Quality Assessment Framework Research Articles

Federated learning based nonlinear two-stage framework for full-reference image quality assessment: An application for biometric

The non-linearity in medical image processing is a critical issue. Because the privacy of the medical image and loss of data is a major concern in recent years. Federated learning is a most advanced form of machine learning in which, rather than transmitting data to local server, a machine learning (ML) algorithm is installed to various devices to train on the information. The parameters from the separate modules will then be transferred to a master ML/ (deep learning) DL model for global training. The research of Image Quality Assessment (IQA) aims to simulate the process of human perception of image quality and construct an objective image quality model as consistent as possible with subjective assessment. The existing IQA methods can be roughly divided into traditional methods and deep learning methods. Traditional methods are knowledge-driven, using prior knowledge or assumptions about the human visual system (HVS) to heuristically design image quality index. Deep learning methods are data-driven, using a large amount of annotated data to learn the mapping from the image to its visual quality end-to-end. To effectively integrate traditional methods into deep networks and investigate the knowledge (model)-driven deep learning methods are the current mainstream trends in IQA research. In this paper, we take the contrary direction and improve traditional methods guided by the cue from deep learning methods. The main works include: 1. the employment of activation function ensure the nonlinear approximation ability of the neural network, here we first extend the two-stage framework widely used in the field of full-reference image quality assessment and propose a nonlinear two-stage framework. 2. Within this framework, we revisit the Edge Strength SIMlarity (ESSIM) algorithm that we previously published in IEEE Signal Processing Letters, and proposed the Nonlinear Edge Strength SIMlarity (NESSIM) algorithm. Experiments on public databases show that NESSIM can obtain good assessment results in traditional methods. • Improve traditional methods guided by the cue from deep learning methods. • Employment of activation function ensure the nonlinear approximation ability of the neural network. • Propose a nonlinear two-stage framework • Proposed the Nonlinear Edge Strength SIMlarity (NESSIM) algorithm.

Error Resilience of H.264/avc Coding Structures for Delivery over Wireless Networks

Video content represents a large portion of global network traffic, and it is expected that this trend will continue as 4K video streaming becomes more popular and more applications such as entertainment and surveillance become available. Wireless networks are becoming the most important technologies for delivering cost-effective content, including video. The unpredictability of wireless channels, as well as the sensitivity of video content to packet losses, errors, and delays makes video delivery over wireless network difficult. Furthermore, when compared to wire links, congestion and complex traffic patterns make wireless channels much worse. Packet losses and network errors can be mitigated with the help of modern codecs. This study looks at the modern video codec H.264/Advanced Video Coding (AVC) and how to use the built-in error resilience tools to improve end-to-end video quality over simulated wireless networks. Network Simulator 3 (NS-3) was used and a framework for video quality assessment based on EvalVid 2.7 tools was employed. We tested the performance of several coding structures in the presence of different intra coding techniques. Results for less active video sequences showed that the IBP coding structure with intra period of 18 achieved best performance at lower loss rates. The addition of an intra MB line or extra random intra MBs did not seem to offer extra protection against errors for these sequences. For more active video sequences, IBBBP coding structure without or with an intra MB line performed better than all other configurations at all loss rates

A Machine Learning Predictive Model to Detect Water Quality and Pollution

The increasing prevalence of marine pollution during the past few decades motivated recent research to help ease the situation. Typical water quality assessment requires continuous monitoring of water and sediments at remote locations with labour-intensive laboratory tests to determine the degree of pollution. We propose an automated water quality assessment framework where we formalise a predictive model using machine learning to infer the water quality and level of pollution using collected water and sediments samples. Firstly, due to the sparsity of sample collection locations, the amount of sediment samples of water is limited, and the dataset is incomplete. Therefore, after an extensive investigation on various data imputation methods’ performance in water and sediment datasets with different missing data rates, we chose the best imputation method to process the missing data. Afterwards, the water sediment sample will be tagged as one of four levels of pollution based on some guidelines and then the machine learning model will use a specific technique named classification to find the relationship between the data and the final result. After that, the result of prediction can be compared to the real result so that it can be checked whether the model is good and whether the prediction is accurate. Finally, the research gave improvement advice based on the result obtained from the model building part. Empirically, we show that our best model archives an accuracy of 75% after accounting for 57% of missing data. Experimentally, we show that our model would assist in automatically assessing water quality screening based on possibly incomplete real-world data.

Review of data quality assessment frameworks experiences around Europe

Abstract In the first phase of its work, WP6 has explored and synthesized existing knowledge and experiences on data quality assurance frameworks (DQAFs) in the context of cross-border sharing of federated secondary use health data. Our aim was to identify good practices within this area and develop a first set of corresponding recommendations. The recommendations reflect the synthesis of results from three parallel lines of work, each utilizing a different methodology: a) thematic workshops and partner meetings, b) analysis of existing data-sharing initiatives, and c) a scoping literature review. Our analysis of literature and existing health data initiatives suggest that currently deployed DQAFs cannot as such serve as platforms or models for an EHDS-wide data quality solution. There is an agreement in principle, that reliability, relevance, timeliness, coherence, coverage and completeness should be incorporated in a DQAF as measurable dimensions of data quality. We aim towards a working definition of data quality, which will be able to reflect the reality of the data, as well as its fitness for purpose from the perspective of potential users. • We recommend focusing efforts on transparency at the level of data holder institutions across Member States in relation to adoption of regular audits, a well-developed DQAF and clear procedures with regard to processing the data. • In the medium to longer-term EHDS nodes could promote and support the development of a benchmarking process, which will assist data managers and data holder institutions with alignment against a Europe-wide approach to measuring data quality within and across Member States.

Assessing the quality of bibliographic data sources for measuring international research collaboration

Abstract Measuring international research collaboration (IRC) is essential to various research assessment tasks but the effect of various measurement decisions, including which data sources to use, has not been thoroughly studied. To better understand the effect of data source choice on IRC measurement, we design and implement a data quality assessment framework specifically for bibliographic data by reviewing and selecting available dimensions and designing appropriate computable metrics, and then validate the framework by applying it to four popular sources of bibliographic data: Microsoft Academic Graph, Web of Science (WoS), Dimensions, and the ACM Digital Library. Successful validation of the framework suggests it is consistent with the popular conceptual framework of information quality proposed by Wang and Strong (1996) and adequately identifies the differences in quality in the sources examined. Application of the framework reveals that WoS has the highest overall quality among the sets considered; and that the differences in quality can be explained primarily by how the data sources are organized. Our study comprises a methodological contribution that enables researchers to apply this IRC measurement tool in their studies and makes an empirical contribution by further characterizing four popular sources of bibliographic data and their impact on IRC measurement.

No-Reference Framework for Image Quality Assessment

The technological advances in computer and communication devices have been an important factor in the appearance for the applications of visual communication. Image quality assessment is one of the pillars for these applications. It is a measure to assess the quality of images. There are many degradations in image quality that occur during the reproduction and transmission of the image. This paper aims to introduce a framework of No-Reference Image Quality Assessment. The framework provides a general estimation for three types of image distortions which are sharpness, blackness, and noisiness. Based on this framework, experiments have been conducted by the use of two datasets (LIVE database, CSIQ database). The results of the experiments have shown that we have introduced a more precise framework of No-Reference Image Quality Assessment.

A neural network based framework for effective laparoscopic video quality assessment.

Video quality assessment is a challenging problem having a critical significance in the context of medical imaging. For instance, in laparoscopic surgery, the acquired video data suffers from different kinds of distortion that not only hinder surgery performance but also affect the execution of subsequent tasks in surgical navigation and robotic surgeries. For this reason, we propose in this paper neural network-based approaches for distortion classification as well as quality prediction. More precisely, a Residual Network (ResNet) based approach is firstly developed for simultaneous ranking and classification task. Then, this architecture is extended to make it appropriate for the quality prediction task by using an additional Fully Connected Neural Network (FCNN). To train the overall architecture (ResNet and FCNN models), transfer learning and end-to-end learning approaches are investigated. Experimental results, carried out on a new laparoscopic video quality database, have shown the efficiency of the proposed methods compared to recent conventional and deep learning based approaches.

Development of entropy and deviation-based water quality index: Case of river Ganga, India

• Development of an objective and rational framework ( M E D -WQI) for water quality assessment that uses entropy and deviation-based approach. • Evaluation of M ED -WQI using synthetic dataset by comparing with existing Composite WQI. • Assessment of river Ganga's water quality status using M ED -WQI. In this study, a comprehensive multiplicative water quality index, M ED -WQI, for surface water quality assessment has been developed that uses sub-index functions based on deviation from maximum contaminant level of respective parameters. The parametric weights in M ED -WQI were computed using entropy based approach to eliminate subjectivity. Using a synthetic dataset the performance of M ED -WQI has been compared with Composite WQI (CWQI) that is based on Saaty’s analytical hierarchical process. The results indicated that in comparison to CWQI, M ED -WQI approach provides an objective and rational framework that eliminates clustering effect in providing the water quality status. Further, M ED -WQI has been applied to an exhaustive water quality dataset of river Ganga, one of the major perennial rivers of India. Out of 224 sampling locations, 167 sites have been associated with excellent or good water quality class whereas 57 sites are identified with either fair, poor, or heavily-polluted water quality class. Furthermore, the water quality classes correlated well with the type of anthropogenic activities carried out at the site.

Framework to assess the quality of mHealth apps: a mixed-method international case study protocol

IntroductionHealthcare professionals (HCPs) often recommend their patients to use a specific mHealth app as part of health promotion, disease prevention and patient self-management. There has been a significant growth in...

Data science and IoT based mobile monitoring framework for hyper-local PM2.5 assessment in urban setting

Ambient air quality shows a high degree of spatiotemporal variation. It is known to impact human life and health. Obtaining meaningful estimates of human exposure to air pollutants is key to mitigating its ill effects. In order to ascertain individual exposure, pollutant information is required at high resolution. Current air quality monitoring approaches utilize static monitoring stations that are sparsely distributed across a city which in turn results in poor spatial resolution. Such stations allow only for gross assessment of air quality across a city. This work proposes a comprehensive framework for hyperlocal assessment of ambient air quality through a mobile monitoring approach. PM2.5 concentration is considered in this work as a specific air pollutant to demonstrate the framework. In order to test the efficacy of the approach, two case studies were undertaken in the city of Chennai, India as a part of this work. The first study aims at locating diurnal, spatio-temporal PM2.5 hotspots in an urban environment. These hotspots are then associated with daily anthropogenic activity. The second study enables an event centric understanding of spatio-temporal short-lived extremities in PM2.5 concentration. The results are then retrospectively associated with land use profile and anthropological activities. The mobile sensing paradigm provides a unique perspective in the assessment of such localized events. This micro-level assessment offers significant insights on the spatio-temporal variations within a city in a regular case and an extreme, event centric case. Real world applications of hyperlocal air quality assessment, such as least exposure route estimation, are also explored at the end of this work.

Monitoring healthcare improvement for mothers and newborns: A quantitative review of WHO/UNICEF/UNFPA standards using Every Mother Every Newborn assessment tools.

BackgroundAssessment tools with the ability to capture WHO/UNICEF/UNFPA standard quality-of-care measures are needed. This study aimed to assess the ability of Every Mother Every Newborn (EMEN) tools to capture WHO/UNICEF/UNFPA maternal and newborn quality improvement standard indicators.MethodsA quantitative study using the EMEN quality assessment framework was applied. The six EMEN tools were compared with the WHO/UNICEF/UNFPA maternal and newborn quality improvement standards. Descriptive statistics analysis was carried out with summaries using tables and figures.ResultsOverall, across all EMEN tools, 100% (164 of 164) input, 94% (103 of 110) output, and 97% (76 of 78) outcome measures were assessed. Standard 2 measures, i.e., actionable information systems, were 100% (17 of 17) completely assessed by the management interview, with 72% to 96% of standard 4–6 measures, i.e., client experiences of care, fulfilled by an exit interview tool.ConclusionThe EMEN tools can reasonably measure WHO/UNICEF/UNFPA quality standards. There was a high capacity of the tools to capture enabling policy environment and experiences of care measures not covered in other available tools which are used to measure the quality of care.

Semi-Supervised Action Quality Assessment With Self-Supervised Segment Feature Recovery

Action Quality Assessment aims to evaluate how well an action performs. Existing methods have achieved remarkable progress on fully-supervised action assessment. However, in real-world applications, with expert’s experience, it is not always feasible to manually label all samples. Therefore, it is important to study the problem of semi-supervised action assessment with only a small amount of samples annotated. A major challenge for semi-supervised action assessment is how to exploit the temporal pattern from unlabeled videos. Inspired by the temporal dependencies of the action execution, we propose a self-supervised learning on the unlabeled videos by recovering the feature of a masked segment of an unlabeled video. Furthermore, we leverage adversarial learning to align the representation distribution of the labeled and the unlabeled samples to close their gap in the sample space since unlabeled samples always come from unseen actions. Finally, we propose an adversarial self-supervised framework for semi-supervised action quality assessment. The extensive experimental results on the MTL-AQA and the Rhythmic Gymnastics datasets will demonstrate the effectiveness of our framework, achieving the state-of-the-art performances of semi-supervised action quality assessment.

Automated Vehicle Detection Systems Data as a Promising Approach for Roadway Construction Planning: The Case of Dallas, Texas, Highway System

Construction activities on roadway infrastructure have adverse impacts on traffic safety and mobility because of often needed lane and shoulder closures. Existing approaches for planning roadway construction operations primarily rely on general guidelines that do not consider the unique traffic demand patterns of individual roadways. This study proposes utilizing data collected through automated vehicle detection systems (AVDS)— big AVDS data—to enable more informed, data-driven construction planning. To operationalize this approach, the study develops an integrated framework for assessing quality (e.g., validity, completeness) and aggregating AVDS data at various temporal and spatial levels. The framework is demonstrated using big AVDS data for the Dallas, Texas, highway network. The analyses demonstrate how integrating data completeness with performance measures’ behavioral patterns can provide improved understanding of the accuracy of information provided by the AVDS data. The traffic pattern analyses reveal evening peaks that are greater than the morning peaks, remaining so past the typical off-peak time according to the general lane closure guidelines. These findings demonstrate that historically used time periods for construction planning may underestimate demand at traditionally off-peak times. Therefore, the framework demonstration emphasizes the significance of using granular traffic data that considers the specifics of individual roadways to better inform construction planning. The study contributes to advances in construction and engineering management by (i) extending existing roadway construction planning approaches through using higher granular traffic data to better inform roadway construction planning, and (ii) developing a much needed integrated quality assessment and aggregation framework that can operationalize this use.

Primary Health Care Quality Assessment Frameworks: State of the Art Review

Context: Quality Assessment Frameworks (QAFs) are essential in monitoring progress in the primary health care (PHC) system. Different QAFs are used in countries to assess PHC quality. Objectives: This study aimed to review and compare the QAFs and highlight the most frequent quality indicators and dimensions. Methods: This state-of-the-art review was conducted on PHCQAFs. Required data were collected through search in Scopus, Web of Science, and PubMed databases, World Health Organization and World Bank websites, and Ministry of Health websites up to January 2022. The main keywords were quality, “primary healthcare”, PHC, “primary care”, “primary health services”, “basic healthcare”, assessment, evaluation, monitoring, measurement, improvement, indicator, OR index, pattern, framework, and model. Comparative tables were used to compare the defined quality dimensions (QDs) and quality assessment indicators (QAIs). Results: Finally, 14 PHCQAFs were retrieved, containing 94 QDs and 785 QAIs. Three PHCQAFs were proposed at the international level and others at the national level. Accessibility, coordination, and safety were the most frequent QDs, and QAIs related to smoking, alcohol and substance abuse, diabetes care, vaccination, chronic heart disease care, respiratory/infectious disease care, hypertension care, population coverage, community participation, customer satisfaction, maternal and child health, adverse event, health information management, staff empowerment, referral system, and patient rights were the most frequent among PHCQAFs. Conclusions: The current study illustrates the similarities and differences between PHCQAFs and highlights important QDs and QIs in PHC. Also, it provides a ready way for health policymakers to address key quality aspects that can help countries accelerate progress in the quality of PHC.

Database quality assessment in research in paramedicine: a scoping review protocol

IntroductionThe paramedic practice environment presents unique challenges to data documentation and access, as well as linkage to other parts of the healthcare system. Variable or unknown data quality can influence...

Local News in Colorado: Comparing Journalism Quality Across Four Counties

Local journalism is a pillar of democratic societies, and its role becomes more critical during crises. But despite its critical importance, local news has faced immense challenges that jeopardize its sustainability in the U.S. This study examines the Colorado media ecology by comparing local news sources across four different counties during Summer 2020 and exploring the factors behind similarities and differences in coverage. Building on Napoli et al.’s (2017) framework to assessing the health of local news, the study uses content analysis to examine original, local reporting and coverage of critical information needs as well as type of framing in over 600 online stories appearing on the home pages of all news sources in the four counties. The findings reveal that the Colorado journalism ecosystem post-Covid outpaces U.S. local news in quality in the pre-Covid era yet aligns with disturbing trends pointing to inequities and disparities. In other words, rural, poorer, and more racially and ethnically diverse Colorado communities tend to have weaker news ecosystems and are more likely to become news deserts. The study also introduces thematic coverage as a necessary dimension to add to journalism quality assessment frameworks and discusses several approaches to salvage local news.

Fingermark quality assessment framework with classic and deep learning ensemble models

The quality assessment of fingermarks (latent fingerprints) is an essential part of a forensic investigation. It indicates how valuable the fingermarks are as forensic evidence, it determines how they should be further processed, and it correlates with the likelihood of successful identification, i.e., finding a matching fingerprint in a reference database. Since the environments in which fingermarks are found are not controlled, this task proves challenging even with modern machine learning solutions. In this work, we propose a predictive framework for automated fingermark quality assessment (AFQA). With this iteration of AFQA, we bridge the gap between the classic machine learning approach with handcrafted features and the modern deep learning paradigm, evaluate the advantages and disadvantages of these methodologies, and provide the rationale and direction for future development of AFQA methods. We present a significantly improved AFQA toolbox and provide a quality aggregation method capable of fusing together multiple predicted quality values from an ensemble of quality assessment models. The proposed ensemble approach provides improved prediction performance while reducing processing time compared to existing state-of-the-art solutions.

Quality assessment framework to rank software projects

Quality assessment framework to rank software projects

Service Innovation and Quality Assessment of Industry 4.0 Microservice through Data Modeling and System Simulation Evaluation Approaches

This study proposes a system construction approach under Industry 4.0 infrastructure that is validated by the proposed framework of microservice quality assessment with framework with data modeling and simulation methodology to achieve innovation and value cocreation goals. The framework, which combines a dynamic process flow with service-dominant logic design and reliability assessment using a multilayer perceptron (MLP) prediction model can assist decision makers in optimizing their service innovation and decision-making processes. The service innovation and evaluation approaches have implications for optimizing the corporation cooperation. The corporation can form a much more comprehensive manufacturing infrastructure or system by considering the requirements and assessment results of third parties. To help the corporation redefine its value proposition and system structure, we must examine the system interaction between different hierarchical layers within the Industry 4.0 system infrastructure. This study used a production dataset from the NASDAQ-listed electronics corporation and two top German and Japanese automobile firms. The proposed system framework had already been validated and introduced to improve 12% of service quality. The system integrated with anticipated functions will accelerate service innovation and optimization by combining MLP and Kaplan–Meier estimation methodologies by extracting the characteristics of realistic datasets.

A quantitative framework for health‐care service quality assessment in India

PurposeThe present situation of COVID-19 pandemic has put the health-care systems under tremendous stress and stringent tests for their ability to offer expected quality of health-care services, as it decides the sustainability and growth of health-care service providers. This study aims to deliver a quantitative framework for service quality assessment in the health-care industry by classifying the health-care service quality parameters into four balanced scorecard (BSC) perspectives.Design/methodology/approachTo determine the service quality for the Indian health-care system, decision-making trial and evaluation laboratory and analytical network process are integrated in a fuzzy environment to contemplate the interaction among BSC perspectives and respective performance measures.FindingsThe results indicate “internal processes” perspective assumes the key role within BSC perspectives, while performance measures “nursing staff turnover” and “staff training” play the key roles. The results also signify that “patient satisfaction” is the most vital issue and can be strongly influenced by measures belonging to the “learning and growth” perspective. In “learning and growth” perspective, “staff training” is the most decisive criteria, very highly influencing “patient satisfaction”, highly influencing “profitability,” “change of cost per patient (both in and out patients)” and “outpatient waiting time” while moderately influencing “staff satisfaction,” “bed occupancy” and “nursing staff turnover”. Moreover, “staff training” criteria have a positive influence on “nursing staff turnover.”Originality/valueThe contributions of this study are in two folds in the domain of quantification of service quality for the health-care system. First, it delivers an assessment framework for Indian health-care service quality. Second, it demonstrates an application of the framework for a case situation and validates the proposed framework.