Data Collection Framework Research Articles

Development of environmental data for land use regression models to assess fine particulate matter pollution in Ho Chi Minh City

Land use regression (LUR) model is a common method for assessing ambient air pollution in metropolis, such as fine particulate matter (PM2.5). The LUR model utilizes PM2.5 concentrations as a dependent variable and traffic, topography, and land use variables..., named as environmental data, as independent variables in multiple linear regression analysis. Currently, the PM2.5 pollution is one of the most concerning environmental issues in Ho Chi Minh City (HCMC). Particularly, in the context of current limited air pollution monitoring resources of the city, the LUR model usage for PM2.5 pollution assessment at the level of the whole city may be a possible solution. However, the environmental data for using in the LUR model in HCMC is not available. Thus, this study was conducted to prepare environmental data for applying in the LUR model in upcoming studies. The study selected potential environmental variables in HCMC and continued to deal with databases selection for developing these environmental variables. The major result of the study is that a database of environmental data in HCMC was developed, including 26 variables categorized into five groups, including (1) Meteorological data, (2) Traffic-related data, (3) Population data, (4) Land use data, and (5) Social-economic data. Moreover, the study established a conceptual framework for environmental data collection. Finally, environmental data of several fixed sites, where PM2.5 concentrations were monitored in our previous studies, was extracted as an illustration.

A real-time green construction optimization strategy for engineering vessels considering fuel consumption and productivity: A case study on a cutter suction dredger

Energy saving in the process of engineering construction is an indispensable measure to achieve industrial low carbon emissions. To reduce fuel consumption in the construction process of engineering vessels, a real-time green construction optimization strategy for engineering vessels considering fuel consumption and productivity is proposed. A cutter suction dredger (CSD) is taken as a case study. Based on its working principle, a series of potential monitoring features are determined. In addition, details of data collection are presented. Then, the original data obtained are cleaned based on human experience. The Hilbert Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) was used to filter out irrelevant and redundant factors to achieve feature dimensionality reduction, followed by data standardization based on the robust scaler function. Five machine learning models, including gradient boosting regression tree (GBRT), extreme gradient boosting (XGBoost), random forest regression (RFR), support vector regression (SVR) and super learner based on stacked strategy (SLBSS), are used to train and test offline data on the basis. The Taguchi test method is used to optimize the hyperparameters of these models. In addition, an online data collection, processing and analysis framework is proposed. The online data collected in real time are used to predict the fuel consumption level based on this framework. Finally, feature importance is analyzed. Aiming at the minimum fuel consumption per unit productivity (FCpup), specific and feasible optimization suggestions are made for CSD construction. The results show that machine learning models can predict the fuel consumption level effectively. The first seven important features should be used for prediction. The productivity, lubricating oil pressure of the pump gearbox in the cabin and slurry density are the three most important factors. Improving the parameters related to productivity and reducing the sailing speed, reamer motor voltage and washing pressure of reamer bearings can improve fuel efficiency, which is beneficial to energy savings and emission reduction. Engineering construction shows that this strategy can reduce the fuel consumption of the CSD by approximately 20%.

Legal issues pertaining to the collection of sociodemographic data in emergency departments.

Legal issues pertaining to the collection of sociodemographic data in emergency departments.

Hybrid synthetic data generation pipeline that outperforms real data

Fine-tuning a pretrained model with real data for a machine learning task requires many hours of manual work, especially for computer vision tasks, where collection and annotation of data can be very time-consuming. We present a framework and methodology for synthetic data collection that is not only efficient in terms of time taken to collect and annotate data, making use of free- and open-source software tools and 3D assets but also beats the state-of-the-art against real data, which is the ultimate test for any similar-to-real approach. We test our approach on a set of image classes from ObjectNet, which is a challenging image classification benchmark test dataset that is designed to be similar in many respects to ImageNet but with a wider variety of viewpoints, rotations, and backgrounds, which can make it more difficult for transfer learning problems. The novelty of our approach stems from the way we create complex backgrounds for 3D models using 2D images laid out as decals in a 3D game engine, where synthetic images are captured programmatically with a large number of systematic variations. We demonstrate that our approach is highly effective, resulting in a deep learning model with a top-1 accuracy of 72% on the ObjectNet data, which is a new state-of-the-art result. In addition, we present an efficient strategy for learning rate tuning that is an order of magnitude faster than regular grid search.

GesturalOrigins: A bottom-up framework for establishing systematic gesture data across ape species

Current methodologies present significant hurdles to understanding patterns in the gestural communication of individuals, populations, and species. To address this issue, we present a bottom-up data collection framework for the study of gesture: GesturalOrigins. By “bottom-up”, we mean that we minimise a priori structural choices, allowing researchers to define larger concepts (such as ‘gesture types’, ‘response latencies’, or ‘gesture sequences’) flexibly once coding is complete. Data can easily be re-organised to provide replication of, and comparison with, a wide range of datasets in published and planned analyses. We present packages, templates, and instructions for the complete data collection and coding process. We illustrate the flexibility that our methodological tool offers with worked examples of (great ape) gestural communication, demonstrating differences in the duration of action phases across distinct gesture action types and showing how species variation in the latency to respond to gestural requests may be revealed or masked by methodological choices. While GesturalOrigins is built from an ape-centred perspective, the basic framework can be adapted across a range of species and potentially to other communication systems. By making our gesture coding methods transparent and open access, we hope to enable a more direct comparison of findings across research groups, improve collaborations, and advance the field to tackle some of the long-standing questions in comparative gesture research.

Trends in abundance indices derived from commercial fisheries for priority marine stocks in the Azores.

Large-scale assessments of fish and shellfish populations may be valuable for identifying the impacts of fishing pressure on stocks, but they are rarely possible due to a lack of long-term datasets. In this study, a two-part mixed-effect model for lognormal data was used to obtain historical abundance indices for 16 priority fish and shellfish stocks in the Azores region derived from catch per unit effort (kg per day at sea per vessel) and landing per unit effort (kg per landing per vessel). This data was obtained over the past 30 years under the EC Data Collection Framework and, in addition to information on effort and catch, included details about the fishing operation such as quarter, vessel length and fishing gear. This information was analysed to investigate how abundance indices changed over the years, verify if trends from different data sources were different and, if possible, relate these results to the population and fishery dynamics. The abundance indices derived from both datasets were generally in agreement with each other. Declining trends have been observed for some commercially important species, such as forkbeard Phycis phycis, European conger Conger conger, parrotfish Sparisoma cretense, red scorpionfish Scorpaena scrofa, offshore rockfish Pontinus kuhlii, common spiny lobster Palinurus elephas, splendid alfonsino Beryx splendens and alfonsino B. decadactylus, which is concerning because they are particularly slow-growing, long-lived and have low natural mortality, making them susceptible to overfishing. The results of this study are very important and should help future stock assessment and management initiatives.

An Optimization Framework for Data Collection in Software Defined Vehicular Networks.

A Software Defined Vehicular Network (SDVN) is a new paradigm that enhances programmability and flexibility in Vehicular Adhoc Networks (VANETs). There exist different architectures for SDVNs based on the degree of control of the control plane. However, in vehicular communication literature, we find that there is no proper mechanism to collect data. Therefore, we propose a novel data collection methodology for the hybrid SDVN architecture by modeling it as an Integer Quadratic Programming (IQP) problem. The IQP model optimally selects broadcasting nodes and agent (unicasting) nodes from a given vehicular network instance with the objective of minimizing the number of agents, communication delay, communication cost, total payload, and total overhead. Due to the dynamic network topology, finding a new solution to the optimization is frequently required in order to avoid node isolation and redundant data transmission. Therefore, we propose a systematic way to collect data and make optimization decisions by inspecting the heterogeneous normalized network link entropy. The proposed optimization model for data collection for the hybrid SDVN architecture yields a 75.5% lower communication cost and 32.7% lower end-to-end latency in large vehicular networks compared to the data collection in the centralized SDVN architecture while collecting 99.9% of the data available in the vehicular network under optimized settings.

Towards Posture and Gait Evaluation through Wearable-Based Biofeedback Technologies

In medicine and sport science, postural evaluation is an essential part of gait and posture correction. There are various instruments for quantifying the postural system’s efficiency and determining postural stability which are considered state-of-the-art. However, such systems present many limitations related to accessibility, economic cost, size, intrusiveness, usability, and time-consuming set-up. To mitigate these limitations, this project aims to verify how wearable devices can be assembled and employed to provide feedback to human subjects for gait and posture improvement, which could be applied for sports performance or motor impairment rehabilitation (from neurodegenerative diseases, aging, or injuries). The project is divided into three parts: the first part provides experimental protocols for studying action anticipation and related processes involved in controlling posture and gait based on state-of-the-art instrumentation. The second part provides a biofeedback strategy for these measures concerning the design of a low-cost wearable system. Finally, the third provides algorithmic processing of the biofeedback to customize the feedback based on performance conditions, including individual variability. Here, we provide a detailed experimental design that distinguishes significant postural indicators through a conjunct architecture that integrates state-of-the-art postural and gait control instrumentation and a data collection and analysis framework based on low-cost devices and freely accessible machine learning techniques. Preliminary results on 12 subjects showed that the proposed methodology accurately recognized the phases of the defined motor tasks (i.e., rotate, in position, APAs, drop, and recover) with overall F1-scores of 89.6% and 92.4%, respectively, concerning subject-independent and subject-dependent testing setups.

Zoonoses, foodborne outbreaks and antimicrobial resistance guidance for reporting 2022 data

This technical report of the European Food Safety Authority (EFSA) presents the guidance to reporting European Union (EU) Member States and non-Member States in data transmission using extensible markup language (XML) data transfer covering the reporting of isolate-based quantitative antimicrobial resistance data, as well as reporting of prevalence data on zoonoses and microbiological agents and contaminants in food, foodborne outbreak data, animal population data and disease status data. For data collection purposes, EFSA has created the Data Collection Framework (DCF) application. The present report provides data dictionaries to guide the reporting of information deriving from 2022 under the framework of Directive 2003/99/EC, Regulation (EU) 2017/625, Commission Implementing Regulation (EU) 2019/627 and Commission Implementing Decision (EU) 2020/1729. The objective is to explain in detail the individual data elements that are included in the EFSA data models to be used for XML data transmission through the DCF. In particular, the data elements to be reported are explained, including information about the data type, a reference to the list of allowed terms and any additional business rule or requirement that may apply.

From text to ties: Extraction of corruption network data from deferred prosecution agreements

Abstract Deferred prosecution agreements (DPAs) are a legal tool for the nontrial resolution of cases of corruption. Each DPA is accompanied by a Statement of Facts that provides detailed and publicly available textual records of the given cases, including summarized evidence of who was involved, what they committed, and with whom. These statements can be translated into networks amenable to social network analysis allowing an analysis of the structure and dynamics of each case. In this study, we show how to extract information about which actors were involved in a given case, the relations and interactions among these actors (e.g., communication or payments), and their relevant individual attributes (gender, affiliation, and sector) from five Statements of Fact. We code the extracted information manually with two independent coders and subsequently, we assess the inter-coder reliability. For assessing the coding reliability of nodes and attributes, we use a matching coefficient, whereas for assessing the coding reliability of ties, we construct a network from the coding of each coder and subsequently calculate the graph correlations of the two resulting networks. The coding of nodes and ties in the five extracted networks turns out to be highly reliable with only slightly lower coding reliability in the case of the largest network. The coding of attributes is highly reliable as well, although it is prone to missing data on actors’ gender. We conclude by discussing the flexibility of our data collection framework and its extension by including network dynamics and nonhuman actors (such as companies) in the network representation.

Data Collection Maximization in IoT-Sensor Networks via an Energy-Constrained UAV

In this paper, we study sensing data collection of IoT devices in a sparse IoT-sensor network, using an energy-constrained Unmanned Aerial Vehicle (UAV), where the sensory data is stored in IoT devices while the IoT devices may or may not be within the transmission range of each other. We formulate two novel data collection problems to fully or partially collect data stored from IoT devices using the UAV, by finding a closed tour for the UAV that consists of hovering locations and the sojourn duration at each of the hovering locations such that the accumulative volume of data collected within the tour is maximized, subject to the energy capacity on the UAV, where the UAV consumes energy on both hovering for data collection and flying from one hovering location to another hovering location. To this end, we first propose a novel data collection framework that enables the UAV to collect sensory data from multiple IoT devices simultaneously if these IoT devices are within the coverage range of the UAV, through adopting the orthogonal frequency division multiple access (OFDMA) technique. We then formulate two data collection maximization problems to deal with full or partial data collection from IoT devices at each hovering location, and show that both defined problems are NP-hard. We instead devise approximation and heuristic algorithms for the problems. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrated that the proposed algorithms are promising.

Readiness data collection, evaluation and reporting system for combat tenders: A conceptual framework

This paper presents a conceptual framework for readiness data collection, evaluation and reporting system for combat tenders. Modules on data gathering areas and the elements of the gathering areas are built; from which a matrix is established.Type of data to be collected namely; state data and data are identified. Readiness Data Collection (RDC) and reporting system are proposed. A dynamic Fleet Readiness Analysis Centre (FRAC) is also proposed. The framework is based on the; method of c-ranting technique for Combat Readiness Assessment.

Moderating Effect of Tactical Asset Allocation on the Risk-Return Relationship in the Nigerian Stock Market

Before making investment decisions, the investor must find out the risk-return characteristics of investments with the help of investment strategies. These investment strategies, including drawdown, tactical asset allocation and short selling, are used by investors and fund managers to improve portfolio performance by maximizing returns and minimizing risks. This study aims to examine the moderating effect of tactical asset allocation on the risk-return relationship in the Nigerian stock market. The study sample is 90 stocks that are consistently traded in the Nigerian stock market within the period of sixteen years on a monthly basis. The study employed time series data which were extracted from the NGX website, annual financial statement, and CBN statistical bulletin. This study's data collection and analysis framework was based on the methodology of Fama and French (2015) using FamaMacbeth's two-step regression. The study used the difference between the logarithmic present value of the price and the previous value of the price to compute the return. Evidence from the result revealed that tactical asset allocation is not significantly priced under the CoFF5F and HMFF5F in the Nigerian stock market. Also, introducing tactical asset allocation as the moderating variable does not improve the relationship between risk and return in the Nigerian stock market. In light of this, the study concluded that tactical asset allocation could not be used to improve the relationship between risk and return through the minimization of risk and maximization of return in the Nigerian stock market. The study recommends that other investment strategies, such as drawdown, short selling etc., can be used by investors to improve the relationship between risk and return in the Nigerian stock market.

Guidance for reporting laboratory data on African swine fever (ASF)

This technical report is aimed at guiding the reporting of data on analytical test results, and related metadata, to EFSA in the context of the activities for the surveillance and monitoring of African Swine Fever. The objective is to explain in detail the individual data elements that are included in the EFSA Standard Sample Description version 2 (SSD2) data model. The guidance is intended to support the reporting countries in data transmission using eXtensible Markup Language (XML) data file transfer through the Data Collection Framework (DCF) according to the protocol described in the EFSA Guidance on Data Exchange version 2 (GDE2). The data elements are explained, including information about data type, list of allowed terms and associated business rules. Instructions about how to report common sampling schemes are also provided to ensure harmonised reporting among the countries.

A Multi-Objective Clustering for Better Data Management in Connected Environment

Over the past decade, the rapid increase in connected devices has enabled the emergence of new digital ecosystems to provide new opportunities for monitoring and managing systems to optimize overall performance. With these connected environments, data collection and management become increasingly challenging. A significant number of works in the literature have addressed data collection and management based on different contexts (e.g., mobile ad hoc, Peer-2-Peer, and IoT networks). Today, a wired network uses all of these protocols simultaneously, thus highlighting the need to build a standard data collection and management framework that considers all potential user preferences. For this purpose, multi-objective clustering has been utilized as a promising solution to ensure the stability of connected devices during the collection and management of data. In this paper, we introduce a new multi-objective clustering (MOC) technique based on various criteria for cluster construction and head selection in connected environments. More precisely, the proposed solution is based hypergraphs to represent the connected environment and clusters according to similarities between heterogeneous devices. Then, a cross-sectional hypergraph algorithm is applied to select the cluster heads. Experiments conducted show that our solution outperforms the pioneering literature methods in terms of performance and effectiveness.

Context-aware IoT-enabled framework to analyse and predict indoor air quality

• An IoT-based framework for data collection and context modelling has been developed. • Extended Kalman Filter (EKF) approach is used to deal with inaccuracies, missing data and eliminate errors. • The EKF-derived indoor pollutant concentrations are employed in context reasoning for proposing a new index. • An adaptive neuro-fuzzy inference system (ANFIS) uses the percent of dissatisfied people (PPD), ventilation rate (VR), and AQI data to identify the present state of IAQ and the percentage of time when the air quality in the classroom is unhealthy. For a productive and healthy life, air quality plays an important role. This paper addresses the requirements to develop a system capable of providing real-time information, predictions, and alerts about the indoor environment using context-awareness. The proposed IoT system serves for data collection, pre-processing, defining rules, and forecasting the predicting states of the indoor environment by giving information to the end-user about the alerts and recommendations. A novel approach based on the indoor pollutants T, RH, CO 2 , PM 2.5 , PM 10 , and CO for the determination of the status of the environment is proposed. The pre-processing is used for filtering data using and extended Kalman filter. Further, the system uses an adaptive neuro-fuzzy inference system (ANFIS) and discrete-time Markov chains (DTMC) to predict the state of the indoor environment with the help of daily air pollution concentrations and environmental parameters. The ANFIS model predictor considers the value of indoor pollutants to form a new index: State of indoor air (SIA). For analysis and forecasting of the new index SIA, the DTMC model is used. The collected and measured data is stored in the IoT cloud using the sensing setup, and sensed information is used to develop the SIA transfer matrix, generating return durations corresponding to each SIA and providing alerts based on the data to the end-user. The models are assessed using the expected and actual return durations. The most frequent interior ventilation states, according to the predictions, are poor and moderate. Only 0.08 percent of the time does the IAQ remain in a good state. Two-thirds of the time (66%), the indoor ventilation is severe (poor, very poor, or hazardous); 19% of the time it is very bad, and 15% of the time it is hazardous, suggesting and warning that there is a very high probability of unhealthy AQI in educational institutions in the Delhi-NCR region. Performance is measured by the comparison between actual and forecasted return periods, and the forecast error for our system is low, with an absolute forecast error of 3.47% on an average.

Coordination and Pilot Implementation of a Standardized Data Collection for Touching Hands

Touching Hands is an American Society for Surgery of the Hand program that provides hand surgeries to the world's underserved communities. The purpose of this study was to develop and implement a systematic data collection method for Touching Hands to assess patient outcomes, volunteer impact, alleviated disease burden, and cost-effectiveness. Research electronic data capture (REDCap) was used as the secure software platform to facilitate data collection. The Quick Disabilities of the Arm, Shoulder and Hand questionnaire was used to assess pre-and postoperative patient-reported outcomes. The Maslach Burnout Inventory-Human Services (Medical Personnel) survey was administered to volunteers before and after the mission to measure impact on volunteers. Case information was collected to calculate disability-adjusted life years and cost-effectiveness. The data collection system was implemented in some capacity in 4 domestic and 3 international mission sites during 2020 and2021. Substantial limitations exist for the implementation of a systematic data collection framework for Touching Hands and warrant further modification and optimization. A REDCap database can be used for standardized and centralized patient and volunteer data collection for Touching Hands missions.

Real-Time Cyber Analytics Data Collection Framework

In cyber security, it is critical that event data is collected in as near real time as possible to enable early detection and response to threats. Performing analytics from event logs stored in databases slows down the response time due to the time cost of database insertion and retrieval operations. The authors present a data collection framework that minimizes the need for long-term storage. Events are buffered in memory, up to a configurable threshold, before being streamed in real time using live streaming technologies. The framework deploys virtualized data collecting agents that ingest data from multiple sources including threat intelligence. The framework enables the correlation of events from various sources, improving detection precision. The authors have tested the framework in a real time, machine-learning-based threat detection system. The results show a time gain of 300 milliseconds in transmission time from event capture to analytics system, compared with storage-based collection frameworks. Threat detection was measured at 95%, which is comparable to the benchmark snort IDS.

A framework for privacy preserving digital trace data collection through data donation

A potentially powerful method of social-scientific data collection and investigation has been created by an unexpected institution: the law. Article 15 of the EU’s 2018 General Data Protection Regulation (GDPR) mandates that individuals have electronic access to a copy of their personal data, and all major digital platforms now comply with this law by providing users with “data download packages” (DDPs). Through voluntary donation of DDPs, all data collected by public and private entities during the course of citizens’ digital life can be obtained and analyzed to answer social-scientific questions – with consent. Thus, consented DDPs open the way for vast new research opportunities. However, while this entirely new method of data collection will undoubtedly gain popularity in the coming years, it also comes with its own questions of representativeness and measurement quality, which are often evaluated systematically by means of an error framework. Therefore, in this paper we provide a blueprint for digital trace data collection using DDPs, and devise a “total error framework” for such projects. Our error framework for digital trace data collection through data donation is intended to facilitate high quality social-scientific investigations using DDPs while critically reflecting its unique methodological challenges and sources of error. In addition, we provide a quality control checklist to guide researchers in leveraging the vast opportunities afforded by this new mode of investigation.

Sustainable and Efficient Fog-Assisted IoT Cloud Based Data Collection and Delivery for Smart Cities

The Internet of Things (IoT) aims to enhance the quality of human life by sensing, processing and delivering data from the physical world. The Internet of Things Cloud (IoTC) is able to enhance sensing capabilities of IoT devices while fog computing can overcome computation and storage limitations of IoT devices. Based on this observation, we propose a fog-assisted IoTC data collection and delivery framework to reduce IoTC-based data communication costs and enhance success rates. The framework exploits the named data networking to deliver IoTC-based data so that multiple IoT devices can retrieve data from the nearest provider via one data delivery process. Moreover, the framework achieves mobility support for IoT devices to guarantee successful receipt of data. The experimental results show that this framework reduces data communication costs by nearly 37.15% and improves success rates by nearly 9.25%.