specific automated query reformulation techniques, which work without developer involvement but are limited by the content of the original query. In other words, these techniques automatically improve the existing query but can not contribute new, previously unmentioned, concepts. Objective: In this paper, we propose a technique to guide software developers in manually improving their own Web search queries. We examine a conversational approach that follows unsuccessful queries with a clarification question aimed at eliciting additional query terms, thus providing to the developer a clear dimension along which the query could be improved. Methods: We describe a set of clarification questions derived from a corpus of software developer queries and a neural approach to recommending them for a newly issued query. Results: Our evaluation indicates that the recommendation technique is accurate, predicting a valid clarification question 80% of the time and outperforms simple baselines, as well as, state-of-the-art Learning To Rank (LTR) baselines. Conclusion: As shown in the experimental results, the described approach is capable at recommending appropriate clarification questions to software developers and considered useful by a sample of developers ranging from novices to experienced professionals.

Enabling Privacy-Preserving Geographic Range Query in Fog-Enhanced IoT Services

The explosive growth of the Internet of Things (IoT) is pushing forward the paradigm of fog computing services today. An important service for most fog-enhanced applications is geographic range-match, which means the fog-nodes can accurately collect sensed data from IoT devices based on their location distances. However, due to the increasing attacking surfaces, outsourcing range query operations to untrusted fog-nodes inevitably raises new privacy concerns about query content and device location exposure. In this article, we devise a new geographic range-match scheme for fog-enhanced services, which allows fog-nodes to securely collect range-based sensed data while protecting the location privacy of IoT devices. Our main idea is to formulate the problem of encrypted geographic queries as range-based pattern matching and carefully craft security schemes to enable efficient range queries in the ciphertext domain. The proposed range-match scheme is provably secure and can reduce accessible information during distance comparisons. We formally analyze the security strengths and complete the prototype implementation. The comprehensive experimental results demonstrate the practicality of our designs.

Computer Image Content Retrieval considering K-Means Clustering Algorithm

The traditional computer image content retrieval technology can only meet the specific requirements of customers; because of its general features, it cannot comply with the requirements of various environments, purposes, and time simultaneously. This study presents a computer image content retrieval method for a K-means clustering algorithm (KCA). The information collected by computer is preprocessed by K-means clustering algorithm, and the unacquired computer image is labeled based on the optimal learning order according to the KCA. The K-means clustering algorithm classifies the color, pattern, shape, and content of computer images and takes advantage of the invariance advantages of image content retrieval such as scale, rotation, illumination, and blur correction to effectively solve the recurring problems of computer images during retrieval and increase its accuracy. The results of the experiment indicate that the proposed K-means clustering algorithm can enhance the efficiency and performance effectively during image retrieval by computer as compared to the traditional content search algorithm and also help to quickly converge to the query content; it also shows that KCA has good robustness.

Bi-directional Bayesian probabilistic model based hybrid grained semantic matchmaking for Web service discovery

Web service discovery is a fundamental task in service-oriented architectures which searches for suitable web services based on users’ goals and preferences. In this paper, we present a novel service discovery approach that can support user queries with various-size-grained text elements. Compared with existing approaches that only support semantics matchmaking in single texture granularity (either word level or paragraph level), our approach enables the requester to search for services with any type of query content with high performance, including word, phrase, sentence, or paragraph. Specifically, we present an unsupervised Bayesian probabilistic model, bi-Directional Sentence-Word Topic Model (bi-SWTM), to achieve semantic matchmaking between possible textual types of queries (word, phrase, sentence, paragraph) and the texts in web service descriptions, by mapping words and sentences in the same semantic space. The bi-SWTM captures textual semantics of the words and sentences in a probabilistic simplex, which provides a flexible method to build the semantic links from user queries to service descriptions. The novel approach is validated using a collection of comprehensive experiments on ProgrammableWeb data. The results demonstrate that the bi-SWTM outperforms state-of-the-art methods on service discovery and classification. The visualization of the nearest-neighbored queries and descriptions shows the capability of our model on capturing the latent semantics of web services.

Updatable privacy-preserving -nearest neighbor query in location-based s-ervice.

The textit{K}-nearest neighbor (textit{K}-NN) query is an important query in location-based service (LBS), which can query the nearest k points to a given point, and provide some convenient services such as interest recommendations. Hence the privacy protection issue of textit{K}-NN query has been a popular research area, protecting the information of queries and the queried results, especially in the information era. However, most of existing schemes fail to consider the privacy protection of location points already stored on servers. Or some schemes support no update of location points. In this paper, we present an updatable and privacy-preserving textit{K}-NN query scheme to address the above two issues. Concretely, our scheme utilizes the textit{K}D-tree (textit{K}-Dimensional tree) to store the location points of data owners in location service provider and encrypts the points with a distributed double-trapdoor public-key cryptosystem. Then, based on the Ciphertext Comparison Protocol and Ciphertext Euclidean Distance Calculation Protocol, our scheme can protect the privacy of location and query contents. Experimental analyses show our proposal supports some new location points for a fixed location service provider. Moreover, the queried results show a high accuracy of more than 95%.