Task Analysis Research Articles

Foundation model for efficient biological discovery in single-molecule data.

Modern data-intensive techniques offer ever deeper insights into biology, but render the process of discovery increasingly complex. For example, exploiting the unique ability of single-molecule fluorescence microscopy (SMFM)1-5. to uncover rare but critical intermediates often demands manual inspection of time traces and iterative ad hoc approaches that are difficult to systematize. To facilitate systematic and efficient discovery from SMFM data, we introduce META-SiM, a transformer-based foundation model pre-trained on diverse SMFM analysis tasks. META-SiM achieves high performance-rivaling best-in-class algorithms-on a broad range of analysis tasks including trace selection, classification, segmentation, idealization, and stepwise photobleaching analysis. Additionally, the model produces high-dimensional embedding vectors that encapsulate detailed information about each trace, which the web-based META-SiM Projector (https://www.simol-projector.org) casts into lower-dimensional space for efficient whole-dataset visualization, labeling, comparison, and sharing. Combining this Projector with the objective metric of Local Shannon Entropy enables rapid identification of condition-specific behaviors, even if rare or subtle. As a result, by applying META-SiM to an existing single-molecule Förster resonance energy transfer (smFRET) dataset6, we discover a previously unobserved intermediate state in pre-mRNA splicing. META-SiM thus removes bottlenecks, improves objectivity, and both systematizes and accelerates biological discovery in complex single-molecule data.

Brexit references in UK financial disclosure discourse (2015-2017): a corpus study

Major political events become a dominant topic of the discussions in the countries involved, and various actors, often other than political parties, take a stance towards them. The withdrawal of the UK from the European Union as a result of the 2016 UK referendum was one of those, and a quite polarised atmosphere from supporters of both sides was observed in the UK and the rest of Europe since the referendum was announced in 2015. Based on the impact of the Brexit on the economy, this study focused into the financial domain of the UK, and the ways that UK financial services referred towards it in their financial disclosures before, during and just after the referendum. For that purpose, I collected the 2015, 2016 and 2017 annual reports from five UK-based financial companies (Barclays, HSBC, Lloyds, Royal Bank of Scotland and Santander UK), and I used thematic keywords to identify all the texts referring to the 2016 UK referendum, its outcome, and the response of the financial services to the exiting process of the UK from the European Union. This set of texts composed the Brexit-related data set, in which different analytical tasks were performed. I explored the context in which the thematic keywords are found, compared statistically the three yearly subsets of the corpus, and searched the significant words of the subset in terms of their keyness strength. This case study revealed that the discourse around a major political event such as Brexit cannot be considered as neutral or objective, and that financial companies clearly expressed negative opinion regarding the 2016 referendum and the UK’s decision to exit the European Union.

An applied cognitive task analysis investigation of professional judgement and decision-making in a small group of high-level, UK-based caving instructors

ABSTRACT The professional judgement and decision-making processes of adventure sports coaches’ leadership practice is a growing area of research. However, the PJDM and underpinning epistemology of caving instructors has recived limited research focus. The decision-making practice of professionals working in complex and constrained environments has significant transferability to domains such as rescue and emergency services. Consequently, this paper investigates the leadership practice of a purposive sample of 3 UK based caving instructors, utilising an Applied Cognitive Task Analysis to access expert cognition. The study aimed specifically to examine the decision-making processes of this sub-group of outdoor professionals who work in difficult, multifaceted and demanding environments. Results supported and built upon previous work in adventure sports, specifically in confirming the complexity of judgement and decision-making underground. In particular, decision-making creates high cognitive loads because of environmental factors inherent in extended caving journeys. Loads are assuaged by the application of heuristics and planning, but naturalistic decision-making processes remain numerous and multifaceted. Findings have implications for National Governing Body instructor training programme design, with particular reference to the development of decision-making expertise in complex environments.

Challenges of Translating Idiomatic Expressions: A Cross-Linguistic Analysis at a University in Hanoi, Vietnam

This study investigates the challenges students face when translating idiomatic expressions between English and Vietnamese and explores how linguistic and cultural factors influence the translation process. Utilizing a combination of translation task analysis and focus group discussions, the research reveals that students frequently rely on literal translations, resulting in inaccurate or nonsensical outcomes due to their inability to grasp the figurative meanings of idioms. Cultural mismatches, particularly the absence of equivalent expressions in Vietnamese, further complicate translation efforts, leading to significant misunderstandings. Additionally, linguistic barriers, such as syntactic differences between English and Vietnamese, hinder students' ability to produce natural and accurate translations of idiomatic expressions. The findings emphasize the critical role of cultural knowledge and syntactic flexibility in effective idiomatic translation. To address these challenges, the study recommends that translation training programs place greater emphasis on idiomatic understanding and cross-cultural competence. This study contributes to the growing body of literature on idiomatic translation by highlighting the interplay of linguistic and cultural factors and suggesting strategies to enhance translation accuracy.

Enhancing Learning Interest in the Collaborative Learning Practitioner Lecture: Training on Determining the Viscosity of Palm Kernel Oil (PKO)

This study aims to determine the effects in physical chemistry class with the workshop sessions on measuring PKO viscosity by CV. Bina Agro Mandiri. The effects are evaluated through the enhancing of student's learning interest in the Physical Chemistry II class in Chemistry Program of the Islamic University of Indonesia, which presents a good avenue for this investigation. The study was conducted by classroom, practical and online action. The data were analyzed using a pie chart. Findings showed that the implementation of the practical was able to increase the student's learning interests. Through case analysis tasks, students participate significantly in the teaching-learning process.

Estimation of Lower Limb Joint Angles Using sEMG Signals and RGB-D Camera.

Estimating human joint angles is a crucial task in motion analysis, gesture recognition, and motion intention prediction. This paper presents a novel model-based approach for generating reliable and accurate human joint angle estimation using a dual-branch network. The proposed network leverages combined features derived from encoded sEMG signals and RGB-D image data. To ensure the accuracy and reliability of the estimation algorithm, the proposed network employs a convolutional autoencoder to generate a high-level compression of sEMG features aimed at motion prediction. Considering the variability in the distribution of sEMG signals, the proposed network introduces a vision-based joint regression network to maintain the stability of combined features. Taking into account latency, occlusion, and shading issues with vision data acquisition, the feature fusion network utilizes high-frequency sEMG features as weights for specific features extracted from image data. The proposed method achieves effective human body joint angle estimation for motion analysis and motion intention prediction by mitigating the effects of non-stationary sEMG signals.

Gut Analysis Toolbox - automating quantitative analysis of enteric neurons.

The enteric nervous system (ENS) consists of an extensive network of neurons and glial cells embedded within the wall of the gastrointestinal (GI) tract. Alterations in neuronal distribution and function are strongly associated with GI dysfunction. Current methods for assessing neuronal distribution suffer from undersampling, partly due to challenges associated with imaging and analyzing large tissue areas, and operator bias due to manual analysis. We present the Gut Analysis Toolbox (GAT), an image analysis tool designed for characterization of enteric neurons and their neurochemical coding using two-dimensional images of GI wholemount preparations. GAT is developed in Fiji, has a user-friendly interface, and offers rapid and accurate segmentation via custom deep learning (DL)-based cell segmentation models developed using StarDist, as well as a ganglia segmentation model in deepImageJ. We apply proximal neighbor-based spatial analysis to reveal differences in cellular distribution across gut regions using a public dataset. In summary, GAT provides an easy-to-use toolbox to streamline routine image analysis tasks in ENS research. GAT enhances throughput, allowing rapid unbiased analysis of larger tissue areas, multiple neuronal markers and numerous samples.

DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax

Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach. It maximizes the mutual information between graph-level and node-level representations, emphasizing flexible sampling and aggregation of nodes and their neighbors. This effectively captures and incorporates local information from nodes into the overall graph structure. Additionally, this paper developed the DGSIST framework, an unsupervised cell clustering method that integrates the DGSI model, SVD dimensionality reduction algorithm, and k-means++ clustering algorithm. This aims to identify cell types accurately. DGSIST fully uses spatial transcriptomics data and outperforms existing methods in accuracy. Demonstrations of DGSIST’s capability across various tissue types and technological platforms have shown its effectiveness in accurately identifying spatial domains in multiple tissue sections. Compared to other spatial clustering methods, DGSIST excels in cell clustering and effectively eliminates batch effects without needing batch correction. DGSIST excels in spatial clustering analysis, spatial variation identification, and differential gene expression detection and directly applies to graph analysis tasks, such as node classification, link prediction, or graph clustering. Anticipation lies in the contribution of the DGSIST framework to a deeper understanding of the spatial organizational structures of diseases such as cancer.

Development and Validation of a Laparoscopic Sacrocolpopexy Training Model.

Proper training is necessary to develop the highly specialized skills required to safely perform laparoscopic sacrocolpopexy. Currently, there is no validated training model for laparoscopic sacrocolpopexy that includes dissection of the presacral space, both vaginal and presacral mesh attachments, and peritoneal closure. This study aimed to create a procedure specific hierarchical task analysis for laparoscopic sacrocolpopexy and then develop and validate a corresponding laparoscopic sacrocolpopexy pelvic training model for the simulation environment. This was an observational simulation study that was divided into 5 phases: (1) development of hierarchical task analysis, (2) model construction, (3) participant recruitment and simulation testing, (4) reliability and validity testing, and (5) creation of a standard passing performance measure. Construct, face, and content validity were established for this model. According to the participating experts, the model was able to replicate the steps of presacral dissection, anterior vaginal and sacral mesh attachment, and peritoneal closure. Thirteen trainees and 5 experts completed the simulation, and all "agreed" or "strongly agreed" that the model seemed useful for improving suturing technique and learning the procedure. Additionally, a passing performance measure was determined through contrasting groups methodology. We developed a novel, reusable, and validated training model that can be utilized as a training resource for the many critical skills necessary to safely and efficiently perform laparoscopic sacrocolpopexy.

Network mutual information measures for graph similarity

A wide range of tasks in network analysis, such as clustering network populations or identifying anomalies in temporal graph streams, require a measure of the similarity between two graphs. To provide a meaningful data summary for downstream scientific analyses, the graph similarity measures used for these tasks must be principled, interpretable, and capable of distinguishing meaningful overlapping network structure from statistical noise at different scales of interest. Here we derive a family of graph mutual information measures that satisfy these criteria and are constructed using only fundamental information theoretic principles. Our measures capture the information shared among networks according to different encodings of their structural information, with our mesoscale mutual information measure allowing for network comparison under any specified network coarse-graining. We test our measures in a range of applications on real and synthetic network data, finding that they effectively highlight intuitive aspects of network similarity across scales in a variety of systems.

Aiming, Pointing, Steering: A Core Task Analysis Framework for Gameplay

Underneath their compelling audiovisual surface, games require players to carry out mundane interaction work, such as pointing, typing, or steering. However, many of these underlying building blocks are not defined rigorously, hampering synthesis and analysis. We elaborate on the origin of tasks within human-computer interaction (HCI) and define tasks' relationship to game terminology (game mechanics, goals, and actions). Our proposed framework draws on systemic-structural theory of activity to aid systematic analysis and exploration of game design by mapping gameplay to abstract core tasks. The framework contains four task tools, applicable when 1) uncovering design properties, 2) designing experimental manipulation, 3) creating behavioral measurements, and 4) describing gameplay in literature reviews of game genres and design techniques. We evaluated our framework as a lens to design purposeful games in three case studies within a scientific education. We invite researchers and practitioners to employ the framework as a microscope, to describe and design games rigorously.

Energy efficient permanence‐based community detection algorithm

SummaryDetecting an accurate community structure is a crucial task in network analysis. With the increasing popularity of social networking sites, it is essential to have a community detection algorithm that is not only efficien but also cost‐effective for running in data centers. There are several metrics for estimating the accuracy of community detection. However, previous research has shown that permanence, a vertex‐centric metric, provides the most precise estimate of a community structure compared to other approaches. Despite this, no study has been conducted on parallelizing a permanence‐based community detection algorithm and analyzing its energy efficiency. This article introduces Amoeba, a task parallel implementation of a permanence‐based community detection algorithm designed for multicore processors. It uses dynamic tasking to schedule the inherent irregular computation, and it can dynamically adapt the total number of parallel threads, which results in improved energy efficiency. We evaluated Amoeba using several real‐world and artificial graphs on a multicore server processor. Our experimental results show that Amoeba achieves a geometric mean speedup of 15.3 over its sequential implementation, and due to thread adaptability, it achieves energy savings of 12.4% and a speedup of 6% over its nonadaptive implementation.

Testing Similarity of Parametric Competing Risks Models for Identifying Potentially Similar Pathways in Healthcare.

The identification of similar patient pathways is a crucial task in healthcare analytics. A flexible tool to address this issue are parametric competing risks models, where transition intensities may be specified by a variety of parametric distributions, thus in particular being possibly time-dependent. We assess the similarity between two such models by examining the transitions between different health states. This research introduces a method to measure the maximum differences in transition intensities over time, leading to the development of a test procedure for assessing similarity. We propose a parametric bootstrap approach for this purpose and provide a proof to confirm the validity of this procedure. The performance of our proposed method is evaluated through a simulation study, considering a range of sample sizes, differing amounts of censoring, and various thresholds for similarity. Finally, we demonstrate the practical application of our approach with a case study from urological clinical routine practice, which inspired this research.

Graph Neural Networks: A Bibliometric Mapping of the Research Landscape and Applications

Graph neural networks (GNNs) are deep learning algorithms that process graph-structured data and are suitable for applications such as social networks, physical models, financial markets, and molecular predictions. Bibliometrics, a tool for tracking research evolution, identifying milestones, and assessing current research, can help identify emerging trends. This study aims to map GNN applications, research directions, and key contributors. An analysis of 40,741 GNN-related publications from the Web Science Core Collection reveals a rising trend in GNN publications, especially since 2018. Computer Science, Engineering, and Telecommunications play significant roles in GNN research, with a focus on deep learning, graph convolutional networks, neural networks, convolutional neural networks, and machine learning. China and the USA combined account for 76.4% of the publications. Chinese universities concentrate on graph convolutional networks, deep learning, feature extraction, and task analysis, whereas American universities focus on machine learning and deep learning. The study also highlights the importance of Chemistry, Physics, Mathematics, Imaging Science & Photographic Technology, and Computer Science in their respective knowledge communities. In conclusion, the bibliometric analysis provides an overview of GNN research, showing growing interest and applications across various disciplines, and highlighting the potential of GNNs in solving complex problems and the need for continued research and collaboration.

Enhanced Input-Doubling Method Leveraging Response Surface Linearization to Improve Classification Accuracy in Small Medical Data Processing

Currently, the tasks of intelligent data analysis in medicine are becoming increasingly common. Existing artificial intelligence tools provide high effectiveness in solving these tasks when analyzing sufficiently large datasets. However, when there is very little training data available, current machine learning methods do not ensure adequate classification accuracy or may even produce inadequate results. This paper presents an enhanced input-doubling method for classification tasks in the case of limited data analysis, achieved via expanding the number of independent attributes in the augmented dataset with probabilities of belonging to each class of the task. The authors have developed an algorithmic implementation of the improved method using two Naïve Bayes classifiers. The method was modeled on a small dataset for cardiovascular risk assessment. The authors explored two options for the combined use of Naïve Bayes classifiers at both stages of the method. It was found that using different methods at both stages potentially enhances the accuracy of the classification task. The results of the improved method were compared with a range of existing methods used for solving the task. It was demonstrated that the improved input-doubling method achieved the highest classification accuracy based on various performance indicators.

QCLUE: a quantum clustering algorithm for multi-dimensional datasets

Clustering algorithms are at the basis of several technological applications, and are fueling the development of rapidly evolving fields such as machine learning. In the recent past, however, it has become apparent that they face challenges stemming from datasets that span more spatial dimensions. In fact, the best-performing clustering algorithms scale linearly in the number of points, but quadratically with respect to the local density of points. In this work, we introduce qCLUE, a quantum clustering algorithm that scales linearly in both the number of points and their density. qCLUE is inspired by CLUE, an algorithm developed to address the challenging time and memory budgets of Event Reconstruction (ER) in future High-Energy Physics experiments. As such, qCLUE marries decades of development with the quadratic speedup provided by quantum computers. We numerically test qCLUE in several scenarios, demonstrating its effectiveness and proving it to be a promising route to handle complex data analysis tasks – especially in high-dimensional datasets with high densities of points.

A Systematic Review on Graph Neural Network-based Methods for Stock Market Forecasting

Financial technology (FinTech) is a field that uses artificial intelligence to automate financial services. One area of FinTech is stock analysis, which aims to predict future stock prices to develop investment strategies that maximize profits. Traditional methods of stock market prediction, such as time series analysis and machine learning, struggle to handle the non-linear, chaotic, and sudden changes in stock data and may not consider the interdependence between stocks. Recently, graph neural networks (GNNs) have been used in stock market forecasting to improve prediction accuracy by incorporating the interconnectedness of the market. GNNs can process non-Euclidean data in the form of a knowledge graph. However, financial knowledge graphs can have dynamic and complex interactions, which can be challenging for graph modeling technologies. This work presents a systematic review of graph-based approaches for stock market forecasting. This review covers different types of stock analysis tasks (classification, regression, and stock recommendation), a generalized framework for solving these tasks, and a review of various features, datasets, graph models, and evaluation metrics used in the stock market. The results of various studies are analyzed, and future directions for research are highlighted.

Better together: Automated app review analysis with deep multi-task learning

Context:User reviews of mobile apps provide an important communication channel between developers and users. Existing approaches to automated app review analysis mainly focus on one task (e.g., bug classification task, information extraction task, etc.) at a time, and are often constrained by the manually defined patterns and the ignorance of the correlations among the tasks. Recently, multi-task learning (MTL) has been successfully applied in many scenarios, with the potential to address the limitations associated with app review mining tasks. Objective:In this paper, we propose MABLE, a deep MTL-based and semantic-aware approach, to improve app review analysis by exploiting task correlations. Methods:MABLE jointly identifies the types of involved bugs reported in the review and extracts the fine-grained features where bugs might occur. It consists of three main phases: (1) data preparation phase, which prepares data to allow data sharing beyond single task learning; (2) model construction phase, which employs a BERT model as the shared representation layer to capture the semantic meanings of reviews, and task-specific layers to model two tasks in parallel; (3) model training phase, which enables eavesdropping by shared loss function between the two related tasks. Results:Evaluation results on six apps show that MABLE outperforms ten commonly-used and state-of-the-art baselines, with the precision of 79.76% and the recall of 79.24% for classifying bugs, and the precision of 79.83% and the recall of 80.33% for extracting problematic app features. The MTL mechanism improves the F-measure of two tasks by 3.80% and 4.63%, respectively. Conclusion:The proposed approach provides a novel and effective way to jointly learn two related review analysis tasks, and sheds light on exploring other review mining tasks.

GeoHi-GNN: Geometry-aware hierarchical graph representation learning for normal estimation

Normal estimation has been one of the key tasks in point cloud analysis, while it is challenging when facing with severe noises or complex regions. The challenges mainly come from the selection of supporting points for estimation, that is, improper selections of points and points' scale will lead to insufficient information, loss of details, etc. To this end, this paper proposes one feature-centric fitting scheme, GeoHi-GNN, by learning geometry-aware hierarchical graph representation for fitting weights estimation. The main functional module is the continuously conducted Hierarchically Geometric-aware (HG) module, consisting of two core operations, namely, the graph node construction (GNC) and the geometric-aware dynamic graph convolution (GDGC). GNC aims to aggregate the feature information onto a smaller number of nodes, providing global-to-local information while avoiding the interferences from noises in larger scales. With these nodes distributed in different scales, GDGC dynamically updates the node features regarding to both intrinsic feature and extrinsic geometric information. Finally, the hierarchical graphical features are cascaded to estimate the weights for supporting points in the surface fitting. Through the extensive experiments and comprehensive comparisons with the state-of-the-arts, our scheme has exhibited many attractive advantages such as being geometry-aware and robust, empowering further applications like more accurate surface reconstruction.

Applications of BERT in sentimental analysis

This research study emphasizes sentiment analysis and examines Natural Language Processing (NLP) by Bidirectional Encoder Representations from Transformers (BERT). BERT's bidirectional Transformer architecture pre-trained utilizes Next Sentence Prediction (NSP) and Masked Language Modeling (MLM) and has achieved a lot in terms of AI transformation. This paper provides a description of the BERT design, pre-training methods, and fine-tuning for sentiment analysis tasks. The study goes ahead and compares BERT's performance with other deep learning models, machine learning algorithms, and traditional rule-based techniques, highlighting the latter's limited ability to handle linguistic nuances and context. Additionally, studies proving the consistency and accuracy of BERT's sentiment analysis are examined, along with the challenges of handling irony, sarcasm, and domain-specific data. Ethical and privacy concerns that sentiment analysis inherently raises and makes recommendations for further research are also examined in the study, which also shows how integrating sentiment analysis with other domains can lead to multidisciplinary breakthroughs that can offer more comprehensive insights and applications.