Problem Of Quality Assessment Research Articles

Protein quality assessment with a loss function designed for high-quality decoys.

Motivation: The prediction of a protein 3D structure is essential for understanding protein function, drug discovery, and disease mechanisms; with the advent of methods like AlphaFold that are capable of producing very high-quality decoys, ensuring the quality of those decoys can provide further confidence in the accuracy of their predictions. Results: In this work, we describe Qϵ, a graph convolutional network (GCN) that utilizes a minimal set of atom and residue features as inputs to predict the global distance test total score (GDTTS) and local distance difference test (lDDT) score of a decoy. To improve the model's performance, we introduce a novel loss function based on the ϵ-insensitive loss function used for SVM regression. This loss function is specifically designed for evaluating the characteristics of the quality assessment problem and provides predictions with improved accuracy over standard loss functions used for this task. Despite using only a minimal set of features, it matches the performance of recent state-of-the-art methods like DeepUMQA. Availability: The code for Qϵ is available at https://github.com/soumyadip1997/qepsilon.

ОЦІНКА ЯКОСТІ ЗОБРАЖЕНЬ ЗГОРТКОВОЮ НЕЙРОННОЮ МЕРЕЖЕЮ ПРИ ВИКОРИСТАННІ БАЗИ TID2013

The article is devoted to the problem of automatic image quality assessment by a convolutional neural network when using the common TID2013 image database for training the neural network. The TID2013 database was chosen for the reason that it contains 25 base real-world images, which were distorted from these images using 24 different distortion methods and with 5 distortion levels, creating a sufficiently large database of 3000 images for training the neural network. For each image, an average expert assessment of its quality is given. All input images for the neural network are divided into two groups - the training set and the validation set. We consider two options for solving the problem of building and training a neural network and the corresponding data structure. The first option is reduced to the task of numerical regression using the numerical values of expert assessments. The second option is the task of classifying the training and validation images into 5 classes according to their quality corresponding to distortion levels. Keras and TensorFlow software tools are used to build and study the neural network. The neural network structures and relevant parameters for training each layer are presented, as well as graphs of accuracy changes for training and validation images during training.

Konx: cross-resolution image quality assessment

Scale-invariance is an open problem in many computer vision subfields. For example, object labels should remain constant across scales, yet model predictions diverge in many cases. This problem gets harder for tasks where the ground-truth labels change with the presentation scale. In image quality assessment (IQA), down-sampling attenuates impairments, e.g., blurs or compression artifacts, which can positively affect the impression evoked in subjective studies. To accurately predict perceptual image quality, cross-resolution IQA methods must therefore account for resolution-dependent discrepancies induced by model inadequacies as well as for the perceptual label shifts in the ground truth. We present the first study of its kind that disentangles and examines the two issues separately via KonX, a novel, carefully crafted cross-resolution IQA database. This paper contributes the following: 1. Through KonX, we provide empirical evidence of label shifts caused by changes in the presentation resolution. 2. We show that objective IQA methods have a scale bias, which reduces their predictive performance. 3. We propose a multi-scale and multi-column deep neural network architecture that improves performance over previous state-of-the-art IQA models for this task. We thus both raise and address a novel research problem in image quality assessment.

External assessment of medical education quality: indicative model development considering paradox of skill.

This study proposes an approach to the external evaluation of medical education programs' quality based on a combination of indicators, including international rankings, external stakeholders' input, and independent agencies' assessments. We modify the success equation with a detailed consideration of the skill component and its decomposition into internal and external quality assurance elements along with authority. We carried out a bibliometric analysis regarding the problem of medical education quality assessment in the context of achieving sustainable development goals. We described the calculation model of external quality assessment indicators through the algorithms of independent education quality assurance agencies' activity and rating indicators shown in the modified Mauboussin's equation. The model considers the economic component (the consequence of achievement) of skill, which is expressed in raising funds from external sources to implement educational and scientific activities. The proposed algorithm for assessing the educational program quality can be applied to benchmark educational program components, complete educational programs within the subject area, and the educational institution for different areas. We propose a "financial" model for educational program quality based on the analysis results. The model makes it possible to determine the need for additional focused funding of the educational program based on the individual analysis of the external evaluation criteria of the achievement level. This study analyzes the accreditation results of more than 110 educational programs in 2020 and 8 months of 2021 within the direction 22 "Medicine" (according to the national classification of fields of knowledge) (state and private Ukrainian medical universities).

Correct for Whom? Subjectivity and the Evaluation of Personalized Image Aesthetics Assessment Models

The problem of image aesthetic quality assessment is surprisingly difficult to define precisely. Most early work attempted to estimate the average aesthetic rating of a group of observers, while some recent work has shifted to an approach based on few-shot personalization. In this paper, we connect few-shot personalization, via Immanuel Kant's concept of disinterested judgment, to an argument from feminist aesthetics about the biased tendencies of objective standards for subjective pleasures. To empirically investigate this philosophical debate, we introduce PR-AADB, a relabeling of the existing AADB dataset with labels for pairs of images, and measure how well the existing groundtruth predicts our new pairwise labels. We find, consistent with the feminist critique, that both the existing groundtruth and few-shot personalized predictions represent some users' preferences significantly better than others, but that it is difficult to predict when and for whom the existing groundtruth will be correct. We thus advise against using benchmark datasets to evaluate models for personalized IAQA, and recommend caution when attempting to account for subjective difference using machine learning more generally.

The Uncertainty of Essential Oil as a Cosmetic Product Market: A Market Sociology Perspective

Essential oil as a cosmetic product is believed to be one of the highest-demand commodities, especially when the back to nature is the current leading trend. However, the essential oil market itself faced an uncertain future. The uncertainty problem in this market can be seen from the fluctuating prices and difficulties in assessing the product. The previous studies mostly saw this problem from the supply side and it can be concluded that there is lack of data and information about the market demand for essential oils. Therefore the aim of this study is to analyze the uncertainty of essential oil as a cosmetic product market. This study tried to answer the central initial problem of market sociology, the constitution of demand. Furthermore, the constitution of demand will discuss about the problem of price and quality assessment of this commodity. Library research was used by the author in this article. The findings shows that the uncertainty problem in the constitution of demand can be seen in the fluctuating price of essential oils and the difficulties faced by experts and non-experts assessing the quality of essential oils. The reason for its difficulties is that the commodities are only assessed from their material qualities. Previous studies about cosmetic and Indonesian Traditional Herbal Medicine (Jamu) and several documents of essential oil reports, will enlarge the constitution of demand side of this essential oil market.

Evaluation of the key factors of effective leadership in the process of implementing public sector reforms of the Republic of Serbia

Public sector reform is one of the Government of the Republic of Serbia strategic goals. The strategy envisages the improvement of the human resources management system, which includes the development of professional capacities. In order for public sector reform to achieve its contribution, it is necessary to develop effective leadership at all levels. The effectiveness of the leadership process is influenced by many factors. One of the factors refers to the characteristics of the leader, his abilities and skills to make and implement optimal decisions. The decision-making process itself also depends on the circumstances, i.e. the situation in which the decision is made, as well as the environment that affects the decisionmaking process. There are situations that are certain, where the decision maker has sufficient information and can immediately see the effects of each alternative before implementation. In that case, decision-making is significantly facilitated, because it boils down to the technical problem of quality assessment of alternatives and their comparison. But, in most cases, the circumstances, that is, the environment is uncertain and complex. The decision-maker in such situations usually does not have enough information and therefore it is difficult or impossible to check their effectiveness and develop potential alternatives. The main goal of the work is to use the new multicriteria decision-making method PIPRECIA to evaluate and define the importance of key factors influencing the behavior of leaders and contributing to the effectiveness of the leadership process important for the implementation of reforms in the Republic of Serbia public sector. The results of this method application indicate that the key factor influencing the leader's behavior is C5-Characteristics of the leaders. The results provide important information to managers of public organizations on how to manage change more effectively.

Quality Assessment of UGC Videos Based on Decomposition and Recomposition

The prevalence of short-video applications imposes more requirements for video quality assessment (VQA). User-generated content (UGC) videos are captured under an unprofessional environment, thus suffering from various dynamic degradations, such as camera shaking. To cover the dynamic degradations, existing recurrent neural network-based UGC-VQA methods can only provide implicit modeling, which is unclear and difficult to analyze. In this work, we consider explicit motion representation for dynamic degradations, and propose a motion-enhanced UGC-VQA method based on decomposition and recomposition. In the decomposition stage, a dual-stream decomposition module is built, and VQA task is decomposed into single frame-based quality assessment problem and cross frames-based motion understanding. The dual streams are well grounded on the two-pathway visual system during perception, and require no extra UGC data due to knowledge transfer. Hierarchical features from shallow to deep layers are gathered to narrow the gaps from tasks and domains. In the recomposition stage, a progressively residual aggregation module is built to recompose features from the dual streams. Representations with different layers and pathways are interacted and aggregated in a progressive and residual manner, which keeps a good trade-off between representation deficiency and redundancy. Extensive experiments on UGC-VQA databases verify that our method achieves the state-of-the-art performance and keeps a good capability of generalization. The source code will be available in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Sissuire/DSD-PRO</uri> .

ЦІННІСНІ ВИМІРИ КУЛЬТУРИ ЯКОСТІ ВИЩОЇ ОСВІТИ

The article analyses the changes in the social and cultural context in the functioning of the modern university, which lead to the actualization of the issues of development of quality culture and its assurance. It is identified that the problems of quality assessment arise due to the insufficient articulation of the essence of concept of "quality" and "quality culture" in international standards and national legislation. The purpose of the study is to define the essence and dimensions of quality culture as a necessary element of the mission of a contemporary university. Results. It is noted that in current processes of quality assurance in the higher education and its assessment there is an excessive focus on purely managerial procedures, which results in threats to the cultural mission of the contemporary university and loss of motivation for authentical academic activity. Consideration of complex educational activities through the prism of the quality of learning outcomes and the quality of research, teaching, learning, and assessment processes opens up prospects for theoretical justification of quality requirements with the use of the theories of consequentialism and virtue ethics. The article demonstrates the possibilities of justification the quality in education from the standpoint of the common good of all stakeholders, firstly, as maximizing the instrumental goods of all participants in the educational process and, secondly, as continuous improvement of members of the university community in complex joint activities based on "intrinsic" values. Thus, the university is able to realize its mission, which can combine instrumental "extrinsic" and self-sufficient "intrinsic" values-goals. In this way the understood quality in education allows to broaden the focus of consideration of the essence of quality culture, which is unique for each university. Quality culture, as an element of organizational culture, bears the imprint of the value system of national culture, the value system of the professional activity for which the university prepares future specialists, the value system of subcultures that arise within the university (academic groups, departments, faculties; teachers, students, administration, etc.) Given the multidirectional incorporation of members of the university community into the system of social relations, it is proposed to consider the culture of quality through the prism of four structural elements - value, normative, sign and symbolic, and managerial components. Taking into account the fact the non-homogeneous nature of the quality culture of each university, its assessment remains problematic both by the university itself and by institutions of external evaluation.

No-Reference Video Quality Assessment Using the Temporal Statistics of Global and Local Image Features.

During acquisition, storage, and transmission, the quality of digital videos degrades significantly. Low-quality videos lead to the failure of many computer vision applications, such as object tracking or detection, intelligent surveillance, etc. Over the years, many different features have been developed to resolve the problem of no-reference video quality assessment (NR-VQA). In this paper, we propose a novel NR-VQA algorithm that integrates the fusion of temporal statistics of local and global image features with an ensemble learning framework in a single architecture. Namely, the temporal statistics of global features reflect all parts of the video frames, while the temporal statistics of local features reflect the details. Specifically, we apply a broad spectrum of statistics of local and global features to characterize the variety of possible video distortions. In order to study the effectiveness of the method introduced in this paper, we conducted experiments on two large benchmark databases, i.e., KoNViD-1k and LIVE VQC, which contain authentic distortions, and we compared it to 14 other well-known NR-VQA algorithms. The experimental results show that the proposed method is able to achieve greatly improved results on the considered benchmark datasets. Namely, the proposed method exhibits significant progress in performance over other recent NR-VQA approaches.

GRAPH-ANALYTICAL MODELS OF COMPLEX SYSTEMS FOR STATE ASSESSMENT “ORGANISM – ENVIRONMENT”

Practicism in business is generally associated with obtaining high-performance goal-setting solutions. Cost reduction in the case of making a decision requires weight components identification in the object “state (environment)  system processes  system end state” associated with the structure or functionality of systems (processes), which themselves arbitrarily determine the target result. The works aim is to determine model type of a complex structured object of any purpose, which adequately reflects system state and interaction with the environment “(environment – system) – process – (system state – environment)” in a situation of varying degrees uncertainty. Assessment of systemic formation state is due to the grouping of methods, their complex application in the study of the interaction “system – environment”, which is determined by the corporative relations between the components. To solve the problem of quality assessment of such system objects, structural and graphical approach information and methodological support was offered as a set of methods.

Research on Regional Basic Education Quality Assessment Based on Deep Convolutional Neural Network

At present, the informatization of basic education quality assessment has become a hot topic in the field of education and is playing an increasingly important role. Based on the theory of deep convolutional neural network, this paper adopts the methods of mathematical analysis and experimental research to construct a regional basic education quality assessment model. The model solves the data informatization problem of education quality assessment. In the simulation process, two key modules of data self-assessment and expert assessment of the deep convolutional neural network are realized by ASENET+SQL SERVER, and the assessment results are integrated by using the weighted average method and the fuzzy comprehensive assessment method. The experimental results show that the quantitative analysis of the quality assessment is carried out by using the logic and support relationship, and the results of comprehensive qualitative analysis and quantitative analysis are realized and segmented when the threshold level is 9, the MIOU obtains the highest value of 0.7501, and the MIOU of the multi-stage method of the quality evaluation model proposed in this paper is 0.8116, which is 6.15% points higher than the traditional multi-stage algorithm, which effectively improves the current stage area quality of basic education.

Dual-Channel Multi-Task CNN for No-Reference Screen Content Image Quality Assessment

Nowadays the problem of image quality assessment (IQA) for screen content images (SCIs) has become a research hotspot as they are ubiquitous in multimedia applications. Although the quality assessment of natural images (NIs) has been continuously developed in the past few decades, few NI-oriented IQA methods can be directly applied on SCIs due to different visual characteristics between them. In this paper, we present a no-reference quality prediction approach considering the content information of SCIs, which is based on dual-channel multi-task convolutional neural network. First, we segment a SCI into small patches and classify them as the textual patches and the pictorial patches. Then, we devise a novel dual-channel convolutional neural network (CNN) to predict the quality of textual patches and pictorial patches. Finally, we propose an effective adaptive weighting strategy for quality score aggregation. The proposed CNN is built on an end-to-end multi-task learning framework, which assists the SCI quality prediction task through the histogram of oriented gradient (HOG) feature prediction task to learn a better mapping between the input patch and its quality score. The adaptive weighting strategy further improves the representation ability of each SCI patch. Experimental results on two largest SCI-oriented databases demonstrate that the proposed method outperforms most of the state-of-the-art no-reference IQA methods and the full-reference IQA methods.

Deep Learning for FAST Quality Assessment.

To determine the feasibility of using a deep learning (DL) algorithm to assess the quality of focused assessment with sonography in trauma (FAST) exams. Our dataset consists of 441 FAST exams, classified as good-quality or poor-quality, with 3161 videos. We first used convolutional neural networks (CNNs), pretrained on the Imagenet dataset and fine-tuned on the FAST dataset. Second, we trained a CNN autoencoder to compress FAST images, with a 20-1 compression ratio. The compressed codes were input to a two-layer classifier network. To train the networks, each video was labeled with the quality of the exam, and the frames were labeled with the quality of the video. For inference, a video was classified as poor-quality if half the frames were classified as poor-quality by the network, and an exam was classified as poor-quality if half the videos were classified as poor-quality. The results with the encoder-classifier networks were much better than the transfer learning results with CNNs. This was primarily because the Imagenet dataset is not a good match for the ultrasound quality assessment problem. The DL models produced video sensitivities and specificities of 99% and 98% on held-out test sets. Using an autoencoder to compress FAST images is a very effective way to obtain features that can be used to predict exam quality. These features are more suitable than those obtained from CNNs pretrained on Imagenet.

Evaluation of Ecological Environment Quality in Chongqing Main City Area Based on Principal Component Analysis

With the rapid development of social productive forces in China, the process of urbanization has been accelerating, resulting in the deterioration of urban environmental quality and the destruction of ecosystems, such as debris flow, drought, water bloom, large-scale haze weather, and other ecological and environmental problems. The quality of the urban ecological environment is not only related to the stability of urban ecological function but also affects the sustainable development of the city. Many scholars have studied the problem of ecoenvironmental quality assessment, but there are still some shortcomings in the evaluation process, such as the construction of the index system and the application of related methods. Taking Chongqing as the research object, after referring to the representative evaluation index system in China, the evaluation index system of ecological environment quality in Chongqing was constructed from three aspects of nature, economy, and society. In this article, the principal component analysis (PCA) method is used to construct the ecoenvironmental quality evaluation system of Chongqing, and the economic development, pollutant control, natural protection and pollution reduction investment, urban and rural cooperation, land area, and other aspects are comprehensively evaluated. The relationship between simulated variables and the regression equation was also assessed. Through the comparative analysis of the two evaluation methods, it can be seen that the evaluation results of the PCA method and the fuzzy comprehensive evaluation method are basically the same in the longitudinal trend, which shows that the application of this method in the field of ecological environment quality evaluation has certain scientific rationality.

WEscore: quality assessment method of multichannel image visualization with regard to angular resolution

This work considers the problem of quality assessment of multichannel image visualization methods. One approach to such an assessment, the Escore quality measure, is studied. This measure, initially proposed for decolorization methods evaluation, can be generalized for the assessment of hyperspectral image visualization methods. It is shown that Escore does not account for the loss of local contrast at the supra-pixel scale. The sensitivity to the latter in humans depends on the observation conditions, so we propose a modified wEscore measure which includes the parameters allowing for the adjustment of the local contrast scale based on the angular resolution of the images. We also describe the adjustment of wEscore parameters for the evaluation of known decolorization algorithms applied to the images from the COLOR250 and the Cadik datasets with given observational conditions. When ranking the results of these algorithms and comparing it to the ranking based on human perception, wEscore turned out to be more accurate than Escore.

Digital fundus image quality assessment

Diabetic retinopathy (DR) is a disease caused by complications of diabetes. It starts asymptomatically and can end in blindness. To detect it, doctors use special fundus cameras that allow them to register images of the retina in the visible range of the spectrum. On these images one can see features, which determine the presence of DR and its grade. Researchers around the world are developing systems for the automated analysis of fundus images. At present, the level of accuracy of classification of diseases caused by DR by systems based on machine learning is comparable to the level of qualified medical doctors.The article shows variants for representation of the retina in digital images by different cameras. We define the task to develop a universal approach for the image quality assessment of a retinal image obtained by an arbitrary fundus camera. It is solved in the first block of any automated retinal image analysis system. The quality assessment procedure is carried out in several stages. At the first stage, it is necessary to perform binarization of the original image and build a retinal mask. Such a mask is individual for each image, even among the images recorded by one camera. For this, a new universal retinal image binarization algorithm is proposed. By analyzing result of the binarization, it is possible to identify and remove imagesoutliers, which show not the retina, but other objects. Further, the problem of no-reference image quality assessment is solved and images are classified into two classes: satisfactory and unsatisfactory for analysis. Contrast, sharpness and possibility of segmentation of the vascular system on the retinal image are evaluated step by step. It is shown that the problem of no-reference image quality assessment of an arbitrary fundus image can be solved.Experiments were performed on a variety of images from the available retinal image databases.

Кросс-культурная адаптация и валидация опросника SPOT-25 для русскоязычной популяции

Life quality assessment is one of the most popular and relevant field in modern medicine. Hearing loss due to otosclerosis may significantly affect different areas of the patient’s life, thereby changing the level of quality of life. At the same time, there is a certain subjectivity in the assessment of the patient’s own condition and the expert opinion of the doctor, including due to the discrepancy between the concept of a satisfactory level of quality of life. One of the problems of life quality assessment of patients with otosclerosis among Russian-speaking population is the lack of validated nosologically specific questionnaires. The aim of our study was to assess the quality of life of patients with otosclerosis by means of cross-cultural adaptation and validation of the Russified version of the SPOT-25 questionnaire (Stapesplasty Outcome Test 25) to assess the quality of life of patients with otosclerosis. During the study, the SPOT-25 questionnaire was translated using a double reverse independent translation, agreed and formed into a single version for further validation work. As part of the study, various observation groups were formed, which included patients suffering from otosclerosis and healthy volunteers. The main internal characteristics of the questionnaire were determined, such as validity, reliability, and sensitivity. The results obtained demonstrated sufficient reliability and sensitivity as well as clinical validity of the new condition assessment tool. Thus, the Russified version of the SPOT-25 questionnaire can be used both in further scientific research and in the daily practice of an otorhinolaryngologist.

Power Quality Assessment Based on Rough AHP and Extension Analysis

Due to the increasing power consumption of whole society and widely using of new non-linear and asymmetric electrical equipment, power quality assessment problem in the new period has attracted more and more attention. The mathematical essence of comprehensive assessment of power quality is a multi-attribute optimal decision-making problem. In order to solve the key problem of determining the indicator weight in the process of power quality assessment, a rough analytic hierarchy process (AHP) is proposed to aggregate the judgment opinions of multiple experts and eliminate the subjective effects of single expert judgment. Based on the advantage of extension analysis for solving the incompatibility problem, extension analysis method is adopted to assess the power quality. The assessment grades of both total power quality and each assessment indicator are obtained by correlation function. Through a case of 110 kV bus of a converting station in a wind farm of China, the feasibility and effectiveness of the propose method are demonstrated. The result shows that the proposed method can determine the overall power quality of power grid, as well as compare the differences among the performance of assessment indicators and provide the basis for further improving of power quality.

Understanding the perceived quality of video predictions

The study of video prediction models is believed to be a fundamental approach to representation learning for videos. While a plethora of generative models for predicting the future frame pixel values given the past few frames exist, the quantitative evaluation of the predicted frames has been found to be extremely challenging. In this context, we study the problem of quality assessment of predicted videos. We create the Indian Institute of Science Predicted Videos Quality Assessment (IISc PVQA) Database consisting of 300 videos, obtained by applying different prediction models on different datasets, and accompanying human opinion scores. We collected subjective ratings of quality from 50 human participants for these videos. Our subjective study reveals that human observers were highly consistent in their judgments of quality of predicted videos. We benchmark several popularly used measures for evaluating video prediction and show that they do not adequately correlate with these subjective scores. We introduce two new features to effectively capture the quality of predicted videos, motion-compensated cosine similarities of deep features of predicted frames with past frames, and deep features extracted from rescaled frame differences. We show that our feature design leads to state-of-the-art quality prediction in accordance with human judgments on our IISc PVQA Database. The database and code are publicly available on our project website: https://nagabhushansn95.github.io/publications/2020/pvqa.