Average Opinion Research Articles

A Novel Perspective on the Quality Index for Fused Satellite Images

Blind Image Quality Assessment for Remote Sensing Applications has garnered a significant amount of interest from the community, presenting a problem that is both persistent and difficult to solve. The current quality indicators are, for the most part, in agreement with each individual's subjective perception. Traditional handcrafted quality measures focus on detecting low-level features such as contour, edge, color, texture, and shape. These are the attributes that will be measured. Nevertheless, it is possible that they will disregard the essential semantics that lie behind the warped image. Within the scope of popular deep learning, the process of acquiring multilayer features is a straightforward exercise. The unfortunate reality is that a significant number of these models either ignore superficial traits or focus only on high-level variables, which ultimately leads to poor prediction performance. In order to successfully portray varying degrees of distortion, the fusion features make advantage of the laws that govern the human visual system in order to extract both local and global components of information. In conclusion, the ultimate quality score is determined by considering the features of both the local and global levels. In this investigation, we developed two different NR-IQA methods. In one, a markov random field is used, while in the other, a sparse approximation variational autoencoder is established as the foundation. The effectiveness of our work is shown by the evaluation of multiple IQA datasets, where our model consistently generates findings that are at the leading edge of the field. This evaluation does not make use of any quality standards; rather, it compares it against a variety of well-known cutting-edge techniques and average opinion ratings based on the opinions of different people. Quality ratings are accurately predicted by the regression models that have been provided.

Perceptions of organizational culture among mental health providers at a Veterans Affairs hospital.

A mixed method approach was utilized to assess the organizational culture of the mental health service line at a large Veterans Affairs (VA) hospital. The goals of the study were to assess the organizational culture, identify how employees differ in perceptions of organizational culture, and identify areas of strength and challenges. Two hundred thirteen participants returned a questionnaire assessing perceptions of organizational culture using the Organizational Culture Assessment Questionnaire (Sashkin & Rosenbach, 2013). Additionally, 22 employees and seven service line leaders participated in five focus groups that assessed for perceptions of culture. Quantitative results suggest that employees had a generally average to high average opinion of the organizational culture of the mental health service line within their VA hospital. Subscale scores suggested that customer orientation was a particular strength of the organization. Relative areas of weakness were that staff felt many decisions were imposed from outside the service line and that hard-working clinical staff were not always sufficiently recognized or rewarded for their actions. In general, demographic variables were not significantly related toward respondent opinion of the organizational culture. Substantial overlap existed between the qualitative and quantitative results. This study suggests that, at this VA's mental health service line, customer service was a strength. However, increased recognition of employees and externally imposed decision-making needed improvement. Although interventions have been implemented in these areas, it would be useful to repeat aspects of this study over time and across different institutions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Toward 3D facial analysis for recognizing Mendelian causes of autism spectrum disorder.

Recognizing Mendelian causes is crucial in molecular diagnostics and counseling for patients with autism spectrum disorder (ASD). We explored facial dysmorphism and facial asymmetry in relation to genetic causes in ASD patients and studied the potential of objective facial phenotyping in discriminating between Mendelian and multifactorial ASD. In a cohort of 152 ASD patients, 3D facial images were used to calculate three metrics: a computational dysmorphism score, a computational asymmetry score, and an expert dysmorphism score. High scores for each of the three metrics were associated with Mendelian causes of ASD. The computational dysmorphism score showed a significant correlation with the average expert dysmorphism score. However, in some patients, different dysmorphism aspects were captured making the metrics potentially complementary. The computational dysmorphism and asymmetry scores both enhanced the individual expert dysmorphism scores in differentiating Mendelian from non-Mendelian cases. Furthermore, the computational asymmetry score enhanced the average expert opinion in predicting a Mendelian cause. By design, our study does not allow to draw conclusions on the actual point-of-care use of 3D facial analysis. Nevertheless, 3D morphometric analysis is promising for developing clinical dysmorphology applications in diagnostics and training.

The asynchronous DeGroot dynamics

AbstractWe analyze the asynchronous version of the DeGroot dynamics: In a connected graph with nodes, each node has an initial opinions in and an independent Poisson clock. When a clock at a node rings, the opinion at is replaced by the average opinion of its neighbors. It is well known that the opinions converge to a consensus. We show that the expected time to reach ‐consensus is poly in undirected graphs and in Eulerian digraphs, but for some digraphs of bounded degree it is exponential. Our main result is that in undirected graphs and Eulerian digraphs, if the degrees are uniformly bounded and the initial opinions are i.i.d., then for every fixed . We give sharp estimates for the variance of the limiting consensus opinion, which measures the ability to aggregate information (“wisdom of the crowd”). We also prove generalizations to non‐reversible Markov chains and infinite graphs. New results of independent interest on fragmentation processes and coupled random walks are crucial to our analysis.

Point Cloud Quality Assessment Using a One-Dimensional Model Based on the Convolutional Neural Network.

Recent advancements in 3D modeling have revolutionized various fields, including virtual reality, computer-aided diagnosis, and architectural design, emphasizing the importance of accurate quality assessment for 3D point clouds. As these models undergo operations such as simplification and compression, introducing distortions can significantly impact their visual quality. There is a growing need for reliable and efficient objective quality evaluation methods to address this challenge. In this context, this paper introduces a novel methodology to assess the quality of 3D point clouds using a deep learning-based no-reference (NR) method. First, it extracts geometric and perceptual attributes from distorted point clouds and represent them as a set of 1D vectors. Then, transfer learning is applied to obtain high-level features using a 1D convolutional neural network (1D CNN) adapted from 2D CNN models through weight conversion from ImageNet. Finally, quality scores are predicted through regression utilizing fully connected layers. The effectiveness of the proposed approach is evaluated across diverse datasets, including the Colored Point Cloud Quality Assessment Database (SJTU_PCQA), the Waterloo Point Cloud Assessment Database (WPC), and the Colored Point Cloud Quality Assessment Database featured at ICIP2020. The outcomes reveal superior performance compared to several competing methodologies, as evidenced by enhanced correlation with average opinion scores.

Balancing homophily and prejudices in opinion dynamics: An extended Friedkin–Johnsen model

In this paper we propose an extended version of the Friedkin–Johnsen (FJ) model where we analyze the effects that a homophily-based influence matrix has on the opinion formation process. In particular, we assume that the influence matrix varies over time, and its entries (that represent the appraisals that individuals have of the other individuals) update based on the comparison between their opinions and can take both positive and negative values. This leads to a system of two difference equations, where the first one corresponds to the standard FJ model, except that the influence matrix is no longer constant, and the second one models the dynamics of this matrix. We show that a necessary and sufficient condition for the convergence of this modified version of the classical FJ model is that the influence matrix becomes constant in a finite number of steps. Moreover, we provide the explicit expression for the agents’ asymptotic opinions in some special cases: the purely cooperative setting, the case of a structurally balanced network and the case of a single discussion topic. Finally, the case when the topics are correlated and the influence matrix depends on the average opinion vectors is investigated.

Transient behavior of gossip opinion dynamics with community structure

We study transient behavior of gossip opinion dynamics, in which agents randomly interact pairwise over a weighted graph with two communities. Edges within a community have identical weights different from edge weights between communities. We first derive an upper bound for the second moment of agent opinions. Using this result, we obtain upper bounds for probability that a large proportion of agents have opinions close to average opinions. The results imply a phase transition of transient behavior of the process: When edge weights within communities are larger than those between communities and those between regular and stubborn agents, most agents in the same community hold opinions close to the average opinion of that community with large probability, at an early stage of the process. However, if the difference between intra- and inter-community weights is small, most of the agents instead hold opinions close to everyone’s average opinion at the early stage. In contrast, when the influence of stubborn agents is large, agent opinions settle quickly to steady state. We then conduct numerical experiments to validate the theoretical results. Different from traditional asymptotic analysis in most opinion dynamics literature, the paper characterizes the influence of stubborn agents and community structure on the initial phase of the opinion evolution.

Controlling Media Player with Hands: A Transformer Approach and a Quality of Experience Assessment

In this article, we propose a Hand Gesture Recognition (HGR) system based on a novel deep transformer (DT) neural network for media player control. The extracted hand skeleton features are processed by separate transformers for each finger in isolation to better identify the finger characteristics to drive the following classification. The achieved HGR accuracy (0.853) outperforms state-of-the-art HGR approaches when tested on the popular NVIDIA dataset. Moreover, we conducted a subjective assessment involving 30 people to evaluate the Quality of Experience (QoE) provided by the proposed DT-HGR for controlling a media player application compared with two traditional input devices, i.e., mouse and keyboard. The assessment participants were asked to evaluate objective (accuracy) and subjective (physical fatigue, usability, pragmatic quality, and hedonic quality) measurements. We found that (i) the accuracy of DT-HGR is very high (91.67%), only slightly lower than that of traditional alternative interaction modalities; and that (ii) the perceived quality for DT-HGR in terms of satisfaction, comfort, and interactivity is very high, with an average Mean Opinion Score (MOS) value as high as 4.4, whereas the alternative approaches did not reach 3.8, which encourages a more pervasive adoption of the natural gesture interaction.

Research on a Mongolian Text to Speech Model Based on Ghost and ILPCnet

The core challenge of speech synthesis technology is how to convert text information into an audible audio form to meet the needs of users. In recent years, the quality of speech synthesis based on end-to-end speech synthesis models has been significantly improved. However, due to the characteristics of the Mongolian language and the lack of an audio corpus, the Mongolian speech synthesis model has achieved few results, and there are still some problems with the performance and synthesis quality. First, the phoneme information of Mongolian was further improved and a Bang-based pre-training model was constructed to reduce the error rate of Mongolian phonetic synthesized words. Second, a Mongolian speech synthesis model based on Ghost and ILPCnet was proposed, named the Ghost-ILPCnet model, which was improved based on the Para-WaveNet acoustic model, replacing ordinary convolution blocks with stacked Ghost modules to generate Mongolian acoustic features in parallel and improve the speed of speech generation. At the same time, the improved vocoder ILPCnet had a high synthesis quality and low complexity compared to other vocoders. Finally, a large number of data experiments were conducted on the proposed model to verify its effectiveness. The experimental results show that the Ghost-ILPCnet model has a simple structure, fewer model generation parameters, fewer hardware requirements, and can be trained in parallel. The average subjective opinion score of its synthesized speech reached 4.48 and the real-time rate reached 0.0041. It ensures the naturalness and clarity of synthesized speech, speeds up the synthesis speed, and effectively improves the performance of the Mongolian speech synthesis model.

Voter model under stochastic resetting

The voter model is a toy model of consensus formation based on nearest-neighbor interactions. A voter sits at each vertex in a hypercubic lattice (of dimension d) and is in one of two possible opinion states. The opinion state of each voter flips randomly, at a rate proportional to the fraction of the nearest neighbors that disagree with the voter. If the voters are initially independent and undecided, the model is known to lead to a consensus if and only if . In this paper the model is subjected to stochastic resetting: the voters revert independently to their initial opinion according to a Poisson process of fixed intensity (the resetting rate). This resetting prescription induces kinetic equations for the average opinion state and for the two-point function of the model. For initial conditions consisting of undecided voters except for one decided voter at the origin, the one-point function evolves as the probability of presence of a diffusive random walker on the lattice, whose position is stochastically reset to the origin. The resetting prescription leads to a non-equilibrium steady state. For an initial state consisting of independent undecided voters, the density of domain walls in the steady state is expressed in closed form as a function of the resetting rate. This function is differentiable at zero if and only if .

The inquiry of the learning support index connotation of adults with autism spectrum disorder

In this study, a questionnaire survey using the Delphi Technique was conducted among 22 expert group members. We construct the connotation of learning support indicators for adults with autism spectrum disorder (ASD) based on survey results. In this study, there are five levels of significance. After three rounds of questionnaire surveying, the average of all questions was >4.0, with a standard deviation of <1, and 37 questions were reserved. The average opinion of the expert members of the Delphi Technique on the importance of connotation topics increased and the standard deviation decreased sequentially, yielding the result of opinion convergence, indicating that the expert members agreed on the importance of the topic of the survey. The research results indicate that the connotation question is reduced from 52 questions to 37 questions through three aspects: writing and expression, simplification and merging of questions, and consideration criteria for various score indicators of the question. On a theoretical level, this study is expected to provide a reference for the connotation construction of learning support indicators for adults with ASD.

Perceptual maps to aggregate assessments from different rating profiles: A hesitant fuzzy linguistic approach

In decision making environments under uncertainty, assessments are frequently expressed in linguistic terms. When people express their opinions using linguistic terms, the meanings ascribed to these terms may not always align. This phenomenon is captured by the concept of a linguistic perceptual map, which draws from the established lattice of hesitant fuzzy linguistic term sets. Each individual or group of people (referred to as a ’profile’) possesses their own distinct perceptual map. By projecting and aggregating the opinions of these individuals or groups onto a common perceptual map, an average opinion and a level of consensus are derived. This paper extensively studies the mathematical properties of the projection function. We prove that it is a monomorphism between lattices, preserving crucial order relations. Additionally, we progress beyond existing research by introducing an interpretation function. This function facilitates the translation of the aggregated result (referred to as the ’centroid’) from the common perceptual map to each individual’s perceptual map. The properties of the interpretation function are also subject to analysis, demonstrating its role in preserving previous order relations, despite not being a morphism. To illustrate the practicality of these concepts, we propose a methodology that we apply to a real-world case study involving data in the form of ratings from the Amazon books platform. The results obtained highlight that utilizing distinct perceptual maps for each user profile statistically enhances the degree of consensus compared to scenarios where perceptual maps are not differentiated.

Relative opinion similarity leads to the emergence of large clusters in opinion formation models

This study considers a variant of the bounded confidence opinion formation model wherein the probability of opinion assimilation is dependent on the relative similarity of opinions. Agents are located on a social network and decide whether or not they adopt the opinion of one of the neighbors (called a role agent). Opinion assimilation is more (less) likely to occur when the distance from the opinion of the role agent is smaller (larger) than the average opinion distance from other neighbors. Thus, assimilation probability is reliant not only on opinion proximity with the role agent considered in conventional models but also on relative similarity that considers other neighbors. The simulation results demonstrate that large weights on relative similarity in determining assimilation probability increase the size of the largest opinion cluster. The size of the threshold parameter of the bounded confidence model displays inverse-U relationships with the largest cluster size. The findings imply that consideration of relative opinion similarity, as observed in recent empirical studies, prevents polarization into small opinion clusters.

Clinical study on the efficacy and tolerability of an oral supplement based on arginine, l-cystine, zinc and B6 vitamin (Cystiphane®) in patients with telogen effluvium.

Telogen effluvium (TE) is a common cause of non-cicatricial hair loss with no treatment-standardized protocol. The aim of our study was to evaluate the efficacy, tolerability, and patient compliance of a treatment with an oral supplement based on arginine, l-cystine, zinc and B<inf>6</inf> vitamin (Cystiphane®, Laboratoires Bailleul, Geneva, Switzerland) with hair-growth properties, administered 4 times daily, in patients affected by TE. We recruited 20 patients, aged between 18 and 70 years old, affected by TE. Patients were asked to take the oral supplement as a monotherapy, four tablets daily, in one or two administrations during meals. The study lasted 3 months. We evaluated the efficacy and tolerability of the treatment both qualitatively by collecting the clinician's opinion through a clinical evaluation and clinical-anamnestic form filled in by the researcher, and quantitatively through global photography and trichoscopy. We collected the patient's opinion through a self-assessment test, at the beginning of the recruitment and after 3 months of treatment. Eighteen patients were evaluated. After 3 months of taking the supplement, the researcher rated an average improvement of 2.89 at the clinical evaluation. For what concerns hair quantity, at the control trichoscopy, the mean trichoscopic value had risen to +2.055, whereas for hair diameter the mean trichoscopic diameter value had increased to +1.83. After 3 months of treatment, patients gave an average efficacy opinion of 3.61. The oral supplement has proved effective as an adjuvant in the treatment of TE in our cohort of patients.

Distribution of consensus in a broadcast-based consensus algorithm with random initial opinions

AbstractWe study the distribution of the consensus formed by a broadcast-based consensus algorithm for cases in which the initial opinions of agents are random variables. We first derive two fundamental equations for the time evolution of the average opinion of agents. Using the derived equations, we then investigate the distribution of the consensus in the limit in which agents do not have any mutual trust, and show that the consensus without mutual trust among agents is in sharp contrast to the consensus with complete mutual trust in the statistical properties if the initial opinion of each agent is integrable. Next, we provide the formulation necessary to mathematically discuss the consensus in the limit in which the number of agents tends to infinity, and derive several results, including a central limit theorem concerning the consensus in this limit. Finally, we study the distribution of the consensus when the initial opinions of agents follow a stable distribution, and show that the consensus also follows a stable distribution in the limit in which the number of agents tends to infinity.

Multi-opinion Markovian agent networks: Parametrization, second order moment and social power

This paper extends the theory of Markovian multi-agent opinion networks, previously studied in the binary opinion case, to the situation of multiple opinions. The first step is the definition of a suitable canonical representation of a multi-state Markov chain, to describe the behavior of any non-interacting agent in terms of its prejudice. Based on this parametrization, the time evolution of both first- and second-order moments of the opinion shares when the agents are connected in a social network is completely characterized, both in transient and at steady-state. The steady-state analysis allows one to introduce an appropriate notion of marginal social power, measuring the sensitivity of the average network opinion to the agents’ prejudices.

Regulating Social Media as a Public Good: Limiting Epistemic Segregation

ABSTRACT The rise of social media has correlated with an increase in political polarization, which many perceive as a threat to public discourse and democratic governance. This paper presents a framework, drawing on social epistemology and the economic theory of public goods, to explain how social media can contribute to polarization, making us collectively poorer, even while it provides a preferable media experience for individual consumers. Collective knowledge and consensus is best served by having richly connected networks that are epistemically integrated: individuals with diverse levels of expertise should be relatively well connected to each other. In epistemically segregated networks, by contrast, we have reason to predict collective epistemic failures. Expert knowledge will be isolated from the majority, leading average opinion to be less informed than is socially optimal, and entrenching disagreements. Because social media enables users to very easily adopt homophilous network connections – connections to those with similar opinions, education levels, and social backgrounds – it is likely to have increased epistemic segregation compared to older media platforms. The paper explains the theoretical foundations of these predictions, and sketches regulatory measures – such as taxes – that might be employed to preserve the public good of a well integrated social media network.

Promoting physical activity for children: an audit among Italian family pediatricians

This survey aimed to investigate the perception of Italian Pediatricians regarding the promotion of physical activity. From January to May 2015, a survey was conducted among family pediatricians working in Puglia, interviewed by a standardized questionnaire, that investigated demographic information, participation in sporting activity by the pediatrician, opinion on obesity and the promotion of physical activity, and attitude towards enquiring about the physical activity of patients with and without chronic diseases. We collected 200 questionnaires (35.7% of target population): 51.5% of enrolled pediatricians stated that they were participating in sporting activities at the time of the survey. The average opinion regarding the relevance of the obesity was 9±1.4/10 and about the promotion of physical activity 9.6±0.8. All enrolled pediatricians asked for their patients' physical activity and encouraged inactive children to begin an appropriate activity. Pediatricians who took part in sport had a high probability of encouraging physical activity, among patients affected by chronic diseases. Family pediatricians declared a good attitude regarding the promotion of physical activity for their patients according to national and international recommendations. The main concern emerging from our results is the risk of an "inappropriate interdiction" of physical activity in children affected by chronic diseases, in particular physicians that have never participated in a sport.

Homophily-Based Social Group Formation in a Spin Glass Self-Assembly Framework.

Homophily, the tendency of humans to attract each other when sharing similar features, traits, or opinions, has been identified as one of the main driving forces behind the formation of structured societies. Here we ask to what extent homophily can explain the formation of social groups, particularly their size distribution. We propose a spin-glass-inspired framework of self-assembly, where opinions are represented as multidimensional spins that dynamically self-assemble into groups; individuals within a group tend to share similar opinions (intragroup homophily), and opinions between individuals belonging to different groups tend to be different (intergroup heterophily). We compute the associated nontrivial phase diagram by solving a self-consistency equation for "magnetization" (combined average opinion). Below a critical temperature, there exist two stable phases: one ordered with nonzero magnetization and large clusters, the other disordered with zero magnetization and no clusters. The system exhibits a first-order transition to the disordered phase. We analytically derive the group-size distribution that successfully matches empirical group-size distributions from online communities.

Image Restoration Quality Assessment Based on Regional Differential Information Entropy.

With the development of image recovery models, especially those based on adversarial and perceptual losses, the detailed texture portions of images are being recovered more naturally. However, these restored images are similar but not identical in detail texture to their reference images. With traditional image quality assessment methods, results with better subjective perceived quality often score lower in objective scoring. Assessment methods suffer from subjective and objective inconsistencies. This paper proposes a regional differential information entropy (RDIE) method for image quality assessment to address this problem. This approach allows better assessment of similar but not identical textural details and achieves good agreement with perceived quality. Neural networks are used to reshape the process of calculating information entropy, improving the speed and efficiency of the operation. Experiments conducted with this study's image quality assessment dataset and the PIPAL dataset show that the proposed RDIE method yields a high degree of agreement with people's average opinion scores compared with other image quality assessment metrics, proving that RDIE can better quantify the perceived quality of images.