Subjective Evaluation Data Research Articles

Exploring the Influence of Object, Subject, and Context on Aesthetic Evaluation through Computational Aesthetics and Neuroaesthetics

Background: In recent years, computational aesthetics and neuroaesthetics have provided novel insights into understanding beauty. Building upon the findings of traditional aesthetics, this study aims to combine these two research methods to explore an interdisciplinary approach to studying aesthetics. Method: Abstract artworks were used as experimental materials. Based on traditional aesthetics and in combination, features of composition, tone, and texture were selected. Computational aesthetic methods were then employed to correspond these features to physical quantities: blank space, gray histogram, Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Gabor filters. An electroencephalogram (EEG) experiment was carried out, in which participants conducted aesthetic evaluations of the experimental materials in different contexts (genuine, fake), and their EEG data were recorded to analyze the impact of various feature classes in the aesthetic evaluation process. Finally, a Support Vector Machines (SVMs) was utilized to model the feature data, Event-Related Potentials (ERPs), context data, and subjective aesthetic evaluation data. Result: Behavioral data revealed higher aesthetic ratings in the genuine context. ERP data indicated that genuine contexts elicited more negative deflections in the prefrontal lobes between 200 and 1000 ms. Class II compositions demonstrated more positive deflections in the parietal lobes at 50–120 ms, while Class I tones evoked more positive amplitudes in the occipital lobes at 200–300 ms. Gabor features showed significant variations in the parieto-occipital area at an early stage. Class II LBP elicited a prefrontal negative wave with a larger amplitude. The results of the SVM models indicated that the model incorporating aesthetic subject and context data (ACC = 0.76866) outperforms the model using only parameters of the aesthetic object (ACC = 0.68657). Conclusion: A positive context tends to provide participants with a more positive aesthetic experience, but abstract artworks may not respond to this positivity. During aesthetic evaluation, the ERP data activated by different features show a trend from global to local. The SVM model based on multimodal data fusion effectively predicts aesthetics, further demonstrating the feasibility of the combined research approach of computational aesthetics and neuroaesthetics.

Urban perception by using eye movement data on street view images

AbstractUnderstanding the spatial distribution patterns of urban perception and analyzing the correlation between human emotional perception and street composition elements are important for accurately understanding how people interact with the urban environment, urban planning, and urban management. Previous studies on urban perception using street view data have not fully considered the actual level of attention to different visual elements when browsing street view images. In this article, we use eye tracking technology to collect eye movement data and subjective perception evaluation data when people browse street view images, and analyze the correlation between the time to first fixation, duration of first fixation, and fixation frequency of different visual elements and the six perceptual outcomes of wealthy, safe, lively, beautiful, boring, and depressing. Furthermore, this article integrates eye movement data with street view semantic data and introduces a novel method for predicting urban perception using a machine learning algorithm. The proposed method outperforms a comparative model that solely relies on semantic data, exhibiting higher accuracy in perception prediction. Additionally, the study presents a perceptual mapping of the prediction results, providing a visual representation of the predicted urban perception outcomes. As vision is the primary perceptual channel, this study achieves a more objective and scientifically reliable urban perception, which is of reference value for the study of physical and mental health due to the urban physical environment.

Study of Lighting Design for Interior Spaces

Due to the continuous progress of lighting fixture technology, people's requirements for commercial space design are also rising, and the comfortable, convenient, artistic, and technological combination of space is more and more emphasized by designers. The author has collected a large amount of visual subjective evaluation data through actual investigation and experimentation and conducted extensive and in-depth research on the light source's low radiation, luminous flux, luminous intensity, illuminance, luminance, color temperature, color deviation, color tolerance, beam angle, and maintenance coefficient. This paper takes the lighting quality parameters as the benchmark, analyzes the formal aesthetic relationship between commercial space and lighting design, and has a certain reference value for both lighting workers and academic research.

Assessing the intention to accept inquiry-based teaching pedagogy among Chinese university students: an extension of technology acceptance model.

Due to the limitations of traditional didactic teaching, inquiry-based teaching has attracted increasing attention and has become an important content of curriculum teaching reform in college education. Nevertheless, it is vital to investigate students' subjective acceptance of inquiry-based instruction and its influencing factors before inquiry-based teaching methods are widely implemented. In light of this, taking into account the psychological factors of students, an acceptance model of inquiry-based teaching pedagogy was established based on the extended technology acceptance model (TAM). Three additional variables, namely self-efficacy, implementation quality, and risk perception, were incorporated into the TAM. Firstly, subjective evaluation data of the influencing factors of inquiry teaching acceptance were obtained through a network questionnaire survey from university students in Guangdong, China, using snowball sampling and convenient sampling. A total of 485 valid questionnaires were retrieved, with an effective response rate of 88.2%. Then, internal consistency and reliability, convergent validity and discriminant validity of the model and its hypothesis were tested with reliability and validity tests. Finally, path analysis was used to examine key determinants of students' acceptance of inquiry teaching and moderators. Results indicated that the constructed model can explain the acceptability of inquiry teaching for college students by 88.6%; Attitude has a positive significant impact on behavioral intention; Perceived ease of use indirectly affects behavioral intention through perceived usefulness, while perceived usefulness indirectly affects behavioral intention through attitude; self-efficacy not only directly affects behavioral intention but also indirectly affects behavioral intention through implementation quality; implementation quality indirectly affects behavioral intention through perceived usefulness and attitude; students' risk perception of inquiry-based teaching has no negative impact on behavioral intention. Overall, this study has implications for policymakers, teachers or learners in terms of the implementation and promotion of inquiry-based teaching in college classroom.

Thermal comfort characteristics of a catalytic combustion heater under wind-chilled exposure

Heating in wind-chilled environments is a serious task. The catalytic combustion heater (CCH) provides more efficient and cleaner in combustion compared to conventional heaters with high pollutant emission and low energy utilization. In this paper, the heating ability of CCH on human body in a wind-chilled environment (apparent temperatures between −14.8 °C and 12.4 °C) was investigated by collecting skin temperature and subjective thermal evaluation data from 23 subjects. The impact of cold air on localized heating of body parts by CCH was clarified using decision trees and dimensionless correlation coefficients. A human-computer interaction regulation strategy was proposed to simultaneously satisfy the requirements of effective heating, low energy consumption, and low pollution. Finally, CCH and a commercial heater were compared in terms of heating effectiveness and energy consumption in a real outdoor environment. The results showed that CCH effectively solved the problem of excessive cold extremities, and the temperature difference between thoracoabdominal area and extremities was reduced by 57.5–87.9% to within 2 °C. In a strong convective environment, wind speed was the decisive factor (coefficient of 0.95), inducing 46% radiant heat loss. Inputting personnel subjective perception, dynamic environmental parameters, and pollutant thresholds to adjust the injected gas flow enabled intelligent control. In a real cold scenario, the CCH saved 65.9% of energy compared to a commercial heater when achieving the same heating effect. This study provides a reference for the application of catalytic combustion technology in cold outdoor heating and also provides an important theoretical basis for the construction of heating strategies for CCH.

Dynamic collective opinion generation framework for digital transformation barrier analysis in the construction industry

The absence of a reliable, dynamic evaluation system has impeded early-stage industrial research progress, particularly in the digital transformation of the construction industry. Moreover, existing research studies rarely explore the impact of digitalt transformation barriers considering the interplays among them. This paper aims to introduce an innovative framework to generate dynamic collective opinions for barrier analysis in such context. The proposed dynamic collective opinion generation framework comprises three key components: Collective Opinion Generation, Prediction with Expert Advice (PEA), and Social Network Analysis. Its goal is to provide dependable decision support when subjective evaluation data from experts is available. Initially, a bi-objective optimization model generates the initial barrier weight vector. The PEA incorporates a loss function to measure the deviation between aggregated probablity density function and actual observed data, updating the weight vector over time. Next, an influence network covering all barriers is established. Node significance is evaluated through metrics like degree centrality, closeness centrality, and eigenvector centrality. The gravity model based on three metrics is used to determine interrelationships among barriers, resulting in a weight vector capturing these interplays. The two weight vectors are combined with Nash equilibrium, yielding the ultimate weight vector for barriers. The effectiveness of the proposed dynamic collective opinion generation framework is showcased through a case study on China Construction Third Bureau. Results indicate that talent structure notably influences construction companies' digital transformation. Additionally, market structure and strategic position significantly impact digital transformation in this industry.

Plate waste in food service: Nudging intervention

Food waste is a threat to global sustainability. The study aim is to determine the effect of nudge interventions to reduce food waste at lunch. In this experimental study, which was planned in this cross-sectional type, survey data on food waste attitudes were collected from the participants. Volunteers who benefited from the cafeteria service were assigned to the survey (n=157), excluding those who did not use the cafeteria regularly, had a history of food allergy, and declared a diagnosis of chewing-swallowing disorder or celiac disease (n=3). The sociodemographic data and subjective waste amounts of the participants based on the survey, the waste was measured rationally in the pre-nudging and nudging period. The primary outcome is that nudge interventions applied to reduce food waste at lunch can contribute to the total amount of food waste and in which foods to reduce waste. Despite the nudging intervention, the total amount of waste increased, only vegetarian food and bread waste decreased. These changes are not statistically significant. According to the subjective evaluation data, vegetarian food and bread group foods are wasted more. Subjective and rational evaluation results are inconsistent. Short-term nudging intervention is not effective and different strategies are needed to reduce the amount of food waste.

Adopting an HMI for overtaking assistance - Impact of distance display, advice, and guidance information on driver gaze and performance

Conveying supporting information by an in-vehicle human–machine interface (HMI) overtaking assistance system represents a promising approach to facilitate safe overtaking maneuvers. Although various researchers have attempted to examine the safety of systems, numerous HMI concepts are not validated and the impacts of systems on driver behavior following exposure to a connected environment remain unclear. Thus, the objective of this study is to assess the effectiveness of various HMI concepts and their effects on drivers' gaze behavior and driving performance in the connected environment. Three types of supporting information, namely distance, advice and guidance, were designed based on implicit and explicit concepts and displayed on the HMI. The guidance information provides more detailed maneuver instructions. A driving simulator experiment was conducted to imitate a connected environment and included an overtaking scenario on a two-lane highway under three distance gaps (short, middle and long) of oncoming vehicles and four HMI conditions including baseline (3*4). 39 participants (mean = 28.3 years, SD = 8.2 years) drove the simulator. Visual attention, driving maneuver, and subjective evaluation data were collected. The results indicated that the supporting information was valid in facilitating overtaking behavior compared without HMI, particularly under the middle distance gap scenario. Advice information was the most effective, required the least attention, exhibited the best performance, and was accepted by drivers. Detailed maneuver instructions were perceived as helpful, although no effect on acceptance was found. The findings of the study provide valuable insights into the development of user-friendly overtaking assistance systems.

Effects of urban waterfront park landscape elements on visual behavior and public preference: Evidence from eye-tracking experiments

High-quality urban blue-green spaces are essential to enhance the well-being of residents. Public preferences in urban landscapes are closely related to visual behavior and can meet human needs. By combining objective eye-movement data with subjective evaluation data, public preferences for blue-green typical spatial waterfront parks were analyzed based on visual behavior. Taking Wuhan, a megacity in China, as an example, four types of landscapes were identified: waterscape, plantscape, hardscape, and integrated landscape. Using visual clustering to identify eye movement indicators, we analyzed the influence of element types, proportions and combinations on public preferences based on visual behavior differences related to landscape elements. The results indicated that: (1) visual behaviors differ among elements and preferences, arbor commands overall attention, and pavilion is the element attended to first. (2) The positive correlation between water and fatigue recovery increased by 0.163 after combination with reflection, while the correlation between arbor and fatigue recovery changed from 0.353 (p < 0.01) to − 0.251 (p < 0.01) after combination with shrub and grass. (3) Plantscape and waterscape were rated as more natural, intimate, and restorative, but the most element-rich integrated landscape had the highest beauty ratings. Hardscape had significantly lower ratings but were generally considered to be fully utilized spaces. (4) The most significant visual behavior factor affecting evaluations was fixation duration, and the most critical landscape elements were arbor and pavilion. This study reveals differences in people's visual perceptions and preferences for waterfront parks through eye-movement experiments, and could lead to more judicious landscape element selections and park space compositions.

基于扩展计划行为理论的驾驶员跟驰意向分析

Road traffic flow will show the phenomenon of mixed driving of autonomous vehicles and traditional vehicles in the future. In order to explore psychological factors that affect traditional vehicle drivers' intention to follow autonomous vehicles in the human-machine hybrid driving environment, three variables of driver's understanding, risk perception, and acceptance of autonomous vehicles are introduced to construct an extended theoretical framework of planning behavior. Three hundred thirty-one subjective evaluation data are obtained through questionnaire survey. The internal consistency and reliability of the data are tested by SPSS and AMOS softwares. Finally, the path analysis and mediation effect analysis are carried out by using structural equation model to test relationship among the influencing factors. The results show that the structural equation model of driver's car-following intention based on the theory of extended planning behavior has good explanatory power for the driver's car-following intention in the mixed driving environment. Driver's behavioral attitude, subjective norm and perceived behavioral control of autonomous vehicles have a significant positive and direct effect on driver's car-following intention. Driver's risk perception and acceptance of autonomous vehicles have a significant indirect effect on driver's car-following intention through intermediary variables, in which the effect of risk perception is negative, and the effect of acceptance is positive. The degree of understanding has both a significant positive direct effect and a significant positive indirect effect on driver's car-following intention. The research results can be used as the basis for vehicle interaction behavior analysis in the human-machine hybrid driving environment.

Color Transfer Algorithm between Images Based on a Two-Stage Convolutional Neural Network.

A color transfer algorithm between images based on two-stage convolutional neural network (CNN) is proposed. The first stage network is based on VGG19 architecture as the backbone. The reference image-based color transfer (RICT) model was used to extract the features of the reference image and the target image, so as to realize the color transfer between them. The second stage is based on progressive convolutional neural network (PCNN) as its backbone. The palette-based emotional color enhancement (PECE) model is adopted to enhance the emotional coloring of the resulting image by comparing the palette, emotional value and the proportion of each color of the reference image. Through five sets of experiments, it is proved that the visual effect processed by our model is obviously better than several main colorization methods in both subjective evaluation and objective data. It can be applied to various complex scenes, and in the near future, it can also be better applied to the fields of digital restoration of old image archives, medical image coloring, art restoration, remote sensing image enhancement, infrared image enhancement and other fields.

Sitting comfort analysis and prediction for high-speed rail passengers based on statistical analysis and machine learning

Various comfort evaluation models have been extensively studied for improving driver seat and office seat designs for the purpose of improving seat comfort. High-speed rail (HSR) seat comfort is influenced by specific environments, activities, and postures, and targeted in-depth research on this topic is lacking. Existing evaluation cannot ensure the validity of data at the early stages of model training because of their direct reliance on subjective evaluation data, thereby limiting the improvement in the accuracy of prediction. In this study, we designed a sitting observation experiment and static sitting comfort experiment for the typical activities of HSR passengers. The sitting posture of HSR passengers under typical activities was classified for different comfort levels based on the factors influencing the sitting posture in terms of bone and muscle biomechanics. The correlations between the subjective discomfort in different sitting positions, objective change patterns, and body pressure distribution parameters were statistically analysed. The possibility of using sitting duration and sitting frequency as objective behavioural indicators of discomfort evaluation was indicated. Based on the verification of the reliability of the evaluation data, the classification performance of various machine learning algorithms was compared and analysed, and a sitting comfort prediction model was established based on the gradient boosting machine algorithm, with an accuracy of 89.5%. This study serves as an effective reference for improvement strategies for HSR passenger comfort and will inspire new ideas for exploring objective indicators for sitting comfort evaluation.

A Study on the Effectiveness of IT Application Education for Older Adults by Interaction Method of Humanoid Robots.

Education using humanoid robots can have a positive impact in many fields, including in medical or physical training. This study investigated the effects of robot interactions with respect to facial expressions, gestures, voices and their combinations on the education of the elderly regarding information and communications technology (ICT) from functional and emotional perspectives. In this study, the robot’s interaction methods were divided into four categories: (1) voice, (2) voice and expression, (3) voice and gesture, and (4) voice and expression and gesture. An experiment involving an educational application with a humanoid robot was conducted with a total of 15 elderly people over the age of 60. The effect of the humanoid robot’s interaction method on education was identified by means of subjective survey evaluation and practice performance data analysis, including error rate, task success rate, and number of retrainings. Through the experiment, functional and emotional aspects of effects were measured. The results showed that performance and perceived effectiveness were not significantly affected by the type of robot interaction, but the degree to which the robot felt like it had emotions, the degree to which the robot felt like a human, and the degree to which the robot was friendly were significantly different according to the interaction type employed by the humanoid robot. The best effect was achieved when voice and gesture were used together during tutoring. Recognizing that ICT education using humanoid robots increases interest and participation in education, such robots are concluded to be a suitable method for performing ICT education. In addition, when designing robotic interactions, the use of the robot’s voice and gestures together is expected to lead to greater anthropomorphism, resulting in a stronger relationship with humanoid robots.

The white noise standard sample method and application for subjective noise evaluation

The anchored scoring method is one of the widely used subjective sound evaluation methods. The comparability between subjective sound evaluation data obtained from different sample sets can be enhanced by selecting the standard sound sample as reference sample in subjective sound evaluation, thus facilitating the horizontal comparison between different evaluation results. However, the applicability of the standard sound sample method is poor in the experiments on evaluating some types of noise because the mechanisms of annoyance caused by different types of noise are discrepant. Therefore, the standard sound sample method for white noise that takes the existing pure tone as standard sample is proposed. Taking the evaluation results of the common anchored scoring method as benchmark, the experimental study of the performances of the standard sound sample method and the anchored scoring method was carried out according to the evaluation of the annoyance degrees of the two types of similar noise combination or dissimilar noise combination that have obviously different spectra and perceptual characteristics. The results show that the standard sound sample method for pure tone is suitable for noise with obvious tonal perception and that the standard sound sample method for white noise is suitable for broadband noise with weak tonal perception. Finally, the method of conversion between the annoyance evaluation results on different reference samples is studied to enhance the applicability of the standard sound sample method.

3D Space Layout Design of Holographic Command Cabin Information Display in Mixed Reality Environment Based on HoloLens 2.

After the command and control information of the command and control cabin is displayed in the form of mixed reality, the large amount of real-time information and static information contained in it will form a dynamic situation that changes all the time. This brings a great burden to the system operator’s cognition, decision-making and operation. In order to solve this problem, this paper studies the three-dimensional spatial layout of holographic command cabin information display in a mixed reality environment. A total of 15 people participated in the experiment, of which 10 were the subjects of the experiment and 5 were the staff of the auxiliary experiment. Ten subjects used the HoloLens 2 generation to conduct visual characteristics and cognitive load experiments and collected and analyzed the subjects’ task completion time, error rate, eye movement and EEG and subjective evaluation data. Through the analysis of experimental data, the laws of visual and cognitive features of three-dimensional space in a mixed reality environment can be obtained. This paper systematically explores the effects of three key attributes: depth distance, information layer number and target relative position depth distance of information distribution in a 3D space, on visual search performance and on cognitive load. The experimental results showed that the optimal depth distance range for information display in the mixed reality environment is: the best depth distance for operation interactions (0.6 m~1.0 m), the best depth distance for accurate identification (2.4 m~2.8 m) and the overall situational awareness best-in-class depth distance (3.4 m~3.6 m). Under a certain angle of view, the number of information layers in the space is as small as possible, and the number of information layers should not exceed five at most. The relative position depth distance between the information layers in space ranges from 0.2 m to 0.35 m. Based on this theory, information layout in a 3D space can achieve a faster and more accurate visual search in a mixed reality environment and effectively reduce the cognitive load.

A FUZZY ANALYSIS OF SPIRITUAL POVERTY ALLEVIATION IN DEEP POVERTY-STRICKEN AREAS OF GANSU

This paper aims to find the importance and contribution of spiritual poverty alleviation using people’s subjective evaluation data of deeply impoverished areas. The research is conducted according to the method of five-level scale evaluation and the fuzzy comprehensive evaluation, combined with on-site questionnaires with the masses, and interviews with village cadres. The main findings reveal that the spiritual measures are more important than the material measures, and some of the second-level indicators have relatively low recognition of poverty alleviation contribution, which is an important basis for our targeted policies.

Trusted Network Evaluation Model Based on Comprehensive Trust

Trusted network is currently evaluated based on the trustworthiness of the nodes in the network; thus, trust in the nodes is required to achieve trust in the network. The methods for calculating node trust mainly utilize the following three types of models: node behavior model, multiple-attribute decision-making model and reputation model. User behavior model always determines trust values based on harmful node behaviors. Multi-attribute decision-making model can be regarded as refinements of node behavior model. The trust values of the nodes are determined based on compromised trust attribute data and not just harmful behavior data. Reputation model calculates the trust values of nodes using subjective evaluation data for nodes. This paper presents a comprehensive trust model that combines subjective evaluation data and objective attribute data to calculate node trust. The experimental results show that malicious nodes can be effectively identified, which improves the service success rate of the system.

Emergency Optimization Decision-Making with Incomplete Probabilistic Information under the Background of COVID-19

At present, the whole world is facing the serious challenge of COVID-19, and it has reached a consensus that taking appropriate measures timely is the key to prevent and control infectious diseases. This paper proposes an algorithm to solve the problem of how to choose the most appropriate alternative from numerous alternatives in the limited time from the perspective of management. First of all, we have compared various data structures for keeping the comparison results of alternatives. After comparisons, we adopt the hesitant fuzzy incomplete probabilistic linguistic preference relation matrix to save the information which can keep the first-hand valuable collected data to the maximum extent; then, we can obtain the missing values with the help of the fault tree analysis method, which can consider both subjective evaluation data and objective historical data simultaneously. Meanwhile, the fault tree analysis method can find development laws with the help of similar infectious diseases that have occurred in the past. The definition of consistency index is also introduced which can measure whether there are contradictions and the degree of contradiction in the decision results. Only those data that meet the consistency requirements can be used for decision-making and then a method is proposed to effectively reduce the degree of inconsistency. The information aggregation method will be adopted subsequently, and we can obtain the ranking of alternatives. An instance with specific execution steps is also introduced to illustrate the feasibility and efficiency of the algorithm proposed in this paper; in the end, several types of comparisons with typical algorithms proposed by other scholars are carried out, and all the experimental results show that the algorithm proposed in this paper is effective and innovative in some aspects.

A Study of Blended Learning Using the Smart Class Teaching Module on Psychosocial Dysfunction Course During the Training of Undergraduate Occupational Therapy Students in China.

BackgroundOnline blended learning, also known as “smart classes”, has benefits when compared with traditional teaching methods that use books and lectures. This study aimed to compare the use of the Smart Class teaching module with traditional teaching on the topic of psychosocial dysfunction during the training of undergraduate occupational therapy (OT) students in China.Material/MethodsWe recruited Grade 2017 OT students as the Smart Class teaching module group and Grade 2016 OT students as the Traditional Class teaching module group to participate in the study. The objective evaluation (assignment score, practical exam score, written exam score, and final score) and subjective evaluation (data from student questionnaires and information from interviews with the lead teacher and assistant teachers) were performed in both groups.ResultsNo significant difference was found in the final scores (P=0.874) and students’ questionnaire results between the 2 groups. However, data from the student questionnaires and teacher interviews indicated a preference for combining the Smart Class teaching module and the Traditional Class teaching module.ConclusionsThe advantage of the Smart Class teaching module is that it can effectively integrate excellent teaching resources across geographical restrictions and it is conducive to promoting independent learning for students and all-around supervision for teaching. The Smart Class teaching module was comparable to traditional teaching methods for the training of undergraduate OT students in China, but was preferred by the students.

Parental time restrictions and the cost of children: insights from a survey among mothers

An important aspect when analyzing economic inequality between households with children is time. At given monetary incomes, the material well-being of families may be very different depending on how much time parents have at their disposal. In this paper, we provide estimates of the subjectively perceived cost of children depending on the extent of parental time restrictions. Building on a study by Koulovatianos, Schröder and Schmidt (J. Bus. Econ. Stat. 27:42–51, 2009) that introduces a novel way of using subjective income evaluation data for such estimations, we conduct a refined version of the underlying survey, focusing on young women with children in Germany. Our study confirms that the perceived monetary cost of children is substantial and increases with parental nonmarket time restrictions. The experienced loss in material living standards associated with supplying time to the labor market is sizeable for families with children.