Quality Assessment Framework Research Articles

Prosocial digital games for youth: A systematic review of interventions

Digital games aiming to improve children health behaviours have received increasing attention from scholars and practitioners due to their societal importance. However, there remains a very limited understanding on the scope, application and relevance of prosocial games on youth. This review systematically investigates the literature base of empirical examinations of prosocial digital game interventions for children and explores the measures applied and their effectiveness. According to PRISMA guidelines, a systematic literature search was performed using five academic databases (EBSCO, Ovid, ProQuest, Scopus and Web of Science). To assess risk of bias in the analysis of included studies Cochrane developed quality assessment framework RoB2 and ROBINS-I were applied. Eleven studies met the inclusion criteria. Discussion of the reviewed studies highlights a heterogeneity of measures and lack of a consistent framework, mixed but promising results, and implications of skills and attitudes impacting prosocial behaviours. Finally, it proposes an agenda for future research on prosocial digital games focused on implementing clear theoretical frameworks and analysing key game design attributes to enhance prosocial digital games’ effectiveness.

A low-complexity psychometric curve-fitting approach for the objective quality assessment of streamed game videos

The increasing popularity of video gaming competitions, the so called eSports, has contributed to the rise of a new type of end-user: the passive game video streaming (GVS) user. This user acts as a passive spectator of the gameplay rather than actively interacting with the content. This content, which is streamed over the Internet, can suffer from disturbing network and encoding impairments. Therefore, assessing the user’s perceived quality, i.e the Quality of Experience (QoE), in real-time becomes fundamental. For the case of natural video content, several approaches already exist that tackle the client-side real-time QoE evaluation. The intrinsically different expectations of the passive GVS user, however, call for new real-time quality models for these streaming services. Therefore, this paper presents a real-time Reduced-Reference (RR) quality assessment framework based on a low-complexity psychometric curve-fitting approach. The proposed solution selects the most relevant, low-complexity objective feature. Afterwards, the relationship between this feature and the ground-truth quality is modelled based on the psychometric perception of the human visual system (HVS). This approach is validated on a publicly available dataset of streamed game videos and is benchmarked against both subjective scores and objective models. As a side contribution, a thorough accuracy analysis of existing Objective Video Quality Metrics (OVQMs) applied to passive GVS is provided. Furthermore, this analysis has led to interesting insights on the accuracy of low-complexity client-based metrics as well as to the creation of a new Full-Reference (FR) objective metric for GVS, i.e. the Game Video Streaming Quality Metric (GVSQM).

Cognitive Framework of Food Quality Assessment in IoT-Inspired Smart Restaurants

Information and communication technology (ICT) empowered by the Internet of Things (IoT) and fog–cloud paradigm has been widely adopted in several domains of logistics, healthcare, and agriculture. Inspired by the enormous benefits of IoT technology, this research proposes a novel notion of smart restaurants for assessing the food quality using the game theory. Specifically, this research presents a smart framework for food quality assessment inside restaurants. Real-time data are acquired using numerous IoT devices for food quality assessment. The data are communicated to the fog nodes backed by the cloud platform. This enables the time-sensitive analysis of food quality for formalizing a quantifiable measure, i.e., food quality estimate (FQE) using the Bayesian modeling technique. FQE presents a quantification factor for assessing the food quality over temporal patterns in terms of the quality support index (QSI). This is followed by the 2-player game model for effective food quality assessment. The presented model is validated by deploying it over four data sets. Based on the comparative analysis with other decision-making techniques, the presented technique has registered superior performance in terms of temporal effectiveness, classification efficacy, statistical efficiency, and reliability.

Sediment metal enrichment and ecological risk assessment of ten ports and estuaries in the World Harbours Project

Ten global harbours were assessed for sediment quality by quantifying the magnitude of anthropogenic change and ecological risk. Anthropogenic change (enrichment) was high for Derwent River and Sydney estuary, moderate for Santander Harbour, Rio de Janeiro and Dublin Port, slight for Hong Kong, minimal for Darwin. All 10 enrichment indices used showed similar results. Derwent River sediment was rated at high ecological risk, followed by Sydney and Santander estuaries with moderate risk. Auckland and Darwin sediments exhibited minimal ecological risk and sediment in the remaining harbours (Dublin, Hong Kong, Ravenna, Ria de Vigo and Rio de Janeiro) were assessed at slight ecological risk.The extraordinary variety of environments and types/quantities/qualities of data investigated resulted in as much a critique and development of methodology, as an assessment of human impact, including unique techniques for elemental normalisation and contaminant classification. Recommendations for an improved technical framework for sediment quality assessment are provided.

Visual attention-aware quality estimation framework for omnidirectional video using spherical Voronoi diagram

Omnidirectional video (ODV) enables viewers to look at every direction from a fixed point and provides a much more immersive experience than traditional 2D video. Assessing the video quality is important for delivering ODV to the end-user with the best possible quality. For this goal, two aspects of ODV should be considered. The first is the spherical nature of ODV and the related projection distortions when the ODV is stored in a planar format. The second is the interactive look-around consumption nature of ODV. Related to this aspect, visual attention, that identifies the regions that attract the viewer’s attention, is important for ODV quality assessment. Considering these aspects, in this paper, we study in particular objective full-reference quality assessment for ODV. To this end, we propose a quality assessment framework based on the spherical Voronoi diagram and visual attention. In this framework, a given ODV is subdivided into multiple planar patches with low projection distortions using the spherical Voronoi diagram. Afterwards, each planar patch is analyzed separately by a quality metric for traditional 2D video, obtaining a quality score for each patch. Then, the patch scores are combined based on visual attention into a final quality score. To validate the proposed framework, we create a dataset of ODVs with scaling and compression distortions, and conduct subjective experiments in order to gather the subjective quality scores and the visual attention data for our ODV dataset. The evaluation of the proposed framework based on our dataset shows that both the use of the spherical Voronoi diagram and visual attention are crucial for achieving state-of-the-art performance.

Quality assessment framework for open government data

Purpose To gain an in-depth understanding and provide direction to governments on their quality measurement practices related to open government data (OGD), this paper aims to develop a common frame of reference for quality assessment of OGD. Design/methodology/approach Qualitative meta-synthesis was used to synthesize previous studies on the quality measurement of OGD. This paper applies a meta-synthesis approach to integrate 10 qualitative studies into a common frame of reference for quality assessment of OGD. Findings Based on a seven-step meta-synthesis, the paper proposes a common frame of reference for quality assessment of OGD, which includes six indicators, namely, accuracy, accessibility, completeness, timeliness, consistency and understandability. Originality/value A common frame of reference for quality assessment of OGD will help researchers better understand the quality assessment of OGD and government agencies to improve the quality of OGD that they publish.

Recommendations for Tissue Microarray Construction and Quality Assurance.

Tissue microarrays (TMAs) are important tools to conserve precious tissue resources from increasingly smaller biopsies and to control experimental costs and variation across sample sets. The quality assurance assessment of TMA materials created at centralized biobanks has not been standardized. Herein, we outline 2 processes for the construction of TMAs ("recipient block" and "tape" methods) and the associated preconstruction quality control measures (pathology review, protein and RNA assessment, map creation, and storage conditions) developed by the AIDS Cancer Specimen Resource (ACSR) Network's Science and Technology Core. These steps provide a suggested framework for quality assessment that allows end-users, receiving materials from tissue banks, confidence in their experimental results.

Learning Effective Skeletal Representations on RGB Video for Fine-Grained Human Action Quality Assessment

In this paper, we propose an integrated action classification and regression learning framework for the fine-grained human action quality assessment of RGB videos. On the basis of 2D skeleton data obtained per frame of RGB video sequences, we present an effective representation of joint trajectories to train action classifiers and a class-specific regression model for a fine-grained assessment of the quality of human actions. To manage the challenge of view changes due to camera motion, we develop a self-similarity feature descriptor extracted from joint trajectories and a joint displacement sequence to represent dynamic patterns of the movement and posture of the human body. To weigh the impact of joints for different action categories, a class-specific regression model is developed to obtain effective fine-grained assessment functions. In the testing stage, with the supervision of the action classifier’s output, the regression model of a specific action category is selected to assess the quality of skeleton motion extracted from the action video. We take advantage of the discrimination of the action classifier and the viewpoint invariance of the self-similarity feature to boost the performance of the learning-based quality assessment method in a realistic scene. We evaluate our proposed method using diving and figure skating videos of the publicly available MIT Olympic Scoring dataset, and gymnastic vaulting videos of the recent benchmark University of Nevada Las Vegas (UNLV) Olympic Scoring dataset. The experimental results show that the proposed method achieved an improved performance, which is measured by the mean rank correlation coefficient between the predicted regression scores and the ground truths.

Game theory based framework of smart food quality assessment

AbstractInternet of Things (IoT) technology backed by fog‐cloud computing has conceptualized the notion of Industry 4.0. Motivated from this, smart restaurants presents a novel vision of provisioning effective food quality assessment based on game‐theoretic decision‐modeling techniques. This research presents an IoT‐inspired framework to analyze restaurants and food‐hubs for food quality perspective. Specifically, IoT technology is used to acquire ubiquitous data inside restaurant environment that can impact food quality directly and indirectly. This data is analyzed over the fog‐cloud computing platform to formulate a quantification measure in terms of probability of food quality using Bayesian modeling technique. In addition to this, food quality assurance index is estimated to present a unifying factor for food quality assessment over temporal scale. Finally, a three‐player game‐model is presented for food quality‐based decision making by the customers, restaurant managers, and monitoring officials. For validation purposes, the proposed model is deployed in four restaurants of Amritsar, India. The comparative analysis was performed with state‐of‐the‐art decision‐making modeling techniques, which showed the superiority of the presented technique in terms of temporal efficiency, statistical performance enhancement, correlation analysis, stability, and reliability. Moreover, a mathematical evaluation has been performed to depict the overall performance enhancement for the proposed decision‐making.

Young people's perceived service quality and environmental performance of hybrid electric bus service

While cities are moving towards environmentally friendly public transport such as electrification of public buses, the attractiveness of such innovative solutions among the bus users is yet to be explored. We study young people’s perception of hybrid electric buses (HEBs) incorporating environmental performance in the bus service quality assessment framework. We collect data using a structured survey questionnaire from young people aged between 18 and 34 years, living in Southern Norway. Methodology-wise, we use the covariance-based structural equation model (SEM). As of contribution, we examine service quality of HEBs, using contextually modified measurement scales adopted from the SERVQUAL framework. We introduce a four-item perceived environmental performance construct in the context of HEBs. We find that tangible features of HEBs, bus service provider’s empathy and perceived environmental performance of HEBs have a significant positive association with customer satisfaction, and customer satisfaction is positively associated with life satisfaction of young people. Finally, a post hoc analysis using multi-group confirmatory factor analysis reveals that the levels of young people’s perceived service with bus drivers’ quality, empathy of the bus service provider and customer satisfaction with HEBs are higher in colder temperature.

Challenges Frequently Encountered in the Secondary Use of Electronic Medical Record Data for Research.

The wide adoption of electronic medical records and subsequent availability of large amounts of clinical data provide a rich resource for researchers. However, the secondary use of clinical data for research purposes is not without limitations. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review to identify current issues related to secondary use of electronic medical record data via MEDLINE and CINAHL databases. All articles published until June 2018 were included. Sixty articles remained after title and abstract review, and four domains of potential limitations were identified: (1) data quality issues, present in 91.7% of the articles reviewed; (2) data preprocessing challenges (53.3%); (3) privacy concerns (18.3%); and (4) potential for limited generalizability (21.7%). Researchers must be aware of the limitations inherent to the use of electronic medical record data for research and consider the potential effects of these limitations throughout the entire study process, from initial conceptualization to the identification of adequate sources that can provide data appropriate for answering the research questions, analysis, and reporting study results. Consideration should also be given to using existing data quality assessment frameworks to facilitate use of standardized data quality definitions and further efforts of standard data quality reporting in publications.

A systemic framework for crowdsourced test report quality assessment

In crowdsourced mobile application testing, crowd workers perform test tasks for developers and submit test reports to report the observed abnormal behaviors. These test reports usually provide important information to improve the quality of software. However, due to the poor expertise of workers and the inconvenience of editing on mobile devices, some test reports usually lack necessary information for understanding and reproducing the revealed bugs. Sometimes developers have to spend a significant part of available resources to handle the low-quality test reports, thus severely reducing the inspection efficiency. In this paper, to help developers determine whether a test report should be selected for inspection within limited resources, we issue a new problem of test report quality assessment. Aiming to model the quality of test reports, we propose a new framework named TERQAF. First, we systematically summarize some desirable properties to characterize expected test reports and define a set of measurable indicators to quantify these properties. Then, we determine the numerical values of indicators according to the contained contents of test reports. Finally, we train a classifier by using logistic regression to predict the quality of test reports. To validate the effectiveness of TERQAF, we conduct extensive experiments over five crowdsourced test report datasets. Experimental results show that TERQAF can achieve 85.18% in terms of Macro-average Precision (MacroP), 75.87% in terms of Macro-average Recall (MacroR), and 80.01% in terms of Macro-average F-measure (MacroF) on average in test report quality assessment. Meanwhile, the empirical results also demonstrate that test report quality assessment can help developers handle test reports more efficiently.

Data quality assessment framework for critical raw materials. The case of cobalt

Critical Raw Materials (CRMs) require a deep understanding of their societal metabolism, for which robust data and information are needed. However, despite the efforts to build reliable data, some CRMs such as cobalt, are still characterised by lack of data harmonization, lack of connection between datasets, and significant data unavailability. Together with data gaps filling, data quality is a crucial aspect to improve Material Flow Analysis (MFA) and Criticality Assessment (CA). Nevertheless, most of the methodologies for Data Quality Assessment (DQA) are not designed for these tools, but for others, e.g. life cycle assessment. The current research addresses the following challenges; a better understanding of the societal metabolism of CRMs; the development and implementation of DQA in MFA and CA; and a better understanding of the available data related to current cobalt flows in the EU technosphere. The underlying life cycle phases of CRMs within the technosphere were identified, together with 15 key parameters. A new DQA matrix was developed, which was subsequently applied to the full dataset collected for cobalt. The dataset was built considering seven high-end applications of cobalt. More than 300 values were gathered, which were analysed in function of different aspects, such as the country/region, and year. Through the data analysis and the application of the DQA framework, data gaps were identified due to low availability and/or low quality. It was concluded that the main deficiency of cobalt data is its reliability, due to lack of information regarding its generation method, and the incomplete stakeholder coverage.

Assessment Framework for Natural Groundwater Contamination in Arid Regions: Development of Indices and Wells Ranking System Using Fuzzy VIKOR Method

Limited groundwater resources in arid regions have been found polluted for drinking purpose due to the presence of natural minerals and radioactive substances, in the sub-soils, higher than the drinking water quality standards. Municipalities in these regions are spending extensive resources to transport (from well fields) and treat this raw water to provide safe water to the community. Regular monitoring of various physical, chemical, and radioactive water quality parameters (WQPs) in raw water generates large datasets, which makes it difficult to come up with convenient findings for both the decision-makers and general public. A hierarchical water quality assessment framework develops three sub-indices, an overall water quality index, and a system for ranking of groundwater wells. Fuzzy analytical hierarchy process (fuzzy-AHP) establishes the importance weights of different WQPs and the sub-indices based on their impacts on human health, treatment processes, distribution system infrastructure, and irrigation applications. Fuzzy VIekriterijumsko KOmpromisno Rangiranje (fuzzy-VIKOR) method aggregates the WQPs’ performance for each well and ranks all the wells in a well field based on their overall pollution levels, i.e., remoteness from the applicable standards. For evaluating the pragmatism of the framework, data of 11 WQPs were obtained for 39 wells operating in three different well fields located along the boundaries and the central part of Buraydah, Qassim, Saudi Arabia. Spatial water quality maps showing physical, chemical, radioactive, and overall water quality assessment results revealed that the oldest well field located in the middle of the city outperforms the other two more recently developed well fields with lesser anthropogenic activities in their catchments. These findings testify that the primary source of contamination in deep aquifers is the natural sub-soil condition. The water quality indices will be useful to demonstrate the current situation of groundwater quality in Qassim Region and will facilitate the decision-makers for defining the intended uses of raw water sources (i.e., drinking, unrestricted irrigation, and restricted irrigation) and rehabilitation and renewal planning of the groundwater wells. The framework is applicable in the Kingdom of Saudi Arabia (KSA), Gulf Region, and elsewhere for groundwater quality assessment with desired modifications.

Quality assessment framework for video contextualisation of personal videos

The world is witnessing rapid transformations in hardware technology. This will keep on improving day by day. The processing power of every handheld device is significantly improved as well as the storage capacity is increased. With all these advancements, the personal video capture and usage of videos have tremendously increased across many applications. The quality assessment of personal videos has become very important. It is a vital task to design a model for assessing the video quality. A novel methodology for detecting damaged video frames is proposed here. The primary objective of the research is to detect uni-coloured frames and frames with ice effect. The novel histograms bin comparison technique is proposed for inter- and intraframe analysis. The shakiness of the video is calculated using motion estimation. The video quality is also assessed using blur detection as well as contrast calculation to spot useful portion in the video. The proposed framework generates the video quality metadata and supports the contextualisation process.

Assessing administrative service quality in higher education: development of an attribute-based framework (HEADSQUAL) in a Brazilian University

ABSTRACT This paper proposed a valid and reliable perceived quality assessment framework of administrative services in Higher Education Institutions (HEI) in the Brazilian context. The method was in two main stages: (i) map a set of quality attributes and dimensions for administrative services in HEI, (ii) build and test the perceived quality assessment framework and data collection instrument of the administrative services in a HEI. The theoretical contributions of this manuscript were the differentiated scope with a focus on administrative services in HEI, since most of the previous research addresses the area of education (final service), and the development of a specific framework for assessing administrative service quality. The managerial implication highlight was that this study can be reapplied to other HEI to improve existing services quality assessment and control instruments. Adopting the proposed structure, the HEI could promote improvements in administrative services quality and advance in the quest for excellence.

Video quality assessment using space–time slice mappings

We develop a full-reference (FR) video quality assessment framework that integrates analysis of space–time slices (STSs) with frame-based image quality measurement (IQA) to form a high-performance video quality predictor. The approach first arranges the reference and test video sequences into a space–time slice representation. To more comprehensively characterize space–time distortions, a collection of distortion-aware maps are computed on each reference–test video pair. These reference-distorted maps are then processed using a standard image quality model, such as peak signal-to-noise ratio (PSNR) or Structural Similarity (SSIM). A simple learned pooling strategy is used to combine the multiple IQA outputs to generate a final video quality score. This leads to an algorithm called Space–TimeSlice PSNR (STS-PSNR), which we thoroughly tested on three publicly available video quality assessment databases and found it to deliver significantly elevated performance relative to state-of-the-art video quality models. Source code for STS-PSNR is freely available at: http://live.ece.utexas.edu/research/Quality/STS-PSNR_release.zip.

Proposed Framework for Quality Assessment of E-government Portals in Saudi Arabia

Proposed Framework for Quality Assessment of E-government Portals in Saudi Arabia

Research on the parallelization of image quality analysis algorithm based on deep learning

Image quality assessment is an indispensable in computer vision applications, such as image classification, image parsing. With the development of Internet, image data acquisition becomes more conveniently. However, image distortion is inevitable due to imperfect image acquisition system, image transmission medium and image recording equipment. Traditional image quality assessment algorithms only focus on low-level visual features such as color or texture, which could not encode high-level features effectively. CNN-based methods have shown satisfactory results in image quality assessment. However, existing methods have problems such as incomplete feature extraction, partial image block distortion, and inability to determine scores. So in this paper, we propose a novel framework for image quality assessment based on deep learning. We incorporate both low-level visual features and high-level semantic features to better describe images. And image quality is analyzed in a parallel processing mode. Experiments are conducted on LIVE and TID2008 datasets demonstrate the proposed model can predict the quality of the distorted image well, and both SROCC and PLCC can reach 0.92 or higher.

Free- Reference Image Quality Assessment Framework Using Metrics Fusion and Dimensionality Reduction

Free- Reference Image Quality Assessment Framework Using Metrics Fusion and Dimensionality Reduction