Search

Nowadays, the web has emerged as an enormous multimedia data resource. Social media platforms are becoming the mass producers of user-generated multimedia content. Web search engines usually organize media-specific information, such as text, images, video, etc., in specialized data repositories (verticals), providing easy access to multimedia content. Web search engines have become efficient in retrieving data across various distinct verticals. However, users still need help formulating queries to retrieve the relevant multimedia results from verticals. Novice users often issue short-length ambiguous queries due to a lack of domain knowledge or query formulation experience, resulting in the retrieval of irrelevant results. The query formulation itself is a time-consuming process for the users. We presented an end-to-end deep-learning automatic query recommendation approach to address the associated issues. The proposed method autonomously extracts the domain knowledge using pseudo-relevance feedback, transforms it into a unified text-to-text summary, and assists users in generating non-ambiguous and well-balanced query recommendations. The proposed system employs Google's real-time dataset and is compared to the Google search engine. The evaluation consists of empirical and usability perspectives. The empirical evaluation of shorter query formulation and reformulation time obtained 89% accuracy scores in automated query recommendation. The usability testing (N=37) reveals 85.4% usefulness & ease-of-use, and "A" category proposed system usability. [ABSTRACT FROM AUTHOR]

Tag recommendation can suggest a collection of tags to users and effectively describe the characteristics of resources or users' preferences, thereby enhancing the accuracy of information retrieval. In the field of recommendation, metric learning models are employed to measure distances among different categories. However, classifiers are susceptible to noise interference, and it is difficult to mine hard negative samples that lie close to the decision boundary in the training set. Resulting in the hard negative samples is incorrectly recommended to users. In this paper, we propose an Adversarial Tag Hard Negative samples framework to synthesize hard negative samples from observed training data, enhancing the diversity of data. We learn the distribution of the raw training data and the features that come from the hard negative samples. Specifically, we utilize the original metric learning model to preserve the effect of learning on normal samples. We also design an adversarial metric learning model, improving the model's ability to adapt to unseen samples and potentially complex contexts, especially preventing the model from misclassifying a specific type of data in a classification. Finally, we conduct extensive experiments on the hetrec2011-MovieLens-2k and hetrec2011-LastFm-2k datasets, as well as an effective ablation experiment to quantify the contributions of adversarial components. The results demonstrate that the proposed method outperforms existing approaches in Top-N tag recommendation. Compared to the most competitive baselines, our method shows improvements of 2.1%, 8.3%, 10.9% and 5.3%, 5.7%, 7% in terms of F1@3, F1@5, F1@10 and NDCG@3, NDCG@5, NDCG@10 evaluation metrics on hetrec2011-MovieLens-2k dataset, respectively. [ABSTRACT FROM AUTHOR]

Quantum private information retrieval (QPIR) for quantum messages is a quantum communication task, in which a user retrieves one of the multiple quantum states from the server without revealing which state is retrieved. In the one-server setting, we find an exponential gap in the communication complexities between the presence and absence of prior entanglement in this problem with the one-server setting. To achieve this aim, as the first step, we prove that the trivial solution of downloading all messages is optimal under QPIR for quantum messages, which is a similar result to that of classical PIR but different from QPIR for classical messages. As the second step, we propose an efficient one-server one-round QPIR protocol with prior entanglement by constructing a reduction from a QPIR protocol for classical messages to a QPIR protocol for quantum messages in the presence of prior entanglement. [ABSTRACT FROM AUTHOR]

The advent of blockchain technology has revolutionized Internet-of-Thing (IoT) systems offering immutable and transparent ledger mechanisms. However, this transformative technology has also brought significant challenges related to user privacy. Though the existing solutions offer various privacy enrichment in blockchain systems through cryptographic methods, the problems of deanonymization (the issue of revealing users' identities) and linkability of transactions still persist. Furthermore, recent reported news also shows privacy leakages in blockchain systems. Thus, we require a comprehensive privacy-assured blockchain framework to address the above-mentioned problems. In this paper, we introduce a novel privacy-preserving framework for blockchain transactions along with private information retrieval (PIR) to combat the above-mentioned privacy issues in blockchains. We call our proposed approach "PRIvacy-ensured framework for Blockchain trANsactions with pIr (PRIBANI)". PRIBANI uses randomizing ring signatures, Public key Encryption-based Keyword Search (PEKS), and PIR techniques to protect the privacy of the user and the history of the transaction data. We run a set of experiments on our proposed PRIBANI to evaluate the privacy attainment of the framework based on user anonymity, transaction anonymity, and unlinkability. We also compare PRIBANI with state-of-the-art models and show that PRIBANI gives a better throughput with an average of 56 transactions per second (TPS) and incurs low latency which is approximately 5% less than the existing schemes and an average communication overhead of 3.2 kilobytes (kbytes). Overall, PRIBANI is efficient in executing privacy-ensured blockchain transactions. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to evaluate the early-detection capabilities of AI in a screening program over its duration, with a specific focus on the detection of interval cancers, the early detection of cancers with the assistance of AI from prior visits, and its impact on workload for various reading scenarios. Materials and methods: The study included 22,621 mammograms of 8825 women within a 10-year biennial two-reader screening program. The statistical analysis focused on 5136 mammograms from 4282 women due to data retrieval issues, among whom 105 were diagnosed with breast cancer. The AI software assigned scores from 1 to 100. Histopathology results determined the ground truth, and Youden's index was used to establish a threshold. Tumor characteristics were analyzed with ANOVA and chi-squared test, and different workflow scenarios were evaluated using bootstrapping. Results: The AI software achieved an AUC of 89.6% (86.1–93.2%, 95% CI). The optimal threshold was 30.44, yielding 72.38% sensitivity and 92.86% specificity. Initially, AI identified 57 screening-detected cancers (83.82%), 15 interval cancers (51.72%), and 4 missed cancers (50%). AI as a second reader could have led to earlier diagnosis in 24 patients (average 29.92 ± 19.67 months earlier). No significant differences were found in cancer-characteristics groups. A hybrid triage workflow scenario showed a potential 69.5% reduction in workload and a 30.5% increase in accuracy. Conclusion: This AI system exhibits high sensitivity and specificity in screening mammograms, effectively identifying interval and missed cancers and identifying 23% of cancers earlier in prior mammograms. Adopting AI as a triage mechanism has the potential to reduce workload by nearly 70%. Clinical relevance statement: The study proposes a more efficient method for screening programs, both in terms of workload and accuracy. Key Points: • Incorporating AI as a triage tool in screening workflow improves sensitivity (72.38%) and specificity (92.86%), enhancing detection rates for interval and missed cancers. • AI-assisted triaging is effective in differentiating low and high-risk cases, reduces radiologist workload, and potentially enables broader screening coverage. • AI has the potential to facilitate early diagnosis compared to human reading. [ABSTRACT FROM AUTHOR]

In a large-scale distributed network, a naming service is used to achieve location transparency and provide effective content discovery. However, fast and accurate name retrieval in the massive name set is laborious. Approximate set membership data structures, such as Bloom filter and Cuckoo filter, are very popular in distributed information systems. They obtain high query performance and reduce memory requirements through the abstract representation of information, but at the cost of introducing query error rates, which will ultimately affect content service quality. In this paper, in order to obtain higher space utilization and a lower query false positive rate, we propose a flexible fingerprint cuckoo filter (FFCF) for information storage and retrieval, which can change the length and type of fingerprints adaptively. In our scheme, FFCF uses longer fingerprints under low occupancy and has the ability to correct errors by changing the type of stored fingerprints. Moreover, we give a theoretical proof and evaluate the performance of FFCF by experimental simulations with synthetic data sets and real network packets. The results demonstrate that FFCF can improve memory utilization, significantly reduce false positive errors by nearly 90 % at 50 % occupancy and outperform Cuckoo filter in the full range of occupancy. [ABSTRACT FROM AUTHOR]

Radiological imaging is a globally prevalent diagnostic method, yet the free text contained in radiology reports is not frequently used for secondary purposes. Natural Language Processing can provide structured data retrieved from these reports. This paper provides a summary of the current state of research on Large Language Model (LLM) based approaches for information extraction (IE) from radiology reports. We conduct a scoping review that follows the PRISMA-ScR guideline. Queries of five databases were conducted on August 1st 2023. Among the 34 studies that met inclusion criteria, only pre-transformer and encoder-based models are described. External validation shows a general performance decrease, although LLMs might improve generalizability of IE approaches. Reports related to CT and MRI examinations, as well as thoracic reports, prevail. Most common challenges reported are missing validation on external data and augmentation of the described methods. Different reporting granularities affect the comparability and transparency of approaches. [ABSTRACT FROM AUTHOR]

Community based question and answer websites; such as, Yahoo Answers, Stack Overflow, Stack Exchange and Quora are briskly generating content in the form of questions and answers posted by users. These online discussion forums allow the users to append and edit the content easily. Therefore, a huge amount of content has accumulated over the time, which is beneficial for the search engine users for addressing their queries to the related content. On the other hand, the task of maintaining the answer quality and ranking of retrieved results has always been a challenging task for the websites that accumulate user's generated data. Here, the problem of ranking the answers based on the user's satisfaction is investigated. The best answers based on their ranking are identified. Three techniques are proposed, which are Relevance Features (REL_FS), Readability Features (RED_FS), and Weighted Answer Rank WEIGHTED ANS_RANK. REL_FS performs a summation of relevance features, RES_FS sums the readability features and WEIGHTED ANS_RANK performs a summation of REL_FS and RED_FS to deal with the task. Proposed methods compute the rank of answers according to both the relevance and readability of answers. Experimental results show proposed methods ability of providing effective ranking of answers for the selection of best answers. Moreover, it also measures the consistency of ranked answers with the help of a detailed correlational analysis. [ABSTRACT FROM AUTHOR]

Clustering is an unsupervised machine learning method grouping data samples into clusters of similar objects, used as a system support tool in numerous applications such as banking customers profiling, document retrieval, image segmentation, and e-commerce recommendation engines. The effectiveness of several clustering techniques is sensible to the initialization parameters, and different solutions have been proposed in the literature to overcome this limitation. They require high computational memory consumption when dealing with big data. In this paper, we propose the application of a recent object detection Deep Learning model (YOLO-v5) for assisting the initialization of classical techniques and improving their effectiveness on two-variate datasets, leveraging the accuracy and reducing dramatically the memory and time consumption of classical clustering methods. [ABSTRACT FROM AUTHOR]

The healthcare industry is increasingly reliant on access to large volumes of patient data, which is critical for improving patient outcomes, reducing costs, and ensuring regulatory compliance. However, there are significant challenges facing healthcare organizations in retrieving patient data from databases and ensuring the security and privacy of this data. Addressing these challenges is vital to the future success of the healthcare industry. This paper proposes two solutions to these challenges: a simplified ontology-based retrieval algorithm for healthcare multimedia data to enhance the retrieval process, and Hierarchical learning: ensemble model-based reinforcement learning to improve patient data security. An ontology-based retrieval algorithm was a method used to retrieve relevant information from large datasets. It filtered out irrelevant data and provided contextually relevant results. Hierarchical learning involved organizing learning tasks in a hierarchical structure, with higher-level policies guiding lower-level policies. Ensemble model-based reinforcement learning combined multiple models to improve the performance of a reinforcement learning system. The Simplified Ontology-Based Retrieval Algorithm had excellent performance with an accuracy of 92%, making it highly effective in information retrieval tasks. The paper highlights the importance of using advanced technologies and prioritizing cyber security in healthcare to maintain trust and optimal patient care. Cutting-edge technologies transformed healthcare by enhancing patient data retrieval and cyber security. Examples included encrypted computations, multi-party privacy, query result anonymization, real-time monitoring through IoMT, and secure data sharing. Adopting these solutions ultimately contributed to the protection of sensitive patient data and the overall security of healthcare organizations. [ABSTRACT FROM AUTHOR]

Implementing a cloud-based data warehouse to store sensitive or critical strategic data presents challenges primarily related to the security of the stored information and the exchange of OLAP queries between the cloud server and users. Although encryption is a viable solution for safeguarding outsourced data, applying it to OLAP queries involving multidimensional data, measures, and Multidimensional Expressions (MDX) operations on encrypted data poses difficulties. Existing searchable encryption solutions are inadequate for handling such complex queries, which complicates the use of business intelligence tools that rely on efficient and secure data processing and analysis.This paper proposes a new privacy-preserving cloud data warehouse scheme called SSF-CDW which facilitates a secure and scalable solution for an encrypted cloud data warehouse. Our SSF-CDW improves the OLAP queries accessible only to authorized users who can decrypt the query results with improved query performance compared to traditional OLAP tools. The approach involves utilizing symmetric encryption and Ciphertext Policy Attribute-Based Encryption (CP-ABE) to protect the privacy of the dimension and fact data modeled in Multidimensional OLAP (MOLAP). To support efficient OLAP query execution, we proposed a new data cube retrieval mechanism using a Redis schema which is an in-memory database. This technique dynamically compiles queries by disassembling them down into multiple levels and consolidates the results mapped to the corresponding encrypted data cube. The caching of dimensional and fact data associated with the encrypted cube is also implemented to improve the speed of frequently queried data. Experimental comparisons between our proposed indexed search strategy and other indexing schemes demonstrate that our approach surpasses alternative techniques in terms of search speed for both ad-hoc and repeated OLAP queries, all while preserving the privacy of the query results. [ABSTRACT FROM AUTHOR]

Large-sample hydrology datasets have become increasingly available, contributing to significant scientific advances. However, in Europe, only a few such datasets have been published, capturing only a fraction of the wealth of information from national data providers in terms of available spatial density and temporal extent. We present "EStreams", an extensive dataset of hydro-climatic variables and landscape descriptors and a catalogue of openly available stream records for 17,130 European catchments. Spanning up to 120 years, the dataset includes streamflow indices, catchment-aggregated hydro-climatic signatures and landscape attributes (topography, soils, geology, vegetation and landcover). The catalogue provides detailed descriptions that allow users to directly access streamflow data sources, overcoming challenges related to data redistribution policies, language barriers and varied data portal structures. EStreams also provides Python scripts for data retrieval, aggregation and processing, making it dynamic in contrast to static datasets. This approach enables users to update their data as new records become available. Our goal is to extend current large-sample datasets and further integrate hydro-climatic and landscape data across Europe. [ABSTRACT FROM AUTHOR]

Writer retrieval has valuable applications in analyzing handwritten documents, such as the verification of authenticity and authorship of unknown manuscripts. Writer retrieval systems are designed to automatically locate all manuscripts attributed to a specific author based on dissimilarity measures between document features. In the present work, we propose a hybrid combination of handcrafted and deep features to achieve robust writer retrieval. Two writer retrieval systems based on deep features are implemented using the VGG-16 and the MobileNetV2 models. Then, in order to bring complementary information, we propose the MO-HOT (Multi Orientated Histogram Of Templates) to develop shape features-based writer retrieval system. The MO-HOT highlights the writing traits in various directions along with various scales. The three systems are combined through a SVM based dissimilarity learning to aggregate a final writer retrieval decisions. Experiments are conducted on four handwritten document datasets that are CVL, ICDAR-2011, ICDAR-2013, and ICDAR-2017. The results obtained evince the effectiveness of the proposed hybrid combination, which outperforms the best state of the art results with up to 2% in the TOP-2 score. Besides, the proposed MO-HOT provides comparable and complementary performance with deep features since it helps to improve the MAP score by at least 3.6% for CVL, ICDAR-2013 and ICDAR-2017 datasets. [ABSTRACT FROM AUTHOR]

In the area of information retrieval, in order to improve search accuracy and reduce communication overhead, there is an increasing tendency to adopt ranked search in engines. Ranked search allows cloud servers to search for the top k most relevant documents based on the relevance score between the query keywords and the documents. Recently, with the increasing popularity of encrypted search technologies, ranked searchable encryption is proposed accordingly which focuses on solving ranked search problem over encrypted databases. However, recent studies show that some privacy protection methods commonly used in ranked searchable encryption, like order-preserving encryption (OPE), have some security problems. These problems may lead to the leakage of the relevant ranking privacy information. Meanwhile, most of the existing ranked searchable encryption schemes do not consider the problem of payment for outsourced services. In this paper, we propose a scheme called ranked searchable encryption based on differential privacy and blockchain (DPB-RSE). Specifically, we first add noise drawn from a Laplace distribution into the relevance score to disturb its value. Then we design a smart contract to verify the correctness of the results returned by the cloud server and realize payment fairness. The experiment results demonstrate that the accuracy of search results in this scheme can reach 94% in a small privacy budge. [ABSTRACT FROM AUTHOR]

The blockchain-based Electronic Medical Record (EMR) data storage system encounters challenges which are data leakage, insufficient access control, and low retrieval efficiency. In response to these issues, this paper proposes a lightweight searchable encryption and access control system for sharing EMR data securely. The lightweight searchable encryption scheme which is achieved by integrating the trapdoor verification phase with the keyword retrieval phase is constructed using BGG13 + and MP12. User access control is implemented through a Boolean circuit to replace arithmetic operations and thus improve arithmetic efficiency. Since logic operations can reduce the computation time, this paper uses Boolean circuits for user rights verification implementation. In addition, this paper adopts the Bloom filter as the system index to enhance the efficiency of block-chain data retrieval. According to the simulation results, there is a performance advantage of this system over similar systems. [ABSTRACT FROM AUTHOR]

Federated learning, an innovative artificial intelligence training method, offers a secure solution for institutions to collaboratively develop models without sharing raw data. This approach offers immense promise and is particularly advantageous for domains dealing with sensitive information, such as patient data. However, when confronted with a distributed data environment, challenges arise due to data paucity or inherent heterogeneity, potentially impacting the performance of federated learning models. Hence, scrutinizing the efficacy of this method in such intricate settings is indispensable. To address this, we harnessed pathological image datasets of endometrial cancer from four hospitals for training and evaluating the performance of a federated learning model and compared it with a centralized learning model. With optimal processing techniques (data augmentation, color normalization, and adaptive optimizer), federated learning exhibited lower precision but higher recall and Dice similarity coefficient (DSC) than centralized learning. Hence, considering the critical importance of recall in the context of medical image processing, federated learning is demonstrated as a viable and applicable approach in this field, offering advantages in terms of both performance and data security. [ABSTRACT FROM AUTHOR]

Deterministic encryption (DET) allows for fast retrieval of encrypted information, but it would cause significant leakage of frequency information of the underlying data, which results in an array of inference attacks. Simply replacing DET with fully randomized encryption is often undesirable in the scenario of an encrypted database since it incurs a large overhead in query and storage. Frequency Smoothing Encryption (FSE) is a practical encryption scheme to protect frequency information. Current FSE constructions still fall short of efficiency and a reasonable security definition. We revisit FSE and propose two security definitions from both theoretical and practical perspectives. Furthermore, we adopt a novel partitioning strategy to construct a new FSE scheme to improve performance. Experimental results show that compared with others, our scheme achieves excellent query performance while attaining security against inference attacks. [ABSTRACT FROM AUTHOR]

A combination of tools and methods known as "graph mining" is used to evaluate real-world graphs, forecast the potential effects of a given graph's structure and properties for various applications, and build models that can yield actual graphs that closely resemble the structure seen in real-world graphs of interest. However, some graph mining approaches possess scalability and dynamic graph challenges, limiting practical applications. In machine learning and data mining, among the unique methods is graph embedding, known as network representation learning where representative methods suggest encoding the complicated graph structures into embedding by utilizing specific pre-defined metrics. Co-occurrence graphs and keyword searches are the foundation of search engine optimizations for diverse real-world applications. Current work on keyword searches on graphs is based on pre-established information retrieval search criteria and does not provide semantic linkages. Recent works on co-occurrence and keyword search methods function effectively on graphs with only one layer instead of many layers. However, the graph neural network has been utilized in recent years as a branch of graph model due to its excellent performance. This paper proposes an Effective Keyword Search Co-occurrence Multi-Layer Graph mining method by employing two core approaches: Multi-layer Graph Embedding and Graph Neural Networks. We conducted extensive tests using benchmarks on real-world data sets. Considering the experimental findings, the proposed method enhanced with the regularization approach is substantially excellent, with a 10% increment in precision, recall, and f1-score. [ABSTRACT FROM AUTHOR]

Entity recognition plays a crucial role in various domains, such as natural language processing, information retrieval, and question-answering systems. While significant progress has been made in recognizing entities from plain text, the exploration of entity recognition from multimodal data remains limited due to disparities in semantic representation. In light of this challenge, given the supportive nature of visual and text data, we propose a novel entity recognition model called Heterogeneous Graph Reasoning(HGR), leveraging the synergistic nature of visual and textual data. HGR utilizes image objects to facilitate text entity extraction by mining the potential pair projection between text entity and image object. This is achieved through the utilization of the Vision Refine and Graph Cross Inference modules. In the Vision Refine module, semantically relevant objects hidden in the image are selected to aid in the text entity extraction. In the Graph Cross Inference module, cross-association inference between visual regions and textual entities is constructed through graph construction, heterogeneous graph fusion, visual region refinement and cross inference. To validate the effectiveness of our model, extensive experiments on four multimodal datasets are conducted. Among these datasets, two originate from Chinese unmanned surface vehicles and journalism(USV and NEWS), while the remaining two are public English multimodal datasets(Twitter-2015 and Twitter-2017). The experimental results demonstrate the superiority of our model, with F1-sore improvements of 1.55%, 0.12%, 0.22%, and 0.99% on the four datasets, respectively, when compared to the second-best state-of-the-art model. [ABSTRACT FROM AUTHOR]

Private information retrieval, which allows users to securely retrieve information stored in a single server or multiple servers without disclosing any query content to the server, has attracted much attention in recent years. However, most of the existing private information retrieval schemes cannot achieve data retrieval and data integrity authentication simultaneously. To address the above challenges, this paper proposes a verifiable private information retrieval scheme based on parity in a single-server architecture. Specifically, the data owner generates parity information for each data and extends the original database. Then the data owner generates hint information for the query client, and according to the inverse of the hint information, the matrix confusion and permutation of the extensible database are carried out on the database and the hint information is sent to the client. The client selects the corresponding element in the hint to generate the query vector and executes the reconstruction and verification phase after receiving the answer to accomplish the retrieval process. A series of security games prove that this scheme meets the privacy requirements defined by the PIR scheme, and experimental analysis shows that compared with related schemes, our scheme has certain advantages in time cost. The time of verification information generation is 0.3% of APIR and FMAPIR, the reconstruction time is 1.6% of APIR and 1.1% of FMAPIR and the query time is much less than them. [ABSTRACT FROM AUTHOR]

The massive internet and web data are increasing day-by-day rapidly, so searching an important information from huge data is a tedious task. Relevant document retrieval for polysemy queries is a primary issue. This paper addressed the polysemy issue for information retrieval and proposed an intelligent FK Hybrid Model. The model used clustering as a tool to group search results into a coherent group that may help the user in effective and efficient search result during browsing. We utilize the traditional clustering algorithm's capabilities and integrate a nature-inspired approach. The FK Hybrid Model combines the Firefly algorithm and the K-means clustering algorithm. In a series of experiments performed on AMBIENT data, the suggested model significantly outperforms to the related methods.. The experimental results show that our model can locate appropriate clustering solutions with the correct number of clusters. [ABSTRACT FROM AUTHOR]

With the increasing importance of multimedia and multilingual data in online encyclopedias, novel methods are needed to fill domain gaps and automatically connect different modalities for increased accessibility. For example, Wikipedia is composed of millions of pages written in multiple languages. Images, when present, often lack textual context, thus remaining conceptually floating and harder to find and manage. In this work, we tackle the novel task of associating images from Wikipedia pages with the correct caption among a large pool of available ones written in multiple languages, as required by the image-caption matching Kaggle challenge organized by the Wikimedia Foundation. A system able to perform this task would improve the accessibility and completeness of the underlying multi-modal knowledge graph in online encyclopedias. We propose a cascade of two models powered by the recent Transformer networks able to efficiently and effectively infer a relevance score between the query image data and the captions. We verify through extensive experiments that the proposed cascaded approach effectively handles a large pool of images and captions while maintaining bounded the overall computational complexity at inference time. With respect to other approaches in the challenge leaderboard, we can achieve remarkable improvements over the previous proposals (+8% in nDCG@5 with respect to the sixth position) with constrained resources. The code is publicly available at https://tinyurl.com/wiki-imcap. [ABSTRACT FROM AUTHOR]

Identifying cases of intellectual property violation in multimedia files poses significant challenges for the Internet infrastructure, especially when dealing with extensive document collections. Typically, techniques used to tackle such issues can be categorized into either of two groups: proactive and reactive approaches. This article introduces an approach combining both proactive and reactive solutions to remove illegal uploads on a platform while preventing legal uploads or modified versions of audio tracks, such as parodies, remixes or further types of edits. To achieve this, we have developed a rule-based focused crawler specifically designed to detect copyright infringement on audio files coupled with a visualization environment that maps the retrieved data on a knowledge graph to represent information extracted from audio files. Our system automatically scans multimedia files that are uploaded to a public collection when a user submits a search query, performing an audio information retrieval task only on files deemed legal. We present experimental results obtained from tests conducted by performing user queries on a large music collection, a subset of 25,000 songs and audio snippets obtained from the Free Music Archive library. The returned audio tracks have an associated Similarity Score, a metric we use to determine the quality of the adversarial searches executed by the system. We then proceed with discussing the effectiveness and efficiency of different settings of our proposed system. [ABSTRACT FROM AUTHOR]

Many evaluations of binary classifiers begin by adopting a pair of indicators, most often sensitivity and specificity or precision and recall. Despite this, we lack a general, pan-disciplinary basis for choosing one pair over the other, or over one of four other sibling pairs. Related obscurity afflicts the choice between the receiver operating characteristic and the precision-recall curve. Here, I return to first principles to separate concerns and distinguish more than 50 foundational concepts. This allows me to establish six rules that allow one to identify which pair is correct. The choice depends on the context in which the classifier is to operate, the intended use of the classifications, their intended user(s), and the measurability of the underlying classes, but not skew. The rules can be applied by those who develop, operate, or regulate them to classifiers composed of technology, people, or combinations of the two. [ABSTRACT FROM AUTHOR]

Recently, surveillance technology was proposed as an alternative to flood monitoring systems. This study introduces a novel approach to flood monitoring by integrating surveillance technology and LiDAR data to estimate river water levels. The methodology involves deep learning semantic segmentation for water extent extraction before utilizing the segmented images and virtual markers with elevation information from light detection and ranging (LiDAR) data for water level estimation. The efficiency was assessed using Spearman's rank-order correlation coefficient, yielding a high correlation of 0.92 between the water level framework with readings from the sensors. The performance metrics were also carried out by comparing both measurements. The results imply accurate and precise model predictions, indicating that the model performs well in closely matching observed values. Additionally, the semi-automated procedure allows data recording in an Excel file, offering an alternative measure when traditional water level measurement is not available. The proposed method proves valuable for on-site water-related information retrieval during flood events, empowering authorities to make informed decisions in flood-related planning and management, thereby enhancing the flood monitoring system in Malaysia. [ABSTRACT FROM AUTHOR]

Automatic information retrieval from written documents is made possible through natural language processing (NLP). Due to the uncertainty and diversity of real languages, the application of information retrieval is challenging. However, with the development of NLP methods based on deep learning, the issues have been alleviated through information learning criteria. Deep learning-based information retrieval technique provides the answers to their queries as a single piece of specialized information rather than a whole document. Therefore, in the proposed framework, a novel and hybrid BERT model is developed to perform the NLP based information retrieval process. Query input uses information retrieval from the entire document through two different stages of paragraph extraction and information retrieval. Here, the paragraph extraction is employed using the syntax Bidirectional encoder representations from transformers (BERT), and the information retrieval is employed through the fine-tuned BERT. The loss function of the fine-tuned BERT is optimized using the proposed Adaptive Improved Arithmetic Optimization (AIAO) algorithm for enhancing retrieval accuracy by minimizing information loss during the learning phase. The proposed AIAO-FT information retrieval method is analyzed based on various assessment measures like Precision, Recall, F1-Score, Bleu_Score, and RIBES_score and accomplished the values of 0.700, 0.767, 0.732, 0.721, and 0.776 respectively. [ABSTRACT FROM AUTHOR]

The rapid development of the digital healthcare and the electronic health records (EHR) requires smooth networking infrastructure to access data using Hypertext Transfer Protocol (HTTP)-based applications. The new HTTP/3 standard should provide performance and security improvements over HTTP/2. The goal of our work was to test the performance of HTTP/2 and HTTP/3 in the context of the EHRs. We used 45,000 test FHIR Patient resources downloaded and uploaded using 20, 50, 100 and 200 resources per Bundle, which resulted in 2251, 901, 451 and 226 HTTP GET and POST requests respectively. The first test downloading 20 resources per Bundle showed that HTTP/3 outperformed HTTP/2 in the local (mean request time 16.57 ms ± 7.2 standard deviation [SD]) and in the remote network (71.45 ms ± 43.5 SD) which is almost 3 times faster. In the 50 and 100 resources per Bundle test the HTTP/3 protocol demonstrated again more than two times gain in downloading performance for remote requests with mean request time 91.13 ms ± 34.54 SD and 88.09 ms ± 21.66 SD respectively. Furthermore, HTTP/3 outperformed HTTP/2 in the constructed clinical dataset remote transfer. In the upload tests HTTP/3 showed only a slight gain in performance merely in the remote network. The HTTP/3 protocol is a relatively new development and a major improvement for the worldwide web. This new technology is still missing in the digital health and EHRs. Its use could offer a major performance gain in situations where data is gathered from multiple remote locations. [ABSTRACT FROM AUTHOR]

This special issue features the selected works of authors who have presented papers at the 2022 iteration of the Joint Conference on Digital Libraries (JCDL) in Cologne, Germany. The motto of the conference was "Bridging Worlds" and was run as a fully hybrid event. Ten papers covering all aspects of Digital Libraries, namely Natural Language Processing, Information Retrieval, User Behavior, Scholarly Communication, Classification, Information Extraction are included in this issue. [ABSTRACT FROM AUTHOR]

Our civilization creates enormous volumes of digital data, a substantial fraction of which is preserved and made publicly available for present and future usage. Additionally, historical born-analog records are progressively being digitized and incorporated into digital document repositories. While professionals often have a clear idea of what they are looking for in document archives, average users are likely to have no precise search needs when accessing available archives (e.g., through their online interfaces). Thus, if the results are to be relevant and appealing to average people, they should include engaging and recognizable material. However, state-of-the-art document archival retrieval systems essentially use the same approaches as search engines for synchronic document collections. In this article, we develop unique ranking criteria for assessing the usefulness of archived contents based on their estimated relationship with current times, which we call contemporary relevance. Contemporary relevance may be utilized to enhance access to archival document collections, increasing the likelihood that users will discover interesting or valuable material. We next present an effective strategy for estimating contemporary relevance degrees of news articles by utilizing learning to rank approach based on a variety of diverse features, and we then successfully test it on the New York Times news collection. The incorporation of the contemporary relevance computation into archival retrieval systems should enable a new search style in which search results are meant to relate to the context of searchers' times, and by this have the potential to engage the archive users. As a proof of concept, we develop and demonstrate a working prototype of a simplified ranking model that operates on the top of the Portuguese Web Archive portal (arquivo.pt). [ABSTRACT FROM AUTHOR]

Scoring rules are a popular method for aggregating rankings; they are frequently used in many settings, including social choice, information retrieval and sports. Scoring rules are parametrized by a vector of weights (the scoring vectors), one for each position, and declare as winner the candidate that maximizes the score obtained when summing up the weights corresponding to the position of each voter. It is well known that properly setting the weights is a crucial task, as different candidates can win with different scoring vectors. In this paper, we provide several methods to identify the winner considering all possible weights. We first propose VolumetricTop, a rule that ranks alternatives based on the hyper-polytope representing the set of weights that give the alternative the highest score, and provide a detailed analysis of the rule from the point-of-view of social choice theory. In order to overcome some of its limitations, we then propose two other methods: Volumetric-runoff, a rule that iteratively eliminates the alternative associated with the smallest region until a winner is found, and Volumetric-tournament, where alternatives are matched in pairwise comparisons; we provide several insights about these rules. Finally we provide some test cases of rank aggregation using the proposed methods. [ABSTRACT FROM AUTHOR]

Recently, the pandemic caused by COVID-19 is severe in the entire world. The prevention and control of crimes associated with COVID-19 are critical for controlling the pandemic. Therefore, to provide efficient and convenient intelligent legal knowledge services during the pandemic, we develop an intelligent system for legal information retrieval on the WeChat platform in this paper. The data source we used for training our system is "The typical cases of national procuratorial authorities handling crimes against the prevention and control of the new coronary pneumonia pandemic following the law", which is published online by the Supreme People's Procuratorate of the People's Republic of China. We base our system on convolutional neural network and use the semantic matching mechanism to capture inter-sentence relationship information and make a prediction. Moreover, we introduce an auxiliary learning process to help the network better distinguish the relation between two sentences. Finally, the system uses the trained model to identify the information entered by a user and responds to the user with a reference case similar to the query case and gives the reference legal gist applicable to the query case. [ABSTRACT FROM AUTHOR]

Abstract—The promotion of open science initiatives contributes to the development of the world information market and the emergence of a variety of open access resources. The search for scientific information and data, on the one hand, is becoming more extensive, while on the other, it is becoming more difficult due to the ever-increasing amount of resources. It seems that different approaches to structuring open access resources will allow information searches to be more productive and less labor-intensive. [ABSTRACT FROM AUTHOR]

To explore the cross-modal semantic relevance, image-text retrieval has attracted much attention from the research community. The most of prominent models have shown that introducing structured graph information is capable of improving the cross-modal retrieval performance. However, the existing models either only focus on exploiting the structured information within individual modality, or learn specific cross-modal graph-level metric functions which fail to construct a shared semantic subspace for efficient retrieval. In this paper, we propose a graph attention network to transform general structured visual and textual graphs into the shared semantic subspace. Specially, a structured semantic enhancement module is proposed to learn the graph-level relevance information between images and sentences, which is further utilized to promote the cross-modal semantic alignment. And the enhancement module only depends on the structured input information at retrieval stage, which endows our model with the flexibility that processing fragment-level data no matter whether the structured information lacks. Besides, a graph-based pooling network is proposed to transform the fragment-level features to the common cross-modal representations for efficient retrieval. When compared with several state-of-the-art baselines, the experiments show that our model achieves competitive performance on two publicly available datasets Flickr30k and MS-COCO. [ABSTRACT FROM AUTHOR]

The use of voice search is proliferating and expected to grow into the foreseeable future; this is why websites increasingly optimize their content associated with voice-based search to improve their ranking. In this era of rapid growth in voice search technology, it is a topical matter that needs research. Moreover, many predictions about its future excite the subject and require systematic investigation. This research aims to analyze important features that contribute to the SEO of webpages. Therefore, there is a need to examine various ranking factors that improve the ranking of the webpages for voice search queries on the Search Engine Results Page (SERP). This study consists of two phases. The first phase comprises systematic data acquisition and identifying important SEO-based ranking factors. The second phase includes a longitudinal case study to evaluate the impact and significance of identified factors. To achieve this goal, we conduct experiments on methodical combinations of features through machine learning algorithms such as Support Vector Machine, Logistic Regression, Naive Bayes Classifier, K-Nearest Neighbors, Decision Trees and Random Forest. Comparing results for multiple feature designs evaluates the contributing nature of specific features in SEO-based optimization for ranking. Results suggest the importance of the newly identified feature set (FF) outperforms baselines (EF and EFN) by a significant margin. A longitudinal case study on a blog over four months confirms that optimizing these features improves page ranking; therefore, webmasters must optimize these features while preparing the webpage. [ABSTRACT FROM AUTHOR]

Sensitive images represent a new challenge in enterprise imaging. These images, often containing nudity or gruesome content, have the potential to cause emotional harm to patients and people who view the images. Unfortunately, the interoperability standards used in imaging informatics have not yet addressed this issue. Because of this, the software solutions used in healthcare information technology are not able to offer patients and other viewers of image protections. In this Health Information Management Systems Society (HIMSS)/Society for Imaging Informatics in Medicine (SIIM) Enterprise Imaging Community Whitepaper, we define sensitive images, identify unique challenges related to their management, and provide recommendations for future solutions to protect our patients. [ABSTRACT FROM AUTHOR]

Curating and integrating data from sources are bottlenecks to procuring robust training datasets for artificial intelligence (AI) models in healthcare. While numerous applications can process discrete types of clinical data, it is still time-consuming to integrate heterogenous data types. Therefore, there exists a need for more efficient retrieval and storage of curated patient data from dissimilar sources, such as biobanks, health records, and sensors. We describe a customizable, modular data retrieval application (RIL-workflow), which integrates clinical notes, images, and prescription data, and show its feasibility applied to research at our institution. It uses the workflow automation platform Camunda (Camunda Services GmbH, Berlin, Germany) to collect internal data from Fast Healthcare Interoperability Resources (FHIR) and Digital Imaging and Communications in Medicine (DICOM) sources. Using the web-based graphical user interface (GUI), the workflow runs tasks to completion according to visual representation, retrieving and storing results for patients meeting study inclusion criteria while segregating errors for human review. We showcase RIL-workflow with its library of ready-to-use modules, enabling researchers to specify human input or automation at fixed steps. We validated our workflow by demonstrating its capability to aggregate, curate, and handle errors related to data from multiple sources to generate a multimodal database for clinical AI research. Further, we solicited user feedback to highlight the pros and cons associated with RIL-workflow. The source code is available at github.com/magnooj/RIL-workflow. [ABSTRACT FROM AUTHOR]

The amount of exploration done for the available medical literature is quite less and at the same time, there is less awareness of information mining in this specific field. The accessibility of immense quantity of biomedical literature has opened up additional opportunities to apply Information Retrieval and NLP methods for mining existing archives. Therefore, a query based retrieval application (QBR) based on hybrid similarity of string and semantic similarity can help medical professionals in their ongoing research. There are multiple benefits of utilizing various NLP applications, for example, information retrieval engine, and clinical diagnosis frameworks for decision support in medical field. These applications depend on the capacity to gauge Hybrid textual similarity (HTS) and N-Gram similarity. Hybrid similarity is the combination of weighting function and word embedding models providing similarity scores with optimum results. In this work, the main focus is on building of a new biomedical document retrieval model which can pull relevant literature for clinical decision support system based on the specific query. There is also an attempt to compare the statistical and NLP based approaches of query based biomedical document retrieval with the baseline systems. Analysis of the proposed method inclusive of semantic word embeddings shows promising results for both of the suggested similarities. [ABSTRACT FROM AUTHOR]

Recent years Person Re-identification(ReID) has attracted attention in industry and acade-mia. Previous works ignore the impact of appearance changes, such as clothing changes, leading to inferior performance in information retrieval under textual descriptions. Hence, we present a novel task called Composed Cross-modal Person Re-identification (CCM-ReID). Notably, there is currently no available dataset containing descriptions of appearance changes. Therefore, we construct a new dataset named CCMReid which is collected from published datasets. Meanwhile, in this paper, we propose a Potential Source-Information Dominated Learning (PSDL) model to excavate underlying essential information and clothing-changing representation by color-grey space contrastive training on source images for robust person ReID. Specifically, we introduce a source-information extraction module to tackle different color gamuts to explore real and abundant source information. To cope with the appearance variations in the open world, we also design potential information-dominated contrastive learning, which leverages different gamut information to greatly explore the latent changes expressed by related captions. The experimental results indicate that our PSDL has achieved excellent performance, and CCMReid dataset will be publicly available to support future research. [ABSTRACT FROM AUTHOR]

One of the most appealing multidisciplinary research areas in Artificial Intelligence (AI) is Sentiment Analysis (SA). Due to the intricate and complementary interactions between several modalities, Multimodal Sentiment Analysis (MSA) is an extremely difficult work that has a wide range of applications. In the subject of multimodal sentiment analysis, numerous deep learning models and different techniques have been suggested, but they do not investigate the explicit context of words and are unable to model diverse components of a sentence. Hence, the full potential of such diverse data has not been explored. In this research, a Context-Sensitive Multi-Tier Deep Learning Framework (CS-MDF) is proposed for sentiment analysis on multimodal data. The CS-MDF uses a three-tier architecture for extracting context-sensitive information. The first tier utilizes Convolutional Neural Network (CNN) for extracting text-based features, 3D-CNN model for extracting visual features and open-Source Media Interpretation by Large feature-space Extraction (openSMILE) tool kit for audio feature extraction.The first tier focuses on extracting the unimodal features from the utterances. This level of extraction ignores context-sensitive data while determining the feature.CNNs are suitable for text data because they are particularly useful for identifying local patterns and dependencies in data.The second tier uses the features extracted from the first tier.The context-sensitive unimodal characteristics are extracted in this tier using the Bi-directional Gated Recurrent Unit (BiGRU), which is used to comprehend inter-utterance links and uncover contextual evidence.The output from tier two is combined and passed to the third tier, which fuses the features from different modalities and trains a single BiGRU model that provides the final classification.This method applies the BiGRU model to sequential data processing, using the advantages of both modalities and capturing their interdependencies.Experimental results obtained on six real-life datasets (Flickr Images dataset, Multi-View Sentiment Analysis dataset, Getty Images dataset, Balanced Twitter for Sentiment Analysis dataset, CMU-MOSI Dataset) show that the proposed CS-MDF model has achieved better performance compared with ten state-of-the-art approaches, which are validated by F1 score, precision, accuracy, and recall metrics.An ablation study is carried out on the proposed framework that demonstrates the viability of the design. The GradCAM visualization technique is applied to visualize the aligned input image-text pairs learned by the proposed CS-MDF model. [ABSTRACT FROM AUTHOR]

Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP). However, the medical field faces challenges in training LMs due to limited data access and privacy constraints imposed by regulations like the Health Insurance Portability and Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR). Federated learning (FL) offers a decentralized solution that enables collaborative learning while ensuring data privacy. In this study, we evaluated FL on 2 biomedical NLP tasks encompassing 8 corpora using 6 LMs. Our results show that: (1) FL models consistently outperformed models trained on individual clients' data and sometimes performed comparably with models trained with polled data; (2) with the fixed number of total data, FL models training with more clients produced inferior performance but pre-trained transformer-based models exhibited great resilience. (3) FL models significantly outperformed pre-trained LLMs with few-shot prompting. [ABSTRACT FROM AUTHOR]

Aim: The present study was conducted to analyze the publications of the Tehran Lipid and Glucose Study (TLGS) and assess its scientific productions during the last 23 years. Methods: The required data were retrieved from the Scopus database. The advanced search was chosen, and the search query included terms related to the TLGS. Search and retrieval of data were conducted on August 30, 2022. Bibliometric indicators have been used at three levels in this research including the level of documents, journals, and authors. Also, the knowledge structure of this set was analyzed at the level of social structure and the level of conceptual structure. Data analysis and visualizations was performed using Bibliometrix and VOSviewer software. Results: A total of 870 documents related to the TLGS have been indexed in the Scopus from 2000 to 2022, and 1148 authors have participated in the relevant studies. 66.4% of the TLGS documents were published in journals with Q1 subject area quartiles. There was an annual growth rate of 20% and average citations per document of 16.5. There was a co-authorship per document of 5.6 and an international co-authorship of 8.7%. According to the co-occurrence network for keywords, the most common areas in the TLGS published documents were nutrition, epidemiologic issues, cardiometabolic-related biomarkers, diabetes, hypertension, lifestyle variables and genetic studies. Conclusion: Over the past 23 years, the TLGS has successfully addressed a wide range of inquiries pertaining to cardiometabolic and nutritional issues in Iran. The remarkable achievements of the TLGS act as a catalyst, advocating for the planning and implementation of additional cohort studies that specifically focus on non-communicable diseases within the Iranian population. [ABSTRACT FROM AUTHOR]

The structure of the prototype information management system developed here for collecting, storing, and processing computerized tomograms with the aim of identifying promising navigation data for medical robots is presented. This software serves to simplify and systematize the process of acquisition of data on the X‑ray density of patients' tissues using the simplest geometric primitives in the form of points and lines, as well as linking them to history data. Understanding the structure of the surgical tissues can qualitatively improve patients' treatment and, in relation to robotics, will contribute to the development of new methods of navigation by medical robots for transpedicular fixation. [ABSTRACT FROM AUTHOR]

Existing natural language processing (NLP) methods to convert free-text clinical notes into structured data often require problem-specific annotations and model training. This study aims to evaluate ChatGPT's capacity to extract information from free-text medical notes efficiently and comprehensively. We developed a large language model (LLM)-based workflow, utilizing systems engineering methodology and spiral "prompt engineering" process, leveraging OpenAI's API for batch querying ChatGPT. We evaluated the effectiveness of this method using a dataset of more than 1000 lung cancer pathology reports and a dataset of 191 pediatric osteosarcoma pathology reports, comparing the ChatGPT-3.5 (gpt-3.5-turbo-16k) outputs with expert-curated structured data. ChatGPT-3.5 demonstrated the ability to extract pathological classifications with an overall accuracy of 89%, in lung cancer dataset, outperforming the performance of two traditional NLP methods. The performance is influenced by the design of the instructive prompt. Our case analysis shows that most misclassifications were due to the lack of highly specialized pathology terminology, and erroneous interpretation of TNM staging rules. Reproducibility shows the relatively stable performance of ChatGPT-3.5 over time. In pediatric osteosarcoma dataset, ChatGPT-3.5 accurately classified both grades and margin status with accuracy of 98.6% and 100% respectively. Our study shows the feasibility of using ChatGPT to process large volumes of clinical notes for structured information extraction without requiring extensive task-specific human annotation and model training. The results underscore the potential role of LLMs in transforming unstructured healthcare data into structured formats, thereby supporting research and aiding clinical decision-making. [ABSTRACT FROM AUTHOR]

Fog computing is a computer architecture consisting of fog nodes, a collection of near-user edge devices. These fog nodes collaborate to perform computational services like data retrieval, processing, storage, etc. Resource allocation is one of the most critical and challenging problems in the fog environment for industrial applications. The significant aspects to consider while allocating resources are response time, throughput, and energy consumption. The proposed work formulated the resource allocation problem as a bi-objective minimization problem. The main aim of the work is to reduce energy consumption and makespan while allocating service requests to virtual machines. The proposed technique uses the league championship algorithm to select an efficient resource for productive and resourceful service allocation of tasks in fog computing. The proposed algorithm's performance is assessed by comparing it to three well-known metaheuristic algorithms. Finally, the simulation results show that the proposed algorithm is superior in terms of makespan and energy consumption. [ABSTRACT FROM AUTHOR]

Motion blur of an image is a common phenomenon that occurs while taking a photograph due to the relative movement of the object and an image acquiring device. It is essential to detect this phenomenon of blurring of images in many applications such as information retrieval. This paper proposes a novel local blur detection technique, and it performs better than the existing works. This technique mainly uses Radon transform and Laplacian of Gaussian on the local neighborhood around each pixel to estimate blur information. Additionally, two new weight functions are introduced based on local geodesic distance and local variance. It is shown that these functions play a significant role in segregating blur and non-blurred parts. Simulation results validate the correctness and accuracy by testing the proposed algorithm on some challenging images with similar color information in the foreground and background. Various quantitative performance measures have determined the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Ontologies serve as comprehensive frameworks for organizing domain-specific knowledge, offering significant benefits for managing clinical data. This study presents the development of the Fall Risk Management Ontology (FRMO), designed to enhance clinical text mining, facilitate integration and interoperability between disparate data sources, and streamline clinical data analysis. By representing major entities within the fall risk management domain, the FRMO supports the unification of clinical language and decision-making processes, ultimately contributing to the prevention of falls among older adults. We used Ontology Web Language (OWL) to build the FRMO in Protégé. Of the seven steps of the Stanford approach, six steps were utilized in the development of the FRMO: (1) defining the domain and scope of the ontology, (2) reusing existing ontologies when possible, (3) enumerating ontology terms, (4) specifying the classes and their hierarchy, (5) defining the properties of the classes, and (6) defining the facets of the properties. We evaluated the FRMO using four main criteria: consistency, completeness, accuracy, and clarity. The developed ontology comprises 890 classes arranged in a hierarchical structure, including six top-level classes with a total of 43 object properties and 28 data properties. FRMO is the first comprehensively described semantic ontology for fall risk management. Healthcare providers can use the ontology as the basis of clinical decision technology for managing falls among older adults. [ABSTRACT FROM AUTHOR]

Why do we prefer some singers to others? We investigated how much singing voice preferences can be traced back to objective features of the stimuli. To do so, we asked participants to rate short excerpts of singing performances in terms of how much they liked them as well as in terms of 10 perceptual attributes (e.g.: pitch accuracy, tempo, breathiness). We modeled liking ratings based on these perceptual ratings, as well as based on acoustic features and low-level features derived from Music Information Retrieval (MIR). Mean liking ratings for each stimulus were highly correlated between Experiments 1 (online, US-based participants) and 2 (in the lab, German participants), suggesting a role for attributes of the stimuli in grounding average preferences. We show that acoustic and MIR features barely explain any variance in liking ratings; in contrast, perceptual features of the voices achieved around 43% of prediction. Inter-rater agreement in liking and perceptual ratings was low, indicating substantial (and unsurprising) individual differences in participants' preferences and perception of the stimuli. Our results indicate that singing voice preferences are not grounded in acoustic attributes of the voices per se, but in how these features are perceptually interpreted by listeners. [ABSTRACT FROM AUTHOR]

Background: This study aims to conduct a scientometric analysis and thematically-based narrative review of a century of Taraxacum officinale research (TOR), uncovering patterns, trends, themes, and advancements in the field to provide insights for future investigations. The study followed PRISMA guidelines and utilized the Scopus database with MeSH terms for bibliographic data retrieval. Scientometric mapping employed VOSviewer and R-package-based Bibliometrix, while extracted themes were reviewed narratively. A detailed analysis of TOR was achieved by including only original studies. Results: The findings include the extensive duration of TOR since 1908 and its significant growth, particularly in the last two decades. China emerges as the most productive country, but the United States leads in recognizable and collaborative TOR. The thematic map displays dynamic and diverse themes, with a rich knowledge structure revealed through the analysis of term co-occurrence. The year 2016 represents a turning point in the thematic map, marked by numerical growth and thematic bifurcation. The study extracted several main research topics within the field of TOR, including germination, antioxidant activity, bioherbicide, oxidative stress, Taraxacum kok-saghyz, and heavy metals. These topics represent key areas of investigation and provide insights into the diverse aspects of research surrounding T. officinale. Additionally, emerging topics in TOR encompass toxicity, metabolomics, dandelion extract, and diabetes mellitus. Conclusions: The study consolidated knowledge, highlighted research gaps, and provided directions for future investigations on TOR. [ABSTRACT FROM AUTHOR]

The aim of the study is to evaluate and compare the quality and readability of responses generated by five different artificial intelligence (AI) chatbots—ChatGPT, Bard, Bing, Ernie, and Copilot—to the top searched queries of erectile dysfunction (ED). Google Trends was used to identify ED-related relevant phrases. Each AI chatbot received a specific sequence of 25 frequently searched terms as input. Responses were evaluated using DISCERN, Ensuring Quality Information for Patients (EQIP), and Flesch-Kincaid Grade Level (FKGL) and Reading Ease (FKRE) metrics. The top three most frequently searched phrases were "erectile dysfunction cause", "how to erectile dysfunction," and "erectile dysfunction treatment." Zimbabwe, Zambia, and Ghana exhibited the highest level of interest in ED. None of the AI chatbots achieved the necessary degree of readability. However, Bard exhibited significantly higher FKRE and FKGL ratings (p = 0.001), and Copilot achieved better EQIP and DISCERN ratings than the other chatbots (p = 0.001). Bard exhibited the simplest linguistic framework and posed the least challenge in terms of readability and comprehension, and Copilot's text quality on ED was superior to the other chatbots. As new chatbots are introduced, their understandability and text quality increase, providing better guidance to patients. [ABSTRACT FROM AUTHOR]

Stakeholder engagement activities are essential for guiding designers' decision-making throughout their design processes. In capstone courses, engineering student designers practice engaging with stakeholders in preparation for their professional careers. However, understandings of (1) how engineering students engage stakeholders to inform design decisions and (2) the factors that lead students to change their engagement approaches over a single project are limited. Our study investigated capstone design teams' interactions with stakeholders at early, middle, and end stages of the capstone course by analyzing interview transcripts and design reports to develop narratives of interactions for seven teams. Comparisons among teams across their capstone course revealed fundamental differences in what drove students toward or away from stakeholder engagement. Our findings highlighted several factors related to design team characteristics and project context that can inform future design pedagogy as well as the development of curricular design projects that provide effective environments for students to practice more human-centered design approaches. [ABSTRACT FROM AUTHOR]