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With the passage of time and advancement of modern technology, new work methods and ideas are introduced and applied regularly in the field of topography and, by association, cadastre. The entirety of cadastral works rely on topographical methods for the obtaining of information necessary for the documentation of real estate such as parcels and constructions. It is within any country’s interest to not only have a national-level registry in which real estate is systematically documented but to also regularly update it. This paper aims to introduce new technological concepts of data retrieval and processing in the process of execution of cadastre and land registry works, integrating Smart technology, namely GNSS and UAV technology, into the efforts of the National Program of Cadastre and Land Book for the systematic registration of real estate. This area in which these efforts have been made is located in Ialomița county, Muntenia, Romania, namely the Territorial Administrative Unit of Albești (Albești, Bataluri and Marisilieni villages). Since the beginning of this project, the National Agency for Cadastre and Real Estate Advertising has taken care of all necessary steps (legislative and operational measures) to accelerate the systematic registration works and to contribute to the conclusion of contracts for cadastral sectors. [ABSTRACT FROM AUTHOR]

There are different ways of dividing psychology into subdisciplines. The purpose of the present paper is to explore one specific metaperspective on psychological science, seen as having three main branches: person psychology, population psychology, and mechanism psychology, linked to three different levels of research. Person-level research focuses on psychological phenomena as experienced and enacted by individual persons in their interaction with other persons and other parts of the environment, and in their development over time. Population-level research focuses on populations of individuals, frequencies of various psychological phenomena in a population, risk factors, and population-level effects of various psychological interventions. Mechanism-level research focuses on psychological functioning as explained in terms of neurophysiological mechanisms and information processes at a sub-personal level. It is argued that the failure to differentiate clearly between research questions at these three levels lead to questionable research practices. Most notably, a failure to differentiate clearly between the population level and the person level leads to problem-method mismatches in the form of researchers trying to answer questions about persons by research on populations. Also, because of a failure to differentiate between the person level and the mechanism level, explanations in terms of sub-personal mechanisms are too often seen as providing answers about what occurs at the person level, thereby failing to study persons as intentional agents in interaction with other persons and other parts of the environment. It is argued that a clear differentiation between three levels of psychological science - population, person, and mechanism - may contribute to an increased clarity in these matters and may thereby contribute to the development and maturation of psychological science. [ABSTRACT FROM AUTHOR]

The paper reports a longitudinal analysis of the topical and methodological development of Library and Information Science (LIS). Its focus is on the effects of researchers' disciplines on these developments. The study extends an earlier cross‐sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706–1722) by a coordinated dataset representing a content analysis of articles published in 31 scholarly LIS journals in 1995, 2005, and 2015. It is novel in its coverage of authors' disciplines, topical and methodological aspects in a coordinated dataset spanning two decades thus allowing trend analysis. The findings include a shrinking trend in the share of LIS from 67 to 36% while Computer Science, and Business and Economics increase their share from 9 and 6% to 21 and 16%, respectively. The earlier cross‐sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706–1722) for the year 2015 identified three topical clusters of LIS research, focusing on topical subfields, methodologies, and contributing disciplines. Correspondence analysis confirms their existence already in 1995 and traces their development through the decades. The contributing disciplines infuse their concepts, research questions, and approaches to LIS and may also subsume vital parts of LIS in their own structures of knowledge production. [ABSTRACT FROM AUTHOR]

Many scientific texts are produced in Persian and available in scientific information databases through the Web. In this paper, FarsAcademic, a test collection of Persian scientific texts, has been built to implement information retrieval models among academic search comprising 102238 documents and 61 topics. While constructing FarsAcademic, we have tried to resolve the problems specific to information retrieval (IR) and natural language processing (NLP) in Persian scientific texts. Domain experts were employed to create queries within their research area, and user relevance and topical relevance were applied to improve the precision of relevance judgment of documents. Further, to improve retrieval performance in Persian scientific texts, automated query expansion was applied using one of the relevant feedback techniques, the Local Context Analysis algorithm. The result showed that query expansion techniques outperformed other information retrieval models in the Persian scientific texts retrieval task. Eventually, FarsAcademic became the only one that has been free of charge for Iranian information retrieval scholars to implement and evaluate different information retrieval models and algorithms on Persian scientific text and academic search. [ABSTRACT FROM AUTHOR]

Objective: In 2002, the National Library of Medicine (NLM) introduced semi-automated indexing of Medline using the Medical Text Indexer (MTI). In 2021, NLM announced that it would fully automate its indexing in Medline with an improved MTI by mid-2022. This pilot study examines indexing using a sample of records in Medline from 2000, and how an early, public version of MTI's outputs compares to records created by human indexers. Methods: This pilot study examines twenty Medline records from 2000, a year before the MTI was introduced as a MeSH term recommender. We identified twenty higher- and lower-impact biomedical journals based on Journal Impact Factor (JIF) and examined the indexing of papers by feeding their PubMed records into the Interactive MTI tool. Results: In the sample, we found key differences between automated and human-indexed Medline records: MTI assigned more terms and used them more accurately for citations in the higher JIF group, and MTI tended to rank the Male check tag more highly than the Female check tag and to omit Aged check tags. Sometimes MTI chose more specific terms than human indexers but was inconsistent in applying specificity principles. Conclusion: NLM's transition to fully automated indexing of the biomedical literature could introduce or perpetuate inconsistencies and biases in Medline. Librarians and searchers should assess changes to index terms, and their impact on PubMed's mapping features for a range of topics. Future research should evaluate automated indexing as it pertains to finding clinical information effectively, and in performing systematic searches. [ABSTRACT FROM AUTHOR]

The widespread adoption of cloud infrastructures has revolutionized data storage and access. However, it has also raised concerns regarding the privacy of sensitive data. To address these concerns, encryption techniques have been widely used. However, traditional encryption schemes limit the efficient search and retrieval of encrypted data. To tackle this challenge, innovative approaches have emerged, such as the utilization of Homomorphic Encryption (HE) in Searchable Encryption (SE) schemes. This paper provides a comprehensive analysis of the advancements in HE-based privacy-preserving techniques, focusing on their application in SE. The main contributions of this work include the identification and classification of existing SE schemes that utilize HE, a comprehensive analysis of the types of HE used in SE, an examination of how HE shapes the search process structure and enables additional functionalities, and the identification of promising directions for future research in HE-based SE. The findings reveal the increasing usage of HE in SE schemes, particularly Partially Homomorphic Encryption. The popularity of this type of HE schemes, especially Paillier's cryptosystem, can be attributed to its simplicity, proven security properties, and widespread availability in open-source libraries. The analysis also highlights the prevalence of index-based SE schemes using HE, the support for ranked search and multi-keyword queries, and the need for further exploration in functionalities such as verifiability and the ability to authorize and revoke users. Future research directions include exploring the usage of other encryption schemes alongside HE, addressing omissions in functionalities like fuzzy keyword search, and leveraging recent advancements in Fully Homomorphic Encryption schemes. [ABSTRACT FROM AUTHOR]

Objective: From medicine via radio to telemedicine, personalized medical care at sea has improved significantly over the years. Currently, very little research has been conducted on telemedicine services and tools at sea. This study aims to review real-time case studies of seafarers' personalized treatment via telemedical devices published in medical journals. Methods: A literature search was conducted using three libraries such as PubMed (Medline), Cumulative Index to Nursing and Allied Health Literature (CINAHL), BioMed Central, and Google Scholar. The Medical Subject Headings (MeSH) were used for information retrieval and document selection was conducted based on the guidelines of preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 flowchart. Selected articles were subjected to quality checks using the Newcastle–Ottawa scale (NOS). Results: The literature search produced 785 papers and documents. The selection was conducted in three stages such as selection, screening, and inclusion. After applying predefined inclusion and exclusion criteria, only three articles on real-time medical assistance with telemedical tools were identified. It is reported that medical attention is delivered to seafarers in real time thanks to advancements in telemedicine, satellite technology, and video conferencing. Conclusions: By improving the quality of medical care and reducing response times for medical emergencies at sea, lives have been saved. There are still several gaps despite these advancements. Medical assistance at sea should therefore be improved to address many of the still unsolved issues. [ABSTRACT FROM AUTHOR]

Recommendation system, or recommender system, is widely used in online Web applications like e-commerce Web sites and movie review Web sites. Sequential recommender put more emphasis upon user's short-term preference through exploiting information from its recent history. By incorporating the user short-term preference into the recommendation, the algorithm achieves a higher accuracy, which proves that a more accurate user portrait or representation boosts the performance to a great extent. Intuitionally, we seek to improve the current item representation modeling via incorporating the item trend information. Most of the recommendation models neglect the importance of the ever-changing item popularity. To this end, this paper introduces a novel sequential recommendation approach dubbed TRec. TRec learns the item trend information from the implicit user interaction history and incorporates the item trend information into the subsequent item recommendation tasks. After that, a self-attention mechanism is used for better representation. We also investigate alternative ways to model the proposed item trend representation; we evaluate two variant models which leverage the power of gated graph neural network upon the item trend representation modeling to boost the representation ability. We conduct extensive experiments with seven baseline methods on four benchmark datasets. The empirical results show that our proposed approach outperforms the state-of-the-art models as high as 18.2%. The experiment result displays the effectiveness in item trend information learning while with low computational complexity as well. Our study demonstrates the importance of item trend information in recommendation system. [ABSTRACT FROM AUTHOR]

Metaheuristic applications for information retrieval research are limited in spite of the importance of this problem domain. Ranking the retrieved documents based on their importance is a vital issue for the scientific and industrial communities. This paper proposes a novel variable neighborhood search (VNS) algorithm with adaptation based on an objective function for the learning to rank (LTR) problem. VNS is a global optimum metaheuristic algorithm that has been engaged to evolve the optimal solutions for heuristic problems based on exploring better neighbor solutions from the current one. The changes from the current to the next optimal solution are made during the perturbation stage to identify the global optimal solutions. The exploration procedure has been made through various mutation step sizes, whereas the exploitation process has been done by checking the quality of the evolved solutions using the fitness function. This research proposes a novel version of VNS based on four random probability distributions with gradient ascent. In addition to using the traditional random generator with gradient ascent for modifying the mutated genes of the neighborhood candidate solution in the following evolving iteration. This novel method in LTR is called gradient variable neighborhood (GVN). In the experiments, we utilized Microsoft Bing search (MSLR-WEB30K), Yahoo, and TREC Million Queries Competitions in 2008 and 2007 (LETOR 4) datasets. From the findings of the results, we can deduce that the GVN method outperformed recent studies on LTR methods. [ABSTRACT FROM AUTHOR]

Tagging can be regarded as the action of connecting relevant user-defined keywords to an item, indirectly improving the quality of the information retrieval services that rely on tags as data sources. Tag recommendation dramatically enhances the quality of tags by assisting users in tagging. Although there exist many studies on tag recommendation for textual content, few of them consider two characteristics in real applications, i.e., the long-tail distribution of tags and the topic-tag correlation. In this paper, we propose a Topic-Guided Tag Recommendation (TGTR) model to recommend tags by jointly incorporating dynamic neural topic. Specifically, TGTR first generates dynamic neural topic that would indicate the tags by a neural topic generator. Then, a sequence encoder is used to distill indicative features from the post. To effectively leverage the topic and alleviate the data imbalance, we design a multi-tag topical attention mechanism to get a tag-specific post representation for each tag with the help of dynamic neural topic. These three modules are seamlessly joined together via an end-to-end multi-task learning model, which is helpful for the three parts to enhance each other and balance the effects of topics and tags. Extensive experiments have been conducted on four real-world datasets and demonstrate that our model outperforms the state-of-the-art approaches by a large margin, especially on tail-tags. The code, data and hyper-parameter settings are publicly released for reproducibility. [ABSTRACT FROM AUTHOR]

On account of prevalence of mobile devices, a variety of mobile-based services have been conducted recently. In this situation, it is possible to serve the information that fits to users' requests according to the users' context using mobile devices. Therefore, it is necessary to research data mining techniques with mobile devices. Therefore, we researched information retrieval system in mobile environment. We applied semantic data mining method with context information ontology. These techniques of semantic information retrieval can provide the information according to users' context information such as location, weather, user preference etc. exactly and rapidly anytime and anyplace. In modern society, it is essential to develop mobile information retrieval system which is efficient to users. Also, the system can provide users' current location and the location information with Google map APIs in mobile devices. Therefore, in this paper, we researched semantic data mining technique with context information ontology using mobile techniques. And we compared and evaluated our proposed model and the existing model in three areas and showed our proposed model has higher performance. [ABSTRACT FROM AUTHOR]

Image reconstruction is an interesting yet challenging optimization problem that has several potential applications. The task is to reconstruct an image using a fixed number of transparent polygons. Traditional gradient-based algorithms cannot be applied to the problem since the optimization objective has no explicit expression and cannot be represented by computational graphs. Metaheuristic search algorithms are powerful optimization techniques for solving complex optimization problems, especially in the context of incomplete information or limited computational capability. In this paper, we developed a novel metaheuristic search algorithm named progressive learning hill climbing (ProHC) for image reconstruction. Instead of placing all the polygons on a blank canvas at once, ProHC starts from one polygon and gradually adds new polygons to the canvas until reaching the number limit. Furthermore, an energy-map-based initialization operator was designed to facilitate the generation of new solutions. To assess the performance of the proposed algorithm, we constructed a benchmark problem set containing four different types of images. The experimental results demonstrated that ProHC was able to produce visually pleasing reconstructions of the benchmark images. Moreover, the time consumed by ProHC was much shorter than that of the existing approach. [ABSTRACT FROM AUTHOR]

Sensor fusion is a technique that combines information from multiple sensors in order to improve the accuracy and reliability of the data being collected. In the context of teleoperation control of an anthropomorphic robotic arm, sensor fusion technology can be used to enhance the precise control of anthropomorphic robotic arms by combining data from multiple sensors, such as cameras, data gloves, force sensors, etc. By fusing and processing this sensing information, it can enable real-time control of anthropomorphic robotic arms and dexterous hands, replicating the motion of human manipulators. In this paper, we present a sensor fusion-based teleoperation control system for the anthropomorphic robotic arm and dexterous hand, which utilizes a filter to fuse data from multiple sensors in real-time. As such, the real-time perceived human arms motion posture information is analyzed and processed, and wireless communication is used to intelligently and flexibly control the anthropomorphic robotic arm and dexterous hand. Finally, the user is able to manage the anthropomorphic operation function in a stable and reliable manner. We also discussed the implementation and experimental evaluation of the system, showing that it is able to achieve improved performance and stability compared to traditional teleoperation control methods. [ABSTRACT FROM AUTHOR]

Query, expressing the user need and requirement, has an important role, in an information retrieval system, for reaching a high accuracy search. In this paper, we present an overview of the different refinement operations that the query may undergo, in the sake to enhance performance of an information retrieval system, such as: automatic query formulation through words prevision, query reformulation, query expansion, and query optimization. [ABSTRACT FROM AUTHOR]

Background: Drug-drug interaction (DDI) information retrieval (IR) is an important natural language process (NLP) task from the PubMed literature. For the first time, active learning (AL) is studied in DDI IR analysis. DDI IR analysis from PubMed abstracts faces the challenges of relatively small positive DDI samples among overwhelmingly large negative samples. Random negative sampling and positive sampling are purposely designed to improve the efficiency of AL analysis. The consistency of random negative sampling and positive sampling is shown in the paper. Results: PubMed abstracts are divided into two pools. Screened pool contains all abstracts that pass the DDI keywords query in PubMed, while unscreened pool includes all the other abstracts. At a prespecified recall rate of 0.95, DDI IR analysis precision is evaluated and compared. In screened pool IR analysis using supporting vector machine (SVM), similarity sampling plus uncertainty sampling improves the precision over uncertainty sampling, from 0.89 to 0.92 respectively. In the unscreened pool IR analysis, the integrated random negative sampling, positive sampling, and similarity sampling improve the precision over uncertainty sampling along, from 0.72 to 0.81 respectively. When we change the SVM to a deep learning method, all sampling schemes consistently improve DDI AL analysis in both screened pool and unscreened pool. Deep learning has significant improvement of precision over SVM, 0.96 vs. 0.92 in screened pool, and 0.90 vs. 0.81 in the unscreened pool, respectively. Conclusions: By integrating various sampling schemes and deep learning algorithms into AL, the DDI IR analysis from literature is significantly improved. The random negative sampling and positive sampling are highly effective methods in improving AL analysis where the positive and negative samples are extremely imbalanced. [ABSTRACT FROM AUTHOR]

Approximate string matching (ASM) has a number of applications in many disciplines, ranging from information retrieval to gene matching. Conventional solution to this problem is based on the dynamic programming-based strategy having quadratic space and time complexity. The complexity of the conventional solution makes it impractical to search queries from the huge sequences having billions of characters. Therefore, many studies have been proposed that improves on the space and time requirement of the basic solution which includes heuristic, filtration, and index-based solutions. These existing solutions obtain the better performance by compromising on the completeness of the search. In this paper, we proposed the linear space algorithm for the approximate string matching problem while retaining the time complexity of conventional solution. The proposed method works in linear space without omitting any regions in the given text; hence, it finds all the possible matches. Conventional dynamic programming solution is modified in such a way that storage of complete trace back table is avoided by keeping only running count of each edit operation in the memory. A variety of laws and facts are discovered in classical dynamic programming table in that regard. We also presented the parallel approach to the proffered algorithm to improve the running time of the algorithm. The algorithm is evaluated on the CUDA-enabled GPUs. DNA sequences of sizes between 250 and 970 MBP are used for evaluation. Moreover, experiments are also performed by using natural language text to highlight the broader applicability of the proposed algorithm. Results show the substantial superiority of the algorithm in terms of performance and scalability compared to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Cloud contamination is a common issue that severely reduces the quality of optical satellite images in remote sensing fields. With the rapid development of deep learning technology, cloud contamination is expected to be addressed. In this paper, we propose Denoising Diffusion Probabilistic Model-Cloud Removal (DDPM-CR), a novel cloud removal network that can effectively remove both thin and thick clouds in optical image scenes. Our network leverages the denoising diffusion probabilistic model (DDPM) architecture to integrate both clouded optical and auxiliary SAR images as input to extract DDPM features, providing significant information for missing information retrieval. Additionally, we propose a cloud removal head adopting the DDPM features with an attention mechanism at multiple scales to remove clouds. To achieve better network performance, we propose a cloud-oriented loss that considers both high- and low-frequency image information as well as cloud regions in the training procedure. Our ablation and comparative experiments demonstrate that the DDPM-CR network outperforms other methods under various cloud conditions, achieving better visual effects and accuracy metrics (MAE = 0.0229, RMSE = 0.0268, PSNR = 31.7712, and SSIM = 0.9033). These results suggest that the DDPM-CR network is a promising solution for retrieving missing information in either thin or thick cloud-covered regions, especially when using auxiliary information such as SAR data. [ABSTRACT FROM AUTHOR]

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

This research proposes a new approach to improve information retrieval systems based on a multinomial naive Bayes classifier (MNBC), Bayesian networks (BNs), and a multi-terminology which includes MeSH thesaurus (Medical Subject Headings) and SNOMED CT (Systematized Nomenclature of Medicine of Clinical Terms). Our approach, which is entitled improving semantic information retrieval (IMSIR), extracts and disambiguates concepts and retrieves documents. Relevant concepts of ambiguous terms were selected using probability measures and biomedical terminologies. Concepts are also extracted using an MNBC. The UMLS (Unified Medical Language System) thesaurus was then used to filter and rank concepts. Finally, we exploited a Bayesian network to match documents and queries using a conceptual representation. Our main contribution in this paper is to combine a supervised method (MNBC) and an unsupervised method (BN) to extract concepts from documents and queries. We also propose filtering the extracted concepts in order to keep relevant ones. Experiments of IMSIR using the two corpora, the OHSUMED corpus and the Clinical Trial (CT) corpus, were interesting because their results outperformed those of the baseline: the P@50 improvement rate was +36.5% over the baseline when the CT corpus was used. [ABSTRACT FROM AUTHOR]

Quantifying the dissimilarity of two texts is an important aspect of a number of natural language processing tasks, including semantic information retrieval, topic classification, and document clustering. In this paper, we compared the properties and performance of different dissimilarity measures D using three different representations of texts—vocabularies, word frequency distributions, and vector embeddings—and three simple tasks—clustering texts by author, subject, and time period. Using the Project Gutenberg database, we found that the generalised Jensen–Shannon divergence applied to word frequencies performed strongly across all tasks, that D's based on vector embedding representations led to stronger performance for smaller texts, and that the optimal choice of approach was ultimately task-dependent. We also investigated, both analytically and numerically, the behaviour of the different D's when the two texts varied in length by a factor h. We demonstrated that the (natural) estimator of the Jaccard distance between vocabularies was inconsistent and computed explicitly the h-dependency of the bias of the estimator of the generalised Jensen–Shannon divergence applied to word frequencies. We also found numerically that the Jensen–Shannon divergence and embedding-based approaches were robust to changes in h, while the Jaccard distance was not. [ABSTRACT FROM AUTHOR]

In this paper, we present a new perspective of single server private information retrieval (PIR) schemes by using the notion of linear error-correcting codes. Many of the known single server schemes are based on taking linear combinations between database elements and the query elements. Using the theory of linear codes, we develop a generic framework that formalizes all such PIR schemes. This generic framework provides an appropriate setup to analyze the security of such PIR schemes. In fact, we describe some known PIR schemes with respect to this code-based framework, and present the weaknesses of the broken PIR schemes in a unified point of view. [ABSTRACT FROM AUTHOR]

The current practice of users searching for different types of geo-resources in a geographic area and wishing to identify the most convenient routes for visiting the most relevant ones, requires the iterative formulation of several queries: first to identify the more interesting resources and then to select the best route to visit them. In order to simplify this process, in this paper a novel functionality for a geographic information retrieval (GIR) system is proposed, which retrieves and ranks several routes for visiting a number of relevant georeferenced resources as a result of a single query, named flexible trip-planning query. An original retrieval model is defined to identify the relevant resources and to rank the most convenient routes by taking into account personal user preferences. To this end, a graph-based algorithm is defined, exploiting prioritized aggregation to optimize the routes' identification and ranking. The proposed algorithm is applied in the proof-of-concept of a Smart cOmmunity-based Geographic infoRmation rEtrievAl SysTem (SO-GREAT) designed to strengthen local communities: it collects and manages open data from regional authorities describing categories of authoritative territorial resources and services, such as schools, hospitals, etc., and from volunteered geographic services (VGSs) created by citizens to offer services in their neighbourhood. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as a promising platform that grants users direct yet shared access to computing resources and services without worrying about the internal complex infrastructure. However, secure data storage and data retrieval is the basic characteristics. Therefore, this paper proposes a novel double keys adapted elliptic curve cryptography (DKECC) and log normalized Gaussian sigmoid adaptive neuro‐fuzzy interference system (LGS‐ANFIS) based secure resource allocation system in decentralized cloud storage. Initially, the input data undergoes data fragmentation. After that, encryption is carried out using DKECC algorithm. Next, hash code generation using Pearson hash function is done. Then, resource availability is estimated via logistic sine chaotic mapping indulged rock hyraxes swarm optimization technique. Afterwards, data is checked for deduplication. Finally, resource allocation through LGS‐ANFIS takes place. The experimental outcomes demonstrated better results compared to baseline techniques. The modelling process showed that the suggested safe resource allocation model achieves the total security level of 96.27% while also having shorter reaction times (3018 ms), greater throughputs (1258), reduced load balancing (0.355874), and reduced latency (4372 ms). [ABSTRACT FROM AUTHOR]

Controllable synthesis of high‐performance materials with low‐cost is critical for the development of functional devices. Herein, a series of sequence‐controlled low‐dispersity oligomers with acid–base chromotropic capability via simple copolymerization between N‐substituted maleimides (NMI) and methyldiallylamine (MDAA) are synthesized. The structure and molecular weight of the oligomers are characterized. Owing to the presence of strong electrophilic carbonyl oxygen and imine nitrogen, a rapid‐responsive color switching system is achieved. This change is attributed to the acid/base‐triggered isomerization between enolate state and the enol or keto tautomer via intramolecular proton transfer. More importantly, the color of P(MDAA/NMI) is regulated by the variation of the substituent group of the N‐substituted maleimide. A new type of rewritable paper based on the designed N‐substituted maleimide oligomers is fabricated by a simple spin‐coating process, on which images with high resolution can be acid‐printed and base‐erased for over ten cycles. The writing and erasure times can be as short as 10 s and the legible time can be more than 90 days under ambient conditions. The as‐formed rewritable paper with excellent rewriting performances is low‐cost and easy for large‐scale production and may find more advanced potential applications in memory devices, rewritable labels, an sensors. [ABSTRACT FROM AUTHOR]

Information retrieval is an important field that aims to provide a relevant document to a user information need, expressed through a query. Arabic is a challenging language that gained much attention recently in the information retrieval domain. To overcome the problems related to its complexity, many studies and techniques have been presented, most of them were conducted to solve the stemming problem. This paper presents an overview of the Arabic information retrieval process, including various text processing techniques, ranking approaches, evaluation measures, and some important information retrieval models. The paper finally presents some recent related studies and approaches in different Arabic information retrieval fields. [ABSTRACT FROM AUTHOR]

The introduction of electronic medical records has created vast opportunities in relation to data storage, visibility and extraction. In Allied Health the collection, storage, display and reporting of service statistics is a key opportunity to utilise the capabilities of the electronic medical record to reduce clinician time completing data entry, improve accuracy and visibility of available data and maximise opportunities to view and utilise service statistic information in clinical and operational decision making. This case study describes service statistic capture and extraction for a speech pathology department, pre- and post- the introduction of a digital dashboard. A new Allied Health digital dashboard was created via clinicians and informaticians working collaboratively to define service delivery elements for data extraction and design dashboard functionality. Descriptive comparison of data capture pre- and post- dashboard implementation was undertaken. The integration of service statistic information into a digital dashboard was found to support service statistic reporting, improve ease of access, and provide greater visibility and timeliness of service information. What is known about the topic? Service statistic data capturing and reporting systems used prior to the digital medical record required additional clinician entry processes, and lacked visibility, transparency and timeliness of reporting. What does this paper add? This paper describes a case study of the use of an electronic dashboard linked to the Electronic Medical Record to capture and report service statistics. What are the implications for practitioners? A real-time service statistic dashboard can remove a significant burden of manual reporting for staff, and help improve the transparency, visibility and timeliness of data availability for staff and managers. [ABSTRACT FROM AUTHOR]

Retrieving mathematical expressions from scientific documents is a challenging task as mathematical expressions or formulae are quite different from the traditional text. Mathematical expressions are highly symbolic and complex. Moreover, the structure of a mathematical formula conveys a semantic meaning which cannot be overlooked. This paper proposes a scientific document retrieval system based on mathematical formula query. The paper explores the concept of Structure Encoded String (SES), which has been employed for mathematical expressions to capture the relations among the formula structures. A pattern based trie indexing scheme has been proposed for faster retrieval. The Jaro-Winkler Similarity has been adopted for matching and ranking. Experiments are conducted, results are reported using standard evaluation measures and compared with similar existing systems. [ABSTRACT FROM AUTHOR]

Modeling information seeking behavior was started in the early 1980s pioneered by Wilson. More than 70 models in the last four decades on information seeking behavior have emerged. The models delineate the concepts from cognate subjects such as psychology and sociology. The authors made an analytical study of these models and identified many concepts of library and information science (LIS); from information processing and retrieval, reference service case studies and the evaluation experiments of information retrieval systems intrinsically are hidden in these models that have not been explicitly expounded as part of information seeking traits. The Chain Indexing devised by Ranganathan has an inclusive concept into information seeking with the phrase "Sought Link" to select the relevant subject heading(s) derived from the document(s) for indexing and to assign as the sought term(s) in the subject catalogue. Lancaster in the evaluation of MEDLARS identified phrases; serendipity and novelty ratio in response to users queries to the system. The paper after an in depth study of these ideas and with an analytical review of the models implies many of the classical concepts from LIS are implicit-in information seeking patterns of users. The paper conjures up to investigate them in the models of information seeking behavior as pragmatic rather only on user perceptions. [ABSTRACT FROM AUTHOR]

In order to improve the effect of personnel archives information management under the background of Industry 4.0, this paper conducts in-depth research on retrieval technology. Aiming at the shortcomings and deficiencies of the existing sorting methods, this paper introduces the basic principle of information retrieval, the retrieval process, and retrieval model and proposes a relevance ranking algorithm based on the user vector calculation method and the record vector calculation method. The algorithm solves the query structure sorting problem of information retrieval, optimizes the retrieval process, and improves the efficiency of information acquisition. Under the guidance of the concept of Industry 4.0, this paper constructs the digital management model of personnel archives information. The experimental results show that the digital management model of personnel archives proposed in this paper has certain effects. [ABSTRACT FROM AUTHOR]

Undertaking ethnographic or phenomenological inquiry under a hovering cloud of dictatorship can often be a mammoth, some might even say life-risking, venture. In such circumstances, researchers are confronted with ethical dilemmas: the need to strike a balance between accessing credible first-hand information and playing it safe. It is against this background that this paper traces the challenges confronting researchers planning to conduct fieldwork in authoritarian regimes. Conducting research under a political culture of fear, polarization and censorship has proven to be something of a heinous task for my own research in my home country of Zimbabwe. Drawing from this experience, the paper discusses the risks, ethical dilemmas and apprehensions that underscore the challenge of carrying out fieldwork in authoritarian regimes. Specifically, the paper discusses how researchers, whose research might be perceived by governments as a threat to national security, can deal with risks, threats, and dangers regarding access to the gathering and retrieval of data. [ABSTRACT FROM AUTHOR]

The amount of scientific papers is growing together with the development of science itself; but, although there is an unprecedented availability of large citation indexes, some daily activities of researchers remain time‐consuming and poorly supported. In this paper, we present Visual Bibliographies (VisualBib), a real‐time visual platform, designed using a zz‐structure‐based model for linking metadata and a narrative, visual approach for showing bibliographies. VisualBib represents a usable, advanced, and visual tool, which simplifies the management of bibliographies, supports a core set of bibliographic tasks, and helps researchers during complex analyses on scientific bibliographies. We present the variety of metadata formats and visualization methods, proposing two use case scenarios. The maturity of the system implementation allowed us two studies, for evaluating both the effectiveness of VisualBib in providing answers to specific data analysis tasks and to support experienced users during real‐life uses. The results of the evaluation are positive and describe an effective and usable platform. [ABSTRACT FROM AUTHOR]

In order to improve the effect of English teaching information retrieval, this paper designs a hierarchical retrieval model of digital English teaching information through ontology and constructs an intelligent system structure that can be used for hierarchical retrieval of digital English teaching information. Moreover, this paper proposes a method for judging the similarity of English teaching information structure based on non-negative matrix factorization and develops a hierarchical retrieval model of digital English teaching information based on ontology according to an improved algorithm. In addition, this paper uses the hierarchical search idea to design the hierarchical structure and study the system framework and hierarchical process. Finally, this paper verifies the performance of the system through simulation experiments. The experiment shows that the hierarchical retrieval model of digital English teaching information based on ontology proposed in this paper meets the basic needs of this paper to build an English teaching information system and can play a certain role in English teaching. [ABSTRACT FROM AUTHOR]

Information transmission and storage have gained traction as unifying concepts to characterize biological systems and their chances of survival and evolution at multiple scales. Despite the potential for an information-based mathematical framework to offer new insights into life processes and ways to interact with and control them, the main legacy is that of Shannon's, where a purely syntactic characterization of information scores systems on the basis of their maximum information efficiency. The latter metrics seem not entirely suitable for biological systems, where transmission and storage of different pieces of information (carrying different semantics) can result in different chances of survival. Based on an abstract mathematical model able to capture the parameters and behaviors of a population of single-celled organisms whose survival is correlated to information retrieval from the environment, this paper explores the aforementioned disconnect between classical information theory and biology. In this paper, we present a model, specified as a computational state machine, which is then utilized in a simulation framework constructed specifically to reveal emergence of a "subjective information", i.e., trade-off between a living system's capability to maximize the acquisition of information from the environment, and the maximization of its growth and survival over time. Simulations clearly show that a strategy that maximizes information efficiency results in a lower growth rate with respect to the strategy that gains less information but contains a higher meaning for survival. [ABSTRACT FROM AUTHOR]

The information retrieval system contains either a list of subject terms (taxonomy) or a list of collaborative tags (folksonomy) or both. The taxonomy and folksonomy come together as called hybrid subject devices. The main purpose of this paper is to apply machine learning techniques in the dataset from the library domain like others and analyse a large quantity of data for critical problems with accuracy. This research reveals to perform EDA (Exploratory data analysis), prediction analysis, and similarity measurement between folksonomy and taxonomy terms with new emerging technologies. Data science deals with big data that means unstructured data, messy data, a large volume of data. The size is of a large amount of data in terms of GB, TB. Machine learning tools manage this type of data. Usually, the Excel, or other spreadsheets package could not manage the file size in GB or TB, and that's why ML tools, and techniques are applied. At present, the library science domain also contains a large amount of data like 20/30 years of circulation data or subject descriptors, collaborative tags etc. Library professionals can apply machine learning tools for analysing this kind of data in the library domain. In this paper, the authors have introduced the applications of tools and techniques in the library domain and they have tested with 2642 taxonomy and folksonomy terms. This research work includes - EDA, prediction analysis, and similarity measurement of a folksonomy and taxonomy dataset. In the EDA part, the research work has performed a lot of analysis that includes frequency of LCSH (Library of Congress Subject Heading - taxonomy) terms, pair plots, joint plots, and heat map of LCSH and folksonomy terms. The logistic regression (LR) model for prediction analysis has been used in the folksonomy and taxonomy dataset. These 2642 terms of folksonomy and taxonomy both terms are taken as data for this research work. The EDA has been performed with the attributes in the dataset. The accuracy value of logistic regression (f1-score) is 0.37 at the training percentage of 69. The percentage of similarity between LCSH terms and folksonomy terms is 30 per cent (0.30151134), and the angle between these two vectors is 27 degrees. The novelty of this research work is that library data has been analysed using machine learning techniques the ever used before. [ABSTRACT FROM AUTHOR]

Since the introduction of the concept of entrepreneurial university in 1980s, the number of studies has dramatically increased, in particular since 2015. This had made the literature on the entrepreneurial university complex, fragmented and difficult to navigate. This paper provides a comprehensive review of all topics covered in the body of literature on the entrepreneurial university and identifies the most salient topics and papers within this literature, making use of text-mining techniques. Our paper employs topic modelling that reveals the underlying semantic structure of texts to identify the different underlying. Our study systematically analyses 1,110 papers over the period 1983–2020 using the Latent Dirichlet Allocation algorithm. Our analysis shows that the entrepreneurial university is fragmented around different topics that are very diverse. We find a total of 20 differentiated topics. Our study suggests that topics related to the overarching theme of academic entrepreneurship, in particular to commercialisation of research and the triple helix model are very popular within the entrepreneurial university literature. Finally, our analysis reveals that case-study type of research is losing momentum, giving path to nascent topics of research in the areas of entrepreneurial capability and university-industry alliances, which are becoming very popular within the entrepreneurial university literature. [ABSTRACT FROM AUTHOR]

This study is the first paper to investigate Turkish translation students' information literacy, with particular focus on the steps ranging from problem detection to (un)acceptable solutions to detected problems. The paper investigated the construct of translation students' information literacy by describing their information retrieval trajectory in view of (1) need for information, (2) search locations by gender, source type, and search items' structures, and (3) search results by search items, participants' genders, search items' structures, and search locations. Due to the recent prominence of electronic media as translators' workbenches, this research was primarily focused on online information retrieval skills. A screen-recording program, Camtasia Studio, was employed to monitor the participants' information mining process. SPSS - a statistical software program - was used to gain insight into the relationships between the operationalized parameters. The sample comprises ten fourth-year translation undergraduates, selected through convenience sampling. [ABSTRACT FROM AUTHOR]

X-ray grating interferometry (XGI) provides complementary information to visualize the internal structure of an object better than conventional absorption-based imaging methods, thereby having the potential for future biomedical applications. In XGI, information retrieval is critical for qualitative and quantitative research. However, information retrieval based on the phase-stepping technique usually requires that the absolute spatial translations of these phase-stepping position series lie in the range of only a few hundred nanometers, making this technique prone to mechanical instabilities and motion artifacts. In this paper, trimodal radiography using sinusoidal phase modulating interferometry is studied. The theoretical analysis of the signal retrieval algorithm using four integrating buckets is derived, and numerical experiments are demonstrated. In the proposed method, the phase modulation is generated by shifting the grating following a sinusoidal curve while the signals are retrieved from the four frames obtained by integrating the time-varying intensity over the four quarters of the modulation period. Compared with the previously proposed method, this method is easier to implement due to relaxed requirements on the phase-shifting device; high speed and continuous data averaging will greatly promote the real applications of the X-ray grating interferometer. [ABSTRACT FROM AUTHOR]

Retrieving mathematical expressions from scientific documents is a challenging task as mathematical expressions or formulae are quite different from the traditional text. Mathematical expressions are highly symbolic and complex. Moreover, the structure of a mathematical formula conveys a semantic meaning which cannot be overlooked. This paper proposes a scientific document retrieval system based on mathematical formula query. The paper explores the concept of Structure Encoded String (SES), which has been employed for mathematical expressions to capture the relations among the formula structures. A pattern based trie indexing scheme has been proposed for faster retrieval. The Jaro-Winkler Similarity has been adopted for matching and ranking. Experiments are conducted, results are reported using standard evaluation measures and compared with similar existing systems. [ABSTRACT FROM AUTHOR]

Objectives: In order to improve the calculation efficiency of linear octree neighbor grid elements, this paper uses Hilbert code to mark the grid elements and proposes a fast calculation algorithm for neighbor grid elements Hilbert code. Methods: Based on the Hilbert primitive curve, the concept of state vector is introduced to record the filling order of the Hilbert curve to all child elements belonging to the same parent element, thereby establishing the hierarchical evolution and degradation function of the state vector. With the hierarchical mapping relationship in the order curve, the state vector and its hierarchical evolution function are finally used to calculate the neighboring grid element Hilbert code. Results: The results of comparison experiments on calculation accuracy and efficiency show that the calculation result of the algorithm is correct. The calculation speed of the state vector decreases with the increase of the level. The calculation of 4 201 grid elements can be completed within 1 ms on the 20th level. In the calculation of the same number of neighbor grid elements at the specified level, the speed of this algorithm is significantly better than the existing Morton code conversion algorithm. At the 15th level, the speed of the algorithm in this paper is about 2.1-2.4 times that of the existing Morton code conversion algorithm. In the calculation of millions of adjacent cells at different levels, the calculation speed of the algorithm in this paper is compared with the existing Morton code conversion. The improvement factor of the algorithm increases as the level increases. At level 20, the efficiency of the proposed algorithm increases by 2.6 times. Conclusions: Experimental results show that this algorithm is superior to the existing Morton code conversion algorithm, and can be used as the basis of spatial operations such as efficient data retrieval, determination of three-dimensional topological relationship and connectivity judgment. [ABSTRACT FROM AUTHOR]

Privacy information security retrieval of multi-party data sources is an urgent problem in network security. The feature of oblivious transfer technology is to ensure the data security of each participant. Therefore, this paper combined oblivious transfer technology with homomorphic encryption and symmetric encryption in cryptography to design an anonymous query protocol for multi-party data sources. Firstly, based on the idea of oblivious transfer, this paper designed a three-party anonymous query protocol, and gave the model of the protocol and the specific process of the protocol. Secondly, this paper proved and analyzed the correctness and security of the protocol. Finally, it extended the three-party anonymous query protocol to the multiparty query protocol, and it applied to the verification of blacklist users in digital product transaction, which solved the problem of malicious transaction of buyer users in the process of network security transaction. Experimental data shows that the algorithm has correct query results and high efficiency under the condition of ensuring the data security of all parties. [ABSTRACT FROM AUTHOR]

As part of the effort to digitally transform, organizations are seeking more and better solutions to long‐standing enterprise content management challenges. Such solutions rarely investigate the relationship between knowledge workers' daily work to capture information and the perceived or actual value of that information to the enterprise per established content management strategy. The study described in this paper seeks to identify gaps in content management practices versus policy by modeling the conventions by which one organization's knowledge workers typically generate, store, and later recover their daily work products. Thirty‐five interviews with knowledge workers at the Jet Propulsion Laboratory were conducted on this subject. The results of these interviews provide an insight as to how knowledge workers interact with enterprise content in their dual roles as both the primary creators and primary consumers of enterprise content. This paper, which outlines various permutations of the digital object creation, description, and storage (CDS) model, provides basic strategies for bringing the value perceptions of knowledge workers into alignment with institutional directives related to improving content findability and reuse in the enterprise. [ABSTRACT FROM AUTHOR]

PURPOSE: Despite advances in molecular therapeutics, few anticancer agents achieve durable responses. Rational combinations using two or more anticancer drugs have the potential to achieve a synergistic effect and overcome drug resistance, enhancing antitumor efficacy. A publicly accessible biomedical literature search engine dedicated to this domain will facilitate knowledge discovery and reduce manual search and review. METHODS: We developed RetriLite, an information retrieval and extraction framework that leverages natural language processing and domain-specific knowledgebase to computationally identify highly relevant papers and extract key information. The modular architecture enables RetriLite to benefit from synergizing information retrieval and natural language processing techniques while remaining flexible to customization. We customized the application and created an informatics pipeline that strategically identifies papers that describe efficacy of using combination therapies in clinical or preclinical studies. RESULTS: In a small pilot study, RetriLite achieved an F 1 score of 0.93. A more extensive validation experiment was conducted to determine agents that have enhanced antitumor efficacy in vitro or in vivo with poly (ADP-ribose) polymerase inhibitors: 95.9% of the papers determined to be relevant by our application were true positive and the application's feature of distinguishing a clinical paper from a preclinical paper achieved an accuracy of 97.6%. Interobserver assessment was conducted, which resulted in a 100% concordance. The data derived from the informatics pipeline have also been made accessible to the public via a dedicated online search engine with an intuitive user interface. CONCLUSION: RetriLite is a framework that can be applied to establish domain-specific information retrieval and extraction systems. The extensive and high-quality metadata tags along with keyword highlighting facilitate information seekers to more effectively and efficiently discover knowledge in the combination therapy domain. [ABSTRACT FROM AUTHOR]

In this paper, we create EMIR, the first-ever Music Information Retrieval dataset for Ethiopian music. EMIR is freely available for research purposes and contains 600 sample recordings of Orthodox Tewahedo chants, traditional Azmari songs and contemporary Ethiopian secular music. Each sample is classified by five expert judges into one of four well-known Ethiopian Kiñits, Tizita, Bati, Ambassel and Anchihoye. Each Kiñit uses its own pentatonic scale and also has its own stylistic characteristics. Thus, Kiñit classification needs to combine scale identification with genre recognition. After describing the dataset, we present the Ethio Kiñits Model (EKM), based on VGG, for classifying the EMIR clips. In Experiment 1, we investigated whether Filterbank, Mel-spectrogram, Chroma, or Mel-frequency Cepstral coefficient (MFCC) features work best for Kiñit classification using EKM. MFCC was found to be superior and was therefore adopted for Experiment 2, where the performance of EKM models using MFCC was compared using three different audio sample lengths. 3s length gave the best results. In Experiment 3, EKM and four existing models were compared on the EMIR dataset: AlexNet, ResNet50, VGG16 and LSTM. EKM was found to have the best accuracy (95.00%) as well as the fastest training time. However, the performance of VGG16 (93.00%) was found not to be significantly worse (P < 0.01). We hope this work will encourage others to explore Ethiopian music and to experiment with other models for Kiñit classification. [ABSTRACT FROM AUTHOR]

Data deduplication has become a promising technique that significantly reduces space utilization of cloud storage servers and optimizes the network bandwidth. A variety of deduplication techniques have been proposed to enhance the cloud server's storage efficiency. Most of the deduplication schemes do not address global indexing and user privacy issues. This paper elucidates a novel three-tier secure data deduplication architecture (referred to as SEEDDUP) for data storage and retrieval in the cloud. SEEDDUP provides data deduplication with proper indexing and user privacy. In this work, the optimal chunk size, for a given file size and format, is calculated using Cuckoo Search Algorithm. The chunk existence in the cloud is verified by the hash value generated via SHA3-512. To improve data deduplication speed, Merkle Hash Tree based MapReduce (MHTMR) is used for fast index lookup, which improves the performance at data storage and retrieval. In addition, data are encrypted based on its sensitivity level (low and high) using Niederreiter Public Key Cryptosystem. With this new SEEDDUP architecture, only legitimate users can request data successfully for limited times (k) and spurious data requests cannot be processed or handled in the cloud. Experimental results show that the proposed SEEDDUP can reduce latency, achieved good throughput and deduplication efficiency (chunk size variance and security measures) in data deduplication. [ABSTRACT FROM AUTHOR]

Adopting semantic technologies has proven several benefits for enabling a better representation of the data and empowering reasoning capabilities over it. However, there are still unresolved issues, such as the shift from heterogeneous documents to semantic data models and the representation of search results. Thus, in this paper, we introduce a novel FramEwork for hybrid molEcule-baseDSEmantic SEARCH, entitled FEED2SEARCH, which facilitates Information Retrieval over a heterogeneous document corpus. We first propose a semantic representation of the corpus, which automatically generates a semantic graph covering both structural and domain-specific aspects. Then, we propose a query processing pipeline based on a novel data structure for query answers, extracted from this graph, which embeds core information together with structural-based and domain-specific context. This provides users with interpretable search results, helping them understand relevant information and track cross document dependencies. A set of experiments conducted using real-world construction projects from the Architecture, Engineering and Construction (AEC) industry shows promising results, which motivates us to further investigate the effectiveness of our proposal in other domains. [ABSTRACT FROM AUTHOR]

The problem of attacks on neural networks through input modification (i.e., adversarial examples) has attracted much attention recently. Being relatively easy to generate and hard to detect, these attacks pose a security breach that many suggested defenses try to mitigate. However, the evaluation of the effect of attacks and defenses commonly relies on traditional classification metrics, without adequate adaptation to adversarial scenarios. Most of these metrics are accuracy-based and therefore may have a limited scope and low distinctive power. Other metrics do not consider the unique characteristics of neural network functionality or measure the effectiveness of the attacks indirectly (e.g., through the complexity of their generation). In this paper, we present two metrics that are specifically designed to measure the effect of attacks, or the recovery effect of defenses, on the output of neural networks in multiclass classification tasks. Inspired by the normalized discounted cumulative gain and the reciprocal rank metrics used in information retrieval literature, we treat the neural network predictions as ranked lists of results. Using additional information about the probability of the rank enabled us to define novel metrics that are suited to the task at hand. We evaluate our metrics using various attacks and defenses on a pre-trained VGG19 model and the ImageNet dataset. Compared to the common classification metrics, our proposed metrics demonstrate superior informativeness and distinctiveness. [ABSTRACT FROM AUTHOR]

Cloud computing, which is distributed, stored and managed, is drawing attention as data generation and storage volumes increase. In addition, research on green computing, which increases energy efficiency, is also widely studied. An index is constructed to retrieve huge dataset efficiently, and the layer-based indexing methods are widely used for efficient query processing. These methods construct a list of layers, so that only one layer is required for information retrieval instead of the entire dataset. The existing layer-based methods construct the layers using a convex hull algorithm. However, the execution time of this method is very high, especially in large, high-dimensional datasets. Furthermore, if the total number of layers increases, the query processing time also increases, resulting in efficient, but slow, query processing. In this paper, we propose an unbalanced-hierarchical layer method, which hierarchically divides the dimensions of input data to increase the total number of layers and reduce the index building time. We demonstrate that the proposed procedure significantly increases the total number of layers and reduces the index building time, compared to existing methods through the various experiments. [ABSTRACT FROM AUTHOR]

QR codes provide potential opportunities to improve interaction in different areas of application. Even so, this technology has been utilised in punctual and specific standalone applications, being barely applied to broader contexts, such as the educational one, where different scenarios can be considered to take advantage of the same QR-code approach. In this paper, key issues for the development of applications that utilise QR codes to improve sharing and availability of resources are presented, specifically focusing on improving interaction and acceptance. In this way, a holistic approach has been developed where the system is aimed at improving the interaction between educators and students. On the one hand, educators use a web interface to manage educational resources and announcements and automatically generate QR codes. On the other hand, students scan those QR codes using a mobile device, obtaining updated information related to their lectures. This scenario has been evaluated by educators and students, obtaining good results in terms of acceptance, cognitive burden, and overall usability. The approach provides development clues and the evaluation method that can be applied to different contexts, thus representing a generalisable solution. [ABSTRACT FROM AUTHOR]