Specification Of Data Types Research Articles

Investigating the impact of data scaling on the k-nearest neighbor algorithm

This study investigates the impact of data scaling techniques on the performance of the k-nearest neighbor (KNN) algorithm using ten different datasets from various domains. Three commonly used data scaling techniques, min-max normalization, Z-score, and decimal scaling, are evaluated based on the KNN algorithm's performance in terms of accuracy, precision, recall, F1-score, runtime, and memory usage. The study aims to provide insights into the applicability and effectiveness of different scaling techniques in different contexts, aid in the design and implementation of machine learning systems, and help identify the strengths and weaknesses of each technique and their suitability for specific types of data. The results show that data scaling significantly affects the performance of the KNN algorithm, and the choice of scaling method can have significant implications for practical applications. Moreover, the performance of the three scaling techniques varies across different datasets, suggesting that the choice of scaling technique should be made based on the specific characteristics of the data. Overall, this study provides a comprehensive analysis of the impact of data scaling techniques on the KNN algorithm's performance and can help practitioners and researchers in the machine learning community make informed decisions when designing and implementing machine learning systems.

Investigating the impact of data scaling on the k-nearest neighbor algorithm

This study investigates the impact of data scaling techniques on the performance of the k-nearest neighbor (KNN) algorithm using ten different datasets from various domains. Three commonly used data scaling techniques, min-max normalization, Z-score, and decimal scaling, are evaluated based on the KNN algorithm's performance in terms of accuracy, precision, recall, F1-score, runtime, and memory usage. The study aims to provide insights into the applicability and effectiveness of different scaling techniques in different contexts, aid in the design and implementation of machine learning systems, and help identify the strengths and weaknesses of each technique and their suitability for specific types of data. The results show that data scaling significantly affects the performance of the KNN algorithm, and the choice of scaling method can have significant implications for practical applications. Moreover, the performance of the three scaling techniques varies across different datasets, suggesting that the choice of scaling technique should be made based on the specific characteristics of the data. Overall, this study provides a comprehensive analysis of the impact of data scaling techniques on the KNN algorithm's performance and can help practitioners and researchers in the machine learning community make informed decisions when designing and implementing machine learning systems.

PoliViews: A comprehensive and modular approach to the conceptual modeling of genomic data

The human genome complexity is captured by many signals, representing for instance DNA variations, the expression of gene activity, or DNA’s structural rearrangements; a rich set of data types and formats is used to record these signals. Conceptual models can support the description and explanation of the genome’s elaborate structure and behavior. Among others, the Conceptual Schema of the Human Genome (CSG) provides a concept-oriented, top-down representation of the genome behavior, which is independent of data formats. The Genomic Conceptual Model (GCM) provides instead a data-oriented, bottom-up representation, targeting a well-organized, unified description of these formats. In this research, we join the two approaches to achieve PoliViews, a comprehensive model that links (1) a concepts layer, describing genome elements and their conceptual connections, with (2) a data layer, describing datasets derived from genome sequencing with specific technologies. Their dynamic connection is established when specific genomic data types are chosen in the data layer, thereby triggering the selection of a view in the concepts layer. The benefit is mutual: data records can be semantically described by high-level concepts exploiting their links and, in turn, the continuously evolving abstract model can be extended thanks to the input provided by real datasets. PoliViews enables expressing queries that employ a holistic conceptual perspective on the genome, directly translated onto data-oriented terms and organization. Here, we demonstrate the approach by linking two major genomic data types, namely DNA variation and gene expression. For each type, we consider different eminent data sources; we describe their mapping with the corresponding view in the concepts layer, enabling an intra-data-type integration. Then, leveraging on the connections available in the concepts layer, we show how the distinct data types can be interoperated, enabling an inter-data-type integration. The PoliViews approach is shown through several examples of biological interest and can be further extended to any kind of genomic information.

Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer

Objective:Lung cancer exhibits unpredictable recurrence in low-stage tumors and variable responses to different therapeutic interventions. Predicting relapse in early-stage lung cancer can facilitate precision medicine and improve patient survivability. While existing machine learning models rely on clinical data, incorporating genomic information could enhance their efficiency. This study aims to impute and integrate specific types of genomic data with clinical data to improve the accuracy of machine learning models for predicting relapse in early-stage, non-small cell lung cancer patients. Methods:The study utilized a publicly available TCGA lung cancer cohort and imputed genetic pathway scores into the Spanish Lung Cancer Group (SLCG) data, specifically in 1348 early-stage patients. Initially, tumor recurrence was predicted without imputed pathway scores. Subsequently, the SLCG data were augmented with pathway scores imputed from TCGA. The integrative approach aimed to enhance relapse risk prediction performance. Results:The integrative approach achieved improved relapse risk prediction with the following evaluation metrics: an area under the precision–recall curve (PR-AUC) score of 0.75, an area under the ROC (ROC-AUC) score of 0.80, an F1 score of 0.61, and a Precision of 0.80. The prediction explanation model SHAP (SHapley Additive exPlanations) was employed to explain the machine learning model’s predictions. Conclusion:We conclude that our explainable predictive model is a promising tool for oncologists that addresses an unmet clinical need of post-treatment patient stratification based on the relapse risk while also improving the predictive power by incorporating proxy genomic data not available for specific patients.

Compressed computations using wavelets for hidden Markov models with continuous observations.

Compression as an accelerant of computation is increasingly recognized as an important component in engineering fast real-world machine learning methods for big data; c.f., its impact on genome-scale approximate string matching. Previous work showed that compression can accelerate algorithms for Hidden Markov Models (HMM) with discrete observations, both for the classical frequentist HMM algorithms-Forward Filtering, Backward Smoothing and Viterbi-and Gibbs sampling for Bayesian HMM. For Bayesian HMM with continuous-valued observations, compression was shown to greatly accelerate computations for specific types of data. For instance, data from large-scale experiments interrogating structural genetic variation can be assumed to be piece-wise constant with noise, or, equivalently, data generated by HMM with dominant self-transition probabilities. Here we extend the compressive computation approach to the classical frequentist HMM algorithms on continuous-valued observations, providing the first compressive approach for this problem. In a large-scale simulation study, we demonstrate empirically that in many settings compressed HMM algorithms very clearly outperform the classical algorithms with no, or only an insignificant effect, on the computed probabilities and infered state paths of maximal likelihood. This provides an efficient approach to big data computations with HMM. An open-source implementation of the method is available from https://github.com/lucabello/wavelet-hmms.

A survey of visualization techniques for comparing event sequences

Event sequence data is a special type of time-dependent data that captures information about the order in which discrete events occur. The time-dimension is one of the factors that makes event sequence data hard to understand. Other factors that contribute to this complexity are the dimensionality in terms of the amount of events and attributes, the frequency of events in terms of consistency and density, varying durations, and parallel occurrences. When end-users need to compare event sequences, all these characteristics need to be considered and justified. In this state-of-the-art report we review visualization techniques for the event sequence comparison task. We focus specifically on comparison, in contrast to general event sequence visualization, and review how different aspects, such as the data attributes or granularity, affect the comparison. We define a taxonomy based on five dimensions: Scale, Comparison Type, Size, Data, and Visualization & Interaction. Based on these dimensions, we provide an overview of the literature of the past 17 years. This overview is accompanied with an analysis of the strengths and weaknesses of the proposed techniques. Furthermore, we identify current gaps and research challenges in the literature.

Native American youth finding self through digital story telling

AbstractThis holistic single‐case study aimed to understand the impact of digital story telling (DST) on the identity expressions of Native American youth. The question that guided the study asks, ‘How do Native American adolescents in a rural, tribal‐run after‐school programme for Indigenous youth explore and express who they are through digital story telling?’ Five Indigenous youth enrolled in a tribal‐run after‐school programme participated in the study and completed a digital story telling project that contained multiple components and interviews. Data sources included funds of knowledge maps, shields, story scripts, storyboards, interview transcripts, and digital videos. Thematic analysis was the overarching method used to identify themes. The researchers also conducted constant comparison, content analysis, and/or intertextual transcription to analyse specific data types. Findings indicate the youth enjoyed the DST process, explored and solidified their personal identities, and discovered personal strength. The findings also suggest that DST enabled the youth to draw upon cultural knowledges, literacies, and personal experiences to establish their identities and make sense of the world around them. Furthermore, findings reveal the continued presence of racism in society and schools and the need to transform schools into sites that embrace and support Indigenous knowledges, languages, literacies, and identity development of Indigenous students.

Exploiting similarity-induced redundancies in correlation topology for channel pruning in deep convolutional neural networks

The paper discusses the high computational costs associated with convolutional neural networks (CNNs) in real-world applications due to their complex structure, primarily in hidden layers. To overcome this issue, the paper proposes a novel channel pruning technique that leverages the correlation topology of feature maps generated by each CNNs layer to construct a network with fewer nodes, reducing computational costs significantly. Redundant channels exhibit a high degree of topological similarity and tend to increase as the number of network layers rises. Removing the channel corresponding to highly correlated feature maps allows retrieval of the ‘base’ set of characteristics needed by subsequent layers. The proposed channel pruning technique provides a promising approach to reducing the computational costs of deep convolutional neural networks while maintaining high performance levels. By designing a network structure optimized for specific input data types, the method results in more efficient and effective machine learning models. The pruning operation requires fine-tuning to optimize network performance, and experiments using X-ray, chest CT, and MNIST images show that the pruned network can eliminate approximately 80% of redundant channels with minimal performance deterioration (maintaining original CNNs performance at 99.2%).

ScCCL: Single-cell data clustering based on self-supervised contrastive learning.

The growing maturity of single-cell RNA-sequencing (scRNA-seq) technology allows us to explore the heterogeneity of tissues, organisms, and complex diseases at cellular level. In single-cell data analysis, clustering calculation is very important. However, the high dimensionality of scRNA-seq data, the ever-increasing number of cells, and the unavoidable technical noise bring great challenges to clustering calculations. Motivated by the good performance of contrastive learning in multiple domains, we propose ScCCL, a novel self-supervised contrastive learning method for clustering of scRNA-seq data. ScCCL first randomly masks the gene expression of each cell twice and adds a small amount of Gaussian noise, and then uses the momentum encoder structure to extract features from the enhanced data. Contrastive learning is then applied in the instance-level contrastive learning module and the cluster-level contrastive learning module, respectively. After training, a representation model that can efficiently extract high-order embeddings of single cells is obtained. We selected two evaluation metrics, ARI and NMI, to conduct experiments on multiple public datasets. The results show that ScCCL improves the clustering effect compared with the benchmark algorithms. Notably, since ScCCL does not depend on a specific type of data, it can also be helpful in clustering analysis of single-cell multi-omics data.

Legal challenges in ensuring the cybersecurity of genetic data in the Russian Federation

The article explores problematic issues of legal regulation of genetic data cybersecurity. The author points out gaps in the legal framework regulating the requirements for collection, storage, processing and transfer of genetic data as a special type of personal data. The author investigates the aspects of insufficient protection of genetic information and the main reasons and sources of threats for private and public interests. The author proposed ways to improve the legislation in order to counteract cyber risks in conditions of a significant growth of genetic data in the Internet space, in particular, to approve mandatory security requirements when dealing with genetic information, to increase responsibility for violation of legislation in the field of personal data, to limit the range of persons having access to genetic databases and other mechanisms of legal regulation.

Understanding Public Attitudes and Willingness to Share Commercial Data for Health Research: Survey Study in the United Kingdom.

Health research using commercial data is increasing. The evidence on public acceptability and sociodemographic characteristics of individuals willing to share commercial data for health research is scarce. This survey study investigates the willingness to share commercial data for health research in the United Kingdom with 3 different organizations (government, private, and academic institutions), 5 different data types (internet, shopping, wearable devices, smartphones, and social media), and 10 different invitation methods to recruit participants for research studies with a focus on sociodemographic characteristics and psychological predictors. We conducted a web-based survey using quota sampling based on age distribution in the United Kingdom in July 2020 (N=1534). Chi-squared tests tested differences by sociodemographic characteristics, and adjusted ordered logistic regressions tested associations with trust, perceived importance of privacy, worry about data misuse and perceived risks, and perceived benefits of data sharing. The results are shown as percentages, adjusted odds ratios, and 95% CIs. Overall, 61.1% (937/1534) of participants were willing to share their data with the government and 61% (936/1534) of participants were willing to share their data with academic research institutions compared with 43.1% (661/1534) who were willing to share their data with private organizations. The willingness to share varied between specific types of data-51.8% (794/1534) for loyalty cards, 35.2% (540/1534) for internet search history, 32% (491/1534) for smartphone data, 31.8% (488/1534) for wearable device data, and 30.4% (467/1534) for social media data. Increasing age was consistently and negatively associated with all the outcomes. Trust was positively associated with willingness to share commercial data, whereas worry about data misuse and the perceived importance of privacy were negatively associated with willingness to share commercial data. The perceived risk of sharing data was positively associated with willingness to share when the participants considered all the specific data types but not with the organizations. The participants favored postal research invitations over digital research invitations. This UK-based survey study shows that willingness to share commercial data for health research varies; however, researchers should focus on effectively communicating their data practices to minimize concerns about data misuse and improve public trust in data science. The results of this study can be further used as a guide to consider methods to improve recruitment strategies in health-related research and to improve response rates and participant retention.

Lack of evidence for the match‐mismatch hypothesis across terrestrial trophic interactions

Climate change has led to widespread shifts in the timing of key life history events between interacting species (phenological asynchrony) with hypothesized cascading negative fitness impacts on one or more of the interacting species-often termed 'mismatch'. Yet, predicting the types of systems prone to mismatch remains a major hurdle. Recent reviews have argued that many studies do not provide strong evidence of the underlying match-mismatch hypothesis, but none have quantitatively analysed support for it. Here, we test the hypothesis by estimating the prevalence of mismatch across antagonistic trophic interactions in terrestrial systems and then examine whether studies that meet the assumptions of the hypothesis are more likely to find a mismatch. Despite a large range of synchrony to asynchrony, we did not find general support for the hypothesis. Our results thus question the general applicability of this hypothesis in terrestrial systems, but they also suggest specific types of data missing to robustly refute it. We highlight the critical need to define resource seasonality and the window of 'match' for the most rigorous tests of the hypothesis. Such efforts are necessary if we want to predict systems where mismatches are likely to occur.

Curriculum Learning for Goal-Oriented Semantic Communications With a Common Language

Goal-oriented semantic communication will be a pillar of next-generation wireless networks. Despite significant recent efforts in this area, most prior works are focused on specific data types (e.g., image or audio), and they ignore the goal and effectiveness aspects of semantic transmissions. In contrast, in this paper, a holistic goal-oriented semantic communication framework is proposed to enable a speaker and a listener to cooperatively execute a set of sequential tasks in a dynamic environment. A common language based on a hierarchical belief set is proposed to enable semantic communications between speaker and listener. The speaker, acting as an observer of the environment, utilizes the beliefs to transmit an initial description of its observation (called event) to the listener. The listener is then able to infer on the transmitted description and complete it by adding related beliefs to the transmitted beliefs of the speaker. As such, the listener reconstructs the observed event based on the completed description, and it then takes appropriate action in the environment based on the reconstructed event. An optimization problem is defined to determine the perfect and abstract description of the events while minimizing the various communication costs with constraints on the task execution time and belief efficiency. Then, a novel bottom-up curriculum learning (CL) framework based on reinforcement learning is proposed to solve the optimization problem and enable the speaker and listener to gradually identify the structure of the belief set and the perfect and abstract description of the events. Simulation results show that the proposed CL method outperforms classical RL and CL without inference scheme in terms of convergence time, task execution cost and time, reliability, and belief efficiency.

AIRR community curation and standardised representation for immunoglobulin and T cell receptor germline sets

Analysis of an individual's immunoglobulin or T cell receptor gene repertoire can provide important insights into immune function. High-quality analysis of adaptive immune receptor repertoire sequencing data depends upon accurate and relatively complete germline sets, but current sets are known to be incomplete. Established processes for the review and systematic naming of receptor germline genes and alleles require specific evidence and data types, but the discovery landscape is rapidly changing. To exploit the potential of emerging data, and to provide the field with improved state-of-the-art germline sets, an intermediate approach is needed that will allow the rapid publication of consolidated sets derived from these emerging sources. These sets must use a consistent naming scheme and allow refinement and consolidation into genes as new information emerges. Name changes should be minimised, but, where changes occur, the naming history of a sequence must be traceable. Here we outline the current issues and opportunities for the curation of germline IG/TR genes and present a forward-looking data model for building out more robust germline sets that can dovetail with current established processes. We describe interoperability standards for germline sets, and an approach to transparency based on principles of findability, accessibility, interoperability, and reusability.

The Effectiveness Of Using Quillbot In Improving Writing For Students Of English Education Study Program

Abstract: The aims of this study was to assess students' proficiency with Quillbot to compose error-free original scientific papers in English. This research is a quantitative study. One particular type of data was used for analysis in this study, and that was responses to questionnaires. As a result, it is very important that all students know this. Some students, however, still haven't learned the best method for paraphrasing. These skills can be honed through regular reading or by making use of various online tools, both free and paid. This research consisted of three stages: planning, implementation, and evaluation (through student questionnaires), all of which were carried out sequentially. The aim of this experiment was to determine whether using Quillbot would help students commit less plagiarism; however, students need to reread the tool to ensure that the meaning of the cited articles remains consistent. Students were able to take advantage of the software and find it useful to prephrase text without changing its meaning, according to survey findings submitted via google forms.

3D nuclei segmentation for multi-cellular quantification of zebrafish embryos using NISNet3D

Advances in imaging of developing embryos in model organisms such as the fruitfly, zebrafish, and mouse are producing massive data sets that contain 3D images with every cell and readouts of signaling activity in every cell of an embryo. In Zebrafish embryos, determining the locations of nuclei is crucial for the study of the spatial-temporal behavior of these cells and the control of gene expression during the developmental process. Traditional image processing techniques suffer from bad generalizations, often relying on heuristic measurements that narrowly applies to specific data types, microscope settings, or other image characteristics. Machine learning techniques, and more specifically convolutional neural networks, have recently revolutionized image processing and computer vision. A well-known challenge in developing theses algorithms is the lack of curated training data. We developed a new, manually-curated nuclei segmentation data set for four complete embryos containing over 8,000 cells each. The whole-mount zebrafish embryos at different development stages were hand-labeled with 3D volumetric segmentation of nuclei. Two full embryo data sets were used for training the 3D nuclei instance segmentation network NISNet3D, and the other two embryos were used to validate the training results. We provide both qualitative and quantitative evaluation results for each of the volumes using multiple evaluation metrics. We also provide the fully curated and manually segmented embryo data sets, along with raw images, for the image processing community.

Nonparametric inference for interval data using kernel methods

ABSTRACT Symbolic data have become increasingly popular in the era of big data. In this paper, we consider density estimation and regression for interval-valued data, a special type of symbolic data, common in astronomy and official statistics. We propose kernel estimators with adaptive bandwidths to account for variability of each interval. Specifically, we derive cross-validation bandwidth selectors for density estimation and extend the Nadaraya–Watson estimator for regression with interval data. We assess the performance of the proposed methods in comparison with existing kernel methods by extensive simulation studies and real data analysis.

Testing Models of Cognition and Action Using Response Conflict and Multinomial Processing Tree Models

Dual-process perspectives have made substantial contributions to our understanding of behavior, but fundamental questions about how and when deliberate and automatic cognition shape action continue to be debated. Among these are whether automatic or deliberate cognition is ultimately in control of behavior, how often each type of cognition controls behavior in practice, and how the answers to each of these questions depends on the individual in question. To answer these questions, sociologists need methodological tools that enable them to directly test competing claims. We argue that this aim will be advanced by (a) using a particular type of data known as response conflict data and (b) analyzing those data using multinomial processing tree models. We illustrate the utility of this approach by reanalyzing three samples of data from Miles et al. (2019) on behaviors related to politics, morality, and race.

Topic Modeling: Perspectives From a Literature Review

Topic modeling is a Natural Language Processing technique that has gained popularity over the last ten years, with applications in multiple fields of knowledge. However, there is insufficient empirical evidence to show how this field of study has developed over the years, as well as the main models that have been applied in different contexts. The objective of this paper is to analyze the evolution of the topic modeling technique, the main areas in which it has been applied, and the models that are recommended for specific types of data. The methodology applied is based on bibliometric analysis. First, we searched the Web of Science and the Scopus databases. We then used scientometric techniques and a Tree of Science methodology, which allowed us to analyze the search results from the perspectives of classics, structure, and trends. The results show that the USA and China are among the most productive countries in this field and the applications have been mainly in the identification of sub-topics in short texts, such as social networks and blogs. The main conclusion of this work is that topic modeling is a versatile technique that can complement systematic literature reviews and that has been well-received in different academic and research contexts. The results of this study will help researchers and academics to recognize the importance of these techniques for reviewing large volumes of unstructured information, such as research articles, and in general, for systematic literature reviews.

Appraising the decision-making process concerning COVID-19 policy in postsecondary education in Canada: A critical scoping review protocol.

Responses to COVID-19 in Canadian postsecondary education have overhauled usual norms and practices, with policies of unclear rationale implemented under the pressure of a reported public health emergency. To critically appraise the decision-making process informing COVID-19 policy in the postsecondary education sector. Our scoping review will draw from macro and micro theories of public policy, specifically the critical tradition exemplified by Carol Bacchi's approach "What is the problem represented to be" and will be guided by Arksey and O'Malley's framework for scoping reviews and the team-based approach of Levan and colleagues. Data will include diverse and publicly available documents to capture multiple stakeholders' perspectives on the phenomenon of interest and will be retrieved from university newsletters and legal websites using combinations of search terms adapted to specific data types. Two reviewers will independently screen, chart, analyze and synthesize the data. Disagreements will be resolved through full team discussion. Despite the unprecedented nature of the mass medical mandates implemented in the postsecondary sector and their dramatic impact on millions of lives-students, faculty, staff and their families, friends and communities-the decision-making process leading to them has not been documented or appraised. By identifying, summarizing and appraising the evidence, our review should inform practices that can contribute to effective and equitable public health policies in postsecondary institutions moving forward.