Average Opinion Research Articles

Selection of Urban Health Equity Assessment and Response Tool indicators using item response theory analysis to assess a city health profile in India

Introduction: City health profile is a useful tool for understanding the health gap and to plan to close the health gap. Of the different tools available to draft a city health profile, the Urban Health Equity Assessment and Response Tool (HEART) developed by the WHO Kobe Centre is one of the best among them. A list of core indicators of the Urban HEART varies from country to country. Aim: In this scenario, an attempt was made with an aim to choose feasible indicators in the Urban HEART to assess a city health profile in Indian setting. Material and Methods: The current study is a cross-sectional observational study conducted among different public health experts in India using an online survey technique. An online survey form was created to collect the public health expert's opinion and sent to public health experts. A total of 105 responses were collected. Statistical Analysis: The average opinion score for each indicator was measured. Item response theory analysis technique was used to calculate the feasibility score to categorize the indicators. Results: The average score of the 60 indicators ranged from 4.54 to 2.77. The feasibility score ranged from 4.13 to − 0.49. Of 60 indicators, 16 indicators were strongly recommended, 41 were recommended, and 3 indicators were optional indicators. Nineteen were highly feasible, 28 feasible, and 13 were less feasible indicators. Conclusion: The Urban HEART can be applied in Indian setting. Data collection can be started with feasible indicators and later can be extended to other indicators.

Abnormality Detection in Musculoskeletal Radiographs by DenseNet and Inception-v3

Background: One of the most remarkable applications of deep learning emerges in medical diagnosis. New improvements in this field have shown that with large enough datasets and the right methods, one can achieve results as reliable as diagnoses made by experienced doctors. One of such developments is MURA, which is a dataset of musculoskeletal radiographs consisting of 14863 studies from 12173 patients, resulting in 40561 multi-view radiograph images. Each one of these studies concerns one of the seven standard upper extremity radiographic study types, namely finger, forearm, elbow, hand, shoulder, homeruns, and wrist. Each study was categorized as normal or abnormal by board-certified radiologists in the diagnostic radiology environment between 2001 and 2012. Abnormality detection in muscular radiography is of great clinical application. This gains more importance in cases in which abnormality detection is difficult for physicians. If the proposed model can help us in detection, the process of treatment will precipitate. This model is termed Inception-v3. Methods: In this study, we evaluated the MURA dataset through Dense NET and inception-v3 methods. Results: The results indicated that the former had better performance and we added a pre-processing module to it to improve the accuracy of the DenseNet method in detecting the abnormality. In this context, we trained the machine to be sensitive to the presence of external objects to be distinguished from actual abnormality such as bone fraction. We achieved this condition by many various radiographs as machine inputs. By this strategy, both techniques (DenseNet and Inception-v3) showed improvements in accuracy. Thus, we sub-grouped abnormality into with or without the presence of external objects. Conclusion: Although the average opinion of radiologists still shows better results, in images with delicate fracture detection, such as finger fracture, the proposed model worked more accurately, and it could be a decision support assistant for physicians in the final detection of fracture. The precision of the proposed model will enhance if the image is separated from normal images using Platinum, a new class is made, and pre-processing is done. Therefore, the model can automatically detect abnormality by identifying that part of the image that is detected to be abnormal. An efficient model can interpret images more efficiently, can reduce errors, and can enhance quality. More studies are needed to evaluate the integration of this model with other models of deep learning in clinical settings.

Differences of opinion, institutional bids, and IPO underpricing

Miller (1977) hypothesizes that IPO underpricing arises because the issue price is based on the average opinion while the aftermarket price is set by a minority of optimistic investors. Using a unique data set of institutional bids for a large sample of Chinese IPOs, we show that the IPO issue price is positively related to the quantity-weighted average bid price and unrelated to the market-clearing bid price. In contrast, the first-day closing price is positively related to the market-clearing bid price and unrelated to the average bid price. Overall, our results provide strong support for Miller's explanation of IPO underpricing.

Optimal Investment Strategies for Competing Camps in a Social Network: A Broad Framework

We study the problem of optimally investing in nodes of a social network in a competitive setting, wherein two camps aim to drive the average opinion of the population in their own favor. Using a well-established model of opinion dynamics, we formulate the problem as a zero-sum game with its players being the two camps. We derive optimal investment strategies for both camps, and show that a random investment strategy is optimal when the underlying network follows a popular class of weight distributions. We study a broad framework, where we consider various well-motivated settings of the problem, namely, when the influence of a camp on a node is a concave function of its investment on that node, when a camp aims at maximizing competitor's investment or deviation from its desired investment, and when one of the camps has uncertain information about the values of the model parameters. We also study a Stackelberg variant of this game under common coupled constraints on the combined investments by the camps and derive their equilibrium strategies, and hence quantify the first-mover advantage. For a quantitative and illustrative study, we conduct simulations on real-world datasets and provide results and insights.

Concordancia interevaluador en la elección de tratamiento profiláctico para la migraña vestibular y papel de un algoritmo asistido para la elección de dicho tratamiento

Background and objectiveVestibular migraine (VM) is a cause of episodic vestibular syndrome. There are many drugs available for its prophylaxis and currently the choice is mainly made according to the patient's comorbidities. The aim of this article was to measure the agreement of a group of otorhinolaryngologists in the choice of a prophylactic treatment and to evaluate the role of an assisted algorithm in the choice of this prophylaxis. Material and methodThe medical records of 10 patients with VM were offered to 10 otolaryngologists who were asked to select for each patient the drug they considered most appropriate among five possible options. The Fleiss’ κ index was calculated among the 10 otolaryngologists alone, recalculating it including the algorithm as the eleventh evaluator, and Cohen's κ index was calculated between each otolaryngologist and the answers of the algorithm. The otolaryngologists were offered the option to change their responses after knowing the responses of the algorithm and then both indexes were calculated again. ResultsThe Fleiss’ κ index was .302. This index was raised to .343 after introducing the algorithm as an evaluator. After offering the responses proposed by the algorithm, Cohen's κ was improved in 9 of the 10 evaluators, and Fleiss’ κ rose to .711. ConclusionsThe agreement between otorhinolaryngologists in choosing prophylaxis for MV can be defined as “fair”. The responses of the algorithm for the choice of prophylaxis were close to the average opinion of the otolaryngologists, raising the agreement between them to “substantial”.

Opinion dynamics with decentralized proportional–integral control strategy

Opinion dynamics as a special type of complex human behavior has received increasing attention in recent years. From a control-theoretic point of view, the opinion dynamical system can be viewed as a decentralized control system. In this paper, we investigate opinion dynamics of the model in which each agent can adjust its opinion by using an incremental proportional–integral (PI) controller, where the opinion is updated according to the deviation between the average opinion of local neighbors’ and the individual one. Simulation results suggest the strong integral effect can promote the system to achieve consensus easily and reduce the number of subsets fast. In contrast, the proportional effect will impede the convergence of the system and prolong the time required to reach consensus. This observation is robust with different system sizes and provides a new way of thinking about guidance in opinion dynamics.

EVALUASI KUALITAS PENYELENGGARAAN BIMTEK PENINGKATAN KAPASITAS ANGGOTA DPRD KAB/KOTA/PROVINSI

The aim of the research is to evaluative and obtain empirical data about the quality of organizing " capacity building technical guidance of Member of Legislative districts/cities/provinces" organized by UIA-LPPM. The method of study using surveys with the target population is all participants technical guidance 2018/2019 during the period (6 months) that there were 6 times technical guidance activities. Sampling is done in a simple random sample, the amount of 50% of the total activities of the technical guidance, namely: three (3) events from event 6 times technical guidance as many as 86 people. Data analysis using SPSS program with a descriptive analysis.
 The research results showed that the participants opinion technical guidance provide that the quality of capacity building organization of Member of Legislative districts/cities/provinces " is a great fit with the needs = 25%; appropriate with needs = 69%; fit =; less appropriate = 0% and highly inappropriate = 0 %. The average opinion of the respondents give score = 4.1222 which means the quality of organizing "capacity building technical guidance of Member of Legislative districts/cities/provinces" in accordance with the needs of the participants technical guidance"; this needs to be maintained the quality of its commissioning and when necessary be present again.

Continuous-Time Opinion Dynamics With Stochastic Multiplicative Noises

Recently, the study of opinion dynamics on complex networks has attracted a great deal of attention. In this brief, we take an interest in how an opinion forms in a social network with stochastic noises. We investigate a stochastic opinion dynamics model, in which individuals obtain information from their neighbors in a social network. We model the social network as a strongly connected graph. Each individual has an initial opinion and iteratively exchanges her opinion based on a stubborn opinion, her neighbors’ opinions and small stochastic noises continually. We first construct a general stochastic opinion evolution problem with multiplicative noises as a stochastic differential equation and then investigate the stochastic stability of this equation. We consider whether an opinion may form as a result of stochastic disturbance and local interactions. We also establish two sufficient conditions which ensure that the average opinion and the error vector system for all individuals are stochastic stable, respectively. Numerical simulations are given to demonstrate the effectiveness of the theoretical results.

Opinion formation and targeting when persuaders have extreme and centrist opinions

We consider a model of competitive opinion formation in which three persuaders characterized by (possibly unequal) persuasion impacts try to influence opinions in a society of individuals embedded in a social network. Two of the persuaders have the extreme and opposite opinions, and the third one has the centrist opinion. Each persuader chooses one individual to target, i.e., he forms a link with the chosen individual in order to spread his own “point of view” in the society and to get the average long run opinion as close as possible to his own opinion. We examine the opinion convergence and consensus reaching in the society. Also the case when the persuaders choose several targets for diffusion of information is discussed. We study the existence and characterization of pure strategy Nash equilibria in the game played by the persuaders with equal impacts. This characterization depends on influenceability and centrality of the targets. We discuss the effect of the centrist persuader on the consensus and symmetric equilibria, compared to the framework with only two persuaders having the extreme opinions. When the persuasion impacts are unequal with one persuader having a sufficiently large impact, the game has only equilibria in mixed strategies.

EVALUASI KUALITAS KERJASAMA AKADEMIK UIA DENGAN MITRA

The aim of this cooperation is the evaluative research to know the level ofeffectiveness and efficiency of the implementation of UIA cooperation with partners, sothat the results can be used to repair either substance and the implementation ofcooperation in the future. Evaluation methods using survey with samples as many as 65people were taken simple random from a population of 168 target fixed lecturer in UIA TA2017/2018. Data analysis using descriptive with SPSS program. The result of thisresearch show that opinions of the fixed lecttures about the quality of cooperation withpartners from both the domestic and overseas: highly optimum = 1.5%;, Optimum =44.7%; moderate optimum = 36.9%; less than optimum = 15.54% and very less thanoptimum = 1.5%. The average opinion of the respondents = 3.23 score , that means thequality of collaboration with UIA partners shows quality of "moderate optimum"; and thisneeds to be improve.

Synthesis of Emotional Speech by Prosody Modification of Vowel Segments of Neutral Speech

Speech is viewed as a combination of voiced and unvoiced regions. Voiced speech is produced due to vibration of the vocal cords. The vibrating pattern of vocal cords is different in different emotions. During production of some consonant sound units, vocal cords do not vibrate. Therefore, consonants are less effective for emotion generation in speech signal. In this paper, we have considered only vowel regions for emotion synthesis using three prosody parameters duration, intensity and pitch patterns. Vowel like regions (VLR) is identified using vowel onset and offset points. Onset and offset points are starting and ending points of the vowel like regions. It is observed that during emotional synthesis from neutral speech mainly vowel regions of speech utterance are modified significantly. Our experimental result shows that the emotion synthesis using only prosody modification of VLR is significantly better than emotion synthesis of prosody modification at syllable level and it is also very effective in time consideration. The average mean opinion score is calculated using only vowel level prosody modification. The average mean opinion scores for angry, happy and fear emotional speeches are 3.85, 3.60 and 4.03, respectively. These mean opinion scores are better than syllable level prosody modification which are 3.56, 3.17 and 3.92 for angry, happy and fear emotions, respectively.

Opinion Formation and Targeting When Persuaders Have Extreme and Centrist Opinions

We consider a model of competitive opinion formation in which three persuaders characterized by (possibly unequal) persuasion impacts try to influence opinions in a society of individuals embedded in a social network. Two of the persuaders have the extreme and opposite opinions, and the third one has the centrist opinion. Each persuader chooses one individual to target, i.e., he forms a link with the chosen individual in order to spread his own “point of view” in the society and to get the average long run opinion as close as possible to his own opinion. We examine the opinion convergence and consensus reaching in the society. Also the case when the persuaders choose several targets for diffusion of information is discussed. We study the existence and characterization of pure strategy Nash equilibria in the game played by the persuaders with equal impacts. This characterization depends on influenceability and centrality of the targets. We discuss the effect of the centrist persuader on the consensus and symmetric equilibria, compared to the framework with only two persuaders having the extreme opinions. When the persuasion impacts are unequal with one persuader having a sufficiently large impact, the game has only equilibria in mixed strategies.

Anisotropic opinion dynamics with an adaptive social rule

We consider an anisotropic opinion formation process where the set of rules $B$, that dictates what is the socially acceptable position, changes following the average voters' opinion. As in the case of a constant $B$, conservative (agreement with $B$) and liberal (agreement with neighbors) voters' attitudes are still represented by stable fixed points in the phase space of the system but with the difference that the conservative fixed point is stable for all possible values of the intervoter interaction. It has been also observed that, when the model is applied to sufficiently large populations, the time needed to consolidate a position in agreement with $B$ is finite. We observed that there is also a range of values of the interaction where the two stable points coexist, opening the door for the modeling of bistability related phenomena, such as stochastic resonance and hysteresis.

Kinetic-exchange-like opinion dynamics in complex networks: roles of the dimensionality and local interaction topology

We study a kinetic-exchange-like opinion dynamics model with both positive and negative interactions in various complex networks. The control parameter p ∈ [0, 1] denotes the probability of the presence of negative outcome in the pairwise interaction, which indicates that the difference between the standpoints of the two focal individuals becomes even more large because of disagreement. Accordingly, with probability 1 − p they become more similar (positive interaction). The average opinion of the population serves as the order parameter of the system. We find that in random homogeneous networks the ordering process displays an anomalous jump at some special value of the control parameter p*, which gives rise to two distinct critical points pc for p p*, respectively. Whenever the underlying interaction network has heterogeneous interaction patterns and/or planar property, the anomalous ordering phenomenon disappears. Finite-size scaling analysis of the simulation results shows that the critical exponents for the opinion dynamics in random networks are in accordance with those of the mean-field Ising model, no matter whether the degree distribution is homogeneous or heterogeneous and the anomalous jump of the order parameter is presence or absence. The critical exponents for the opinion dynamics in spatially embedded networks (in two dimensions) belong to different universality classes, which depend closely on the configuration of local interactions. Particularly, whenever the local interactions are homogeneously distributed, two-dimensional Ising model universality class is recovered. Mean-field theoretical analysis corroborates well our findings. Our results highlight the importance of both the dimensionality and the local topology of the underlying interaction network in the phase transition behavior of the opinion dynamics.

THE DARK SIDE OF GOSSIPS: HINTS FROM A SIMPLE OPINION DYNAMICS MODEL

We consider a simple model of agents modifying their opinion about themselves and about the others during random pair interactions. Two unexpected patterns emerge: (1) without gossips, starting from zero, agents’ opinions tend to grow and stabilize on average at a positive value; (2) when introducing gossips, this pattern is inverted; the opinions tend to decrease and stabilize on average at a negative value. We show that these patterns can be explained by the relative influence of a positive bias on self-opinions and of a negative bias on opinions about others. Without gossips, the positive bias on self-opinions dominates, leading to a positive average opinion. Gossips increase the negative bias about others, which can dominate the positive bias on self-opinions, leading to a negative average opinion.

A Room of One’s Own: Need for Uniqueness Counters Online WoM

We examine how consumers’ desire to be different reduces their reliance on others’ suggestions and thus increases their tendency to diverge from the average opinion. While the extant literature focuses on the role of need for uniqueness in attitude formation and choice behavior, not much has been done to test the effect of uniqueness seeking on reactions to persuasive, word of mouth (WoM) messages. In four studies, we find converging evidence for a uniqueness effect. Specifically, the uniqueness motivation interacts with the valence of the average opinion such that when uniqueness motivation is low, consumers follow others’ advice and thus their attitudes depend primarily on the valence of the average opinion; meanwhile, the uniqueness seekers rely less on the valence and are more likely to form less favorable attitudes after reading positive reviews and to hold less unfavorable ones when the reviews are negative. These effects are found when trait need for uniqueness is measured as well as when situational need for uniqueness is manipulated. We further examine the process through which uniqueness motivation results in nonconformist attitudes. Uniqueness seekers perceive minority opinions as more diagnostic. Thus, these minority opinions are disproportionately represented in uniqueness seekers’ nonconformist views. These findings are important to the hospitality industry as consumers often rely on others’ experiences by reading online reviews to help make decisions concerning their own hospitality needs, which are highly experiential in nature.

Student Perceptions of, and Attitudes toward, Bats in Barak Valley, Assam, India

ABSTRACTBats are commonly found all over the world and provide significant ecosystem services. These animals are facing serious threats due to various anthropogenic activities, including hunting. Additionally, bats are often not particularly well liked due to their non-aesthetic characteristics. A positive attitude to these animals is essential to protect them through appropriate conservation measures involving public intervention. Current high school students are the conservationists of the future, and the extent of support for bat conservation received from this and future generations is dependent upon their attitude toward these animals. It is therefore important to assess their perceptions of bats so that appropriate awareness programs can be devised, and negative attitudes (if any) rectified. The present study aimed to understand this aspect in order to provide inputs for shaping positive attitudes toward bat conservation. The study was undertaken in Barak Valley, Assam, India, where 65 bat colonies had already been recorded. A total of 3,059 high school students from 36 schools situated in their vicinity were questioned using a close-ended questionnaire to determine their perceptions. The responses received were divided among three categories: familiarity with bats, opinion on bats, and attitudes toward conservation, and were ranked on a scale of 10, based on selected aspects. Most students were awarded a score of four, indicating an average knowledge and opinion of bats. These findings suggest that awareness programs are urgently needed in schools to improve the general knowledge of and attitudes toward bats, and to inspire the next generation of bat conservationists.

Visual-based change detection in scene regions using statistical-based approaches

Detecting changes of the same scene taken at different time instances is crucial and demanding for medical, remote sensing, infrastructure, agriculture, and planogram compliance applications. We propose a statistical-based approach by exploiting the linear relationship. Initially, region of interest is identified using a graph-cut-based technique followed by geometrical alignment via area-based registration. To perform statistical correlation, we adopt features such as block-wise average coefficient value of the first level of the discrete wavelet transform (DWT-LL1) and the map obtained using hybrid saliency approaches. In the former approach, Pearson’s correlation measure is calculated for the DWT-LL1, and in the latter, PCC has been calculated using the saliency value. Change has been detected using optimal PCC value while minimizing the error rate. Experimental results on datasets reveal that saliency feature and DWT-LL1 perform better for normal and noise corrupted images, respectively. The efficiency of the proposed method is validated by user study with average mean opinion score of 70%. Hybrid saliency-based change detection gives 92.9% of correct classification and hence useful for the vision-based applications like damage detection in a car.

Social Influence Makes Self-Interested Crowds Smarter: An Optimal Control Perspective

It is very common to observe crowds of individuals solving similar problems with similar information in a largely independent manner. We argue here that crowds can become “smarter,” i.e., more efficient and robust, by partially following the average opinion. This observation runs counter to the widely accepted claim that the wisdom of crowds deteriorates with social influence. The key difference is that individuals are self-interested, and hence, can reject feedback that does not improve performance. We propose a control-theoretic methodology to compute the degree of social influence, i.e., the level to which one accepts the population feedback, that optimizes performance. We conducted an experiment with human subjects ( $N=194$ ), where the participants were first asked to solve an optimization problem independently, i.e., with no social influence. Our theoretical methodology estimates a 30% degree of social influence to be optimal, resulting in a 29% improvement in the crowd’s performance. We then let the same cohort solve a new problem and have access to the average opinion. Surprisingly, we find the average degree of social influence in the cohort to be 32% with a 29% improvement in performance: In other words, the crowd self-organized into a near-optimal setting. We believe this new paradigm for making crowds “smarter” has the potential for significant impact on a diverse set of fields from population health to government planning. We include a case study to show how a crowd of states potentially could collectively learn the level of taxation and expenditure that optimizes economic growth.

Heuristics-based influence maximization for opinion formation in social networks

Influence maximization for opinion formation (IMOF) is a significant issue in social networks. In general, the main goal of the IMOF is to determine a set of optimal nodes in an effective way for the maximum propagation of the most ideal opinions. The current studies mainly focus on the control methods for the opinion formation, for example, the informed agents, the improved opinion formation models and the effective initial-node-determination algorithms. However, there are few researches on the mathematical modeling of the IMOF. Additionally, the effective mechanisms should be specifically designed to solve the IMOF. In this paper, a general IMOF model is formulated by the informed agents, and then a 3-hop heuristic algorithm is proposed to deal with the IMOF. Firstly, the IMOF is formulated mathematically as an optimization model. Then, in order to satisfy various preferences among nodes, the weighted bounded confidence model is devised to calculate the opinion of each node effectively. Moreover, the influence spread obeys 3-hop rule, and node influence gradually dissipates and stops beyond 3 hop. Therefore, based on node influence, a 3-hop heuristic algorithm is proposed to effectively determine the top-m influential nodes. With the opinion set initialized, a discount strategy is presented to accelerate the propagation of the selected top-m influential nodes. Finally, the experimental results demonstrate that the proposed method obtains better performance on the average of opinions than the chosen benchmarks.