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Deep learning technologies have brought us many models that outperform human beings on a few benchmarks. An interesting question is: can these models well solve real‐world problems with similar settings (e.g., identical input/output) to the benchmark datasets? We argue that a model is trained to answer the same information need in a similar context (e.g., the information available), for which the training dataset is created. The trained model may be used to solve real‐world problems for a similar information need in a similar context. However, information need is independent of the format of dataset input/output. Although some datasets may share high structural similarities, they may represent different research tasks aiming for answering different information needs. Examples are question–answer pairs for the question answering (QA) task, and image‐caption pairs for the image captioning (IC) task. In this paper, we use the QA task and IC task as two case studies and compare their widely used benchmark datasets. From the perspective of information need in the context of information retrieval, we show the differences in the dataset creation processes and the differences in morphosyntactic properties between datasets. The differences in these datasets can be attributed to the different information needs and contexts of the specific research tasks. We encourage all researchers to consider the information need perspective of a research task when selecting the appropriate datasets to train a model. Likewise, while creating a dataset, researchers may also incorporate the information need perspective as a factor to determine the degree to which the dataset accurately reflects the real‐world problem or the research task they intend to tackle. [ABSTRACT FROM AUTHOR]

The exponential growth of social media text information presents a challenging issue in terms of retrieving valuable information efficiently. Utilizing deep learning models, we can automatically generate keywords that express core content and topics of social media text, thereby facilitating the retrieval of critical information. However, the performance of deep learning models is limited by the labeled text data in the social media domain. To address this problem, this paper presents a hierarchical keyword generation method for low-resource social media text. Specifically, the text segment is introduced as a hierarchical unit of social media text to construct a hierarchical model structure and design a text segment recovery task for self-supervised training of the model, which not only improves the ability of the model to extract features from social media text, but also reduces the dependence of the keyword generation model on the labeled data in the social media domain. Experimental results from publicly available social media datasets demonstrate that the proposed method can effectively improve the keyword generation performance even given limited social media labeled data. Further discussions demonstrate that the self-supervised training stage based on the text segment recovery task indeed benefits the model in adapting to the social media text domain. [ABSTRACT FROM AUTHOR]

Trust is a critical element when it comes to news articles, and an important problem is how to ensure trust in the published information on news websites. First, this paper describes the inner workings of a proposed news-retrieval and aggregation architecture employed by a blockchain-based solution for fighting disinformation; this includes a comparison between existing information retrieval solutions. The decentralized nature of the solution is achieved by separating the crawling (i.e., extracting the web page links) from the scraping (i.e., extracting the article information) and having third-party actors extract the data. A majority-rule mechanism is used to determine the correctness of the information, and the blockchain network is used for traceability. Second, the steps needed to deploy the distributed components in a cloud environment seamlessly are discussed in detail, with a special focus on the open-source OpenStack cloud solution. Lastly, novel methods for achieving a truly decentralized architecture based on community input and blockchain technology are presented, thus ensuring maximum trust and transparency in the system. The results obtained by testing the proposed news-retrieval system are presented, and the optimizations that can be made are discussed based on the crawling and scraping test results. [ABSTRACT FROM AUTHOR]

Orosius includes a note on the hero Perseus that neglects the usually prominent features of his myth, saying little more than that Perseus led an expedition into Asia and, once victorious, named the people he conquered there Persians after himself. He is the earliest of the surviving Latin authors to say either of these things about Perseus. And he is apparently the first author to combine these pieces of information – until, that is, John Malalas, who says much the same things about Perseus not once, but twice. This paper will survey Orosius' usual sources for possible influences, trace the development of the notions that Perseus invaded Asia and that the Persians were named for him, and explore the possibility that Orosius and Malalas might have shared a source on Perseus and the history of Assyria. [ABSTRACT FROM AUTHOR]

This paper examines the decision points over the course of ten years of development of an institutional repository. Specifically, the focus is on the impact and influence from the open-source community, the needs of the local institution, the role that team dynamics plays, and the chosen platform. Frequently, the discussion revolves around the technology stack and its limitations and capabilities. Inherently, any technology will have several features and limitations, and these are important in determining a solution that will work for your institution. However, the people running the system and developing the software, and their enthusiasm to continue work within the existing software environment in order to provide features for your campus and the larger open-source community, will play a bigger role than the technical platform. These lenses are analyzed through three points in time: the initial roll out of our institutional repository, our long-term running and maintenance, and eventual new development and why we made the decisions we made at each of those points in time. [ABSTRACT FROM AUTHOR]

In recent years, searchable encryption technology and attribute encryption technology have been widely used in cloud storage environments, and attribute-based searchable encryption schemes can both achieve the retrieval of encrypted data and effectively solve the access control problem. Considering that existing attribute-based searchable encryption schemes for cloud storage only support keyword search and do not support attribute revocation, most of the schemes that support attribute revocation only consider the computational overhead of users and ignore the large amount of computational resources consumed by attribute authorization centers when updating keys. In addition, keyword search may lead to partial errors in the returned search results, leading to the wastage of computational and broadband resources. To solve these issues, this paper proposes an attribute-based searchable encryption scheme that supports attribute revocation and is verifiable. To realize fine-grained ciphertext search of encrypted data, support scenarios of dynamic changes of user attributes, and ensure that third-party servers perform the search process reliably and honestly while minimizing computation and storage costs, first, this paper implements attribute revocation with the attribute authorization center by creating a user revocation list and an attribute key revocation list. At the same time, the system updates the attribute key at the time of user search request, which effectively reduces the computational overhead. Second, a third-party auditor is introduced to ensure the correctness of the search results. Finally, the security of this paper is verified by theoretical analysis, and the efficiency and practicality of this paper are verified by comparing it to other schemes through simulation experiments. [ABSTRACT FROM AUTHOR]

Deck bridges with encased steel beams are commonly used for short-spans bridge structures. This type of bridge structures minimizes steel consumption without significantly affecting the overall resistance of the structure [1 and 2]. The paper presents basic information and partial results from experimental research on composite steelconcrete deck bridges with different types of encased steel beams. The paper also provides an overview about the experimental program, the production process of the tested members, an explanation of the applied load and the measurement of monitored strains and deflections of the tested members. [ABSTRACT FROM AUTHOR]

Retrieving information from document collections is necessary in many contexts, e.g. researchers search for papers on a topic, physicians search for records of patients with a certain condition and police investigators seek relationships between different criminal reports. Finding relevant textual content in a corpus can be challenging in scenarios where the users expect a retrieval process with high recall. Visual Analytics (VA) systems that integrate interactive visualizations and machine learning algorithms are often advocated to support retrieval tasks in such complex scenarios. However, few studies report an end-user perspective on the utility of such systems. We present results from observational studies on VA-supported information retrieval conducted with graduate students and researchers using a system to explore collections of scientific papers. While users have, in general, positive views of the system's potential to facilitate their retrieval tasks, some faced practical difficulties in using it effectively, and we found considerable variation in their assessment of specific functionalities. Our findings reinforce the potential of VA systems and also the importance of carefully informing users of the underlying conceptual models in such systems and their limitations. [ABSTRACT FROM AUTHOR]

At present, the research and application of biped robots is more and more popular. The popularity of biped robots can be better promoted by improving the motion performance of low-cost biped robots. In this paper, the method of the Linear Quadratic Regulator (LQR) is used to track a robot's center of mass (COM). At the same time, the whole-body-control method and value function generated in the process of tracking COM are used to construct the quadratic programming (QP) model of a biped robot. Through the above method, the torque feedforward of the robot is obtained in the Drake simulation platform. The torque feedforward information of the robot is transformed into position feedforward information by spring compensation. In this paper, open loop control and spring compensation are used, respectively, to make the robot perform simple actions. Generally, after the compensation method of spring compensation is adopted, the roll angle and pitch angle of the upper body of the robot are closer to 0 after the robot performs an action. However, as the selected motion can introduce more forward and lateral motions, the robot needs more spring clearance compensation to improve performance. For improving the motion performance of a low-cost biped robot, the experimental results show that the spring compensation method is both reasonable and effective. [ABSTRACT FROM AUTHOR]

In the intelligent transformation of the mechanical manufacturing, a large amount of spare parts models is inevitably generated, which brings huge challenges to the efficient retrieval of data. Considering the user-friendly and lightweight characteristics of design sketches, this paper conducted model retrieval of mechanical parts based on design sketches by constructing a deep cross-domain representation model. Aiming at the problem of cross-modal information association between sketches and 3D models, this paper proposed a feature joint learning method, which controlled the differences within and between classes of retrieved models. By this method, feature descriptors could acquire single-domain features as well as fuse the cross-domain information, and thus, the method established a consistent association representation of cross-modal data in a co-embedding space. Finally, it constructed the index table by using hash coding to realize fast retrieval of massive data. The experimental results on the parts data show that the proposed retrieval method based on the design sketch can not only achieve the most accurate retrieval results, but also have higher retrieval efficiency among the comparison methods. The method not only improves the accuracy of cross-modal spare parts information retrieval, but also improves the efficiency of data management, thereby indirectly improving the efficiency and convenience of product design. The related systems have been applied in some enterprises and have received good feedback. [ABSTRACT FROM AUTHOR]

This paper presents a geometric approach to pitch estimation (PE) – an important problem in music information retrieval (MIR), and a precursor to a variety of other problems in the field. Though there exist a number of highly accurate methods, both mono-pitch estimation and multi-pitch estimation (particularly with unspecified polyphonic timbre) prove computationally and conceptually challenging. A number of current techniques, while incredibly effective, are not targeted towards eliciting the underlying mathematical structures that underpin the complex musical patterns exhibited by acoustic musical signals. Tackling the approach from both theoretical and experimental perspectives, we present a novel framework, a basis for further work in the area, and results that (while not state of the art) demonstrate relative efficacy. The framework presented in this paper opens up a completely new way to tackle PE problems and may have uses both in traditional analytical approaches as well as in the emerging machine learning (ML) methods that currently dominate the literature. [ABSTRACT FROM AUTHOR]

Typically, the amount of health data increases by their size, computational complexity, and speed. A traditional healthcare system often requires the delivery of medical data to the large storage system, which is comprised of patient's sensitive information and causes communication overhead. Basically, medical sharing is a serious threat and also a challenging issue. For these reasons, a range of big data analytical approaches such as Machine Learning and Text Mining are often required for organizing huge amounts of data. In this paper, we focus on two big data analytics strategies: Data Retrieval and Data Management over Hadoop Distributed File System (HDFS). The functions of this paper include Privacy Protection, Data Sharing, and Management. In both stages of data retrieval and management, we first authenticate data requestors and data owners who want to store data and share the stored data in the HDFS. Secondly, we present a new model to help the requestor to select trustable and similar partners who want to access the health data stored in the HDFS clusters and give them a proper and accurate set of partners. Thirdly, we cluster patients' medical data stored in the HDFS and find out their health status (diseases or any serious threats) using a Deep learning algorithm over Spark environment. Finally, in order to prevent the patients from serious diseases, a report is generated and forwarded to stakeholders. Our experiments illustrate the effectiveness of the proposed scheme in terms of accuracy, precision, recall, and f-measure. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the role of the chromatic information in CT scans in COVID-19 detection and we aim to confirm the inclusion of the artificial intelligence findings in assisting COVID-19 diagnosis. This paper proposes a freezing-based convolutional neural network learning using a morphological transformation of CT images to classify COVID-19 cohorts to help in prognostication pneumonia disease monitoring. The experiments made on the collected CT images from previous works have proven to be a powerful aid to recognize the lesions in CT images which works at comprehensively greater accuracy and speed. The proposed CNN architecture has reflected the viral proliferation in infected patients and archives an accuracy of 87.56% with an improvement by 3% compared to the baseline method on the available database of CT images. [ABSTRACT FROM AUTHOR]

Objectives: Federal open-data initiatives that promote increased sharing of federally collected data are important for transparency, data quality, trust, and relationships with the public and state, tribal, local, and territorial partners. These initiatives advance understanding of health conditions and diseases by providing data to researchers, scientists, and policymakers for analysis, collaboration, and use outside the Centers for Disease Control and Prevention (CDC), particularly for emerging conditions such as COVID-19, for which data needs are constantly evolving. Since the beginning of the pandemic, CDC has collected person-level, de-identified data from jurisdictions and currently has more than 8 million records. We describe how CDC designed and produces 2 de-identified public datasets from these collected data. Methods: We included data elements based on usefulness, public request, and privacy implications; we suppressed some field values to reduce the risk of re-identification and exposure of confidential information. We created datasets and verified them for privacy and confidentiality by using data management platform analytic tools and R scripts. Results: Unrestricted data are available to the public through Data.CDC.gov, and restricted data, with additional fields, are available with a data-use agreement through a private repository on GitHub.com. Practice Implications: Enriched understanding of the available public data, the methods used to create these data, and the algorithms used to protect the privacy of de-identified people allow for improved data use. Automating data-generation procedures improves the volume and timeliness of sharing data. [ABSTRACT FROM AUTHOR]

People access and share information over the web and in other digital environments, including digital libraries, in the form of documents such as books, articles, technical reports, etc. These documents are in a variety of formats, of which the Portable Document Format (PDF) is most widely used because of its emphasis on preserving the layout of the original material. The retrieval of relevant material from these derivative documents is challenging for information retrieval (IR) because the rich semantic structure of these documents is lost. The retrieval of important units such as images, figures, algorithms, mathematical formulas, and tables becomes a challenge. Among these elements, tables are particularly important because they can add value to the resource description, discovery, and accessibility of documents not only on the web but also in libraries if they are made retrievable and presentable to readers. Sighted users comprehend tables for sensemaking using visual cues, but blind and visually impaired users must rely on assistive technologies, including textto- speech and screen readers, to comprehend tables. However, these technologies do not pay sufficient attention to tables in order to effectively present tables to visually impaired individuals. Therefore, ways must be found to make tables in PDF documents not only retrievable but also comprehensible. Before developing such solutions, it is necessary to review the available assistive technologies, tools, and frameworks for their capabilities, strengths, and limitations from the comprehension perspective of blind and visually impaired people, along with suitable environments like digital libraries. We found no such review article that critically and analytically presents and evaluates these technologies. To fill this gap in the literature, this review paper reports on the current state of the accessibility of PDF documents, digital libraries, assistive technologies, tools, and frameworks that make PDF tables comprehensible and accessible to blind and visually impaired people. The study findings have implications for libraries, information sciences, and information retrieval. [ABSTRACT FROM AUTHOR]

The new area of Learned Data Structures consists of mixing Machine Learning techniques with those specific to Data Structures, with the purpose to achieve time/space gains in the performance of those latter. The perceived paradigm shift in computer architectures, that would favor the employment of graphics/tensor units over traditional central processing units, is one of the driving forces behind this new area. The advent of the corresponding branch-free programming paradigm would then favor the adoption of Neural Networks as the fundamental units of Classic Data Structures. This is the case of Learned Bloom Filters. The equally important field of Learned Indexes does not appear to make use of Neural Networks at all. In this paper, we offer a comparative experimental investigation regarding the potential uses of Neural Networks as a fundamental building block of Learned Indexes. Our results provide a solid and much-needed evaluation of the role Neural Networks can play in Learned Indexing. Based on our findings, we highlight the need for the creation of highly specialised Neural Networks customised to Learned Indexes. Because of the methodological significance of our findings and application of Learned Indexes in strategic domains, such as Computer Networks and Databases, care has been taken to make the presentation of our results accessible to the general audience of scientists and engineers working in Neural Networks and with no background about Learned Indexing. [ABSTRACT FROM AUTHOR]

In this paper, we propose a user-friendly encrypted storage scheme named EStore, which is based on the Hadoop distributed file system. Users can make use of cloud-based distributed file systems to collaborate with each other. However, most data are processed and stored in plaintext, which is out of the owner's control after it has been uploaded and shared. Meanwhile, simple encryption guarantees the confidentiality of uploaded data but reduces availability. Furthermore, it is difficult to deal with complex key management as there is the problem whereby a single key encrypts different files, thus increasing the risk of leakage. In order to solve the issues above, we put forward an encrypted storage model and a threat model, designed with corresponding system architecture to cope with these requirements. Further, we designed and implemented six sets of protocols to meet users' requirements for security and use. EStore manages users and their keys through registration and authentication, and we developed a searchable encryption module and encryption/decryption module to support ciphertext retrieval and secure data outsourcing, which will only minimally increase the calculation overhead of the client and storage redundancy. Users are invulnerable compared to the original file system. Finally, we conducted a security analysis of the protocols to demonstrate that EStore is feasible and secure. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach that uses a spectral clustering method to cluster patients with e-health IoT devices based on their similarity and distance and connect each cluster to an SDN edge node for efficient caching. The proposed MFO-Edge Caching algorithm is considered for selecting the near-optimal data options for caching based on considered criteria and improving QoS. Experimental results demonstrate that the proposed approach outperforms other methods in terms of performance, achieving decrease in average time between data retrieval delays and the cache hit rate of 76%. Emergency and on-demand requests are prioritized for caching response packets, while periodic requests have a lower cache hit ratio of 35%. The approach shows improvement in performance compared to other methods, highlighting the effectiveness of SDN-Edge caching and clustering for optimizing e-health network resources. [ABSTRACT FROM AUTHOR]

Introduction: Observational data are increasingly seen as a valuable source for integrated care research. Especially since the growing availability of routinely collected data and quasi-experimental methods. The aim of this paper is to describe the potentials and challenges when using observational data for integrated maternity care research, based on our experience from developing and working with the Data-InfrAstructure for ParEnts and childRen (DIAPER). Methods and Results: We provide a description of DIAPER, which is a linked datainfrastructure on the individual level based on maternity care claims data, quality and utilization of maternity care and data from municipal registries, covering the life course from preconception to adulthood. We then discuss potentials and practical applications of DIAPER such as to evaluate alternative payment models for integrated maternity care, to set the policy agenda regarding postpartum care, to provide insights into value of care and into provider variation, and to evaluate (policy) interventions designed to promote and support integrated maternity care. This is relevant for several stakeholders: policy makers, payers, providers and clients/patients. Based on experiences with DIAPER, we identify remaining challenges: missing data sources (especially self-reported outcomes), suboptimal quality of data, privacy concerns and potential biases introduced during data linkage, and describe how these challenges were tackled within the applications of DIAPER. Conclusions: With DIAPER we demonstrated that using observational data can be of added value for integrated care research, but also that challenges remain. It is essential to keep exploring and developing the possibilities of observational data and continue the discussions in the scientific community. Learning from each other's successes and failures will be critical. [ABSTRACT FROM AUTHOR]

Many academic libraries collect art exhibition catalogues and juvenile books to support the curriculum, but academic library review sources and book vendors have limited coverage of these niche areas. For more than a decade, Wichita State University has used purchase plans from Worldwide Art Books and Junior Library Guild to acquire print books. This paper discusses the assessment of both plans, how experience with this assessment has influenced development of an assessment plan, and reasons other libraries may want to assess their own niche collecting plans. [ABSTRACT FROM AUTHOR]

The Association for Library Collections & Technical Services (ALCTS) Board of Directors approved the Core Competencies for Cataloging and Metadata Professional Librarians, hereafter referred to as the "Core Competencies," in January 2017. The Core Competencies lists the skills required of professionals performing cataloging and metadata work in libraries of all types. In the six years since the document's release, the cataloging and metadata community has adopted new cataloging standards, experimented with new tools, and engaged in conversations and reparative efforts around inclusive metadata. In this paper, we, the authors of the Core Competencies, report the results of our survey research that assessed the current use of the document within the cataloging and metadata community and solicited comments on ways in which the document might be revised. We conclude with recommendations for immediate changes to the document, and for its future use and maintenance. [ABSTRACT FROM AUTHOR]

Treating patients with up-to-date medical knowledge is an ongoing goal for healthcare workers and implies efficient knowledge management at the point of care. Widely available mobile wireless technologies influence practices but a significant gap remains between technological possibilities and actual usage. The purpose of this study was to analyze residents' baseline practices in managing medical knowledge and to evaluate the use and impact of an innovative multiplatform application dedicated to anesthesiology and intensive care residents. This study took place in Rennes Teaching Hospital and comprised two distinct surveys. First, in April 2018, all residents received a ten-items online survey focusing on managing medical knowledge. Then, through a second online survey constituted of ten items, we sought to assess the use of a new multiplatform cloud-based application named "DansMaBlouse", dedicated to sharing and indexing medical knowledge, in anesthesiology and intensive care residents. Among 148 residents that answered the evaluation survey, the most sought out pieces of information in clinical setting were a phone or fax number (74%), drugs' characteristics (68%) and expert guidelines (57%). The main sources were senior staff (68%), medical databases (60%) and an Internet search engine (59%). Computers and smartphones were more frequently used than bound paper notebooks. After implementation of the multiplatform application DansMaBlouse, fifty-nine (82%) of the 72 residents that answered the evaluation survey reported using the application and 39% used it more than ten times. Among application users, 90% found it easy to use and 92% agreed that it improved point-of-care access to knowledge. Accessing appropriate medical knowledge at the point of care remains an issue for residents and can be improved by a multiplatform application combining personal and shared up-to-date resources. [ABSTRACT FROM AUTHOR]

Query reformulation is a well-known technique intended to improve the performance of Information Retrieval Systems. Among the several available techniques, Query Expansion (QE) reformulates the initial query by adding similar terms, drawn from several sources (corpus, knowledge resources), to the query terms in order to retrieve more relevant documents. Most QE methods are based on the relationships between the original query term and candidate terms (new terms) in order to select the most similar expansion terms. In this paper, we suggested a new hybrid query reformulation through QE and term re-weighting techniques. The suggested approach aimed to demonstrate the effectiveness of QE with a semantic selection of candidate terms according to the specificity of original query terms in the improvement of retrieval performance. To this end, we exploited both relationships defined by knowledge resources and the distributed semantics, recently revealed by neural network analysis. For term re-weighting, we proposed a new semantic method based on semantic similarity measure that assigns a weight to each term of the expanded query. The conducted experiments on OHSUMED and TREC 2014 CDS test collections, including long and short queries, yielded significant results that outperformed the baseline and state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Clustering is used to analyze the intrinsic structure of a dataset based on the similarity of datapoints. Its widespread use, from image segmentation to object recognition and information retrieval, requires great robustness in the clustering process. In this paper, a novel clustering method based on adjacent grid searching (CAGS) is proposed. The CAGS consists of two steps: a strategy based on adaptive grid-space construction and a clustering strategy based on adjacent grid searching. In the first step, a multidimensional grid space is constructed to provide a quantization structure of the input dataset. The noise and cluster halo are automatically distinguished according to grid density. Moreover, the adaptive grid generating process solves the common problem of grid clustering, in which the number of cells increases sharply with the dimension. In the second step, a two-stage traversal process is conducted to accomplish the cluster recognition. The cluster cores with arbitrary shapes can be found by concealing the halo points. As a result, the number of clusters will be easily identified by CAGS. Therefore, CAGS has the potential to be widely used for clustering datasets with different characteristics. We test the clustering performance of CAGS through six different types of datasets: dataset with noise, large-scale dataset, high-dimensional dataset, dataset with arbitrary shapes, dataset with large differences in density between classes, and dataset with high overlap between classes. Experimental results show that CAGS, which performed best on 10 out of 11 tests, outperforms the state-of-the-art clustering methods in all the above datasets. [ABSTRACT FROM AUTHOR]

Proofs of Data Reliability are cryptographic protocols that provide assurance to a user that a cloud storage system correctly stores her data and has provisioned sufficient redundancy to be able to guarantee reliable storage service. In this paper, we consider distributed cloud storage systems that make use of erasure codes to guarantee data reliability. We propose two novel Proof of Data Reliability schemes, namely POROS and PORTOS, that on the one hand guarantees the retrieval of the outsourced data in their entirety through the use of proofs of data possession and on the other hand ensures the actual storage of redundancy. As a result, POROS and PORTOS are compatible with the current cloud computing model where the cloud autonomously performs all maintenance operations without any interaction with the user. Moreover, POROS and PORTOS delegate the burden of generating the redundancy to the cloud as well. The security of both solutions is proved in the face of a rational adversary whereby the cheating cloud provider tries to gain storage savings without increasing its total operational cost. [ABSTRACT FROM AUTHOR]

Evaluating the performance of Content-Based Image Retrieval (CBIR) systems is a challenging and intricate task, even for experts in the field. The literature presents a vast array of CBIR systems, each applied to various image databases. Traditionally, automatic metrics employed for CBIR evaluation have been borrowed from the Text Retrieval (TR) domain, primarily precision and recall metrics. However, this paper introduces a novel quantitative metric specifically designed to address the unique characteristics of CBIR. The proposed metric revolves around the concept of grouping relevant images and utilizes the entropy of the retrieved relevant images. Grouping together relevant images holds great value from a user perspective, as it enables more coherent and meaningful results. Consequently, the metric effectively captures and incorporates the grouping of the most relevant outcomes, making it highly advantageous for CBIR evaluation. Additionally, the proposed CBIR metric excels in differentiating between results that might appear similar when assessed using other metrics. It exhibits a superior ability to discern subtle distinctions among retrieval outcomes. This enhanced discriminatory power is a significant advantage of the proposed metric. Furthermore, the proposed performance metric is designed to be straightforward to comprehend and implement. Its simplicity and ease of use contribute to its practicality for researchers and practitioners in the field of CBIR. To validate the effectiveness of our metric, we conducted a comprehensive comparative study involving prominent and well-established CBIR evaluation metrics. The results of this study demonstrate that our proposed metric exhibits robust discrimination power, outperforming existing metrics in accurately evaluating CBIR system performance. [ABSTRACT FROM AUTHOR]

Whilst arguing for sounds as repeatable objects does not seem suitable to our auditory experience, considering them as events can then help us understand some of their main features. In this sense, sounds are events happening to material objects; they have a beginning and an end; they are ephemeral entities that we cannot grasp as ordinary objects. Nevertheless, supporters of event theory usually focus on the autonomous status that sounds manifest from the things in the world. Conversely, when we hear sounds, we hear what and where they are sounding even in those theories that I will call detached-sound theories; in hearing them we hear how different materials create different sounds. Within the different positions of the event theory to pure-event or acousmatic proposal, the importance and role that the material has in the creation of diverse sounds does not always seem to be recognized. In this paper, I therefore aim to show how the materiality of resonating bodies and objects is given in all forms of auditory experience, not until analyzed the differences between object and event accounts in the philosophy of sound. [ABSTRACT FROM AUTHOR]

To realize the great flexibility and cost savings for providing location-based service, data owners are incentivized to migrate their data to cloud servers. However, direct data outsourcing to untrusted servers may pose significant privacy risks. This paper proposes a practical privacy-preserving nearest neighbor searching method over encrypted spatial data. We simultaneously protect data and location privacy (access and pattern privacy) by encrypting data using asymmetric scalar-product-preserving encryption (ASPE) and performing computational private information retrieval (CPIR) on encrypted subspace datasets. To mitigate the performance degradation introduced by the combination of ASPE and CPIR, we propose a hierarchical index that enables users to safely obtain encrypted subspace datasets with configurable privacy, where different degrees of privacy can be traded off against query processing performance. Experiments demonstrate that our method outperforms the state-of-the-art method in efficiency while allowing for a flexible trade-off between performance and privacy. [ABSTRACT FROM AUTHOR]

The analysis of the hyperlink structure of the web has led to significant improvements in web information retrieval. This survey study evaluates and analyzes relevant research publications on link analysis in web information retrieval utilizing diverse methods. These factors include the research year, the aims of the research article, the algorithms utilized to complete their study, and the findings received after using the algorithms. The findings revealed that Page Rank, Weighted Page Rank, and Weighted Page Content Rank are extensively employed by academics to properly analyze hyperlinks in web information retrieval. Finally, this paper analyzes the previous studies. [ABSTRACT FROM AUTHOR]

We consider the problem of multi-access cache-aided multi-user Private Information Retrieval (MACAMuPIR) with cyclic wraparound cache access. In MACAMuPIR, several files are replicated across multiple servers. There are multiple users and multiple cache nodes. When the network is not congested, servers fill these cache nodes with the content of the files. During peak network traffic, each user accesses several cache nodes. Every user wants to retrieve one file from the servers but does not want the servers to know their demands. This paper proposes a private retrieval scheme for MACAMuPIR and characterizes the transmission cost for multi-access systems with cyclic wraparound cache access. We formalize privacy and correctness constraints and analyze transmission costs. The scheme outperforms the previously known dedicated cache setup, offering efficient and private retrieval. Results demonstrate the effectiveness of the multi-access approach. Our research contributes an efficient, privacy-preserving solution for multi-user PIR, advancing secure data retrieval from distributed servers. [ABSTRACT FROM AUTHOR]

Facets are widely encountered in information and knowledge organization, but there is much disparity in the use and understanding of concepts such as "facet," "facet analysis," and "faceted classification." The paper traces the history of these ideas and how they have been employed in different contexts. What may be termed the classical school of faceted classification is given some prominence, through the ideas of Ranganathan and the Classification Research Group, but other interpretations are also explored. Attention is paid not only to the idea of what facet analysis is, and what purpose it serves, but also the language utilized to describe and explain it. [ABSTRACT FROM AUTHOR]

The poesia in ottava rima ('poetry in octave rhyme') is a living tradition of improvised-sung poetry widespread in the rural areas of Central Italy. In this article, I describe and interpret the interaction that takes place between two poet-performers and between poet-performers and the audience, highlighting the importance of embodiment and gesture during the poetic duel and the active role of listeners. This paper aims to highlight the strong binding of speech and gesture and the bodily expressiveness of the onstage improvised sung poetic duel, analysing the contrasto as a 'vocal-gestural performance'. The ultimate aim of this study is to examine the ways in which visual information in the form of gestures contributes to the way musical performance is experienced. Furthermore, we apply insights from ethnomusicology and gesture studies, together with digital video and audio recording and associated analysis software, to the study of contrasto performance. [ABSTRACT FROM AUTHOR]

The importance of involving humans in the data science process has been widely discussed in the literature. However, studies lack details on how to involve humans in the process. Using a design science approach, this paper proposes and evaluates a human‐supervised data science framework in the context of local governments. Our findings suggest that the involvement of a stakeholder group, public managers in this case, in the process of data science project enhanced quality of data science outcomes. Public managers' detailed knowledge on both the data and context was beneficial for improving future data science infrastructure. In addition, the study suggests that local governments can harness the value of data‐driven approaches to policy and decision making through focalized investments in improving data and data science infrastructure, which includes culture and processes necessary to incorporate data science and analytics into the decision‐making process. [ABSTRACT FROM AUTHOR]

The semantic query problem is commonly called the semantic gap and is one of the significant problems in multimedia data retrieval. In this study, we focus on multimedia data retrieval by combining semantic information with data content to solve the semantic gap problem effectively. The main idea behind the combination of low-level content descriptors and the concept of multimedia data is to represent the content information with the semantic information by adding a low-level content descriptor as a new dimension to the index structure. This new dimension is represented by constructing an array index structure that uses a fuzzy clustering algorithm. Thus, a new high-dimensional index structure, named MM-FOOD, supporting querying of multimedia data, including fuzzy querying, is presented in this paper. This proposed index structure's construction and query algorithms are explained throughout this paper. Our experiments show that our indexing mechanism is considerably efficient compared to the basic indexing approach, which stores low-level content and semantic concept descriptors in separate structures when the data size is large. [ABSTRACT FROM AUTHOR]

Near-real-time precipitation retrieval plays an important role in the study of the evolutionary process of precipitation and the prevention of disasters caused by heavy precipitation. Compared with ground-based precipitation observations, the infrared precipitation estimations from geostationary satellites have great advantages in terms of geographical coverage and temporal resolution. However, precipitation retrieved from multispectral infrared data still faces challenges in terms of accuracy, especially in extreme cases. In this paper, we propose a new paradigm for satellite multispectral infrared data retrieval of precipitation and construct a new model called PrecipGradeNet. This model uses FY-4A L1 FDI data as the input, IMERG precipitation data as the training target, and improves the precipitation retrieval accuracy by grading the precipitation intensity through Res-UNet, a semantic segmentation network. To evaluate the precipitation retrieval of the model, we compare the retrieval results with the FY-4A L2 QPE operational product to the IMERG precipitation. IMERG is considered as the ground truth. We evaluate the precipitation retrieval from the precipitation fall area identification, the precipitation intensity interval discrimination, and the precipitation quantification. Experimental results show that PrecipGradeNet has better overall performance compared with the FY-4A QPE product in precipitation fall area identification with POD increased by 48% and CSI and HSS improved by 21% and 14%. PrecipGradeNet also has better performance in light precipitation with POD increased by 114% and CSI and HSS improved by 64% and 52%, and better overall precipitation quantification, with RMSE and CC improved by 16% and 15%. In addition, PrecipGradeNet avoids the overall bias in the low and extreme high precipitation cases. Therefore, the new paradigm proposed in this paper has the potential to improve the retrieval accuracy of satellite precipitation estimation products. This study suggests that the application of semantic segmentation methods may provide a new path to correct the intensity bias of the satellite-based precipitation products. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to explore the realm of literature about personalization of digital library services. This paper focuses on users' unique needs and will identify different types of personalized services. Therefore, this study has identified different types of services personalization in the context of digital academic libraries. Design/methodology/approach: In this research, the systematic review method has been used to obtain the relevant indicators of different types of personalization in the context of libraries. To explain basic indicators, a Delphi method has been used. The Delphi panel's members consisted of 15 experts (faculty members, researchers, professional users and software designers). A purposeful sampling and the Delphi fulfillment process were performed in three rounds. After collecting data, descriptive statistics (mean and standard deviation), inferential statistics (binomial distribution test) and the Kendall coordination coefficient were used to determine the consensus rate among experts. Findings: A total of 103 indicators were extracted for different types of personalization through a systematic literature review. Of these, 90 indicators were considered significant in the experts' view. Generally, content personalization, interactive personalization, collaborative personalization and information retrieval personalization are the main components of personalization types, each of which has its own indicators. Originality/value: This study has dealt with the issue of what is personalized in the context of digital academic library. The findings should be helpful and effective in the development of a holistic view on personalization of services in digital libraries. [ABSTRACT FROM AUTHOR]

This paper investigates the fact that construction projects, due to their specificity, are complex, temporary and dynamic. Over their course, participants change, successive construction works are done and new information becomes available. This carries over to difficulties in communication. In the literature, numerous studies note the fact that a network-based approach to the analysis and monitoring of communication as a part of complex construction projects is commendable. Relations between agents, knowledge and tasks in the context of communication within a construction project can be visualized in the form of a meta-network, and suitably developed structural measures can be used to analyze them. In this paper, the authors used meta-network theory to analyze relations between project participants, knowledge and tasks in the context of communication within a construction project, on the basis of the construction of a housing estate located in Katowice, Poland. Meta-network structural analysis allowed for a deeper understanding of these relations and the detection of essential information about the level of communication in the project under investigation, which was a basis for further discussion. The authors also stress the benefits from the approach presented and argue that it should be a starting point for effective management in the sphere of communication in construction companies. [ABSTRACT FROM AUTHOR]

Abstract Participating in research and teaching activities of universities, and then providing valuable information and policy suggestions are an important task of academic librarians. This paper demonstrates systematically in practice that how academic librarians can involve and contribute in the research activities of universities. Our demonstration is launched through comparative studies of research productivities and research impacts among universities. These universities are divided into two counterparts. One is the Chinese universities which aim to construct the world-class mathematics discipline, and the other is the university which already owns the world-class mathematics discipline. During these comparative studies, we demonstrate in practice that how to seek the research question, how to collect and process the data, how to design the research methods, how to analyze the data, and how to propose policy suggestions. Contributions of this paper are twofold. The first aspect is a systematic demonstration in practice. This demonstration shows to academic librarians that how to involve and contribute in the research activities of universities. The academic librarianship thus can be strengthened, as it provides a way for us academic librarians to promote our roles in teaching and research activities of universities. The second aspect is to gain insights in the research productivities and research impacts of Chinese universities which aim to construct the world-class mathematics discipline. During this process, we design a bibliometric indicator for evaluating the research impacts of universities based on academic papers, launch comparative studies with the university that already owns the world-class mathematics discipline, and propose policy suggestions for China to scientifically achieve its goal. This also provides references for other countries to launch similar studies. [ABSTRACT FROM AUTHOR]

Scientific research plays an important role in the achievement of a sustainable society. However, grasping the trends in sustainability research is difficult because studies are not devised and conducted in a top-down manner with Sustainable Development Goals (SDGs). To understand the bottom-up research activities, we analyzed over 300,000 publications concerned with sustainability by using citation network analysis and natural language processing. The results suggest that sustainability science's diverse and dynamic changes have been occurring over the last few years; several new topics, such as nanocellulose and global health, have begun to attract widespread scientific attention. We further examined the relationship between sustainability research subjects and SDGs and found significant correspondence between the two. Moreover, we extracted SDG topics that were discussed following a convergent approach in academic studies, such as "inclusive society" and "early childhood development", by observing the convergence of terms in the citation network. These results are valuable for government officials, private companies, and academic researchers, empowering them to understand current academic progress along with research attention devoted to SDGs. [ABSTRACT FROM AUTHOR]

This paper discusses the importance of search engine optimization (SEO) for digital repositories. We first describe the importance of SEO in the academic environment. Online systems, such as institutional digital repositories, are established and used to disseminate scientific information. Next, we present a case study of our own institution's DSpace repository, performing several SEO tests and identifying the potential SEO issues through a group of three independent audit tools. In this case study, we attempt to resolve most of the SEO problems that appeared within our research and propose solutions to them. After making the necessary adjustments, we were able to improve the quality of SEO variables by more than 59% compared to the non-optimized state (a fresh installation of DSpace). Finally, we apply the same software audit tools to a sample of global institutional repositories also based on DSpace. In the discussion, we compare the SEO sample results with the average score of the semi-optimized DSpace repository (from the case study) and make conclusions. [ABSTRACT FROM AUTHOR]

Our research objective is to improve the Huffman coding efficiency by adjusting the data using a Poisson distribution, which avoids the undefined entropies too. The scientific value added by our paper consists in the fact of minimizing the average length of the code words, which is greater in the absence of applying the Poisson distribution. Huffman Coding is an error-free compression method, designed to remove the coding redundancy, by yielding the smallest number of code symbols per source symbol, which in practice can be represented by the intensity of an image or the output of a mapping operation. We shall use the images from the PASCAL Visual Object Classes (VOC) to evaluate our methods. In our work we use 10,102 randomly chosen images, such that half of them are for training, while the other half is for testing. The VOC data sets display significant variability regarding object size, orientation, pose, illumination, position and occlusion. The data sets are composed by 20 object classes, respectively: aeroplane, bicycle, bird, boat, bottle, bus, car, motorbike, train, sofa, table, chair, tv/monitor, potted plant, person, cat, cow, dog, horse and sheep. The descriptors of different objects can be compared to give a measurement of their similarity. Image similarity is an important concept in many applications. This paper is focused on the measure of similarity in the computer science domain, more specifically information retrieval and data mining. Our approach uses 64 descriptors for each image belonging to the training and test set, therefore the number of symbols is 64. The data of our information source are different from a finite memory source (Markov), where its output depends on a finite number of previous outputs. When dealing with large volumes of data, an effective approach to increase the Information Retrieval speed is based on using Neural Networks as an artificial intelligent technique. [ABSTRACT FROM AUTHOR]

Traditional Chinese medicine (TCM) is a summary of the diagnosis and treatment experience formed by the working people in the long-term struggle against diseases, so it is very important to protect the intangible cultural heritage of TCM. How to extract valuable knowledge accurately and conveniently from the massive medical records of TCM is one of the important issues in the current research on the development of TCM. Due to the large amount of data of TCM medical records, many feature attributes, and diverse patterns, the existing classification technology has high computational complexity, low mining efficiency, and poor universality. Therefore, this paper proposed to quantify the medical records of TCM and obtained the main symptoms according to the improved hierarchical clustering feature selection algorithm. This paper also proposed a support vector machine (SVM) classification method using improved particle swarm algorithm to classify TCM information, which not only improves the efficiency and accuracy of TCM information classification but also discovers the potential dialectical and symptom patterns in diagnosis and treatment, so that the intangible cultural heritage protection of TCM can be developed sustainably. This paper showed that the information acquisition accuracy of the improved algorithm was very high. Before the improved algorithm was used, the accuracy of information mining for TCM was 67.90% at the highest and 65.53% at the lowest, but after using the improved algorithm, the accuracy rate of information mining for TCM was 88.02% at the highest and 82.45% at the lowest. It can be seen that using the improved algorithm to mine TCM information can quickly process effective information. [ABSTRACT FROM AUTHOR]

A functional $k$ -batch code of dimension $s$ consists of $n$ servers storing linear combinations of $s$ linearly independent information bits. Any multiset request of size $k$ of linear combinations (or requests) of the information bits can be recovered by $k$ disjoint subsets of the servers. The goal under this paradigm is to find the minimum number of servers for given values of $s$ and $k$. A recent conjecture states that for any $k=2^{s-1}$ requests the optimal solution requires $2^{s}-1$ servers. This conjecture is verified for $s \leqslant 5$ but previous work could only show that codes with $n=2^{s}-1$ servers can support a solution for $k=2^{s-2} + 2^{s-4} + \left \lfloor{ \frac { 2^{s/2}}{\sqrt {24}} }\right \rfloor $ requests. This paper reduces this gap and shows the existence of codes for $k=\lfloor \frac {5}{6}2^{s-1} \rfloor - s$ requests with the same number of servers. Another construction in the paper provides a code with $n=2^{s+1}-2$ servers and $k=2^{s}$ requests, which is an optimal result. These constructions are mainly based on extended Simplex codes and equivalently provide constructions for parallel Random I/O (RIO) codes. [ABSTRACT FROM AUTHOR]

Aiming at the traditional ciphertext retrieval scheme in cloud environment, which generates file vectors and retrieval vectors based on statistical model, and does not consider the deep-seated semantic information of files and requests, this paper proposes a deep-seated semantic ciphertext retrieval model based on hybrid Cloud Architecture. The vector generation model is constructed through the private cloud federated learning neural network model, and the ciphertext data is stored through the public cloud. In addition, this paper proposes a secret inverted index table to store file vectors, so as to improve the efficiency of retrieval without ensuring that the retrieval information is not leaked in the retrieval process of public cloud. The analysis and experiments on real data sets show that this scheme is better than the current ciphertext retrieval schemes of the same type in terms of security and search efficiency. [ABSTRACT FROM AUTHOR]

Due to the heterogeneity of program language and natural language query, it is difficult to identify the semantic relationship between them, which leads to the low efficiency of code recommendation. In order to solve the problems of the above code recommendation technology, a code recommendation method based on joint embedded attention network (JEAN) is proposed in this paper. The method uses GRU Network to embed code snippets and describe queries into vector representation, which solves the problem of heterogeneous code snippets and natural language queries. The Attention mechanism is then used to distribute totally different weights to different components of every mode of the code snippet. The reason for the Attention mechanism is that different components of every mode of the code snippet contribute differently to the semantic vector of the final code snippet, making it interpretable. Finally, two commonly used evaluation indexes of information retrieval, SuccessRate@k and MRR, are used for experimental comparison with other baseline models. The experimental results show that the code recommendation method based on joint embedded attention network proposed in this paper can effectively recommend appropriate code snippets according to the needs of developers, and its performance is better than other baseline methods. [ABSTRACT FROM AUTHOR]

Implementation of ocean observation sensors are booming in recent years for encouraging research among coastal areas all over the world. This results in a copious amount of big data which makes it difficult for traditional data processing applications to manage them. The complexity in ocean observing community is heterogeneity and interpretation, which directs to a high-end information retrieval system. World Wide Web Consortium (W3C) spreads the usage of Semantic Web (SW) that provide easier way to search, reuse, combine and share information by integrating the data into a single platform. The use of semantic web in big data management helps to increase end-users ability for self-management of data from various sources, to handle the concepts and relationships of a domain and to manage the terminologies while connecting data from a varied data sources. This paper focuses on integrating big data with semantic web technology by developing a knowledge base system through ontology to solve the problem of heterogeneity in ocean observing communities. Ontology refers to a set of machine-readable controlled vocabularies which interprets big data by combining the data concepts with ontology classes. The proposed data model upgrades the information system in terms of improvising data analysis, discovery, retrieval and decision making. In addition to that, this paper also evaluates the quality of proposed ontology and found to be 39.28% improved in completeness, 45.29% reduced in structural complexity, 11% and 37.7% decreased in conciseness and correctness, respectively. [ABSTRACT FROM AUTHOR]

Outsourcing data to the cloud offers various advantages, such as improved reliability, enhanced flexibility, accelerated deployment, and so on. However, data security concerns arise due to potential threats such as malicious attacks and internal misuse of privileges, resulting in data leakage. Data encryption is a recognized solution to address these issues and ensure data confidentiality even in the event of a breach. However, encrypted data presents challenges for common operations like access control and range queries. To address these challenges, this paper proposes Secure Multi-dimensional Data Retrieval with Access Control and Range Search in the Cloud (SMDR). In this paper, we propose SMDR policy, which supports both access control and range queries. The design of the SMDR policy cleverly utilizes the minimum and maximum points of buckets, enabling the SMDR policy is highly appropriate for supporting range queries on multi-dimensional data. Additionally, we have made modifications to Ciphertext Policy-Attribute Based Encryption (CP-ABE) to enable effective integration with the SMDR policy, and then constructed a secure index using the SMDR policy and CP-ABE. By utilizing the secure index, access control and range queries can be effectively supported over the encrypted multi-dimensional data. To evaluate the efficiency of SMDR, extensive experiments have been conducted. The experimental results demonstrate the effectiveness and suitability of SMDR in handling encrypted multi-dimensional data. Additionally, we provide a detailed security analysis of SMDR. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of maximizing the effectiveness of the ranking produced by information retrieval or recommender systems and at the same time maximizing two fairnesses, that of the group and that of the individual. The context of this paper is therefore that of access to information carried out by users, who aim to satisfy their own information needs, to documents produced by authors and curators who aim to be exposed in a fair manner, i.e. without discriminating between groups nor individuals. The paper describes a general method based on the spectral decomposition of mixtures of symmetric matrices, each of which represents a variable to be maximized, and experiments conducted with the use of a test collection. The method described in this paper has explained if and how the trade-offs between effectiveness, group fairness and individual fairness manifest themselves. The experimental results show that maintaining an acceptable level of effectiveness and fairness at the same time is feasible and (b) the trade-offs exist but the order of magnitude of the variations depends on the measure of effectiveness used and therefore by what the user's model of access to information is as well as on the fairness measures and therefore on how authors or editors should be exposed. • Modern information access systems should balance fairness and effectiveness. • A single eigensystem achieves simultaneous maximization. • Fairness, effectiveness, and access measures are crucial in trade-offs. [ABSTRACT FROM AUTHOR]

The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data around k-cluster centres that typically are selected randomly. Recently, the issues behind DC have called for a search for an alternative solution. Recently, a nature-based optimization algorithm named Black Hole Algorithm (BHA) was developed to address the several well-known optimization problems. The BHA is a metaheuristic (population-based) that mimics the event around the natural phenomena of black holes, whereby an individual star represents the potential solutions revolving around the solution space. The original BHA algorithm showed better performance compared to other algorithms when applied to a benchmark dataset, despite its poor exploration capability. Hence, this paper presents a multi-population version of BHA as a generalization of the BHA called MBHA wherein the performance of the algorithm is not dependent on the best-found solution but a set of generated best solutions. The method formulated was subjected to testing using a set of nine widespread and popular benchmark test functions. The ensuing experimental outcomes indicated the highly precise results generated by the method compared to BHA and comparable algorithms in the study, as well as excellent robustness. Furthermore, the proposed MBHA achieved a high rate of convergence on six real datasets (collected from the UCL machine learning lab), making it suitable for DC problems. Lastly, the evaluations conclusively indicated the appropriateness of the proposed algorithm to resolve DC issues. [ABSTRACT FROM AUTHOR]

Space-based measurements, such as the Greenhouse gases Observing SATellite (GOSAT) and the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite, provide global observations of the column-averaged CH4 concentration (XCH4). Due to the irregular observations and data gaps in the retrievals, studies on the spatial and temporal variations of regional atmospheric CH4 concentrations are limited. In this paper, we mapped XCH4 data over monsoon Asia using GOSAT and TROPOMI observations from April 2009 to December 2021 and analyzed the spatial and temporal pattern of atmospheric CH4 variations and emissions. The results show that atmospheric CH4 concentrations over monsoon Asia have long-term increases with an annual growth rate of roughly 8.4 ppb. The spatial and temporal trends of XCH4 data are significantly correlated with anthropogenic CH4 emissions from the bottom-up emission inventory of EDGAR. The spatial pattern of gridded XCH4 temporal variations in China presents a basically consistent distribution with the Heihe–Tengchong Line, which is mainly related to the difference in anthropogenic emissions in the eastern and western areas. Using the mapping of XCH4 data from 2019 to 2021, this study further revealed the response of atmospheric CH4 concentrations to anthropogenic emissions in different urban agglomerations. For the urban agglomerations, the triangle of Central China (TCC), the Chengdu–Chongqing City Group (CCG), and the Yangtze River Delta (YRD) show higher CH4 concentrations and emissions than the Beijing–Tianjin–Hebei region and nearby areas (BTH). The results reveal the spatial and temporal distribution of CH4 concentrations and quantify the differences between urban agglomerations, which will support further studies on the drivers of methane emissions. [ABSTRACT FROM AUTHOR]