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Exam period can be a stressful time for students, and having examples of past papers to help prepare for the tests can be extremely helpful. It is possible that past exams are already shared on your campus--by professors in their specific courses, via student unions or groups, or between individual students. In this article, we will go over the workflows and infrastructure to support the systematic collection, provision of access to, and repository management of past exam papers. We will discuss platform-agnostic considerations of opt-in versus opt-out submission, access restriction, discovery, retention schedules, and more. Finally, we will share the University of Toronto setup, including a dedicated instance of DSpace, batch metadata creation and ingest scripts, and our submission and retention workflows that take into account the varying needs of stakeholders across our three campuses. [ABSTRACT FROM AUTHOR]

Objectives: Information professionals have supported medical providers, administrators and decision-makers, and guideline creators in the COVID-19 response. Searching COVID-19 literature presented new challenges, including the volume and heterogeneity of literature and the proliferation of new information sources, and exposed existing issues in metadata and publishing. An expert panel developed best practices, including recommendations, elaborations, and examples, for searching during public health emergencies. Methods: Project directors and advisors developed core elements from experience and literature. Experts, identified by affiliation with evidence synthesis groups, COVID-19 search experience, and nomination, responded to an online survey to reach consensus on core elements. Expert participants provided written responses to guiding questions. A synthesis of responses provided the foundation for focus group discussions. A writing group then drafted the best practices into a statement. Experts reviewed the statement prior to dissemination. Results: Twelve information professionals contributed to best practice recommendations on six elements: core resources, search strategies, publication types, transparency and reproducibility, collaboration, and conducting research. Underlying principles across recommendations include timeliness, openness, balance, preparedness, and responsiveness. Conclusions: The authors and experts anticipate the recommendations for searching for evidence during public health emergencies will help information specialists, librarians, evidence synthesis groups, researchers, and decision-makers respond to future public health emergencies, including but not limited to disease outbreaks. The recommendations complement existing guidance by addressing concerns specific to emergency response. The statement is intended as a living document. Future revisions should solicit input from a broader community and reflect conclusions of meta-research on COVID-19 and health emergencies. [ABSTRACT FROM AUTHOR]

Background and Objective: It is necessary to study the emerging trends and areas of knowledge and to predict the direction of future research on the concept of health literacy in order to promote community health. Therefore, this study was conducted with the aim of mapping research knowledge in the field of health literacy and showing the structure of knowledge and their evolution over time. Materials and Methods: This scientometrics research was conducted using Co-word analysis technique includes the steps include: Data retrieval, Data analysis and Mapping and Visualization Papers from the MEDLINE database during (1970-2018) retrieved and analyzed. The bibliographic data analyzed using the visualization of similarities technique by text-mining and visualization techniques of VOS viewer. Results: A total of 7,429 documents, growth of scientific publications related to the field of health literacy has been increased rapidly over the past 10 years and continues to grow in recent years. The stronger studies were mainly conducted at research institutions of higher education in the Canada and United States. Four Core authors groups with a higher influence were identified. The keyword of “communication”, “depression”, “health education”, and “internet” had respectively the highest frequency. The results of cluster analysis identified and categorized them into three major clusters. Health literacy field had a close relationship with the lifestyle dimensions, health information technology, mental health literacy and chronic diseases. Conclusions: Given the interdisciplinary nature of health literacy the areas of education, health, Information Communication Technology (ICT) science, and mental health will help the cooperation of experts in these areas to enrich scientific research and make them more applicable. [ABSTRACT FROM AUTHOR]

Mercury intrusion porosimetry (MIP) is an inexpensive and common technique to characterize porous structures like paper. One major limitation of MIP is the lack of information about the arrangement of pores in the structure, information that is particularly relevant for multilayer structures such as thickness-structured paper. In this article, results from Synchrotron X-ray 3D microtomography are combined with MIP data to provide in-depth and improved information about the structures. [ABSTRACT FROM AUTHOR]

Purpose -- The paper aims to explore and identify the trends in E-Learning research at the global level. Design/methodology/approach -- The data were collected from the Web of Science database covering the period from 1989- 2018 in order to identify substantial contributions that have been published in the field of E-Learning. A total of 9826 records were retrieved. The data was analyzed to reveal different trends prevailing in E-Learning research including prominent contributing countries, authorship patterns adopted, the degree of collaboration, collaborative index, prominent sources for publication of research, visibility of research in term of citation trends like citations received/citations per paper etc. Findings --The analysis revealed a positive growth in literature. It is clear that USA and UK have contributed to more than half of the research output with PEI of 1.07 and 1.45 respectively. Computers & Education and Journal of Chemical Education were the two most used journals. The study also found out that Bradford's Law of scattering does not hold good to the journals cited in the three journals [ABSTRACT FROM AUTHOR]

The dynamic behavior of a stochastic epidemic model with information intervention and vertical transmission was the concern of this paper. The threshold to judge the extinction and persistence of the disease was obtained. Specifically, when Δ < 0 ( Δ appears in Section 3), the three classes I t , M t , and R t appearing in the model go extinct at an exponential rate, and the susceptible class S t almost surely converges to the solution of the boundary equation exponentially. When Δ > 0 , the result that the disease in the model is persistent in the mean and the existence of invariant probability measure are proved by constructing a new form of Lyapunov functions, which results in getting sufficient and nearly necessary conditions for different properties. Moreover, one of the main characteristics of this article was the study of the critical case of Δ = 0 under some conditions. Some examples were listed to confirm the obtained results. The dynamic behavior of a stochastic epidemic model with information intervention and vertical transmission was the concern of this paper. The threshold to judge the extinction and persistence of the disease was obtained. Specifically, when ( appears in Section 3), the three classes , , and appearing in the model go extinct at an exponential rate, and the susceptible class almost surely converges to the solution of the boundary equation exponentially. When , the result that the disease in the model is persistent in the mean and the existence of invariant probability measure are proved by constructing a new form of Lyapunov functions, which results in getting sufficient and nearly necessary conditions for different properties. Moreover, one of the main characteristics of this article was the study of the critical case of under some conditions. Some examples were listed to confirm the obtained results. [ABSTRACT FROM AUTHOR]

The revolution of the Internet of Things (IoT) and the Web of Things (WoT) has brought new opportunities and challenges for the information retrieval (IR) field. The exponential number of interconnected physical objects and real-time data acquisition requires new approaches and architectures for IR systems. Research and prototypes can be crucial in designing and developing new systems and refining architectures for IR in the WoT. This paper proposes a unified and holistic approach for IR in the WoT, called IR.WoT. The proposed system contemplates the critical indexing, scoring, and presentation stages applied to some smart cities' use cases and scenarios. Overall, this paper describes the research, architecture, and vision for advancing the field of IR in the WoT and addresses some of the remaining challenges and opportunities in this exciting area. The article also describes the design considerations, cloud implementation, and experimentation based on a simulated collection of synthetic XML documents with technical efficiency measures. The experimentation results show promising outcomes, whereas further studies are required to improve IR.WoT effectiveness, considering the WoT dynamic characteristics and, more importantly, the heterogeneity and divergence of WoT modeling proposals in the IR domain. [ABSTRACT FROM AUTHOR]

This paper presents a method for developing a malware ontology structure by detecting malware instances on Twitter. The ontology represents a semi-automatic classifier fed by the data extracted from tweets. In particular, the automatic part of the presented methodology relies on a pattern-based approach to detect trigger expressions leading to new information about malware, whilst the manual one covers the evaluation of the results by domain-experts, who also validate the reliability of the semantic relationships within the ontology framework. We present preliminary results on the application of our methodology to tweets extracted from MalwareBazaar database showing how the documents’ collection analysis, through Natural Language Processing (NLP) tasks, can support the knowledge retrieval and documents’ classification procedures for building early warning system of detected malware. Results obtained from this research paper within the time framework of 2023 are referred to the previous version of the current social network X. [ABSTRACT FROM AUTHOR]

Scientific papers published in journals or conferences, also considered academic publications, are the manifestation of scientific research achievements. Lots of scientific papers published in digital form bring new challenges for academic evaluation and information retrieval. Therefore, research on the ranking method of scientific papers is significant for the management and evaluation of academic resources. In this paper, we first identify internal and external factors for evaluating scientific papers and propose a publication ranking method based on an analysis of a heterogeneous academic network. We use four types of metadata (i.e., author, venue (journal or conference), topic, and title) as vertexes for creating the network; in there, the topics are trained by the SentenceLDA algorithm with the metadata of the abstract. We then use the Gibbs sampling method to create a heterogeneous academic network and apply the ConNetClus algorithm to calculate the probability value of publication ranking. To evaluate the significance of the method proposed in this paper, we compare the ranking results with BM25, PageRank, etc., and homogeneous networks in MAP and NDCG. As shown in our evaluation results, the performance of the method we propose in this paper is better than other baselines for ranking publications. [ABSTRACT FROM AUTHOR]

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list. [ABSTRACT FROM AUTHOR]

Purpose: The expansion of research across various disciplines has led to a substantial increase in published papers and journals, highlighting the necessity for reliable text mining platforms for database construction and knowledge acquisition. This abstract introduces GPDMiner(Gene, Protein, and Disease Miner), a platform designed for the biomedical domain, addressing the challenges posed by the growing volume of academic papers. Methods: GPDMiner is a text mining platform that utilizes advanced information retrieval techniques. It operates by searching PubMed for specific queries, extracting and analyzing information relevant to the biomedical field. This system is designed to discern and illustrate relationships between biomedical entities obtained from automated information extraction. Results: The implementation of GPDMiner demonstrates its efficacy in navigating the extensive corpus of biomedical literature. It efficiently retrieves, extracts, and analyzes information, highlighting significant connections between genes, proteins, and diseases. The platform also allows users to save their analytical outcomes in various formats, including Excel and images. Conclusion: GPDMiner offers a notable additional functionality among the array of text mining tools available for the biomedical field. This tool presents an effective solution for researchers to navigate and extract relevant information from the vast unstructured texts found in biomedical literature, thereby providing distinctive capabilities that set it apart from existing methodologies. Its application is expected to greatly benefit researchers in this domain, enhancing their capacity for knowledge discovery and data management. [ABSTRACT FROM AUTHOR]

In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance. [ABSTRACT FROM AUTHOR]

Spatial similarity provides an important basis for geographic information processing and is widely applied in multi-source data fusion and update, data retrieval and query, and cartographic generalization. To address the shape description and similarity measurement of polygon entities, this study presents a new hierarchical shape description approach and examines its application in similarity measurement of polygon entities. Using the rotation and segmentation methods, we first constructed a hierarchical shape description model for target polygon entities, followed by measurement of global and hierarchical shape description of polygon entities, respectively, using the farthest-point-distance and geometric feature description methods. Finally, we constructed a comprehensive similarity measurement model through a weighted integration of position, size, direction, and shape. The hierarchical shape description approach proposed in this paper can be applied to the shape similarity measurement of polygon elements, similarity measurement after spatial object simplification, and multi-scale polygon entity matching. The experimental results showed that the hierarchical shape description approach and similarity measurement model are able to effectively measure spatial similarity between different polygon entities, and have obtained good application results. [ABSTRACT FROM AUTHOR]

Cosmological simulations serve as invaluable tools for understanding the Universe. However, the technical complexity and substantial computational resources required to generate such simulations often limit their accessibility within the broader research community. Notable exceptions exist, but most are not suited for simultaneously studying the physics of galaxy formation and cosmic reionization during the first billion years of cosmic history. This is especially relevant now that a fleet of advanced observatories (e.g. James Webb Space Telescope, Nancy Grace Roman Space Telescope , SPHEREx, ELT, SKA) will soon provide an holistic picture of this defining epoch. To bridge this gap, we publicly release all simulation outputs and post-processing products generated within the thesan simulation project at www.thesan-project.com. This project focuses on the z ≥ 5.5 Universe, combining a radiation-hydrodynamics solver (arepo-rt), a well-tested galaxy formation model (IllustrisTNG) and cosmic dust physics to provide a comprehensive view of the Epoch of Reionization. The thesan suite includes 16 distinct simulations, each varying in volume, resolution, and underlying physical models. This paper outlines the unique features of these new simulations, the production and detailed format of the wide range of derived data products, and the process for data retrieval. Finally, as a case study, we compare our simulation data with a number of recent observations from the James Webb Space Telescope , affirming the accuracy and applicability of thesan. The examples also serve as prototypes for how to utilize the released data set to perform comparisons between predictions and observations. [ABSTRACT FROM AUTHOR]

Stance detection is a crucial task in natural language processing and social computing, focusing on classifying expressed attitudes towards specific targets based on the input text. Conventional methods predominantly view stance detection as a task of target-oriented, sentence-level text classification. On popular social media platforms like Twitter, users often express their opinions through hashtags in addition to textual content within tweets. However, current methods primarily treat hashtags as data retrieval labels, neglecting to effectively utilize the semantic information they carry. In this paper, we propose a large language model knowledge-enhanced stance detection framework (LKESD) for stance detection. LKESD contains three main components: an instruction-prompted background knowledge acquisition module (IPBKA) that retrieves background knowledge of hashtags by providing handcrafted prompts to large language models (LLMs); a graph convolutional feature-enhancement module (GCFEM) is designed to extract the semantic representations of words that frequently co-occur with hashtags in the dataset by leveraging textual associations; an a knowledge fusion network (KFN) is proposed to selectively integrate graph representations and LLM features using a prompt-tuning framework. Extensive experimental results on three benchmark datasets demonstrate that our LKESD method outperforms 2.7% on all setups over compared methods, validating its effectiveness in stance detection tasks. [ABSTRACT FROM AUTHOR]

Public clouds favor sharing of storage resources, in which many tenants acquire bandwidth and storage capacity from a shared storage pool. To provide high availability, data are often encoded to provide fault tolerance with low storage costs. Regarding this, efficiently organizing an encoded storage system for shared I/Os is critical for application performance. This is usually hard to achieve as different applications have different stripe configurations and fault tolerance levels. In this paper, we first study the block trace from the Alibaba cloud, and find that I/O patterns of modern applications prefer the resource sharing scheme. Based on this, we propose a globally shared resource paradigm for encoded storage system in the public cloud. The globally shared resource paradigm can provide balanced load and fault tolerance for numerous disk pool sizes and arbitrary application stripe configurations. Furthermore, we demonstrate with two case studies that our theory can help address the device-specific problems of HDD and SSD RAID arrays with slight modifications: comparing the existing resource partition and resource sharing methods, our theory can promote the rebuild speed of the HDD RAID arrays by 2.5 ×, and reduce the P99 tail latency of the SSD arrays by up to two orders of magnitude. [ABSTRACT FROM AUTHOR]

The appearance and wide use of memory hardware bring significant changes to the conventional vertical memory hierarchy that fails to handle contentions for shared hardware resources and expensive data movements. To deal with these problems, existing schemes have to rely on inefficient scheduling strategies that also cause extra temporal, spatial and bandwidth overheads. Based on the insights that the shared hardware resources trend to be uniformly and hierarchically offered to the requests for co-located applications in memory systems, we present an efficient abstraction of memory hierarchies, called Label, which is used to establish the connection between the application layer and underlying hardware layer. Based on labels, our paper proposes LaMem, a labeled, resource-isolated and cross-tiered memory system by leveraging the way-based partitioning technique for shared resources to guarantee QoS demands of applications, while supporting fast and low-overhead cache repartitioning technique. Besides, we customize LaMem for the learned index that fundamentally replaces storage structures with computation models as a case study to verify the applicability of LaMem. Experimental results demonstrate the efficiency and efficacy of LaMem. [ABSTRACT FROM AUTHOR]

The extraction of effective classification features from high-dimensional hyperspectral images, impeded by the scarcity of labeled samples and uneven sample distribution, represents a formidable challenge within hyperspectral image classification. Traditional few-shot learning methods confront the dual dilemma of limited annotated samples and the necessity for deeper, more effective features from complex hyperspectral data, often resulting in suboptimal outcomes. The prohibitive cost of sample annotation further exacerbates the challenge, making it difficult to rely on a scant number of annotated samples for effective feature extraction. Prevailing high-accuracy algorithms require abundant annotated samples and falter in deriving deep, discriminative features from limited data, compromising classification performance for complex substances. This paper advocates for an integration of advanced spectral–spatial feature extraction with meta-transfer learning to address the classification of hyperspectral signals amidst insufficient labeled samples. Initially trained on a source domain dataset with ample labels, the model undergoes transference to a target domain with minimal samples, utilizing dense connection blocks and tree-dimensional convolutional residual connections to enhance feature extraction and maximize spatial and spectral information retrieval. This approach, validated on three diverse hyperspectral datasets—IP, UP, and Salinas—significantly surpasses existing classification algorithms and small-sample techniques in accuracy, demonstrating its applicability to high-dimensional signal classification under label constraints. [ABSTRACT FROM AUTHOR]

With the rapid development of remote sensing technology, remote sensing products have found increasingly widespread applications across various fields. Nevertheless, as the volume of remote sensing image data continues to grow, traditional data retrieval techniques have encountered several challenges such as substantial query results, data overlap, and variations in data quality. Users need to manually browse and filter a large number of remote sensing datasets, which is a cumbersome and inefficient process. In order to cope with these problems of traditional remote sensing image retrieval methods, this paper proposes a remote sensing image retrieval algorithm for dense data (DD-RSIRA). The algorithm establishes evaluation metrics based on factors like imaging time, cloud coverage, and image coverage. The algorithm utilizes the global grids to create an ensemble coverage relation between images and grids. A locally optimal initial solution is obtained by a greedy algorithm, and then a local search is performed to search for the optimal solution by combining the strategies of weighted gain-loss scheme and novel mechanism. Ultimately, it achieves an optimal coverage of remote sensing images within the region of interest. In this paper, it is shown that the method obtains a smaller number of datasets with lower redundancy and higher data utilization and ensures the data quality to a certain extent in order to accurately meet the requirements of the regional coverage of remote sensing images. [ABSTRACT FROM AUTHOR]

We present in this paper a public data release of an unprecedentedly large set of core-collapse supernova (CCSN) neutrino emission models, comprising 100 detailed 2D axisymmetric radiation-hydrodynamic simulations evolved out to as late as ∼5 s post-bounce and spanning an extensive range of massive-star progenitors. The motivation for this paper is to provide a physically and numerically uniform benchmark data set to the broader neutrino detection community to help it characterize and optimize subsurface facilities for what is likely to be a once-in-a-lifetime galactic supernova burst event. With this release, we hope to (1) help the international experiment and modelling communities more efficiently optimize the retrieval of physical information about the next galactic CCSN, (2) facilitate the better understanding of core-collapse theory and modelling among interested experimentalists, and (3) help further integrate the broader supernova neutrino community. [ABSTRACT FROM AUTHOR]

The Earth Cloud, Aerosol and Radiation Explorer mission (EarthCARE) is a multi-instrument cloud–aerosol–radiation process study mission embarking a high spectral resolution lidar, a cloud profiling radar, a multi-spectral imager, and a three-view broadband radiometer. An important aspect of the EarthCARE mission is its focus on instrument synergy. Many L2 products are the result of L1 inputs from one or more instruments. Since no existing complete observational proxy data sets comprised of co-located and co-temporal "EarthCARE-like" data exists, it has been necessary to create synthetic data sets for the testing and development of various retrieval algorithms and the data processing chain. Given the synergistic nature of the processing chain, it is important that the test data are physically consistent across the various instruments. Within the EarthCARE project, a version of the EarthCARE simulator multi-instrument framework (ECSIM) has been used to create unified realistic test data frames. These simulations have been driven using high-resolution atmospheric model data (described in a companion paper). In this paper, the methods used to create the test data scenes are described. In addition, the simulated L1 data corresponding to each scene are presented and discussed. [ABSTRACT FROM AUTHOR]

Microwave hyperspectral instruments represent one of the main atmospheric sounders of China's next-generation Fengyun meteorological satellites. In order to better apply microwave hyperspectral observations in the fields of atmospheric parameter retrieval and data assimilation, this paper analyzes the sensitivity of trace gases to five selected bandwidth channels using a radiative transfer model based on the simulated data of microwave hyperspectral radiances at 50–60 GHz. This method uses information entropy and a weighting function to select channels and analyze the impact of this on the retrieval accuracy of atmospheric profiles before and after channel selection. The experimental results show that channel selection can reduce the number of channels by approximately 74.05% while maintaining a large amount of information content, and this retrieval effect is significantly better than that of MWTS-III. After channel selection, the 10 MHz, 30 MHz, and 50 MHz bandwidths have the best retrieval results in the stratosphere, whole atmosphere, and troposphere, respectively. When considering the number of channels, computational scale, and retrieval results comprehensively, the channel selection method is effective. [ABSTRACT FROM AUTHOR]

The retrieval of cloud parameters from the atmospheric Sentinel missions require Earth reflectance measurements from a set of spectral bands. Frequently, the ground pixel footprints of the involved spectral bands are not fully aligned and therefore, special treatment is required within the operational algorithms. This so-called inter-band spatial mis-registration of passive spectrometers is present when the Earth reflectance measurements in different spectral bands are captured by different spectrometers. The cloud retrieval algorithm requires reflectance measurements in the UV (ultraviolet)/VIS (visible) band, where the first cloud parameter (i.e., radiometric cloud fraction) is retrieved from the OCRA (Optical Cloud Recognition Algorithm) algorithm. In addition, Earth reflectances in the NIR (near-infrared) band are needed for the retrieval of two additional cloud parameters (i.e., cloud height and cloud albedo or cloud-top height and optical thickness) from the ROCINN (Retrieval of Cloud Information using Neural Networks) algorithm. In the former TROPOMI (TROPOspheric Monitoring Instrument)/S5P (Sentinel-5 Precursor) retrieval, a co-registration scheme of the derived cloud parameters from the source band to the target band based on pre-calculated mapping weights from UV/VIS to NIR, and vice versa, is applied. In this paper we present a new scheme for the co-registration of the TROPOMI cloud parameters using collocated VIIRS (Visible Infrared Imaging Radiometer Suite)/SNPP (Suomi National Polar-orbiting Partnership) information. A great benefit of the new co-registration scheme based on the VIIRS data is that it improves the overall quality of the TROPOMI cloud products and, in addition, it allows the re-construction of the cloud parameters on the first UV/VIS detector pixel, which was impossible with the former scheme based on the static mapping tables. The latter practically means that a significant number of valid data points are added to the TROPOMI cloud, total ozone, SO2 and HCHO product since November 26th 2023 (orbit 31705), when the UPAS version 2.6 with the new co-registration scheme was activated operationally. From a comparison analysis between the two techniques, we found that the largest differences mainly appear for inhomogeneous scenes. From a validation exercise of TROPOMI against VIIRS in the across-track flight direction, we found that the old co-registration scheme tends to smooth out cloud structures along the scanline, whereas such structures can be maintained with the new scheme. The need to implement a similar inter-band spatial co-registration scheme is foreseen for the Sentinel-4/MTG-S (Meteosat Third Generation - Sounder) and Sentinel-5/MetOp-SG (Meteorological Operational Satellite - Second Generation) missions. In the case of Sentinel-4 instrument, the external cloud information will originate from collocated FCI (Flexible Combined Imager) data, on board the MTG-I (Meteosat Third Generation - Imager) satellite. [ABSTRACT FROM AUTHOR]

Background: Libraries provide access to databases with auto-cite features embedded into the services; however, the accuracy of these auto-cite buttons is not very high in humanities and social sciences databases. Case Presentation: This case compares two biomedical databases, Ovid MEDLINE and PubMed, to see if either is reliable enough to confidently recommend to students for use when writing papers. A total of 60 citations were assessed, 30 citations from each citation generator, based on the top 30 articles in PubMed from 2010 to 2020. Conclusions: Error rates were higher in Ovid MEDLINE than PubMed but neither database platform provided error-free references. The auto-cite tools were not reliable. Zero of the 60 citations examined were 100% correct. Librarians should continue to advise students not to rely solely upon citation generators in these biomedical databases. [ABSTRACT FROM AUTHOR]

The improvement of search engines for geospatial data on the World Wide Web has been a subject of research, particularly concerning the challenges in discovering and utilizing geospatial web services. Despite the establishment of standards by the Open Geospatial Consortium (OGC), the implementation of these services varies significantly among providers, leading to issues in dataset discoverability and usability. This paper presents a proof of concept for a search engine tailored to geospatial services in Switzerland. It addresses challenges such as scraping data from various OGC web service providers, enhancing metadata quality through Natural Language Processing, and optimizing search functionality and ranking methods. Semantic augmentation techniques are applied to enhance metadata completeness and quality, which are stored in a high-performance NoSQL database for efficient data retrieval. The results show improvements in dataset discoverability and search relevance, with NLP-extracted information contributing significantly to ranking accuracy. Overall, the GeoHarvester proof of concept demonstrates the feasibility of improving the discoverability and usability of geospatial web services through advanced search engine techniques. [ABSTRACT FROM AUTHOR]

Remote sensing image is a vital basis for land management decisions. The protection of remote sensing images has seen the application of blockchain's notarization function by many scholars. Yet, research on efficient retrieval of such images on the blockchain remains sparse. Addressing this issue, this paper introduces a blockchain-based spatial index verification method using Hyperledger Fabric. It linearizes the spatial information of remote sensing images via Geohash and integrates it with LSM trees for effective retrieval and verification. The system also incorporates IPFS as an underlying storage unit for Hyperledger Fabric, ensuring the safe storage and transmission of images. The experiments indicate that this method significantly reduces the latency in data retrieval and verification without impacting the write performance of Hyperledger Fabric, enhancing throughput and providing a solid foundation for efficient blockchain-based verification of remote sensing images in land registry systems. [ABSTRACT FROM AUTHOR]

With the widespread adoption of cloud computing, the face verification process often requires the client to upload the face to an untrusted cloud server to obtain the verification results. Privacy leakage issues may arise if the client's private information is not protected. This paper proposes a secure and anonymous face verification scheme using fully homomorphic encryption technology and SealPIR. Our scheme is a three-party solution that requires a third-party server trusted by the client. This scheme not only prevents the client's facial data from being obtained by untrusted data servers but also prevents the data server from learning the index corresponding to the face that the client wants to verify. In a single-face verification process, the client only needs to perform one upload operation and one download operation, with a communication volume of 264 KB. We can complete a privacy-protected anonymous face verification process in 84.91 ms. [ABSTRACT FROM AUTHOR]

In challenging environments, feature-based visual SLAM encounters frequent failures in frame tracking, introducing unknown poses to robotic applications. This paper introduces an immediate approach for recovering untracked camera poses. Through the retrieval of key information from elapsed untracked frames, lost poses are efficiently restored with a short time consumption. Taking account of reconstructed poses and map points during local optimizing, a denser local map is constructed around ambiguous frames to enhance the further SLAM procedure. The proposed method is implemented in a SLAM system, and monocular experiments are conducted on datasets. The experimental results demonstrate that our method can reconstruct the untracked frames in nearly real time, effectively complementing missing segments of the trajectory. Concurrently, the accuracy and robustness for subsequent tracking are improved through the integration of recovered poses and map points. [ABSTRACT FROM AUTHOR]

The article analyses the functioning of the war-time Ukrainian humorous discourse in general and the cognitive mechanisms of humor, including the "Availability Heuristic" cognitive pattern in particular. The study aims to examine 12 Ukrainian war-time jokes and to reveal main cognitive patterns and accompanying stylistic figures producing the comic effect. All jokes manifest the application of the "Availability heuristic" cognitive pattern inherent to the war period when old pre-war realities are changed with new war-time realities and explained in a funny way. The study addresses some cognitive and pragmatic aspects of the war-time Ukrainian humor, focusing on the role of cognitive patterns in selecting stylistic humor devices, while processing the incoming information and shaping the cognitive frameworks of humor perception and creation. The paper reveals the main cognitive patterns, including the "Availability Heuristic," the "Distinct contrast," the "Negativity Thinking," the "Superiority or Illusionary Superiority," the "Easel," and their humorous actualization through such stylistic figures as paraprosdokian, irony, bathos, double entendre, pun, metaphor, and pastiche in its narrow meaning. The researched Ukrainian jokes are interwoven with the situational context of the dramatic events within the temporality of the speedy streaming news of the Russian-Ukrainian war. The paper explores the main pragmatic functions of Ukrainian war-time humorous discourse and makes additional remarks about a number of affirmations from some humor theories. Plain Language Summary: Cognitive patterns of Ukrainian wartime humor The article analyses cognitive, pragmatic and stylistic features of war-time Ukrainian Humorous discourse in general and the "Availability Heuristic" cognitive pattern in particular. The purpose of the research is to determine the role of this cognitive pattern in the creation of the comic effect in war-time verbal humor in Ukraine. The paper presents the results of the cognitive, pragmatic and stylistic analyses of twelve war-time Ukrainian jokes. The study determined that twelve researched jokes apply the "Availability Heuristic" cognitive pattern, which is characterized by solution search through pursuing fresh experience or information reflecting recent war events. It is accompanied by the "Distinct contrast" cognitive pattern, both of which are the manifestations of the incongruity mechanism. Besides, it is used together with the "Negativity Thinking" pattern, focusing on negative intensions, the cognitive "Easel" pattern, responsible for the dominance of visual pictures and images, and the cognitive pattern of "Superiority or Illusionary Superiority." All cognitive patterns have sets of stylistic figures standing behind it. In the researched jokes, the cognitive patterns are exhibited through paraprosdokian, irony, bathos, allusions, double entendre, puns, metaphors, pastiche in narrow meaning. The analyzed Ukrainian jokes are interwoven with the situational context of the dramatic events within a temporality of the speedy streaming news of the Russian-Ukrainian war. [ABSTRACT FROM AUTHOR]

Background: A proposal to encourage the preregistration of research on research integrity was developed and adopted as the Amsterdam Agenda at the 5th World Conference on Research Integrity (Amsterdam, 2017). This paper reports on the degree to which abstracts of the 6th World Conference in Research Integrity (Hong Kong, 2019) reported on preregistered research. Methods: Conference registration data on participants presenting a paper or a poster at 6th WCRI were made available to the research team. Because the data set was too small for inferential statistics this report is limited to a basic description of results and some recommendations that should be considered when taking further steps to improve preregistration. Results: 19% of the 308 presenters preregistered their research. Of the 56 usable cases, less than half provided information on the six key elements of the Amsterdam Agenda. Others provided information that invalidated their data, such as an uninformative URL. There was no discernable difference between qualitative and quantitative research. Conclusions: Some presenters at the WCRI have preregistered their research on research integrity, but further steps are needed to increase frequency and completeness of preregistration. One approach to increase preregistration would be to make it a requirement for research presented at the World Conferences on Research Integrity. [ABSTRACT FROM AUTHOR]

We hope to contribute to the field of research in information technology and digital libraries by analyzing the connections between Thematic Digital Universities and digital user-learner profiles. Thematic Digital Universities are similar to digital libraries, and focus on creating and indexing open educational resources, as well as improving learning in the information age. The digital user profile relates to the digital representation of a person's identity and characteristics. In this paper we present the design of an ontology for the digital User-Learner Profile (OntoULP) and its application program. OntoULP is used to structure a user-learner's digital profile. The application provides each user-learner with tailor-made analyses based on informational behaviors, needs, and preferences. We rely on an exploratory research approach and on methods of ontologies, user modeling, and semantic matching to design the OntoULP and its application program. Any user-learner could use the OntoULP and its application program. [ABSTRACT FROM AUTHOR]

In object detection, Transformer-based models such as DETR have exhibited state-of-the-art performance, capitalizing on the attention mechanism to handle spatial relations and feature dependencies. One inherent challenge these models face is the intertwined handling of content and positional data within their attention spans, potentially blurring the specificity of the information retrieval process. We consider object detection as a comprehensive task, and simultaneously merging content and positional information like before can exacerbate task complexity. This paper presents the Multi-Task Fusion Detector (MTFD), a novel architecture that innovatively dissects the detection process into distinct tasks, addressing content and position through separate decoders. By utilizing assumed fake queries, the MTFD framework enables each decoder to operate under a presumption of known ancillary information, ensuring more specific and enriched interactions with the feature map. Experimental results affirm that this methodical separation followed by a deliberate fusion not only simplifies the task difficulty of the detection process but also augments accuracy and clarifies the details of each component, providing a fresh perspective on object detection in Transformer-based architectures. [ABSTRACT FROM AUTHOR]

Monitoring the technical condition of hydrotechnical facilities is crucial for ensuring their safe usage. This process typically involves tracking environmental variables (e.g., concrete damming levels, temperatures, piezometer readings) as well as geometric and physical variables (deformation, cracking, filtration, pore pressure, etc.), whose long-term trends provide valuable information for facility managers. Research on the methods of analyzing geodetic monitoring data (manual and automatic) and sensor data is vital for assessing the technical condition and safety of facilities, particularly when utilizing new measurement technologies. Emerging technologies for obtaining data on the changes in the surface of objects employ laser scanning techniques (such as LiDAR, Light Detection, and Ranging) from various heights: terrestrial, unmanned aerial vehicles (UAVs, drones), and satellites using sensors that record geospatial and multispectral data. This article introduces an algorithm to determine geometric change trends using terrestrial laser scanning data for both concrete and earth surfaces. In the consecutive steps of the algorithm, normal vectors were utilized to analyze changes, calculate local surface deflection angles, and determine object alterations. These normal vectors were derived by fitting local planes to the point cloud using the least squares method. In most applications, surface strain and deformation analyses based on laser scanning point clouds primarily involve direct comparisons using the Cloud to Cloud (C2C) method, resulting in complex, difficult-to-interpret deformation maps. In contrast, preliminary trend analysis using local normal vectors allows for rapid threat detection. This approach significantly reduces calculations, with detailed point cloud interpretation commencing only after detecting a change on the object indicated by normal vectors in the form of an increasing deflection trend. Referred to as the cluster algorithm by the authors of this paper, this method can be applied to monitor both concrete and earth objects, with examples of analyses for different object types presented in the article. [ABSTRACT FROM AUTHOR]

With the rapid growth of social media, textual content is increasingly growing. Unstructured texts are a rich source of latent spatial information. Extracting such information is useful in query processing, geographical information retrieval (GIR), and recommender systems. In this paper, we propose a novel approach to infer spatial information from salient features of non-spatial nature in text corpora. We propose two methods, namely DCS and RCS, to represent place-based concepts. In addition, two measures, namely the Shannon entropy and the Moran's I, are proposed to calculate the degree of geo-indicativeness of terms in texts. The methodology is compared with a Latent Dirichlet Allocation (LDA) approach to estimate the accuracy improvement. We evaluated the methods on a dataset of rental property advertisements in Iran and a dataset of Persian Wikipedia articles. The results show that our proposed approach enhances the relative accuracy of predictions by about 10% in case of the renting advertisements and by 13% in case of the Wikipedia articles. The average distance error is about 13.3 km for the advertisements and 10.3 km for the Wikipedia articles, making the method suitable to infer the general region of the city in which a property is located. The proposed methodology is promising for inferring spatial knowledge from textual content that lacks spatial terms. [ABSTRACT FROM AUTHOR]

Zero frequency is a fundamental problem in information retrieval using language models and smoothing is applied to deal with this problem. The cluster-based smoothing method is found to be effective for information retrieval using language models. Since the effectiveness of cluster-based smoothing depends on clustering quality, there is scope for improvement by enhancing the clustering algorithm. In this paper, we present a study on how to improve cluster-based smoothing using a histogram-based incremental clustering algorithm and word embeddings. To our knowledge, this is the first study on the cluster-based smoothing method which is integrated with a language model for developing an effective IR system for the Bengali language which is one of the most spoken Indian languages. The proposed method has been tested on two benchmark Bengali IR datasets. The experimental results show that our proposed model for Bengali document retrieval is effective and it outperforms several baseline IR models. [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]

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]

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]

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]

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]