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

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

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

Efficient document recognition and sharing remain challenges in the healthcare, insurance, and finance sectors. One solution to this problem has been the use of deep learning techniques to automatically extract structured information from paper documents. Specifically, the structured extraction of a medical examination report (MER) can enhance medical efficiency, data analysis, and scientific research. While current methods focus on reconstructing table bodies, they often overlook table headers, leading to incomplete information extraction. This paper proposes MSIE-Net (multi-stage-structured information extraction network), a novel structured information extraction method, leveraging refined attention transformers and associated entity detection to enhance comprehensive MER information retrieval. MSIE-Net includes three stages. First, the RVI-LayoutXLM (refined visual-feature independent LayoutXLM) targets key information extraction. In this stage, the refined attention accentuates the interaction between different modalities by adjusting the attention score at the current position using previous position information. This design enables the RVI-LayoutXLM to learn more specific contextual information to improve extraction performance. Next, the associated entity detection module, RIFD-Net (relevant intra-layer fine-tuned detection network), identifies each test item's location within the MER table body. Significantly, the backbone of RIFD-Net incorporates the intra-layer feature adjustment module (IFAM) to extract global features while homing in on local areas, proving especially sensitive for inspection tasks with dense and long bins. Finally, structured post-processing based on coordinate aggregation links the outputs from the prior stages. For the evaluation, we constructed the Chinese medical examination report dataset (CMERD), based on real medical scenarios. MSIE-Net demonstrated competitive performance in tasks involving key information extraction and associated entity detection. Experimental results validate MSIE-Net's capability to successfully detect key entities in MER and table images with various complex layouts, perform entity relation extraction, and generate structured labels, laying the groundwork for intelligent medical documentation. [ABSTRACT FROM AUTHOR]

During the last two decades, a systematic re-examination of the whole information science field has taken place around the FIS--Foundations of Information Science--initiative. With the occasion of its Fourth Conference in Beijing 2010, a group of selected contributors and leading practitioners of those fields have been invited to contribute to this Special Issue. What is the status of information science today? What is the relationship between information and the laws of nature? Is information merely "physical"? What is the difference between information and computation? Has the genomic revolution changed the contemporary views on information and life? And what about the nature of social information? Cogent answers to these questions and to quite many others are attempted in the contributions that follow. [ABSTRACT FROM AUTHOR]

Knowledge communication in cultural heritage and digital humanities currently faces two challenges, which this paper addresses: On the one hand, data-driven storytelling in these fields has mainly focused on human protagonists, while other essential entities (such as artworks and artifacts, institutions, or places) have been neglected. On the other hand, storytelling tools rarely support the larger chains of data practices, which are required to generate and shape the data and visualizations needed for such stories. This paper introduces the InTaVia platform, which has been developed to bridge these gaps. It supports the practices of data retrieval, creation, curation, analysis, and communication with coherent visualization support for multiple types of entities. We illustrate the added value of this open platform for storytelling with four case studies, focusing on (a) the life of Albrecht Dürer (person biography), (b) the Saliera salt cellar by Benvenuto Cellini (object biography), (c) the artist community of Lake Tuusula (group biography), and (d) the history of the Hofburg building complex in Vienna (place biography). Numerous suggestions for future research arise from this undertaking. [ABSTRACT FROM AUTHOR]

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

With the continuous breakthrough of natural language processing, the application of intelligent question-answering technology in electric power systems has attracted wide attention. However, at present, the traditional question-answering system has poor performance and is difficult to apply in engineering practice. This paper proposes an improved BERTserini algorithm for the intelligent answering of electric power regulations based on a BERT model. The proposed algorithm is implemented in two stages. The first stage is the text-segmentation stage, where a multi-document long text preprocessing technique is utilized that accommodates the rules and regulations text, and then Anserini is used to extract paragraphs with high relevance to the given question. The second stage is the answer-generation and source-retrieval stage, where a two-step fine-tuning based on the Chinese BERT model is applied to generate precise answers based on given questions, while the information regarding documents, chapters, and page numbers of these answers are also output simultaneously. The algorithm proposed in this paper eliminates the necessity for the manual organization of professional question–answer pairs, thereby effectively reducing the manual labor cost compared to traditional question-answering systems. Additionally, this algorithm exhibits a higher degree of exact match rate and a faster response time for providing answers. [ABSTRACT FROM AUTHOR]

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

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

The performance of emotive text classification using affective hierarchical schemes (e.g., WordNet-Affect) is often evaluated using the same traditional measures used to evaluate the performance of when a finite set of isolated classes are used. However, applying such measures means the full characteristics and structure of the emotive hierarchical scheme are not considered. Thus, the overall performance of emotive text classification using emotion hierarchical schemes is often inaccurately reported and may lead to ineffective information retrieval and decision making. This paper provides a comparative investigation into how methods used in hierarchical classification problems in other domains, which extend traditional evaluation metrics to consider the characteristics of the hierarchical classification scheme, can be applied and subsequently improve the classification of emotive texts. This study investigates the classification performance of three widely used classifiers, Naive Bayes, J48 Decision Tree, and SVM, following the application of the aforementioned methods. The results demonstrated that all the methods improved the emotion classification. However, the most notable improvement was recorded when a depth-based method was applied to both the testing and validation data, where the precision, recall, and F1-score were significantly improved by around 70 percentage points for each classifier. [ABSTRACT FROM AUTHOR]

Keyphrase extraction is a critical task in text information retrieval, which traditionally employs both supervised and unsupervised approaches. Supervised methods generally rely on large corpora, which introduce the problems of availability, while unsupervised methods are independent of out-sources but also lead to defects like imperfect statistical features or low accuracy. Particularly in short-text scenarios, limited text features often result in low-quality candidate ranking. To address this issue, this paper proposes Y-Rank, a lightweight unsupervised keyphrase extraction method that extracts the average information content of candidate sentences as the key statistical features from a single document, and follows a graph construction approach based on similarity to obtain the semantic features of keyphrase with high-quality and ranking accuracy. Finally, the top-ranked keyphrases are acquired by the fusion of these features. The experimental results on five datasets illustrate that Y-Rank outperforms the other nine unsupervised methods, achieves enhancements on six accuracy metrics, including Precision, Recall, F-Measure, MRR, MAP, and Bpref, and performs the highest improvement in short text scenarios. [ABSTRACT FROM AUTHOR]

Analyst sensemaking research typically focuses on individual or small groups conducting intelligence tasks. This has helped understand information retrieval tasks and how people communicate information. As a part of the grand challenge of the Summer Conference on Applied Data Science (SCADS) to build a system that can generate tailored daily reports (TLDR) for intelligence analysts, we conducted a qualitative interview study with analysts to increase understanding of information passing in the intelligence community. While our results are preliminary, we expect that this work will contribute to a better understanding of the information ecosystem of the intelligence community, how institutional dynamics affect information passing, and what implications this has for a TLDR system. This work describes our involvement in and work completed during SCADS. Although preliminary, we identify that information passing is both a formal and informal process and often follows professional networks due especially to the small population and specialization of work. We call attention to the need for future analysis of information ecosystems to better support tailored information retrieval features. [ABSTRACT FROM AUTHOR]

Open-domain question answering requires the task of retrieving documents with high relevance to the query from a large-scale corpus. Deep learning-based dense retrieval methods have become the primary approach for finding related documents. Although deep learning-based methods have improved search accuracy compared to traditional techniques, they simultaneously impose a considerable increase in computational burden. Consequently, research on efficient models and methods that optimize the trade-off between search accuracy and time to alleviate computational demands is required. In this paper, we propose a Korean document retrieval method utilizing ColBERT's late interaction paradigm to efficiently calculate the relevance between questions and documents. For open-domain Korean question answering document retrieval, we construct a Korean dataset using various corpora from AI-Hub. We conduct experiments comparing the search accuracy and inference time among the traditional IR (information retrieval) model BM25, the dense retrieval approach utilizing BERT-based models for Korean, and our proposed method. The experimental results demonstrate that our approach achieves a higher accuracy than BM25 and requires less search time than the dense retrieval method employing KoBERT. Moreover, the most outstanding performance is observed when using KoSBERT, a pre-trained Korean language model that learned to position semantically similar sentences closely in vector space. [ABSTRACT FROM AUTHOR]

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

The primary research question driving this paper is the following: "What are the schooling experiences of young people who are at risk of or experiencing homelessness?" Through interviews with 28 young people in two cities in Ontario, Canada, the authors identified several common experiences, including the following: lack of available information that could help them cope with their housing difficulties; prejudice and bullying from other students, sometimes stemming from their housing problems but also due to factors such as racialization, gender identity, poverty, and substance use; and individual support from some teachers and support staff, although this was dependent on being in school. They proposed changes to help young people still in school, including the inclusion of non-judgmental information and guidance on dealing with poverty and homelessness in school curricula, educating school staff about the 'symptoms' of homelessness to help them identify students at risk, and creating more safe and supportive school environments overall. [ABSTRACT FROM AUTHOR]

The online environment, where the boundaries between the domains of home, school, work, and leisure are blurred, poses new challenges for youth work practice. Due to limited research on this subject matter, the theoretical underpinnings of the online youth work practice are constrained. The fulfilment of youth work's aims online, the position it can take in the online context, and its relation to its partners in the online lifeworld need a theoretical base. This paper seeks to analyse the role of youth work in the online lifeworld according to adolescents and youth work's partners. The research was conducted in the Netherlands in collaboration with 14 youth work organisations. A qualitative research design was used: group conversations with young people and semi-structured interviews with youth work's partners (i.e., parents, schools, informal networks, neighbourhood support teams, police, and municipal officials). The findings indicate that youth work in the online lifeworld, according to the respondents, is part of the general youth work practice, with a primary role of addressing the developmental needs of young people and creating new developmental opportunities. This role is expected to be fulfilled by engaging and connecting with young people in the online lifeworld and providing them instrumental, informational, socioemotional, and cognitive support. To do so, according to the partners, youth workers can make use of their vantage position in the online relationship with adolescents in order to access online information relevant for support and prudent prevention aimed at adolescents' development. This vantage position may potentially encourage a collaboration between young people and partners, and between the online and offline youth work practice. [ABSTRACT FROM AUTHOR]

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

Software vulnerability detection aims to proactively reduce the risk to software security and reliability. Despite advancements in deep-learning-based detection, a semantic gap still remains between learned features and human-understandable vulnerability semantics. In this paper, we present an XAI-based framework to assess program code in a graph context as feature representations and their effect on code vulnerability classification into multiple Common Weakness Enumeration (CWE) types. Our XAI framework is deep-learning-model-agnostic and programming-language-neutral. We rank the feature importance of 40 syntactic constructs for each of the top 20 distributed CWE types from three datasets in Java and C++. By means of four metrics of information retrieval, we measure the similarity of human-understandable CWE types using each CWE type's feature contribution ranking learned from XAI methods. We observe that the subtle semantic difference between CWE types occurs after the variation in neighboring features' contribution rankings. Our study shows that the XAI explanation results have approximately 78% Top-1 to 89% Top-5 similarity hit rates and a mean average precision of 0.70 compared with the baseline of CWE similarity identified by the open community experts. Our framework allows for code vulnerability patterns to be learned and contributing factors to be assessed at the same stage. [ABSTRACT FROM AUTHOR]

Nursing students can access massive amounts of online health data to drive cutting-edge evidence-based practice in clinical placement, to bridge the theory–practice gap. This activity requires investigation to identify the strategies nursing students apply to evaluate online health information. Online Think-Aloud sessions enabled 14 participants to express their cognitive processes in navigating various educational resources, including online journals and databases, and determining the reliability of sources, indicating their strategies for information-seeking, which helped to create this scoring system. Easy access and user convenience were clearly the instrumental factors in this behavior, which has troubling implications for the lack of use of higher-quality resources (e.g., from peer-reviewed academic journals). The identified challenges encountered during resource access included limited skills in the critical evaluation of information credibility and reliability, signaling a requirement for improved information literacy skills. Participants acknowledged the importance of evidence-based, high-quality information, but faced numerous barriers, such as restricted access to professional and specialty databases, and a lack of academic skills training. This paper develops and critiques a Performative Tool for assessing the process of seeking health information using an online Think-Aloud method, and explores factors and strategies contributing to evidence-based health information access and utilization in clinical practice, aiming to provide insight into individuals' information-seeking behaviors in online health contexts. [ABSTRACT FROM AUTHOR]

This paper aims to enhance the Graphical Set-based model (GSB) for ranking and classification tasks by incorporating node and word embeddings. The model integrates a textual graph representation with a set-based model for information retrieval. Initially, each document in a collection is transformed into a graph representation. The proposed enhancement involves augmenting the edges of these graphs with embeddings, which can be pretrained or generated using Word2Vec and GloVe models. Additionally, an alternative aspect of our proposed model consists of the Node2Vec embedding technique, which is applied to a graph created at the collection level through the extension of the set-based model, providing edges based on the graph's structural information. Core decomposition is utilized as a method for pruning the graph. As a byproduct of our information retrieval model, we explore text classification techniques based on our approach. Node2Vec embeddings are generated by our graphs and are applied in order to represent the different documents in our collections that have undergone various preprocessing methods. We compare the graph-based embeddings with the Doc2Vec and Word2Vec representations to elaborate on whether our approach can be implemented on topic classification problems. For that reason, we then train popular classifiers on the document embeddings obtained from each model. [ABSTRACT FROM AUTHOR]

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

Aircraft dispatch involves determining the optimal dispatch option when an aircraft experiences an unexpected failure. Currently, maintenance technicians at the apron have limited access to support information and finding the right information in extensive maintenance manuals is a time-consuming task, often leading to technically induced delays. This paper introduces a novel web-based prototype decision support system to aid technicians during aircraft dispatch decision-making and subsequent maintenance execution. A system architecture for real-time dispatch decision support is established and implemented. The developed system is evaluated through a case study in an operational environment by licensed maintenance technicians. The system fully automates information retrieval from multiple data sources, performs alternative identification and evaluation for a given fault message, and provides the technician with on-site access to relevant information, including the related maintenance tasks. The case study indicates a potential time saving of up to 98% per dispatch decision. Moreover, it enables digitalization of the—currently mostly paper-based—dispatch decision process, thereby reducing logistics and paper waste. The prototype is the first to provide operational decision support in the aircraft maintenance domain and addresses the lack of correlation between theory and practice often found in decision support systems research by providing a representative case study. The developed custom parser for SGML-based documents enables efficient identification and extraction of relevant information, vastly contributing to the overall reduction of the decision time. [ABSTRACT FROM AUTHOR]

Achieving scientific document retrieval by considering the wealth of mathematical expressions and the semantic text they contain has become an inescapable trend. Current scientific document matching models focus solely on the textual features of expressions and frequently encounter hurdles like proliferative parameters and sluggish reasoning speeds in the pursuit of improved performance. To solve this problem, this paper proposes a scientific document retrieval method founded upon hesitant fuzzy sets (HFS) and local semantic distillation (LSD). Concretely, in order to extract both spatial and semantic features for each symbol within a mathematical expression, this paper introduces an expression analysis module that leverages HFS to establish feature indices. Secondly, to enhance contextual semantic alignment, the method of knowledge distillation is employed to refine the pretrained language model and establish a twin network for semantic matching. Lastly, by amalgamating mathematical expressions with contextual semantic features, the retrieval results can be made more efficient and rational. Experiments were implemented on the NTCIR dataset and the expanded Chinese dataset. The average MAP for mathematical expression retrieval results was 83.0%, and the average nDCG for sorting scientific documents was 85.8%. [ABSTRACT FROM AUTHOR]

Derivative-free iterative methods are useful to approximate the numerical solutions when the given function lacks explicit derivative information or when the derivatives are too expensive to compute. Exploring the convergence properties of such methods is crucial in their development. The convergence behavior of such approaches and determining their practical applicability require conducting local as well as semi-local convergence analysis. In this study, we explore the convergence properties of a sixth-order derivative-free method. Previous local convergence studies assumed the existence of derivatives of high order even when the method itself was not utilizing any derivatives. These assumptions imposed limitations on its applicability. In this paper, we extend the local analysis by providing estimates for the error bounds of the method. Consequently, its applicability expands across a broader range of problems. Moreover, the more important and challenging semi-local convergence not investigated in earlier studies is also developed. Additionally, we survey recent advancements in this field. The outcomes presented in this paper can be proved valuable to practitioners and researchers engaged in the development and analysis of derivative-free numerical algorithms. Numerical tests illuminate and validate further the theoretical results. [ABSTRACT FROM AUTHOR]

The concepts of information and media literacy have been central components of digital literacy since the digitization of information began. However, the increasing influence of artificial intelligence on how individuals locate, evaluate, and create content has significant implications for what it means to be information and media literate. This paper begins by exploring the role artificial intelligence plays at the various stages of information retrieval and creation processes. Following this, the paper reviews existing digital literacy frameworks to ascertain their definitions of information and media literacy and the potential impact of artificial intelligence on them. We find that digital literacy frameworks have been slow to react to artificial intelligence and its repercussions, and we recommend a number of strategies for the future. These strategies center around a more agile, responsive, and participatory approach to digital literacy framework development and maintenance. [ABSTRACT FROM AUTHOR]

To address the issue of insufficient testing caused by the continuous reduction of software development cycles, many organizations maintain bug repositories and bug tracking systems to ensure real-time updates of bugs. However, each day, a large number of bugs is discovered and sent to the repository, which imposes a heavy workload on bug fixers. Therefore, effective bug deduplication and triage are of great significance in software development. This paper provides a comprehensive investigation and survey of the recent developments in bug deduplication and triage. The study begins by outlining the roadmap of the existing literature, including the research trends, mathematical models, methods, and commonly used datasets in recent years. Subsequently, the paper summarizes the general process of the methods from two perspectives—runtime information-based and bug report-based perspectives—and provides a detailed overview of the methodologies employed in relevant works. Finally, this paper presents a detailed comparison of the experimental results of various works in terms of usage methods, datasets, accuracy, recall rate, and F1 score. Drawing on key findings, such as the need to improve the accuracy of runtime information collection and refine the description information in bug reports, we propose several potential future research directions in the field, such as stack trace enrichment and the combination of new NLP models. [ABSTRACT FROM AUTHOR]

Biomedical entity linking is an important research problem for many downstream tasks, such as biomedical intelligent question answering, information retrieval, and information extraction. Biomedical entity linking is the task of mapping mentions in medical texts to standard entities in a given knowledge base. Recently, BERT-based models have achieved state-of-the-art results on the biomedical entity linking task. Although this type of method is effective, it brings challenges for fine-tuning and online services in practical industries due to a large number of model parameters and long inference time. In addition, due to the numerous surface variants of biomedical mentions, it is difficult for a single matching module to achieve good results. To address the challenge, we propose an efficient biomedical entity linking method that integrates inter- and intra-entity attention to better capture the information between medical entity mentions and candidate entities themselves and each other, and the model in this paper is more lightweight. Experimental results show that our method achieves competitive performance on two biomedical benchmark datasets, NCBI and ADR, with an accuracy rate of 91.28% and 93.13%, respectively. Moreover, it also achieves comparable or even better results compared to the BERT-based entity linking method while having far fewer model parameters and very high inference speed. [ABSTRACT FROM AUTHOR]

At present, the mine filling system is a mostly automated operation process, and it has many problems, such as centralized data storage and low safety performance. Aiming at solving the above problems, this paper proposes a mine filling overlimit warning system based on blockchain to achieve the functions of an equipment operation overlimit warning, data storage, and data retrieval. Firstly, the original data model is trained based on transfer learning to obtain the overlimit early warning model and predict the overlimit of equipment operation. Then, the interplanetary file system (IPFS) storage device is used to store the running data and overlimit data, and the corresponding file identifier CID is stored on the blockchain. At the same time, alliance chain technology is used to allow administrators and users to retrieve data based on credentials to ensure the privacy of the data retrieval process. System experiments and analysis evaluations show that the combination of blockchain and IPFS to store monitoring data can effectively save storage space and improve the efficiency of storage and retrieval. The application of an overlimit early warning model can optimize the mine filling process and prolong the operational life of system equipment. [ABSTRACT FROM AUTHOR]

In the past decade, only a small number of papers have elaborated on the application of physiologically based pharmacokinetic (PBPK) modeling across different areas. In this review, an in-depth analysis of the distribution of PBPK modeling in relation to its application in various research topics and model validation was conducted by text mining tools. Orange 3.32.0, an open-source data mining program was used for text mining. PubMed was used for data retrieval, and the collected articles were analyzed by several widgets. A total of 2699 articles related to PBPK modeling met the predefined criteria. The number of publications per year has been rising steadily. Regarding the application areas, the results revealed that 26% of the publications described the use of PBPK modeling in early drug development, risk assessment and toxicity assessment, followed by absorption/formulation modeling (25%), prediction of drug-disease interactions (20%), drug-drug interactions (DDIs) (17%) and pediatric drug development (12%). Furthermore, the analysis showed that only 12% of the publications mentioned model validation, of which 51% referred to literature-based validation and 26% to experimentally validated models. The obtained results present a valuable review of the state-of-the-art regarding PBPK modeling applications in drug discovery and development and related fields. [ABSTRACT FROM AUTHOR]

To date, information retrieval methods in the medical field have mainly focused on English medical reports, but little work has studied Chinese electronic medical reports, especially in the field of obstetrics and gynecology. In this paper, a dataset of 180,000 complete Chinese ultrasound reports in obstetrics and gynecology was established and made publicly available. Based on the ultrasound reports in the dataset, a new information retrieval method (IKAR) is proposed to extract key information from the ultrasound reports and automatically generate the corresponding ultrasound diagnostic results. The model can both extract what is already in the report and analyze what is not in the report by inference. After applying the IKAR method to the dataset, it is proved that the method could achieve 89.38% accuracy, 91.09% recall, and 90.23% F-score. Moreover, the method achieves an F-score of over 90% on 50% of the 10 components of the report. This study provides a quality dataset for the field of electronic medical records and offers a reference for information retrieval methods in the field of obstetrics and gynecology or in other fields. [ABSTRACT FROM AUTHOR]

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

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

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

Featured Application: In the biomedical field, accessing data by classical methods is getting more difficult day by day, as it is in any other field, due to the data growth rate. Different methods are needed to access the desired data more quickly. In particular, more specific methods need to be developed for question answering systems. In this study, a model is proposed for the document retrieval and answer extraction modules which are a part of biomedical question answering systems. In this paper, we describe our biomedical document retrieval system and answers extraction module, which is part of the biomedical question answering system. Approximately 26.5 million PubMed articles are indexed as a corpus with the Apache Lucene text search engine. Our proposed system consists of three parts. The first part is the question analysis module, which analyzes the question and enriches it with biomedical concepts related to its wording. The second part of the system is the document retrieval module. In this step, the proposed system is tested using different information retrieval models, like the Vector Space Model, Okapi BM25, and Query Likelihood. The third part is the document re-ranking module, which is responsible for re-arranging the documents retrieved in the previous step. For this study, we tested our proposed system with 6B training questions from the BioASQ challenge task. We obtained the best MAP score on the document retrieval phase when we used Query Likelihood with the Dirichlet Smoothing model. We used the sequential dependence model at the re-rank phase, but this model produced a worse MAP score than the previous phase. In similarity calculation, we included the Named Entity Recognition (NER), UMLS Concept Unique Identifiers (CUI), and UMLS Semantic Types of the words in the question to find the sentences containing the answer. Using this approach, we observed a performance enhancement of roughly 25% for the top 20 outcomes, surpassing another method employed in this study, which relies solely on textual similarity. [ABSTRACT FROM AUTHOR]

The growing significance of sensor data in the development of information technology services finds obstacles due to disparate data presentations and non-adherence to FAIR principles. This paper introduces a novel approach for sensor data gathering and retrieval. The proposal leverages large language models to convert sensor data into FAIR-compliant formats and to provide word embedding representations of tabular data for subsequent exploration, enabling semantic comparison. The proposed system comprises two primary components. The first focuses on gathering data from sensors and converting it into a reusable structured format, while the second component aims to identify the most relevant sensor data to augment a given user-provided dataset. The evaluation of the proposed approach involved comparing the performance of various large language models in generating representative word embeddings for each table to retrieve related sensor data. The results show promising performance in terms of precision and MRR (0.90 and 0.94 for the best-performing model, respectively), indicating the system's ability to retrieve pertinent sensor data that fulfil user requirements. [ABSTRACT FROM AUTHOR]

In recent years, there has been a significant increase in the use of structural health monitoring (SHM) technologies as systems for monitoring the integrity of aircraft's structures. The use of compact and embeddable sensor networks, like the ones based on fibre optics (FO), is particularly attractive from the perspective of releasing an integrated structural system with intrinsic sensing capacity. Usually, an SHM system architecture is completed by a dedicated algorithm that processes the data gathered from the sensors to elaborate on the level of damage currently suffered by the structure, with the further possibility of providing information to the relevant specialists involved with its supervision. One of the main SHM applications that is attracting major interest is related to the inspection and detection of anomalies in bonded joints, which is extremely relevant in many composite realizations. Aeronautical regulations allow the use of bonded joints on an aircraft's primary structure but require the implementation of a means to ensure their absolute safety, such as the introduction of further mechanical links aimed at stopping the propagation of a possible flaw or the availability of Non-Destructive Inspection (NDI) systems to prove the absence of relevant damaged areas. Generally, the main typical defects occurring during the manufacturing of bonded joints include adhesive curing, kissing bonds, poor porosity, and poor surface preparation. The current NDI systems more widely used and available to detect defects are still inaccurate due to the lack of standard procedures for the creation of representative defects in a controlled manner, which would allow for the development of reliable methodologies and tools able to ensure the safety of a bonded joint, as required by safety regulations. This paper shows the results relative to the implementation of an SHM system developed by the Italian Aerospace Research Centre (CIRA) aimed at monitoring the bonding lines between spar caps and panels of a typical composite wingbox section and detecting faults in location and length. The work was performed during typical ground static tests by using a fibre optical sensing network embedded within relevant adhesive paste layers during the manufacturing process of the structure. In the reported investigation, the SHM system assumed the function of an NDI system tool. The results show that the developed SHM system has good reliability for the detection of both the position and size of damage areas that were artificially inserted within the test article during the bonding phase, showing its potential as a candidate to be used as a tool to verify the conditions of a bonded joint, as required by aviation authorities' regulations. [ABSTRACT FROM AUTHOR]

5G networks, pivotal for our digital mobile societies, are transitioning from 4G to 5G Stand-Alone (SA) networks. However, during this transition, 5G Non-Stand-Alone (NSA) networks are widely used. This paper examines potential security vulnerabilities in 5G NSA networks. Through an extensive literature review, we identify known 4G attacks that can theoretically be applied to 5G NSA. We organize these attacks into a structured taxonomy. Our findings reveal that 5G NSA networks may offer a false sense of security, as most security and privacy improvements are concentrated in 5G SA networks. To underscore this concern, we implement three attacks with severe consequences and successfully validate them on various commercially available smartphones. Notably, one of these attacks, the IMSI Leak, consistently exposes user information with no apparent security mitigation in 5G NSA networks. This highlights the ease of tracking individuals on current 5G networks. [ABSTRACT FROM AUTHOR]

Using a safe subset of C++ is a promising direction for increasing the safety of the programming language while maintaining its performance and productivity. In this paper, we examine how close existing C/C++ code is to conforming to a safe subset of C++. We examine the rules presented in existing safe C/C++ standards and safe C/C++ subsets. We analyze the code characteristics of 5.8 million code samples from the Exebench benchmark suite, two C/C++ benchmark suites, and five modern C++ applications using a static analysis tool. We find that raw pointers, unsafe casts, and unsafe library functions are used in both C/C++ code at large and in modern C++ applications. In general, C/C++ code at large does not differ much from modern C++ code, and continued work will be required to transition from existing C/C++ code to a safe subset of C++. [ABSTRACT FROM AUTHOR]

Women are underrepresented in many areas of journalistic newsrooms. In this paper, we examine if this established effect persists in the new forms of journalistic communication, namely social media networks. We use mentions, retweets, and hashtags as measures of journalistic amplification and legitimation. Furthermore, we compare two groups of journalists in different stages of development: political and data journalists in Germany in 2021. Our results show that journalists identified as women tend to favor other women journalists in mentions and retweets on Twitter (now called X), compared to men. While both professions are dominated by men, with a high share of tweets authored by men, women mention and retweet other women more than their male colleagues. Female data journalists also leverage different sources than men. In addition, we found data journalists to be more inclusive of non-member sources in their networks compared to political journalists. [ABSTRACT FROM AUTHOR]

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

Blockchain technology has been successfully applied in recent years to promote the immutability, traceability, and authenticity of previously collected and stored data. However, the amount of data stored in the blockchain is usually limited for economic and technological issues. Namely, the blockchain usually stores only a fingerprint of data, such as the hash of data, while full, raw information is stored off-chain. This is generally enough to guarantee immutability and traceability, but misses to support another important property, that is, data availability. This is particularly true when a traditional, centralized database is chosen for off-chain storage. For this reason, many proposals try to properly combine blockchain with decentralized IPFS storage. However, the storage of data on IPFS could pose some privacy problems. This paper proposes a solution that properly combines blockchain, IPFS, and encryption techniques to guarantee immutability, traceability, availability, and data privacy. [ABSTRACT FROM AUTHOR]

Few plays make such varied or such bravura use of soliloquies as Shakespeare's Richard III. The opening forty-one-line monologue by Richard himself allows an actor to show what he can do and to capture his audience and offers a view on processes of historical causation: having started with six uses of the word 'our', Richard not only moves on to say 'I' nine times (supplemented by 'my' and 'me'), but also explains that his plans are going to affect the future of others, too. His plot to set his brothers against each other is going to change the course of history; moreover, it will do so by using the stalking-horse of a prophecy, a form of speech which presumes that the future is already unalterably fixed. Other soliloquies in the play also offer insights into historical process. This paper examines the differing tonality of the play's soliloquies and the kind of information offered in them to argue that while Richard III officially subscribes to Tudor myths of the past, it not only implicitly urges the audience to a more sceptical take, but in fact raises questions about whether we can ever be sure about how history was made. [ABSTRACT FROM AUTHOR]

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

This paper presents methods of retrieving Twitter data, both streaming and archive data, using Application Programming Interfaces. Twitter data are a kind of Location Based Social Network Data that, nowadays, is emerging in transportation demand modeling. Data regarding the locations of trip makers represent the most crucial step in the modeling. No research article has specifically addressed this topic with an up-to-date method; hence, this paper aims to refresh methods for retrieving Twitter data that can capture relevant data. The method is unique as the data are gathered for trip production modeling in zonal urban areas. Python script programs were built for both data retrieving methods. The programs were run for streaming data from May 2020 to April 2021 and archive data from 2018. The data were collected within Serang City, which is the nearest provincial city to Jakarta, the capital of Indonesia. In order to gather streaming data with no loss, the program has been run with referencing on sub-district office coordinate locations. Retrieving the intended data produces 1,090,623 documents, of which 54,103 are geotagged data from 2495 users. The study concluded that streaming data produce more geolocation data, while historical data capture more Twitter user data with relatively very little geotagged data and greater textual data than the period covered in this research. Thus, both techniques of retrieving Twitter data for urban personal trip modeling are necessary. Obtaining sufficient data collection using data streaming retrieval resulted in the most effective data preprocessing. This research contributes to Location Based Social Network data mining knowledge, both geolocation and text mining, and is useful for insight into developing trip production modeling in passenger transportation demand modeling using Machine Learning. This study also aims to provide useful methods for transportation system researchers and data scientists in utilizing Location Based Social Network data. [ABSTRACT FROM AUTHOR]

This paper presents a benchmarking survey on query expansion techniques for social media information retrieval, with a focus on comparing the performance of methods using semantic web technologies. The study evaluated query expansion techniques such as generative AI models and semantic matching algorithms and how they are integrated in a semantic framework. The evaluation was based on cosine similarity metrics, including the Discounted Cumulative Gain (DCG), Ideal Discounted Cumulative Gain (IDCG), and normalized Discounted Cumulative Gain (nDCG), as well as the Mean Average Precision (MAP). Additionally, the paper discusses the use of semantic web technologies as a component in a pipeline for building thematic knowledge graphs from retrieved social media data with extended ontologies integrated for the refugee crisis. The paper begins by introducing the importance of query expansion in information retrieval and the potential benefits of incorporating semantic web technologies. The study then presents the methodologies and outlines the specific procedures for each query expansion technique. The results of the evaluation are presented, as well as the rest semantic framework, and the best-performing technique was identified, which was the curie-001 generative AI model. Finally, the paper summarizes the main findings and suggests future research directions. [ABSTRACT FROM AUTHOR]

The increased interest in secure and reliable communications has turned the analysis of network traffic data into a predominant topic. A high number of research papers propose methods to classify traffic, detect anomalies, or identify attacks. Although the goals and methodologies are commonly similar, we lack initiatives to categorize the data, methods, and findings systematically. In this paper, we present Network Traffic Analysis Research Curation (NTARC), a data model to store key information about network traffic analysis research. We additionally use NTARC to perform a critical review of the field of research conducted in the last two decades. The collection of descriptive research summaries enables the easy retrieval of relevant information and a better reuse of past studies by the application of quantitative analysis. Among others benefits, it enables the critical review of methodologies, the detection of common flaws, the obtaining of baselines, and the consolidation of best practices. Furthermore, it provides a basis to achieve reproducibility, a key requirement that has long been undervalued in the area of traffic analysis. Thus, besides reading hard copies of papers, with NTARC, researchers can make use of a digital environment that facilitates queries and reviews over a comprehensive field corpus. [ABSTRACT FROM AUTHOR]

To solve the problems exposed by the application of blockchain technology under complex scenarios, such as fraudulent use of data, hard to store huge amounts of data, and low traceability efficiency under an ultra-huge number of traceability requests, this paper constructs an image-based interactive traceability structure by using images as an enhancement. By adding pointers to raw image files, a specific file structure is formed for traceability, and the traceability process is separated from the verification process, therefore realizing the distributed traceability of "traceability off the chain and verification on the chain". The experimental results show that, compared with the traditional blockchain traceability mode, the interactive traceability structure can reduce the data retrieval pressure and greatly improve the traceability efficiency of a specific transaction chain. With the growth of the span of the transaction chain, the traceability efficiency advantage of the interactive traceability structure becomes more obvious. [ABSTRACT FROM AUTHOR]

A systematic literature review is a synthesis of the available evidence, in which a review of quantitative and qualitative aspects of primary studies is carried out, to summarize the existing information regarding a particular topic. The researchers extract key criteria from papers collected about their study area, answering research questions and conducting document analysis. Nonetheless, in some cases, these criteria are improperly justified, unknowing their true level of importance in the study subject. Hence, an additional study is necessary to explain the criteria relevance in the papers studied using qualitative and quantitative premises. The correct identification of these key criteria is a critical factor in prioritizing and achieving appropriate results in any scientific research work. In our paper, a new method to determine key criteria from a literature review is proposed, composed of three components: input-process-output. First, the inputs are a set of criteria to evaluate and a set of documents to analyze. Next, the process component examines the document set to indicate whether the criteria to be analyzed are found. The process component produces a Boolean matrix, which is the input of the mathematical logic process that will get the key criteria considered necessary and sufficient as the output component. The iKeyCriteria method has been applied in different computing domains, particularly for serious games design and virtual organizations, giving positive results in each context. Finally, we developed an online tool that provides global support to the execution of our method. [ABSTRACT FROM AUTHOR]

Software bugs are a noteworthy concern for developers and maintainers. When a failure is detected late, it costs more to be fixed. To repair the bug that caused the software failure, the location of the bug must first be known. The process of finding the defective source code elements that led to the failure of the software is called bug localization. Effective approaches for automatically locating bugs using bug reports are highly desirable, as they would reduce bug-fixing time, consequently lowering software maintenance costs. With the increasing size and complexity of software projects, manual bug localization methods have become complex, challenging, and time-consuming tasks, which motivates research on automated bug localization techniques. This paper introduces a novel bug localization model, which works on two levels. The first level localizes the buggy classes using an information retrieval approach and it has two additional sub-phases, namely the class-level feature scoring phase and the class-level final score and ranking phase, which ranks the top buggy classes. The second level localizes the buggy methods inside these classes using an information retrieval approach and it has two sub-phases, which are the method-level feature scoring phase and the method-level final score and ranking phase, which ranks the top buggy methods inside the localized classes. A model is evaluated using an AspectJ dataset, and it can correctly localize and rank more than 350 classes and more than 136 methods. The evaluation results show that the proposed model outperforms several state-of-the-art approaches in terms of the mean reciprocal rank (MRR) metrics and the mean average precision (MAP) in class-level bug localization. [ABSTRACT FROM AUTHOR]