Domain-specific Knowledge Research Articles

When text mining meets science mapping in the bibliometric analysis: A review and future opportunities

The increasing volume of scientific publications has created a need for more efficient and effective literature review processes. Bibliometric analysis is a quantitative approach to analysing bibliographic data extracted from research studies to identify publishing patterns and trends within specific knowledge domains. Science mapping is a widespread technique in bibliometric analysis that enables researchers to reveal the structure of their respective fields and identify dominant themes. However, there is still a lack of clarity and transparency in describing the science mapping process, which can hinder continued refinement and improvement of this critical field of research. This study provides a comprehensive overview of science mapping in bibliometric analysis based on published review studies from prestigious international journals. It outlines the science mapping mechanism and explores challenges and opportunities, focusing on incorporating text mining approaches to support the analytical literature review process. The study sheds light on previously unexplored mechanisms in the literature of bibliometric analysis, revealing gaps in existing research. The study contributes to the growing body of research on bibliometric analysis by highlighting the need for continuous improvement and the deployment of text mining techniques to support the analysis of scientific publication data. This study offers valuable insights for researchers, policymakers, and practitioners seeking to enhance their understanding of science mapping and bibliometric analysis.

Learning Dynamical Systems with Side Information

We present a mathematical and computational framework for learning a dynamical system from noisy observations of a few trajectories and subject to side information. Side information is any knowledge we might have about the dynamical system we would like to learn, besides trajectory data, and is typically inferred from domain-specific knowledge or basic principles of a scientific discipline. We are interested in explicitly integrating side information into the learning process in order to compensate for scarcity of trajectory observations. We identify six types of side information that arise naturally in many applications and lead to convex constraints in the learning problem. First, we show that when our model for the unknown dynamical system is parameterized as a polynomial, we can impose our side information constraints computationally via semidefinite programming. We then demonstrate the added value of side information for learning the dynamics of basic models in physics and cell biology, as well as for learning and controlling the dynamics of a model in epidemiology. Finally, we study how well polynomial dynamical systems can approximate continuously differentiable ones while satisfying side information (either exactly or approximately). Our overall learning methodology combines ideas from convex optimization, real algebra, dynamical systems, and functional approximation theory, and can potentially lead to new synergies among these areas.

Merging nucleus datasets by correlation-based cross-training

Fine-grained nucleus classification is challenging because of the high inter-class similarity and intra-class variability. Therefore, a large number of labeled data is required for training effective nucleus classification models. However, it is challenging to label a large-scale nucleus classification dataset comparable to ImageNet in natural images, considering that high-quality nucleus labeling requires specific domain knowledge. In addition, the existing publicly available datasets are often inconsistently labeled with divergent labeling criteria. Due to this inconsistency, conventional models have to be trained on each dataset separately and work independently to infer their own classification results, limiting their classification performance. To fully utilize all annotated datasets, we formulate the nucleus classification task as a multi-label problem with missing labels to utilize all datasets in a unified framework. Specifically, we merge all datasets and combine their labels as multiple labels. Thus, each data has one ground-truth label and several missing labels. We devise a base classification module that is trained using all data but sparsely supervised by the ground-truth labels only. We then exploit the correlation among different label sets by a label correlation module. By doing so, we can have two trained basic modules and further cross-train them with both ground-truth labels and pseudo labels for the missing ones. Importantly, data without any ground-truth labels can also be involved in our framework, as we can regard them as data with all labels missing and generate the corresponding pseudo labels. We carefully re-organized multiple publicly available nucleus classification datasets, converted them into a uniform format, and tested the proposed framework on them. Experimental results show substantial improvement compared to the state-of-the-art methods. The code and data are available at https://w-h-zhang.github.io/projects/dataset_merging/dataset_merging.html.

Deep representation learning of scientific paper reveals its potential scholarly impact

Citation and citation-based metrics are traditionally used to quantify the scholarly impact of scientific papers. However, for documents without citation data, i.e., newly published papers, the citation-based metrics are not available. By leveraging deep representation techniques, we propose a text-content based approach that may reveal the scholarly impact of papers without human domain-specific knowledge. Specifically, a large-scale Pre-Trained Model (PTM) with 110 million parameters is utilized to automatically encode the paper into the vector representation. Two indicators, τ (Topicality) and σ (Originality), are then proposed based on the learned representations. These two indicators leverage the spatial relations of paper representations in the semantic space to capture the impact-related characteristics of a scientific paper. Extensive experiments have been conducted on a COVID-19 open research dataset with 1,056,660 papers. The experimental results demonstrate that the deep representation learning method can better capture the scientific content in the published literature; and the proposed indicators are positively and significantly associated with a paper’s potential scholarly impact. In the multivariate regression analysis for the potential impact of a paper, the coefficients of σ and τ are 5.4915 (P<0.001) and 6.6879 (P<0.001) for next 6 months prediction, 12.9964 (P<0.001) and 13.8678 (P<0.001) for next 12 months prediction. The proposed framework may facilitate the study of how scholarly impact is generated, from a textual representation perspective.

Automatic Construction Hazard Identification Integrating On-Site Scene Graphs with Information Extraction in Outfield Test

Construction hazards occur at any time in outfield test sites and frequently result from improper interactions between objects. The majority of casualties might be avoided by following on-site regulations. However, workers may be unable to comply with the safety regulations fully because of stress, fatigue, or negligence. The development of deep-learning-based computer vision and on-site video surveillance facilitates safety inspections, but automatic hazard identification is often limited due to the semantic gap. This paper proposes an automatic hazard identification method that integrates on-site scene graph generation and domain-specific knowledge extraction. A BERT-based information extraction model is presented to automatically extract the key regulatory information from outfield work safety requirements. Subsequently, an on-site scene parsing model is introduced for detecting interaction between objects in images. An automatic safety checking approach is also established to perform PPE compliance checks by integrating detected textual and visual relational information. Experimental results show that our proposed method achieves strong performance in various metrics on self-built and widely used public datasets. The proposed method can precisely extract relational information from visual and text modalities to facilitate on-site hazard identification.

Cultural specialization as a double-edged sword: division into specialized guilds might promote cultural complexity at the cost of higher susceptibility to cultural loss.

The transition to specialization of knowledge within populations could have facilitated the accumulation of cultural complexity in humans. Specialization allows populations to increase their cultural repertoire without requiring that members of that population increase their individual capacity to accumulate knowledge. However, specialization also means that domain-specific knowledge can be concentrated in small subsets of the population, making it more susceptible to loss. Here, we use a model of cultural evolution to demonstrate that specialized populations can be more sensitive to stochastic loss of knowledge than populations without subdivision of knowledge, and that demographic and environmental changes have an amplified effect on populations with knowledge specialization. Finally, we suggest that specialization can be a double-edged sword; specialized populations may have an advantage in accumulating cultural traits but may also be less likely to expand and establish themselves successfully in new demes owing to the increased cultural loss that they experience during the population bottlenecks that often characterize such expansions. This article is part of the theme issue 'Human socio-cultural evolution in light of evolutionary transitions'.

Utilization of temporal autoencoder for semi-supervised intracranial EEG clustering and classification

Manual visual review, annotation and categorization of electroencephalography (EEG) is a time-consuming task that is often associated with human bias and requires trained electrophysiology experts with specific domain knowledge. This challenge is now compounded by development of measurement technologies and devices allowing large-scale heterogeneous, multi-channel recordings spanning multiple brain regions over days, weeks. Currently, supervised deep-learning techniques were shown to be an effective tool for analyzing big data sets, including EEG. However, the most significant caveat in training the supervised deep-learning models in a clinical research setting is the lack of adequate gold-standard annotations created by electrophysiology experts. Here, we propose a semi-supervised machine learning technique that utilizes deep-learning methods with a minimal amount of gold-standard labels. The method utilizes a temporal autoencoder for dimensionality reduction and a small number of the expert-provided gold-standard labels used for kernel density estimating (KDE) maps. We used data from electrophysiological intracranial EEG (iEEG) recordings acquired in two hospitals with different recording systems across 39 patients to validate the method. The method achieved iEEG classification (Pathologic vs. Normal vs. Artifacts) results with an area under the receiver operating characteristic (AUROC) scores of 0.862 ± 0.037, 0.879 ± 0.042, and area under the precision-recall curve (AUPRC) scores of 0.740 ± 0.740, 0.714 ± 0.042. This demonstrates that semi-supervised methods can provide acceptable results while requiring only 100 gold-standard data samples in each classification category. Subsequently, we deployed the technique to 12 novel patients in a pseudo-prospective framework for detecting Interictal epileptiform discharges (IEDs). We show that the proposed temporal autoencoder was able to generalize to novel patients while achieving AUROC of 0.877 ± 0.067 and AUPRC of 0.705 ± 0.154.

Style over substance: A psychologically informed approach to feature selection and generalisability for author classification

Author profiling, or classifying user generated content based on demographic or other personal attributes, is a key task in social media-based research. Whilst high-accuracy has been achieved on many attributes, most studies tend to train and test models on a single domain only, ignoring cross-domain performance and research shows that models often transfer poorly into new domains as they tend to depend heavily on topic-specific (i.e., lexical) features. Knowledge specific to the field (e.g., Psychology, Political Science) is often ignored, with a reliance on data driven algorithms for feature development and selection.Focusing on political affiliation, we evaluate an approach that selects stylistic features according to known psychological correlates (personality traits) of this attribute. Training data was collected from Reddit posts made by regular users of the political subreddits of r/republican and r/democrat. A second, non-political dataset, was created by collecting posts by the same users but in different subreddits.Our results show that introducing domain specific knowledge in the form of psychologically informed stylistic features resulted in better out of training domain performance than lexical or more commonly used stylistic features.

Multi-objective distributed Web service composition—A link-dominance driven evolutionary approach

Service-oriented architecture (SOA) encourages the creation of modular applications involving Web services as the reusable components. Data-intensive Web services manipulate and deal with the massive data that emerged by technological advances and their various applications. Distributed Data-intensive Web Service Composition (DWSC) includes the selection of data-intensive Web services from diverse locations on the network and composing them into a service to accomplish a complex task. Optimising Quality of Service (QoS) while satisfying the functional requirements is a fundamental challenge for service developers. The multi-objective and distributed nature of the DWSC problem demands satisfying algorithms to automatically produce the Pareto optimal set of composite services. In this paper, we propose a new Evolutionary Computation (EC) approach based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) and a novel local search technique to effectively solve the multi-objective distributed DWSC problems. Our local search technique employs an innovative concept, communication link dominance, to incorporate domain-specific knowledge. The performance of our proposed link-dominance driven EC approach is evaluated on benchmark datasets. The results show that our proposed method has the highest quality with acceptable execution time for most of the composition tasks among competing algorithms.

Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering

Abstract Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia-based external knowledge base and is not optimized for use in other specialized domains such as healthcare and news. In this paper, we evaluate the impact of joint training of the retriever and generator components of RAG for the task of domain adaptation in ODQA. We propose RAG-end2end, an extension to RAG that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training. In addition, we introduce an auxiliary training signal to inject more domain-specific knowledge. This auxiliary signal forces RAG-end2end to reconstruct a given sentence by accessing the relevant information from the external knowledge base. Our novel contribution is that, unlike RAG, RAG-end2end does joint training of the retriever and generator for the end QA task and domain adaptation. We evaluate our approach with datasets from three domains: COVID-19, News, and Conversations, and achieve significant performance improvements compared to the original RAG model. Our work has been open-sourced through the HuggingFace Transformers library, attesting to our work’s credibility and technical consistency.

Functional classification of bitcoin addresses

A classification model for predicting the main activity of bitcoin addresses based on their balances is proposed. Since the balances are functions of time, methods from functional data analysis are applied; more specifically, the features of the proposed classification model are the functional principal components of the data. Classifying bitcoin addresses is a relevant problem for two main reasons: to understand the composition of the bitcoin market, and to identify addresses used for illicit activities. Although other bitcoin classifiers have been proposed, they focus primarily on network analysis rather than curve behavior. The proposed approach, on the other hand, does not require any network information for prediction. Furthermore, functional features have the advantage of being straightforward to build, unlike expert-built features. Results show improvement when combining functional features with scalar features, and similar accuracy for the models using those features separately, which points to the functional model being a good alternative when domain-specific knowledge is not available.

Extraction of clinical phenotypes for Alzheimer's disease dementia from clinical notes using natural language processing.

There is much interest in utilizing clinical data for developing prediction models for Alzheimer's disease (AD) risk, progression, and outcomes. Existing studies have mostly utilized curated research registries, image analysis, and structured electronic health record (EHR) data. However, much critical information resides in relatively inaccessible unstructured clinical notes within the EHR. We developed a natural language processing (NLP)-based pipeline to extract AD-related clinical phenotypes, documenting strategies for success and assessing the utility of mining unstructured clinical notes. We evaluated the pipeline against gold-standard manual annotations performed by 2 clinical dementia experts for AD-related clinical phenotypes including medical comorbidities, biomarkers, neurobehavioral test scores, behavioral indicators of cognitive decline, family history, and neuroimaging findings. Documentation rates for each phenotype varied in the structured versus unstructured EHR. Interannotator agreement was high (Cohen's kappa = 0.72-1) and positively correlated with the NLP-based phenotype extraction pipeline's performance (average F1-score = 0.65-0.99) for each phenotype. We developed an automated NLP-based pipeline to extract informative phenotypes that may improve the performance of eventual machine learning predictive models for AD. In the process, we examined documentation practices for each phenotype relevant to the care of AD patients and identified factors for success. Success of our NLP-based phenotype extraction pipeline depended on domain-specific knowledge and focus on a specific clinical domain instead of maximizing generalizability.

Scientometric-based Knowledge Map of Food Science and Technology Research in India

The study aims to explore domain specific knowledge map in the area of Food Science and Technology (FST) using scientometric tools and techniques. The study was based on the data extracted from Scopus in this area for the period 2011–2020. Global research output shows a highly skewed distribution with research concentrated in a few countries. The top two countries China and USA accounts for almost 25% of the global research output; India with 3.44% of output is ranked at 7th position globally. Indian papers are not making significant impact globally as seen through citations. One interesting characteristic of Indian research activity is high co-authorship. Highly cited Indian papers exhibit high degree of international collaboration underscoring that international collaboration is a significant factor in making global impact. The Indian research activity is also highly skewed in terms of institutions and authors involved. Most of the papers are concentrated in a few journals. The co-word analysis of highly cited papers based on keywords helps to identify topics which have high contemporary relevance. Antioxidant effects of plant foods, dietary health benefits, food processing, food safety and security, food bioactive compounds, malnutrition, functional foods were areas addressed prominently in the highly cited papers. The study is thus able to bring different facets of research activity in this area in India situating it within the global context.

Aquila Optimization with Machine Learning-Based Anomaly Detection Technique in Cyber-Physical Systems

Cyber-physical system (CPS) is a concept that integrates every computer-driven system interacting closely with its physical environment. Internet-of-things (IoT) is a union of devices and technologies that provide universal interconnection mechanisms between the physical and digital worlds. Since the complexity level of the CPS increases, an adversary attack becomes possible in several ways. Assuring security is a vital aspect of the CPS environment. Due to the massive surge in the data size, the design of anomaly detection techniques becomes a challenging issue, and domain-specific knowledge can be applied to resolve it. This article develops an Aquila Optimizer with Parameter Tuned Machine Learning Based Anomaly Detection (AOPTML-AD) technique in the CPS environment. The presented AOPTML-AD model intends to recognize and detect abnormal behaviour in the CPS environment. The presented AOPTML-AD framework initially pre-processes the network data by converting them into a compatible format. Besides, the improved Aquila optimization algorithm-based feature selection (IAOA-FS) algorithm is designed to choose an optimal feature subset. Along with that, the chimp optimization algorithm (ChOA) with an adaptive neuro-fuzzy inference system (ANFIS) model can be employed to recognise anomalies in the CPS environment. The ChOA is applied for optimal adjusting of the membership function (MF) indulged in the ANFIS method. The performance validation of the AOPTML-AD algorithm is carried out using the benchmark dataset. The extensive comparative study reported the better performance of the AOPTML-AD technique compared to recent models, with an accuracy of 99.37%.

Enhancing traditional Chinese medicine diagnostics: Integrating ontological knowledge for multi-label symptom entity classification.

In traditional Chinese medicine (TCM), artificial intelligence (AI)-assisted syndrome differentiation and disease diagnoses primarily confront the challenges of accurate symptom identification and classification. This study introduces a multi-label entity extraction model grounded in TCM symptom ontology, specifically designed to address the limitations of existing entity recognition models characterized by limited label spaces and an insufficient integration of domain knowledge. This model synergizes a knowledge graph with the TCM symptom ontology framework to facilitate a standardized symptom classification system and enrich it with domain-specific knowledge. It innovatively merges the conventional bidirectional encoder representations from transformers (BERT) + bidirectional long short-term memory (Bi-LSTM) + conditional random fields (CRF) entity recognition methodology with a multi-label classification strategy, thereby adeptly navigating the intricate label interdependencies in the textual data. Introducing a multi-associative feature fusion module is a significant advancement, thereby enabling the extraction of pivotal entity features while discerning the interrelations among diverse categorical labels. The experimental outcomes affirm the model's superior performance in multi-label symptom extraction and substantially elevates the efficiency and accuracy. This advancement robustly underpins research in TCM syndrome differentiation and disease diagnoses.

Analyses of Tabular AlphaZero on Strongly-Solved Stochastic Games

The AlphaZero algorithm achieved superhuman levels of play in chess, shogi, and Go by learning without domain-specific knowledge except for game rules. This paper targets stochastic games and investigates whether AlphaZero can learn theoretical values and optimal play. Since the theoretical values of stochastic games are expected win rates, not a simple win, loss, or draw, it is worth investigating the ability of AlphaZero to approximate expected win rates of positions. This paper also thoroughly studies how AlphaZero is influenced by hyper-parameters and some implementation details. The analyses are mainly based on AlphaZero learning with lookup tables. Deep neural networks (DNNs) like the ones in the original AlphaZero are also experimented and compared. The tested stochastic games include reduced and strongly-solved variants of Chinese dark chess and EinStein würfelt nicht!. The experiments showed that AlphaZero could learn policies that play almost optimally against the optimal player and could learn values accurately. In more detail, such good results were achieved by different hyper-parameter settings in a wide range, though it was observed that games on larger scales tended to have a little narrower range of proper hyper-parameters. In addition, the results of learning with DNNs were similar to lookup tables.

Exploring Sparse Visual Odometry Acceleration With High-Level Synthesis

Visual Odometry (VO) systems are widely used to determine the position and orientation of a robot or camera in an unknown environment. They are deployed on resource-constrained platforms, such as drones and Virtual Reality (VR) or Augmented Reality (AR) headsets. VO systems harnessing modern System-on-Chip (SoCs) with integrated Field Programmable Gate Array (FPGA) have the potential to improve the overall systems performance. This paper explores the FPGA acceleration of sparse VO kernels using High-level Synthesis (HLS) as this kind of VO system has been designed to use with low-power SoCs. We show that both computational and data transfer overheads between the processing cores of the CPU of the SoC and the accelerators on the FPGA need to be optimized to obtain better end-to-end performance. This is a result of the additional data movement incurred when using an FPGA accelerator and also because of the sparse computational nature with predictable or random memory access patterns of the kernels involved. However, state-of-the-art HLS tools are not yet able to perform the required optimizations automatically because they usually assume that the kernels to be accelerated have dense computational patterns with regular memory access. In this paper we propose three, potentially generic, methods to reduce the data transfer between the CPU and the customised hardware kernels on the FPGA; these methods are: (a) approximation based on domain-specific knowledge, (b) image compression, and (c) the use of on-the-fly computation. We present a case study of the use of these methods on SVO, a state-of-the-art sparse VO system with a semi-direct front-end. We demonstrate that our proposed methods can reduce data transfer overhead to achieve better end-to-end performance and that they can be applied not only when using standard Xilinx HLS tools but also with other state-of-the-art HLS tools, such as HeteroFlow. Compared to the baseline performance of the original SVO software on an Arm CPU, our proposed methods assist the HLS and HeteroFlow designs to achieve a speedup of 2.4x and 2.14x, respectively, without noticeable accuracy loss. The HLS and HeteroFlow designs also achieve a 1.85x and 1.89x, respectively, improvement in energy efficiency on the SoC system used. Compared to the SVO software baseline running on the Intel Xeon CPU, our proposed methods assist the HLS and HeteroFlow designs to achieve 8.2x and 8.3x improvement in energy efficiency, respectively.

Consistency and Diversity Induced Human Motion Segmentation.

Subspace clustering is a classical technique that has been widely used for human motion segmentation and other related tasks. However, existing segmentation methods often cluster data without guidance from prior knowledge, resulting in unsatisfactory segmentation results. To this end, we propose a novel Consistency and Diversity induced human Motion Segmentation (CDMS) algorithm. Specifically, our model factorizes the source and target data into distinct multi-layer feature spaces, in which transfer subspace learning is conducted on different layers to capture multi-level information. A multi-mutual consistency learning strategy is carried out to reduce the domain gap between the source and target data. In this way, the domain-specific knowledge and domain-invariant properties can be explored simultaneously. Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance. Moreover, to preserve the temporal correlations, an enhanced graph regularizer is imposed on the learned representation coefficients and the multi-level representations of the source data. The proposed model can be efficiently solved using the Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive experimental results on public human motion datasets demonstrate the effectiveness of our method against several state-of-the-art approaches.

Health literacy, cancer literacy, comprehensions and knowledge among men attending a urology clinic

Health literacy (HL) is essential for men receiving urological treatment so that they can be involved in the shared decision making process. HL is supported by domain specific background knowledge which also informs cancer literacy and comprehension. Comprehension is in turn a determinant of HL. This study aimed to assess the level of HL among a group of men receiving urological treatment and to investigate if there were any correlations between the two different measures of HL, cancer literacy and comprehension, and prostate cancer knowledge. A survey was mailed to 200 men attending a urological clinic. The survey included: demographic questions, two validated tests of HL, (1) the Brief Health Literacy Score (BHLS) and (2) the Health Literacy Management Scale (HeLMS); a test of cancer comprehension; the Cancer Message Literacy Tests Reading (CMLT); and a prostate cancer knowledge test. Descriptive statistics were used to analyse the data. Surveys from 72 respondents, average age of 65 years, were included in the final analysis. Based on the BHLS, 22%of respondents had inadequate HL and 50% of respondents had inadequate HL in one or more of the HeLMS domains. Overall, the study participants had relatively high cancer literacy, comprehensions, and knowledge. However, for men with inadequate HL, based on the BHLS and the HeLMS, there were strong correlations with poor cancer literacy, comprehension, and knowledge. Our study highlights that many men receiving urological treatment with inadequate HL, require additional support to access health information which they can understand and act on to be part of the shared decision-making process.

10.17159/2223-0386/2023/n30a3

This article argues that complex substantive knowledge in South African school history textbooks may challenge learners who struggle with reading and comprehension. While debates continue about the balance of substantive and procedural knowledge, both fundamental elements of history knowledge (Lee, 2004), this study employs a qualitative analysis of the substantive content within a Bernsteinian framework. Seven purposively sampled history textbooks, covering grade 3 to grade 9, across the foundation, intermediate, and senior phases of the South African school curriculum are analysed using Maton's (2013) language descriptions of context and semantics as conceptual tools. Additionally, nominalisation techniques (Coffin, 2006) are used to examine language in the text. Findings indicate significant growth in substantive knowledge manifesting through time, space, context, and semantics.1 Substantive knowledge shifts from a rudimentary and contextualised nature to a more abstract and dense form, including domain-specific conceptual knowledge. Advancing grades produce decontextualised knowledge with heightened semantic density. Increased events under study accompany greater participant diversity. A History student working with these materials would need to be highly proficient in language skills and also capable of processing substantial volumes of abstract content knowledge. Alarming statistics from the Progress in International Reading Literacy Study (Department of Basic Education, 2023) reveal that 81% of grade 4 learners struggle with comprehension in any language, ranking South Africa at the bottom of 57 countries. It is likely that learners would encounter difficulties with the substantive knowledge evident in these textbooks.