Comprehensive Taxonomy Research Articles

Decoding business analytics: discovering the hidden core through a novel taxonomy

PurposeThis study develops a comprehensive taxonomy of the business analytics (BA) discipline, uncovering its intellectual core and revealing its evolution over the past 12 years.Design/methodology/approachUsing stakeholder-driven identity formation theory, this study explores how organizational identity emerges through stakeholder negotiations. It investigates how top scholarly journals shape the BA discipline’s image and influence perceptions. High-quality articles from top journals listed by the Australian Business Deans Council are analyzed using latent Dirichlet allocation (LDA), a natural language processing and topic modeling method.FindingsThe study outlines key research areas identified as analytics methods, marketing, finance, operations and decision support analytics, along with 12 subareas. An analysis of the top 100 topics reveals prevalent research themes, showcasing the breadth of BA. A 12-year time-series review shows initial growth followed by maturation across most areas, except for decision support analytics, which maintained steady growth. These findings provide empirical evidence of BA’s development as a distinct discipline, highlighting its interdisciplinary nature and evolving research focus.Originality/valueThis study presents the first comprehensive, data-driven taxonomy of BA research, distilling the intellectual core into five key areas and 12 subareas, while identifying 100 supporting themes. It extends the stakeholders’ approach to identity development theory in the context of BA, providing empirical support for discussions on the field’s identity and diversity. The findings offer valuable insights for scholars, industries, managers and professionals, guiding curriculum development, research directions and practical applications of BA.

Decoding the molecular, cellular, and functional heterogeneity of zebrafish intracardiac nervous system

The proper functioning of the heart relies on the intricate interplay between the central nervous system and the local neuronal networks within the heart itself. While the central innervation of the heart has been extensively studied, the organization and functionality of the intracardiac nervous system (IcNS) remain largely unexplored. Here, we present a comprehensive taxonomy of the IcNS, utilizing single-cell RNA sequencing, anatomical studies, and electrophysiological techniques. Our findings reveal a diverse array of neuronal types within the IcNS, exceeding previous expectations. We identify a subset of neurons exhibiting characteristics akin to pacemaker/rhythmogenic neurons similar to those found in Central Pattern Generator networks of the central nervous system. Our results underscore the heterogeneity within the IcNS and its key role in regulating the heart’s rhythmic functionality. The classification and characterization of the IcNS presented here serve as a valuable resource for further exploration into the mechanisms underlying heart functionality and the pathophysiology of associated cardiac disorders.

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection.

Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. With the recent advancements in graph neural networks (GNNs), there has been a surge in GNN-based approaches for time series analysis. These approaches can explicitly model inter-temporal and inter-variable relationships, which traditional and other deep neural network-based methods struggle to do. In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation. Our aim is to guide designers and practitioners to understand, build applications, and advance research of GNN4TS. At first, we provide a comprehensive task-oriented taxonomy of GNN4TS. Then, we present and discuss representative research works and introduce mainstream applications of GNN4TS. A comprehensive discussion of potential future research directions completes the survey. This survey, for the first time, brings together a vast array of knowledge on GNN-based time series research, highlighting foundations, practical applications, and opportunities of graph neural networks for time series analysis.

Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy.

Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In recent years, deep learning-based approaches have propelled results to a new level causing a rapid growth in the field with 121 methods proposed in the medical imaging domain alone. In this review, we provide a structured overview of this emerging field featuring a comprehensive taxonomy, a systematic review of existing methods, and an in-depth analysis of current practices. Based on these contributions, we discuss the challenges and opportunities in the field. For instance, we find that there is a severe lack of comparison across methods which needs to be tackled by standardized baselines and benchmarks.

A Taxonomy and Archetypes of AI-Based Health Care Services: Qualitative Study.

To cope with the enormous burdens placed on health care systems around the world, from the strains and stresses caused by longer life expectancy to the large-scale emergency relief actions required by pandemics like COVID-19, many health care companies have been using artificial intelligence (AI) to adapt their services. Nevertheless, conceptual insights into how AI has been transforming the health care sector are still few and far between. This study aims to provide an overarching structure with which to classify the various real-world phenomena. A clear and comprehensive taxonomy will provide consensus on AI-based health care service offerings and sharpen the view of their adoption in the health care sector. The goal of this study is to identify the design characteristics of AI-based health care services. We propose a multilayered taxonomy created in accordance with an established method of taxonomy development. In doing so, we applied 268 AI-based health care services, conducted a structured literature review, and then evaluated the resulting taxonomy. Finally, we performed a cluster analysis to identify the archetypes of AI-based health care services. We identified 4 critical perspectives: agents, data, AI, and health impact. Furthermore, a cluster analysis yielded 13 archetypes that demonstrate our taxonomy's applicability. This contribution to conceptual knowledge of AI-based health care services enables researchers as well as practitioners to analyze such services and improve their theory-led design.

Unveiling species diversity within early-diverging fungi from China III: Six new species and a new record of Gongronella (Cunninghamellaceae, Mucoromycota).

Gongronella had accommodated only two species for more than half a century and as many as 17 new species have been described in this genus since 2015. However, no systematic studies were conducted for this genus so far. The distribution, substrate and morphology of all known species in Gongronella are analysed herein. Meanwhile, with the support of phylogenetic and morphological evidence, six new species (G.abortosporangia sp. nov., G.apophysata sp. nov., G.bawanglingensis sp. nov., G.inconstans sp. nov., G.pingtangensis sp. nov. and G.reniformis sp. nov.) are proposed and G.pamphilae is recorded from China for the first time. The phylogenetic tree was constructed using ITS+LSU+TEF+ACT+RPB1 and the results were basically the same as ITS+LSU. All species of Gongronella, except G.namwonensis from fresh water, were isolated from soil. The genus is distributed worldwide, mainly in tropical and subtropical regions. A synoptic key is provided for a total of 24 species (18 species previously published and six species newly described herein), except for G.banzhaoae due to unavailable protologue, type and living culture. No morphologies were described when G.pamphilae was proposed. Thanks to the strains isolated in this study, G.pamphilae is included in the key and reported as a Chinese new record. This is the first comprehensive taxonomy and phylogeny of the genus Gongronella.

A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions

Clustering is a fundamental machine learning task, which aim at assigning instances into groups so that similar samples belong to the same cluster while dissimilar samples belong to different clusters. Shallow clustering methods usually assume that data are collected and expressed as feature vectors within which clustering is performed. However, clustering high-dimensional data, such as images, texts, videos, and graphs, poses significant challenges for clustering tasks, such as indiscriminate representation and intricate relationships among instances. Over the past decades, deep learning has achieved remarkable success in effective representation learning and modeling complex relationships. Motivated by these advancements, Deep Clustering seeks to improve clustering outcomes through deep learning techniques, garnering considerable interest from both academia and industry. Despite many contributions to this vibrant area of research, the lack of systematic analysis and a comprehensive taxonomy has hindered progress in this field. In this survey, we first explore how deep learning can be integrated into deep clustering and identify two fundamental components: the representation learning module and the clustering module. Then, we summarize and analyze the representative design of these two modules. Furthermore, we introduce a novel taxonomy of deep clustering based on how these two modules interact, specifically through multistage, generative, iterative, and simultaneous approaches. In addition, we present well-known benchmark datasets, evaluation metrics, and open-source tools to clearly demonstrate different experimental approaches. Finally, we examine the practical applications of deep clustering and propose challenging areas for future research.

On Efficient Training of Large-Scale Deep Learning Models

The field of deep learning has witnessed significant progress in recent times, particularly in areas such as computer vision (CV), natural language processing (NLP), and speech. The use of large-scale models trained on vast amounts of data holds immense promise for practical applications, enhancing industrial productivity and facilitating social development. However, it suffers extremely from the unstable training process and stringent requirements of computational resources. With the increasing demands on the adaption of computational capacity, though numerous studies have explored the efficient training field to a certain extent, a comprehensive summarization/guideline on those general acceleration techniques of training large-scale deep learning models is still much anticipated. In this survey, we present a detailed review of the general techniques for training acceleration. We consider the fundamental update formulation and split its basic components into five main perspectives: (1) “data-centric,” including dataset regularization, data sampling, and data-centric curriculum learning techniques, which can significantly reduce the computational complexity of the data samples; (2) “model-centric,” including acceleration of basic modules, compression training, model initialization, and model-centric curriculum learning techniques, which focus on accelerating the training via reducing the calculations on parameters and providing better initialization; (3) “optimization-centric,” including the selection of learning rate, the employment of large batch size, the designs of efficient objectives, and model average techniques, which pay attention to the training policy and improving the generality for the large-scale models; (4) “budgeted training,” including some distinctive acceleration methods on source-constrained situations, e.g., for limitation on the total iterations; and (5) “system-centric,” including some efficient distributed frameworks and open source libraries that provide adequate hardware support for the implementation of the above-mentioned acceleration algorithms. By presenting this comprehensive taxonomy, our survey presents a comprehensive review to understand the general mechanisms within each component and their joint interaction. Meanwhile, we further provide a detailed analysis and discussion of future works on the development of general acceleration techniques, which could inspire us to re-think and design novel efficient paradigms. Overall, we hope that this survey will serve as a valuable guideline for general efficient training.

Cybersecurity at Sea: A Literature Review of Cyber-Attack Impacts and Defenses in Maritime Supply Chains

The maritime industry is constantly evolving and posing new challenges, especially with increasing digitalization, which has raised concerns about cyber-attacks on maritime supply chain agents. Although scholars have proposed various methods and classification models to counter these cyber threats, a comprehensive cyber-attack taxonomy for maritime supply chain actors based on a systematic literature review is still lacking. This review aims to provide a clear picture of common cyber-attacks and develop a taxonomy for their categorization. In addition, it outlines best practices derived from academic research in maritime cybersecurity using PRISMA principles for a systematic literature review, which identified 110 relevant journal papers. This study highlights that distributed denial of service (DDoS) attacks and malware are top concerns for all maritime supply chain stakeholders. In particular, shipping companies are urged to prioritize defenses against hijacking, spoofing, and jamming. The report identifies 18 practices to combat cyber-attacks, categorized into information security management solutions, information security policies, and cybersecurity awareness and training. Finally, this paper explores how emerging technologies can address cyber-attacks in the maritime supply chain network (MSCN). While Industry 4.0 technologies are highlighted as significant trends in the literature, this study aims to equip MSCN stakeholders with the knowledge to effectively leverage a broader range of emerging technologies. In doing so, it provides forward-looking solutions to prevent and mitigate cyber-attacks, emphasizing that Industry 4.0 is part of a larger landscape of technological innovation.

Scientific workflow scheduling algorithms in cloud environments: a comprehensive taxonomy, survey, and future directions

Scientific workflow scheduling algorithms in cloud environments: a comprehensive taxonomy, survey, and future directions

Backdoor Attacks against Voice Recognition Systems: A Survey

Voice Recognition Systems (VRSs) employ deep learning for speech recognition and speaker recognition. They have been widely deployed in various real-world applications, from intelligent voice assistance to telephony surveillance and biometric authentication. However, prior research has revealed the vulnerability of VRSs to backdoor attacks, which pose a significant threat to the security and privacy of VRSs. Unfortunately, existing literature lacks a thorough review on this topic. This paper fills this research gap by conducting a comprehensive survey on backdoor attacks against VRSs. We first present an overview of VRSs and backdoor attacks, elucidating their basic knowledge. Then we propose a set of evaluation criteria to assess the performance of backdoor attack methods. Next, we present a comprehensive taxonomy of backdoor attacks against VRSs from different perspectives and analyze the characteristic of different categories. After that, we comprehensively review existing attack methods and analyze their pros and cons based on the proposed criteria. Furthermore, we review classic backdoor defense methods and generic audio defense techniques. Then we discuss the feasibility of deploying them on VRSs. Finally, we figure out several open issues and further suggest future research directions to motivate the research of VRSs security.

Measuring Human-AI Value Alignment in Large Language Models

This paper seeks to quantify the human-AI value alignment in large language models. Alignment between humans and AI has become a critical area of research to mitigate potential harm posed by AI. In tandem with this need, developers have incorporated a values-based approach towards model development where ethical principles are integrated from its inception. However, ensuring that these values are reflected in outputs remains a challenge. In addition, studies have noted that models lack consistency when producing outputs, which in turn can affect their function. Such variability in responses would impact human-AI value alignment as well, particularly where consistent alignment is critical. Fundamentally, the task of uncovering a model’s alignment is one of explainability – where understanding how these complex models behave is essential in order to assess their alignment. This paper examines the problem through a case study of GPT-3.5. By repeatedly prompting the model with scenarios based on a dataset of moral stories, we aggregate the model’s alignment with human values to produce a human-AI value alignment metric. Moreover, by using a comprehensive taxonomy of human values, we uncover the latent value profile represented by these outputs, thereby determining the extent of human-AI value alignment.

A Comprehensive Taxonomy of Modern Public Cloud Services for Infrastructure Selection

The rapid adoption of public cloud services has transformed the digital landscape, offering diverse solutions to meet different business needs. However, this diversity of services also presents a challenge: the need for a structured, comprehensive taxonomy to navigate and understand the offerings. This paper presents a comprehensive taxonomy for public cloud services, aiming to provide clarity and structure in this domain. Leveraging extensive market research, analysis of leading industry reports, and insights from top cloud vendors, we have identified key criteria that form the foundation of our taxonomy. The taxonomy passed the validation process, involving industry experts from Ukraine's largest telco operator, ensuring its practical relevance. The results have proven the taxonomy's effectiveness in classifying a wide range of cloud services, highlighting its potential as a valuable tool for businesses, researchers, and cloud practitioners. This research serves as a foundation for future improvement of complex processes of cloud provider selection.

Molecular logic for cellular specializations that initiate the auditory parallel processing pathways.

The cochlear nuclear complex (CN), the starting point for all central auditory processing, comprises a suite of neuronal cell types that are highly specialized for neural coding of acoustic signals, yet molecular logic governing cellular specializations remains unknown. By combining single-nucleus RNA sequencing and Patch-seq analysis, we reveal a set of transcriptionally distinct cell populations encompassing all previously observed types and discover multiple new subtypes with anatomical and physiological identity. The resulting comprehensive cell-type taxonomy reconciles anatomical position, morphological, physiological, and molecular criteria, enabling the determination of the molecular basis of the remarkable cellular phenotypes in the CN. In particular, CN cell-type identity is encoded in a transcriptional architecture that orchestrates functionally congruent expression across a small set of gene families to customize projection patterns, input-output synaptic communication, and biophysical features required for encoding distinct aspects of acoustic signals. This high-resolution account of cellular heterogeneity from the molecular to the circuit level illustrates molecular logic for cellular specializations and enables genetic dissection of auditory processing and hearing disorders with unprecedented specificity.

Deciphering refactoring branch dynamics in modern code review: An empirical study on Qt

Context:Modern code review is a widely employed technique in both industrial and open-source projects, serving to enhance software quality, share knowledge, and ensure compliance with coding standards and guidelines. While code review is extensively studied for its general challenges, best practices, outcomes, and socio-technical aspects, little attention has been paid to how refactoring is reviewed and what developers prioritize when reviewing refactored code in the ‘Refactor’ branch. Objective:The goal is to understand the review process for refactoring changes in the ‘Refactor’ branch and to identify what developers care about when reviewing code in this branch. Method:In this study, we present a quantitative and qualitative examination to understand the main criteria developers use to decide whether to accept or reject refactored code submissions and identify the challenges inherent in this process. Results:Analyzing 2154 refactoring and non-refactoring reviews across Qt open-source projects, we find that reviews involving refactoring from the ‘Refactor’ branch take significantly less time to resolve in terms of code review efforts. Additionally, documentation of developer intent is notably sparse within the ‘Refactor’ branch compared to other branches. Furthermore, through thematic analysis of a substantial sample of refactoring code review discussions, we construct a comprehensive taxonomy consisting of 12 refactoring review criteria. Conclusion:Our findings underscore the importance of developing precise and efficient tools and techniques to aid developers in the review process amidst refactorings.

Applications of knowledge distillation in remote sensing: A survey

With the ever-growing complexity of models in the field of remote sensing (RS), there is an increasing demand for solutions that balance model accuracy with computational efficiency. Knowledge distillation (KD) has emerged as a powerful tool to meet this need, enabling the transfer of knowledge from large, complex models to smaller, more efficient ones without significant loss in performance. This review article provides an extensive examination of KD and its innovative applications in RS. KD, a technique developed to transfer knowledge from a complex, often cumbersome model (teacher) to a more compact and efficient model (student), has seen significant evolution and application across various domains. Initially, we introduce the fundamental concepts and historical progression of KD methods. The advantages of employing KD are highlighted, particularly in terms of model compression, enhanced computational efficiency, and improved performance, which are pivotal for practical deployments in RS scenarios. The article provides a comprehensive taxonomy of KD techniques, where each category is critically analyzed to demonstrate the breadth and depth of the alternative options, and illustrates specific case studies that showcase the practical implementation of KD methods in RS tasks, such as instance segmentation and object detection. Further, the review discusses the challenges and limitations of KD in RS, including practical constraints and prospective future directions, providing a comprehensive overview for researchers and practitioners in the field of RS. Through this organization, the paper not only elucidates the current state of research in KD but also sets the stage for future research opportunities, thereby contributing significantly to both academic research and real-world applications.

Lockdown Stories: A Qualitative Assessment and Comprehensive Taxonomy of Career Resources

Career resources are receiving increasing attention in the context of career development. This paper utilizes M. E. Ford’s (1992) ten components of effective functioning to provide a comprehensive typology of factors likely to act as career resources and test this proposition in a context of career shock with a narrative design. In the weeks following the first COVID-19 lockdown, 42 participants were asked to complete a questionnaire about their well-being, perceived employability, and emotional anticipation of their career future, as well as to write three stories about their experience with the lockdown. M. E. Ford’s categories were used to identify and code the resources and obstacles mentioned in the stories. Results show the relevance of such a taxonomy to classify both career resources and obstacles. Additionally, the type of story (general story, positive or negative story) in which career resources and obstacles were mentioned played a significant role in their association with the quantitative measures. Conceptual and practical implications are discussed.

Creation of a System Taxonomy for Advanced Air Mobility Operations

Advanced Air Mobility (AAM) is an emerging field that envisions highly automated, high-tempo, passenger-carrying aircraft operating in urban environments. Numerous stakeholders have emerged in this space, each proposing unique concepts of operation (CONOPS). While this technology will enable a revolutionary leap in modern transportation, many challenges remain unaddressed. Discrepancies exist between envisioned AAM operations, current technology, and outdated federal regulations. This has led to an unclear vision within the industry of the current maturity level of technology and the path of development needed to achieve proposed CONOPS. We seek to fill this gap by integrating multiple proposed conceptual systems into a single, unified path forward for the field. The literature on AAM covers a diverse array of interconnected themes, each containing a multitude of considerations that require extensive exploration and understanding. In the present work, a literature integration was conducted to collate the numerous proposed concepts and envisioned works that exist within the AAM literature. With this information, a comprehensive AAM taxonomy was created to represent this integration of concepts and rectify the inconsistencies within industry CONOPS. This taxonomy will assist future stakeholders in navigating this intricate field while contributing to the safe and efficient development and deployment of AAM.

Graph pooling in graph neural networks: methods and their applications in omics studies

Graph neural networks (GNNs) process the graph-structured data using neural networks and have proven successful in various graph processing tasks. Currently, graph pooling operators have emerged as crucial components that bridge the gap between node representation learning and diverse graph-level tasks by transforming node representations into graph representations. Given the rapid growth and widespread adoption of graph pooling, this review aims to summarize the existing graph pooling operators for GNNs and their representative applications in omics. Specifically, we first present a comprehensive taxonomy of existing graph pooling algorithms, expanding the categorization for both global and hierarchical pooling operators, and for the first time reviewing the inverse operation of graph pooling, named unpooling. Next, we describe the general evaluation framework for graph pooling operators, encompassing three fundamental aspects: experimental setup, ablation analysis, and model interpretation. We also discuss open issues that significantly influence the design of graph pooling operators, including complexity, connectivity, adaptability, additional loss, and attention mechanisms. Finally, we summarize bioinformatics applications of graph pooling operators in omics, including graphs of gene interaction, medical images, and protein structures for drug discovery and disease diagnosis. Furthermore, we showcase the impact of graph pooling operators on research in specific real-world domains, with a focus on prediction performance and model interpretability. This review provides methodological insights in machine learning based graph modeling and related omics research, as well as an ongoing resource by gathering related papers and code in a dedicated GitHub repository (https://github.com/Hou-WJ/Graph-Pooling-Operators-and-Bioinformatics-Applications).

Evidence based management of medical devices: A follow-up experiment

Ensuring the optimal operation and longevity of medical devices is essential for maintaining high safety standards in healthcare. This study presents a significant advancement in the field by enhancing an existing evidence-based maintenance (EBM) framework, which is crucial for the effective management of medical equipment. Building upon previous methodologies, this research introduces a novel, comprehensive taxonomy for categorizing maintenance reports by revising and updating existing failure codes. This refinement addresses gaps in previous models, enabling more precise and effective maintenance strategies.A key innovation of this study is the development of a standard XML protocol, which addresses the current lack of standardization in the field, providing a consistent and unified approach to data management. This standardization is critical for improving communication and data exchange across different healthcare systems and technologies.To validate the enhanced EBM framework, the study applies it to corrective maintenance work orders related to Digital Angiography Systems, Magnetic Resonance Imaging, and Computed Tomography, using data from the Pisa University Hospital’s Computerized Maintenance Management System from 2021 to 2022. This validation demonstrates the reproducibility and adaptability of the framework across different medical equipment, healthcare contexts, and timeframes.The findings represent a significant advancement in healthcare technology management, offering a robust, standardized approach that optimizes the safety, functionality, and longevity of medical devices. This research not only expands maintenance standards in the healthcare sector but also provides a critical tool for addressing the current challenges in medical device maintenance, paving the way for more reliable and effective healthcare technologies.