Dataset Of Users Research Articles

Retrospective cross-sectional analysis of concurrent VTE diagnosis in hospitalised socially excluded individuals in Ireland

ObjectiveSocial exclusion (such as that experienced by people who are homeless, incarcerated or use drugs) increases morbidity across a range of diseases but is poorly captured in routine data sets....

Finding Spatiotemporal Patterns of Mobile Application Usage

Understanding mobile application usage patterns is significant for producing better services and enriching user experience. The understanding of spatiotemporal patterns of application usage is still limited. In this paper, we aim at finding spatiotemporal mobile app usage patterns and propose a framework to capture who, when, where, and what applications are used. We first collect a large-scale and real-world application usage dataset covering over 400 thousand active users and 600 million records. In order to introduce spatial features, we partition the collection area into small regions. By grouping regions of similar point-of-interest attributes, we then map 796 regions onto 13 region clusters with semantic meanings. As a result, the original data is reformed as a tensor of four dimensions, i.e., users, application categories, region clusters, and time-slots. We then leverage a multi-way clustering algorithm on the tensor to extract coupling relations between different dimensions. Finally, we discover 508 distinct spatiotemporal application usage patterns with meaningful labels, including E- readers, Digital payers, Uber/DiDi drivers, Young parents, Travellers and Travel planners. The results produced by our framework can serve a series of applications, e.g., identifying user habits and inferring user demographics, which are helpful in customized services like personalized recommendations.

Survey of image deduplication for cloud storage

Increased growth of real-life communication has motivated the creation, transmission, and digital storage of vast volumes of images and video data on the cloud. The explosive increase in virtual/visual image data on cloud servers requires efficient storage utilization that can be addressed using image deduplication technology. Even though the virtual and visual image properties are different, the existing literature uses a similar approach for deduplication checks, which motivated us to consider both image types for this review. This article aims to provide a detailed survey of state-of-the-art visuals as well as virtual image deduplication techniques in a cloud environment, summarizing and organizing them by developing a five-dimensional taxonomy for analysing the features and performance with several non-overlapping categories in each dimension. These include: 1) location of applying deduplication; 2) image feature extraction; 3) time of application; 4) image data partitioning strategy; 5) involvement of user dataset level. Existing image deduplication techniques are categorized into two main categories based on whether the technique involves security. A comparison of techniques is discussed across a set of functional and performance parameters. The current issues are highlighted with the possible future directions to motivate further research studies on the topic.

An Animated Visualization Method for Large-Scale Unstructured Unsteady Flow

Animation visualization is one of the primary methods for analyzing unsteady flow fields. In this paper, we addressed the issue of data visualization for large-scale unsteady flow fields using animation. Loading and rendering individual time steps sequentially can result in substantial frame delay, whereas loading and rendering all time steps simultaneously can result in excessive memory usage. To address these issues, the proposed method analyzes the variable description information in the data files to bypass redundant variables and read the flow field data as required. Second, a hash table is constructed to derive the two-dimensional surface mesh of the flow field and complex mesh cells are simplified into simple linear cells to reduce the mesh’s complexity. This paper presents a method for reducing the memory usage of complex data sets by more than 90%, compared with the ParaView data reading method. The proposed method is tested on four sets of unstructured unsteady flow field data with different data structures. The animation visualization method based on simplified data can achieve an average frame rate of less than 100ms and supports real-time user interaction on personal computers. It extends the ability of personal computers to analyze large-scale unstructured unsteady flow fields.

CVFL: A Chain-like and Verifiable Federated Learning Scheme with Computational Efficiency Based on Lagrange Interpolation Functions

Data privacy and security concerns have attracted significant attention, leading to the frequent occurrence of data silos in deep learning. To address this issue, federated learning (FL) has emerged. However, simple federated learning frameworks still face two security risks during the training process. Firstly, sharing local gradients instead of private datasets among users does not completely eliminate the possibility of data leakage. Secondly, malicious servers could obtain inaccurate aggregation parameters by forging or simplifying the aggregation process, ultimately leading to model training failures. To address these issues and achieve high-performance training models, we have designed a verifiable federated learning scheme called CVFL, where users exist in a serial manner to resist inference attacks and further protect the privacy of user dataset information through serial encryption. We ensure the secure aggregation of models through a verification protocol based on Lagrange interpolation functions. The serial transmission of local gradients effectively reduces the communication burden on cloud servers, and our verification protocol avoids the computational overhead caused by a large number of encryption and decryption operations without sacrificing model accuracy. Experimental results on the MNIST dataset demonstrate that, after 10 epochs of training with 100 users, our solution achieves a model accuracy of 90.63% for MLP architecture under IID data distribution and 87.47% under non-IID data distribution. For CNN architecture, our solution achieves a model accuracy of 96.72% under IID data distribution and 93.53% under non-IID data distribution. Experimental evaluations corroborate the practical performance of the presented scheme with high accuracy and efficiency.

Generation and detection of manipulated multimodal audiovisual content: Advances, trends and open challenges

Generative deep learning techniques have invaded the public discourse recently. Despite the advantages, the applications to disinformation are concerning as the counter-measures advance slowly. As the manipulation of multimedia content becomes easier, faster, and more credible, developing effective forensics becomes invaluable. Other works have identified this need but neglect that disinformation is inherently multimodal. Overall in this survey, we exhaustively describe modern manipulation and forensic techniques from the lens of video, audio and their multimodal fusion. For manipulation techniques, we give a classification of the most commonly applied manipulations. Generative techniques can be exploited to generate datasets; we provide a list of current datasets useful for forensics. We have reviewed forensic techniques from 2018 to 2023, examined the usage of datasets, and given a comparative analysis of each modality. Finally, we give another comparison of end-to-end forensics tools for end-users. From our analysis clear trends are found with diffusion models, dataset granularity, explainability techniques, synchronisation improvements, and learning task diversity. We find a roadmap of deep challenges ahead, including multilinguality, multimodality, improving data quality (and variety), all in an adversarial ever-changing environment.

Agreement between Patient-reported Pain Medication Use and Electronic Medical Record Data in Surgical Amputation Patients.

Opioid misuse after surgery remains a public health crisis in the United States. Recent efforts have focused on tracking pain medication use in surgical populations. However, accurate interpretations of medication use remain quite challenging given inconsistent usage of different datasets. The purpose of this study was to investigate the agreement between electronic medical records (EMR) versus patient self-reported use of pain medications in a surgical amputation population. Patients undergoing major lower extremity amputation or amputation-related procedures were included in this study. Both self-reported and EMR data for pain medication intake were obtained for each patient at three time points (preoperatively, 4 months postoperatively, and 12 months postoperatively). Percentage agreement and the kappa statistic were calculated for both usage (yes/no) and dose categories. Forty-five patients were included in this study, resulting in 108 pairs of self-reported and EMR datasets. Substantial levels of agreement (>70% agreement, kappa >0.61) for opioid use was seen at preoperative and 12 months postoperative. However, agreement dropped at 4 months postoperatively. Anticonvulsant medication showed high levels, whereas acetaminophen showed lower levels of agreements at all time points. Either self-reported or EMR data may be used in research and clinical settings for preoperative or 12-month postoperative patients with little concern for discrepancies. However, at time points immediately following the expected end of acute surgical pain, self-reported data may be needed for more accurate medication reporting. With these findings in mind, usage of datasets should be driven by study objectives and the dataset's strength (eg, accuracy, ease, lack of bias).

Dysphonia in Pediatric Professional Voice Users: Is It Just Nodules?

Objectives/HypothesisProfessional voice users of any age are often concerned about nodules, particularly in pediatric singers. However, an accurate diagnosis allows formulation of an optimal management plan and a successful continuation of these young patients’ careers. There is very little literature regarding pediatric professional singers; we aimed to share our experience of over a decade of referrals to our tertiary pediatric voice clinic. Study DesignThis was a retrospective review. MethodsA retrospective review was undertaken of all consecutive patients aged 0–18 years who self-identified as professional voice users and attended our tertiary pediatric voice clinic between December 2010 and December 2021. We analyzed demographics, professional singing status, diagnosis, management, and clinical voice scores. The patients were subdivided into those aged 0–9, 10–16, and 17–18 years. ResultsA total of 113 pediatric professional voice users attended the tertiary voice clinic in the study period. The commonest self-reported voice use was as a singer. Within the 0–9 years age group, there was a strong male predominance (22 males and three females) and mostly organic (52%) causes. For ages 10–16 years, there was a noted female predominance (15 males and 29 females). In the 17–18 years age group, there was a strong female predominance (10 males and 34 females), with predominantly functional diagnoses (48%). Of significance, only five of the 113 patients had nodules (4.4%). ConclusionsWe present a large data set of pediatric professional voice users and demonstrate the numerous underlying diagnoses for their dysphonia, particularly functional disorders. Our experience highlights the need for adequate vocal training for pediatric professional voice users and the need for a multidisciplinary diagnostic and management approach.

NLP BASED TEXT SUMMARIZATION USING BART MODEL

User interest and response prediction are critical tasks in online advertising, Advertisers may more effectively target their advertising, increase their click-through rates (CTRs), and boost their conversion rates by accurately predicting user interests and behaviors. In recent years, deep learning has developed into a potent tool for predicting consumer interest and response. Deep learning models can accurately predict user interests and responses because they are able to understand complicated patterns from huge quantities of user behavior data. In this study, we suggest a deep learning-based system for online advertising response and interest prediction. Two deep-learning models make up our framework: Thismodel forecasts the likelihood that a user will click on an advertisement. Model for predicting user interest: This model forecasts the likelihood that a user would click on a particular kind of advertising campaign. The models are developed using data from a sizable datasetof user behavior, such as user clicks, ad views, anddemographic data. Compared to conventional approaches, our system has several advantages for predicting user interest and response. First, unlike existing methods, our system can identify Key Words: Deep learning, LST model, user interest prediction, response prediction.

End-to end decision support system for sleep apnea detection and Apnea-Hypopnea Index calculation using hybrid feature vector and Machine learning

Sleep apnea is a disease that occurs due to the decrease in oxygen saturation in the blood and directly affects people's lives. Detection of sleep apnea is crucial for assessing sleep quality. It is also an important parameter in the diagnosis of various other diseases (diabetes, chronic kidney disease, depression, and cardiological diseases). Recent studies show that detection of sleep apnea can be done via signal processing, especially EEG and ECG signals. However, the detection accuracy needs to be improved. In this paper, a ML model is used for the detection of sleep apnea using 19 static sensor data and 2 dynamic data (Sleep score and Arousal). The sensor data is recorded as a discrete signal and the sleep process is divided into 4.8 M segments. In this work, 19 different sensor data sets were recorded with polysomnography (PSG). These data sets have been used to perform sleep scoring. Then, arousal status marking is done. Model training was carried out with the feature vector consisting of 21 data obtained. Tests were performed with eight different machine learning techniques on a unique dataset consisting of 113 patients. After all, it was automatically determined whether people were diseased (a kind of apnea) or healthy. The proposed model had an average accuracy of 97.27%, while the recall, precision, and f-score values were 99.18%, 95.32%, and 97.20%, respectively. After all, the model that less feature engineering, less complex classification model, higher dataset usage, and higher classification performance has been revealed.

Hourglass, a rapid analysis framework for heterogeneous bioimaging data, identifies sex disparity in IL-6/STAT3-associated immune phenotypes in pancreatic cancer.

Integration and mining of bioimaging data remains a challenge and lags behind the rapidly expanding digital pathology field. We introduce Hourglass, an open-access analytical framework that streamlines biology-driven visualization, interrogation, and statistical assessment of multiparametric datasets. Cognizant of tissue and clinical heterogeneity, Hourglass systematically organizes observations across spatial and global levels and within patient subgroups. Applied to an extensive bioimaging dataset, Hourglass promptly consolidated a breadth of known interleukin-6 (IL-6) functions via its downstream effector STAT3 and uncovered a so-far unknown sexual dimorphism in the IL-6/STAT3-linked intratumoral T-cell response in human pancreatic cancer. As an R package and cross-platform application, Hourglass facilitates knowledge extraction from multi-layered bioimaging datasets for users with or without computational proficiency and provides unique and widely accessible analytical means to harness insights hidden within heterogeneous tissues at the sample and patient level. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework

As greener mobility becomes the norm with the advent of electric vehicles (EVs), a natural question arises: how big of a change are we seeing in terms of the stochastic energy demands imposed by EVs? There have been considerable difficulties in analyzing the adoption of all types of EV infrastructure due to the lack of publicly available individual EV user data across various service providers, such as distribution network operators, EV aggregators, EV users, utilities, regulators, and academics. In this study, we introduce the JETPANN (Joint EV energy consumption and charging duration Training Prediction using Artificial Neural Networks), a novel jointly trainable artificial deep neural network framework for predicting stochastic EV user behavior by predicting their stay/charging duration and energy consumption simultaneously. We used a large-scale dataset of individual EV users’ charging transactions collected at a multi-site UCLA campus, the Los Angeles Department of Water and Power (LADWP), the Port of LA, the City of Santa Monica, and the City of Pasadena over a five-year period. This includes more than 50 EVSEs (Electric Vehicle Supply Equipment) and 216 EV charging points. Over 50,000 real-time transactions were recorded in our database with 341 distinct EV users. In this research, essential attributes of each charging session were identified, extracted from our database, and then fed into our JETPANN network. Using a jointly trained framework, our AI-enabled network predicts the stay/charging duration and the energy consumption of all the recorded EV users with an accuracy of above 99 percent. The proposed technique was tested and validated using all the collected historical charging data from individual EV users via realizing mean-absolute errors of training loss versus validation loss. Utilizing hyperparameter tuning and semi-grid search, the JETPANN with semi-optimized hyperparameters was achieved with the lowest mean-absolute errors of 0.927 and 0.068 in predicting stay/charging durations and energy consumptions, respectively, in a jointly trainable framework. This study demonstrates the potential of the JETPANN framework in the prediction of EV users’ behavior by using large-scale real-world data collected from a diverse pool of EV users, including faculty and students, occasional and frequent users, and early and late adopters.

Machine learning-based energy use prediction for the smart building energy management system

Smart building is a building development approach utilizing digital and communication technology to improve occupants' comfort inside the building and help increase energy usage efficiency in building operations. Despite its benefits, the smart building concept is still slowly adopted, particularly in developing countries. The advancement of computational techniques such as machine learning (ML) has helped building owners simulate and optimize various building performances in the building design process more accurately. Therefore, this study aims to assist energy efficiency design strategies in a building by identifying the features of the smart building characteristics that can potentially foster building energy efficiency. Furthermore, an ML model based on the features identified is then developed to predict the level of energy use. K-Nearest Neighbor (k-NN) algorithm is employed to develop the model with the openly accessible smart building energy usage datasets from Chulalongkorn University Building Energy Management System (CU-BEMS) as the training and testing datasets. The validation result shows that the predictive model has an average relative error value of 17.76%. The energy efficiency levels obtained from applying identified features range from 34.5% to 45.3%, depending on the reviewed floor. This paper also proposed the dashboard interface design for ML-based smart building energy management.

Analysis of Application Used on Smartphones

As smartphones have become indispensable personal devices, the number of smartphone users has increased dramatically over the last decade. These personal devices, which are supported by a variety of smartphone apps, allow people to access Internet services in a convenient and ubiquitous manner. App developers and service providers can collect fine-grained app usage traces, revealing connections between users, apps, and smartphones. We present a comprehensive review of the most recent research on smartphone app usage analysis in this survey. Our survey summarizes advanced technologies and key patterns in smartphone app usage behaviors, all of which have significant implications for all relevant stakeholders, including academia and industry. We begin by describing four data collection methods: surveys, monitoring apps, network operators, and app stores, as well as nine publicly available app usage datasets. We then systematically summarize the related studies of app usage analysis in three domains: app domain, user domain, and smartphone domain. We make a detailed taxonomy of the problem studied, the datasets used, the methods used, and the significant results obtained in each domain. Finally, we discuss future directions in this exciting field by highlighting research challenges.

Efficient continuous kNN join over dynamic high-dimensional data

Given a user dataset U\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{U}$$\\end{document} and an object dataset I\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{I}$$\\end{document}, a kNN join query in high-dimensional space returns the k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{k}$$\\end{document} nearest neighbors of each object in dataset U\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{U}$$\\end{document} from the object dataset I\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{I}$$\\end{document}. The kNN join is a basic and necessary operation in many applications, such as databases, data mining, computer vision, multi-media, machine learning, recommendation systems, and many more. In the real world, datasets frequently update dynamically as objects are added or removed. In this paper, we propose novel methods of continuous kNN join over dynamic high-dimensional data. We firstly propose the HDR+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^+$$\\end{document} Tree, which supports more efficient insertion, deletion, and batch update. Further observed that the existing methods rely on globally correlated datasets for effective dimensionality reduction, we then propose the HDR Forest. It clusters the dataset and constructs multiple HDR Trees to capture local correlations among the data. As a result, our HDR Forest is able to process non-globally correlated datasets efficiently. Two novel optimisations are applied to the proposed HDR Forest, including the precomputation of the PCA states of data items and pruning-based kNN recomputation during item deletion. For the completeness of the work, we also present the proof of computing distances in reduced dimensions of PCA in HDR Tree. Extensive experiments on real-world datasets show that the proposed methods and optimisations outperform the baseline algorithms of naive RkNN join and HDR Tree.

Exploratory Data Analysis and Feature Selection for Social Media Hackers Prediction Problem

In machine learning, the intelligence of a developed model is greatly influenced by the dataset used for the target domain on which the developed model will be deployed. Social media platform has experienced more of hackers’ attacks on the platform in recent time. To identify a hacker on the platform, there are two possible ways. The first is to use the activities of the user while the second is to use the supplied details the user registered the account with. To adequately identify a social media user as hacker proactively, there are relevant user details called features that can be used to determine whether a social media user is a hacker or not. In this paper, an exploratory data analysis was carried out to determine the best features that can be used by a predictive model to proactively identify hackers on the social media platform. A web crawler was developed to mine the user dataset on which exploratory data analysis was carried out to select the best features for the dataset which could be used to correctly identify a hacker on a social media platform.

Pilot Projects Enhancing Utility and Usage of Common Fund Data Sets (NIH)

Pilot Projects Enhancing Utility and Usage of Common Fund Data Sets (NIH)

The role of the espoused tight culture in value assessment of social media use

ABSTRACT To advance the stream of culture-themed Information Systems (IS) research, this study ushers in the cultural tightness-looseness (CTL) theory and conceptualises the focal construct, the cultural tightness (CT), into the social media usage context. By integrating the CTL theory and the customer value perspective, this study proposes a research model to examine how an espoused tight culture affects individuals’ value assessment and behaviour of social media use. The model is validated using a cross-sectional field survey dataset of Chinese social media users, sampling a typical tight culture. The findings demonstrate that the espoused tight culture plays a paramount role in influencing users’ value assessment and usage behaviour related to social media. This study contributes to the cultural IS literature by providing a context-specific understanding of overall cultural interactions of social media use in a tight culture.

Is There Any Relation Between Smartphone Usage and Loneliness During the COVID-19 Pandemic?: A Study by Exploring Two Objective App Usage Datasets

BACKGROUND: Though smartphone is popular and loneliness is higher among the youth, in low-and-middle income countries (LMICs) such as Bangladesh, the relation of loneliness with actual app usage is unexplored amid pandemic. Also, the studies conducted in developed countries are limited by exploration of some app categories.
 METHODS: We conducted two studies in Bangladesh: in 2020 (N1=100) and 2021 (N2=105). We collected participant’s ULS-8 score and 7 days’ actual app usage. We extracted app usage behavioral data from 1.69 million events and did semi-partial and partial correlation analyses.
 RESULTS: Our analysis did not present any significant relation which may indicate a negative impact on loneliness. However, we found higher usage of Social Media, Communication, Education, Books, and Shopping apps and higher entropy of Browser apps had significant (q<.05) relation with lower loneliness.
 CONCLUSION: Smartphone may not negatively impact loneliness. Instead, some app categories can play a role to mitigate loneliness.

Is Workload Associated With Internal Impingement in Professional Baseball Pitchers? An Analysis of Days of Rest, Innings Pitched, and Batters Faced.

Internal impingement is a common cause of shoulder pain and dysfunction in baseball pitchers. The purpose of this study was to determine if the amount of rest days between outings, the number of innings pitched in each outing, and the number of batters faced in each outing are risk factors for internal impingement in professional baseball pitchers. It was hypothesized that a higher workload would significantly increase a pitcher's risk of developing internal impingement. Case-control study; Level of evidence, 3. All professional (Major and Minor League) baseball pitchers who were diagnosed with internal impingement between 2011 and 2017 were identified using the Major League Baseball's Health and Injury Tracking System. A separate player usage data set was used to determine workload. The authors compared workload variables (days of rest, innings pitched, and batters faced per game) between each of 4 injury-exposure groups (pitchers with documented internal impingement <2, <6, <12, and >12 weeks after a game) and a control group of pitchers with no internal impingement using Student t tests or Mann-Whitney U tests. In a within-pitcher paired analysis of the injury groups, the workload variables during 3 acute periods (<2, <6, and <12 weeks preinjury) were compared with the baseline values (>12 weeks preinjury). Overall, there were 624 professional baseball pitchers diagnosed with internal impingement during the study period. Compared with pitcher games (n = 213,964), pitchers in all 4 injury-exposure groups had significantly more innings pitched per game (P ≤ .003 for all) and faced significantly more batters per game (P < .001 for all). There were significantly more starting pitchers in each of the injury-exposure groups when compared with the controls (P < .001 for all). However, when comparing acute and baseline workloads in the pitchers diagnosed with internal impingement, there were no significant differences in the number of innings pitched, batters faced, or days of rest. This analysis revealed significantly greater pitcher workload and percentage of starting pitchers in professional baseball players diagnosed with internal impingement when compared with healthy controls. However, this injury did not seem to be associated with an acute increase in workload.