Model Of Perception Research Articles

MODEL OF MAXIMAL WEIGHTS INVERSE CHAINS FOR THE ANALYSIS OF THE INFLUENCE FACTORS OF THE SOFTWARE COMPLEXES SUPPORT

Context. The problem of identification, formation and restoration of the boundaries of influencing factors, lost as a result of the implementation of multi-layer perceptron models into the models of subjective perception of the object of software complexes support, as well as the applied practical problem of primary monitoring of the frequency manifestation of a given influencing factor in the post-real-time mode, is considered. The object of research is the influencing factors of support of software complexes. Objective – the goal of the work is to develop a model of inverse chains of maximum weights for the analysis of influencing factors of the software complexes support. Method. A model of maximal weights inverse chains for the analysis of the influence factors of the software complexes support was developed for the analysis of the influencing factors of the software complexes support. The developed model provides possibility to identify and form feedback chains of maximum weights for the identification and further analysis of influencing factors that are reflected into the results of the object perception (the supported software complex or its support processes), by the relevant subjects of interaction which directly or indirectly interact with it. Results. Results of the resolved applied practical problem of primary monitoring of the frequency manifestation of a given influencing factor in the post-real-time mode have been provided as an example of the applied practical use of the developed model. The output results of the developed models functioning – are the reverse chains of maximum weights. In the future, the results obtained by the developed model are used to solve the applied-scientific problem of identification, formation and restoration of the boundaries of influencing factors, lost as a result of the implementation of the appropriate models of multilayer perceptron inside the models of subjective perception of the software complexes support. So the developed model of maximal weights inverse chains for the analysis of the influence factors of the software complexes support resolves this applied-scientific problem, initially caused by the implementation of the corresponding multilayer perceptron models inside the model of the subjective perception of the object of software complexessupport. The developed model provides the possibility of carrying out a qualitative analysis of the transformation of the input characteristics of the object of support into the output resulting characteristics of its subjective perception. Conclusions. Developed model allows to resolve the described problems. At the same time, the developed model improves the classical understanding of multilayer perceptron artificial neural networks, as it introduces an additional value to the neurons of hidden layers, which (starting from now) are able to perform a completely new role of influencing factors markers, while in the classical understanding of multilayer perceptron artificial neural networks they did not perform any functions other than arithmetic to ensure the possibility of correct learning and functioning of a multilayer perceptron artificial neural networks.

Office soundscape assessment: A model of acoustic environment perception in open-plan officesa).

The soundscape approach has been utilised in recent research for residential environments, but applications to indoor working environments remain limited. This study investigated the suitability of affective quality evaluations of open-plan office acoustics. Perceptual assessments were completed by occupants of eight open-plan office floorplates, using affective dimensions outlined in PD ISO/TS 12913-3:2019 and indoor soundscape studies. Participant demographic, contextual, work-related quality, and psychological well-being data were collected. Workstation noise levels were measured following BS ISO 22955:2021. Principal component analysis identified Pleasantness, Eventfulness, and Emptiness as the main perceptual dimensions, cumulatively explaining 56% of the total variance. Results indicate the suitability of the PD ISO/TS 12913-3:2019 two-dimensional model for open-plan offices. The perceived presence of human sounds negatively correlated with ISO Pleasantness yet positively correlated with ISO Eventfulness. Participant gender and aural diversity mediated ISO Pleasantness. Psychological well-being and work-related quality correlated positively with soundscape pleasantness. No correlations were found between level-based acoustical indicators-equivalent continuous sound level (LAeq), level exceeded 10% (LA10) and 90% (LA90) of the time, level variability (LA10-LA90) and Liveliness-and perceptual scores. A conceptual representation of open-plan office soundscape perception is presented, offering a framework for evaluating and designing workspaces to enhance well-being.

Perceived risk of death among patients with advanced cancer: a qualitative directed content analysis

BackgroundRisk perception with respect to death is a prerequisite for patients with advanced cancer when the time comes to make medical decisions. However, the nature of death risk perception remains unclear.MethodIn-depth interviews were conducted with 28 patients with advanced cancer who were recruited from two hospitals and one home-based hospice in Fujian, China. Interviews were transcribed and directed content analysis applied. The Tripartite Model of Risk Perception was used as a theoretical framework.ResultsPatients with advanced cancer perceived their risk of death in different ways. Professional communication about death risk and data-driven risk perception were common in clinical settings. Affective influences, inherent cognition, and comparisons to others or oneself also contributed to the subjects’ self-perceived death risk.ConclusionThis theory-informed qualitative study clarifies the nature of the perceived risk of death among patients with advanced cancer. The study findings offer healthcare providers a more nuanced understanding of the perceived risk of death among patients with advanced cancer.

Multi-modal scene graph inspired policy for visual navigation

Visual navigation needs the agent locate the given target with visual perception. To enable robots to effectively execute tasks, combining large language models (LLMs) with multi-modal inputs in navigation is necessary. While LLMs offer rich semantic knowledge, they lack specific real-world information and real-time interaction capabilities. This paper introduces a Multi-modal Scene Graph (MMSG) navigation framework that aligns LLMs with visual perception models to predict next steps. Firstly, a multi-modal scene dataset is constructed, containing triplets of object-relations-target words. We provide target words and lists of existing objects in the scene to generate a large number of instructions and corresponding action plans for GPT-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}3.5. The generated data is then utilized for pre-train LLM for path planning. During inference, we discover objects in the scene by extending the DETR visual object detector to multi-view RGB image collected from different reachable positions. Experimental results show that path planning generated from MMSG outperforms state-of-the-art methods, indicating its feasibility in complex environments. We evaluate our methods on the ProTHOR dataset and show superior navigation performance.

A review on depth perception techniques in organoid images

Organoids are an in vitro model that can simulate the complex structure and function of tissues in vivo. Functions such as classification, screening and trajectory recognition have been realized through organoid image analysis, but there are still problems such as low accuracy in recognition classification and cell tracking. Deep learning algorithm and organoid image fusion analysis are the most advanced organoid image analysis methods. In this paper, the organoid image depth perception technology is investigated and sorted out, the organoid culture mechanism and its application concept in depth perception are introduced, and the key progress of four depth perception algorithms such as organoid image and classification recognition, pattern detection, image segmentation and dynamic tracking are reviewed respectively, and the performance advantages of different depth models are compared and analyzed. In addition, this paper also summarizes the depth perception technology of various organ images from the aspects of depth perception feature learning, model generalization and multiple evaluation parameters, and prospects the development trend of organoids based on deep learning methods in the future, so as to promote the application of depth perception technology in organoid images. It provides an important reference for the academic research and practical application in this field.

T-Smade: A Two-Stage Smart Detector for Evasive Spectre Attacks Under Various Workloads

Evasive Spectre attacks have used additional nop or memory delay instructions to make effective hardware performance counter based detectors with lower attack detection successful rate. Interestingly, the detection performance gets worse under different workloads. For example, the attack detection successful rate is only 59.8% for realistic applications, while it is much lower 27.52% for memory stress test. Therefore, this paper proposes a two-stage smart detector T-Smade designed for evasive Spectre attacks (e.g., evasive Spectre nop and evasive Spectre memory) under various workloads. T-Smade uses the first-stage detector to identify the type of workloads and then selects the appropriate second-stage detector, which uses four hardware performance counter events to characterize the high cache miss rate and low branch miss rate of Spectre attacks. More importantly, the second stage detector adds one dimension of reusing cache miss rate and branch miss rate to exploit the characteristics of various workloads to detect evasive Spectre attacks effectively. Furthermore, to achieve the good generalization for more unseen evasive Spectre attacks, the proposed classification detector T-Smade is trained by the raw data of Spectre attacks and non-attacks in different workloads using simple Multi-Layer Perception models. The comprehensive results demonstrate that T-Smade makes the average attack detection successful rate of evasive Spectre nop under different workload return from 27.52% to 95.42%, and that of evasive Spectre memory from 59.8% up to 100%.

Active Models in Speech Perception: A Critical Review in Phonetics

This critical review examines four prominent active models of speech perception: quantal theory, motor theory, hyper-hypo-articulation theory, and exemplar theory. Each of these models offers distinctive insights into how listeners perceive, interpret, and categorize speech sounds. Quantal theory explores the non-linear relationship between articulation and acoustic output, emphasizing stable and unstable regions in speech production. Motor theory posits that speech perception is grounded in articulatory gestures, suggesting a direct link between production and perception. The hyper-hypo-articulation theory focuses on the speaker’s adaptive articulation, which ranges from hyper- to hypo-articulation depending on the listener's needs and the communicative context. Exemplar theory, rooted in cognitive psychology, highlights the role of memory and experience, with individual speech sounds compared to stored exemplars in memory to aid perception. Despite their contributions, none of these models offers a comprehensive solution to all speech perception challenges. Each theory is suited to specific contexts, speech features, or tasks, making it crucial for researchers to understand their limitations. Furthermore, the review underscores the importance of selecting the appropriate model for particular research or practical applications, given the variability in their functionality. Finally, it calls for future research to delve deeper into passive models of speech perception and suggests that comparative studies between active and passive models could yield valuable insights.

A following model considering multiple vehicles from the driver's front and rear perspectives

In the current traffic environment dominated by manual driving, existing models of drivers' rear-view perceptions are inadequate. Existing car-following models that incorporate rear-view information are primarily focused on the Internet of Vehicles (IoV) and automated driving environments. However, they fail to realistically reflect the visual processing mechanisms of human drivers, limiting their effectiveness in realistic traffic scenarios. Therefore, we propose a new following model, the multi-vehicle influence from front and rear perspectives (MVFR), that considers the influence of multiple vehicles. The MVFR model combines information from both front and rear vehicles, integrating views from the front, side front and rear. It provides an in-depth analysis of the effects of relative state differences between a vehicle and its surrounding vehicles on speed, including the effects of perspectives in both the lateral and longitudinal directions. Linear stability analysis and numerical simulation demonstrate that considering the perspectives of rear-following vehicles and lateral offset angles can improve traffic flow stability to a certain extent. Furthermore, properly considering the lateral offset distance and the number of vehicles ahead also positively affects traffic flow stability. This study reveals that observing following vehicles and considering information from multiple front vehicles enhances system stability, especially when there is no or minimal lateral offset. In contrast, focusing on fewer front vehicles is more effective for traffic flow stability when there is a large lateral offset. Experimental results using the CKQ4up dataset show that the MVFR model achieves higher accuracy than the conventional FVD model, the front-view-only improved FVD model (MFVD-RV), and the MFRHVAD-RV and MFRHVAD-AV models. Compared with models relying solely on front-view or non-visual perception, the MVFR model demonstrates a better fit, validating the advantages of this full-view perception model in manual driving environments. This innovation addresses the shortcomings of existing research, thereby enhancing the reliability of models under manual driving conditions on highways.

Lifestyle Changes, Individual Mobility, and Interactions in Suburban Settlement Areas of Makassar City, Indonesia: Perceptual Model Approach

Lifestyle Changes, Individual Mobility, and Interactions in Suburban Settlement Areas of Makassar City, Indonesia: Perceptual Model Approach

The Nitty Gritty of Stress and Mental Health: The Role of Grit in Moderating the Relationship Between Stress, Anxiety, and Depression

ABSTRACT College students in the United States contend with unprecedented mental health difficulties. College-specific challenges and stressors associated with emerging adulthood exacerbate these mental health struggles. In the ABC-X model of stress, cognitive appraisal or perception mediates the relationship between stressful events and degraded mental health. In this model, a resource, such as grit, may strengthen student resilience. Using data collected from 844 US college students, the path analysis results identified the mediation effect of the perception of stress on the association between college life challenges and mental health difficulties (depression and anxiety). These mediation effects were not significantly affected by the level of grit. However, the relationships between the perception of stress and both anxiety and depression were weaker at higher levels of grit. The implications of these findings, relative to grit interventions and attrition, are considered.

Research on Game Players' Willingness to Purchase Based on the Perspective of Ambivalent Attitudes

Consumer ambivalence not only reflects the emotional and cognitive responses formed after perceiving external marketing stimuli but also influences subsequent purchase intentions. Based on the TAM (Technology Acceptance Model) and S-O-R (Stimulus-Organism-Response) models, this study introduces ambivalent attitudes as a mediating variable to construct and test a theoretical model of external marketing stimuli, consumer perception, ambivalent attitudes, and purchase intentions. Using 716 valid responses from gaming communities, the measurement and structural models were evaluated using AMOS 26.0 and SPSS 26.0 software. The empirical results show that the clarity and total volume of external marketing information can significantly influence purchase intentions by affecting consumer perceptions. Ambivalent attitudes directly influence purchase intentions and serve as a mediating factor in the pathways where perceptions affect purchase intentions.

Speckle-free holography with a diffraction-aware global perceptual model

Computer-generated holography (CGH) based on neural networks has been actively investigated in recent years, and convolutional neural networks (CNNs) are frequently adopted. A convolutional kernel captures local dependencies between neighboring pixels. However, in CGH, each pixel on the hologram influences all the image pixels on the observation plane, thus requiring a network capable of learning long-distance dependencies. To tackle this problem, we propose a CGH model called Holomer. Its single-layer perceptual field is 43 times larger than that of a widely used 3×3 convolutional kernel, thanks to the embedding-based feature dimensionality reduction and multi-head sliding-window self-attention mechanisms. In addition, we propose a metric to measure the networks’ learning ability of the inverse diffraction process. In the simulation, our method demonstrated noteworthy performance on the DIV2K dataset at a resolution of 1920×1024, achieving a PSNR and an SSIM of 35.59 dB and 0.93, respectively. The optical experiments reveal that our results have excellent image details and no observable background speckle noise. This work paves the path of high-quality hologram generation.

DEVELOPMENT OF MARKETING IN AZERBAIJAN

The main goal of the article is to study activities aimed at marketing development in Azerbaijan. It is noted that benefiting from the synthesis of the national characteristics of the economy (including the experience of the Soviet era) and the world experience is one of the urgent requirements of the modern era. Method and methodologies. In the analysis process, comparative analysis of statistical indicators, analysis of legal-normative documents necessary for the development of marketing, classification of existing problems in the system of formation of market relations in Azerbaijan, forecasting and projecting methods were used. Novelty on the article. The article focuses on some rational and non-rational activities that are neglected in the scientific literature and necessitates the development of marketing. Conclusion and suggestions. The article emphasizes that marketing promotion is a step that serves to make companies popular among the population. These steps are one of the necessary steps to be carried out and updated both thoroughly and systematically. It is noted that 90% of managers of companies operating in Azerbaijan think that there is no need for marketing research. Thus, the decision to implement marketing measures is not based on the analysis of research data, and this, in turn, does not justify the effectiveness of marketing projects. Businessmen rely too much on their intuition and make unreasonable expenses based only on their own experience and unsystematized information that reaches them over a period of time. As a result: 1. Unsuccessful image campaigns are organized that do not produce any desired intangible effect; 2. Sales of newly launched products are falling; 3. Preference is given to advertising campaigns mainly aimed at financial impact; 4. Absurd models of brand perception emerge as a result of lack of study of brand perception after marketing activities; 5. Low level of service and, as a result, customer satisfaction. At best, marketers refer to a company's to-do list.

A taxonomy for human social perception: Data-driven modeling with cinematic stimuli.

Every day, humans encounter complex social situations that need to be encoded effectively to allow interaction with others. Yet, principles for organizing the perception of social features from the external world remain poorly characterized. In this large-scale study, we investigated the principles of social perception in dynamic scenes. In the primary data set, we presented 234 movie clips (41 min) containing various social situations to 1,140 participants and asked them to evaluate the presence of 138 social features in each clip. Analyses of the social feature ratings revealed that some features are perceived categorically (present or absent) and others continuously (intensity) and simple social features requiring immediate response are perceived most consistently across participants. To establish the low-dimensional perceptual organization for social features based on movies, we used principal coordinate analysis and consensus clustering for the feature ratings. These dimension reduction analyses revealed that the social perceptual structure can be modeled with eight main dimensions and that behaviorally relevant perceptual categories emerge from these main dimensions. This social perceptual structure generalized from the perception of unrelated Hollywood movie clips to the perception of a full Finnish movie (70 min) and to the perception of static images (n = 468) and across three independent sets of participants (n = 2,254). Based on the results, we propose eight basic dimensions of social perception as a model for rapid social perception where social situations are perceived along eight orthogonal perceptual dimensions (most importantly emotional valence, empathy vs. dominance, and cognitive vs. physical behavior). (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Soundscape design methodology using a perception model for activity transition in future mobility

In the context of Future Mobility, a methodology for soundscape design using a perception model has been developed to facilitate the implementation of performance spaces among various activity transitions. This approach involved evaluating the soundscape and room acoustics of orchestra performances and solo singing that penetrate the vehicle interior, aimed at comparing with the auditory and visual environments of a concert hall. The performance hall being compared was a large, rectangular concert hall, and the evaluation point was located in the center of the audience. To investigate audio-visual preferences, variables related to sound pressure level and spatial acoustic quality were initially defined. Following this, in-car human perception in Concert Hall mode was assessed, grounded on indoor acoustic parameters relevant to music listening, encompassing acoustic satisfaction and audio-visual components. To enhance the poor audio-visual environmental conditions inside the vehicle from a UX perspective, implementations of transparent displays and stereoscopic sound were implemented. The methodology was validated based on the psychophysiological responses of participants to the audio-visual soundscape factors, aiming for performance enhancement.

Azimuth perception of virtual sources in automotive environment : speech and musical stimulus

The spatial reproduction of musical media in car environment presents a number of challenges, due to the fact that the loudspeakers are not symmetrical in relation to the listeners and to the unique acoustic characteristics. In particular, the positioning of virtual sources is difficult to apprehend, unlike in conventional stereophonic configurations. In an initial study, it was shown that an azimuth perception model, the extended energy vector, produced results that were consistent with experimental data. As the first experiment was carried out using only a short burst of pink noise, it seemed interesting to compare this model with other data from more natural signals. New experiments were therefore carried out using audio samples such as speech and instruments. The subjects, seated in the driving seat, pointed their noses in the perceived direction of the signal, their head movements being recorded with a headtracker. The model will be compared with these new experimental data.

Стандартизация методики «Перцептивное моделирование» для детей 5-6 и 6-7 лет

<p>In modern sociocultural conditions, reliable diagnostic tools are needed to assess the mental development of a child, which would at the same time make it possible to adjust the objectives of educational work. The article reflects the results of adaptation and standardization of the "Perceptual modeling" test included in the diagnostic set for assessing the mental development of preschoolers (L.A. Wenger and colleagues). The study sample consisted of two groups: children 5-6 (N=1003) and children 6-7 (N=1516) years old. In these groups, children were offered various test options based on their age-related perceptual modeling capabilities. The adaptation of the "Perceptual modeling" test involved converting it into electronic form, changing the system for evaluating the results obtained and statistically verifying the possibility of using it in this form to solve diagnostic problems. The test standardization procedure has shown good external and internal validity, and its diagnostic reliability. Statistical analysis revealed the absence of significant gender differences in the performance of the test and the presence of significant differences between children with normal development and children with disabilities (speech, hearing impairment and mental retardation). As a result of standardization, age norms for mastering perceptual modeling actions for children with normal development and children with disabilities were determined by children of the sixth and seventh years of life.</p>

The Social Contagion of Risk Perceptions of Emerging Technologies through Evolutionary Game in Networks

Emerging technologies are leading the fourth industrial revolution, bringing profound changes to modern life. However, the contagion of public risk perceptions pertaining to these technologies may result in new social stability risks according to the social amplification of risk framework (SARF). As such, understanding the formation and contagion mechanisms behind public risk perceptions of emerging technologies is critical to promoting their increased receptivity. Based on an evolutionary game theoretical approach to complex networks, this paper constructs a social contagion model of the risk perception of emerging technologies and uses simulations to analyze the influence of factors such as individual cognition and social environment. Results indicate the following: (1) the rate of risk perception contagion gradually increases with the level of individual risk perception and trust in communicators; (2) the risk perception contagion rate gradually decreases as the level of risk governance participation increases; and (3) comparing different social networks, kinship networks have a greater effect than social media networks on risk perception contagion.

Probing the link between vision and language in material perception using psychophysics and unsupervised learning.

We can visually discriminate and recognize a wide range of materials. Meanwhile, we use language to describe what we see and communicate relevant information about the materials. Here, we investigate the relationship between visual judgment and language expression to understand how visual features relate to semantic representations in human cognition. We use deep generative models to generate images of realistic materials. Interpolating between the generative models enables us to systematically create material appearances in both well-defined and ambiguous categories. Using these stimuli, we compared the representations of materials from two behavioral tasks: visual material similarity judgments and free-form verbal descriptions. Our findings reveal a moderate but significant correlation between vision and language on a categorical level. However, analyzing the representations with an unsupervised alignment method, we discover structural differences that arise at the image-to-image level, especially among ambiguous materials morphed between known categories. Moreover, visual judgments exhibit more individual differences compared to verbal descriptions. Our results show that while verbal descriptions capture material qualities on the coarse level, they may not fully convey the visual nuances of material appearances. Analyzing the image representation of materials obtained from various pre-trained deep neural networks, we find that similarity structures in human visual judgments align more closely with those of the vision-language models than purely vision-based models. Our work illustrates the need to consider the vision-language relationship in building a comprehensive model for material perception. Moreover, we propose a novel framework for evaluating the alignment and misalignment between representations from different modalities, leveraging information from human behaviors and computational models.

Deep Ring-Block-Wise Network for Hyperspectral Image Classification.

Deep learning has achieved many successes in the field of the hyperspectral image (HSI) classification. Most of existing deep learning-based methods have no consideration of feature distribution, which may yield lowly separable and discriminative features. From the perspective of spatial geometry, one excellent feature distribution form requires to satisfy both properties, i.e., block and ring. The block means that in a feature space, the distance of intraclass samples is close and the one of interclass samples is far. The ring represents that all class samples are overall distributed in a ring topology. Accordingly, in this article, we propose a novel deep ring-block-wise network (DRN) for the HSI classification, which takes full consideration of feature distribution. To obtain the good distribution used for high classification performance, in this DRN, a ring-block perception (RBP) layer is built by integrating the self-representation and ring loss into a perception model. By such way, the exported features are imposed to follow the requirements of both block and ring, so as to be more separably and discriminatively distributed compared with traditional deep networks. Besides, we also design an optimization strategy with alternating update to obtain the solution of this RBP layer model. Extensive results on the Salinas, Pavia Centre, Indian Pines, and Houston datasets have demonstrated that the proposed DRN method achieves the better classification performance in contrast to the state-of-the-art approaches.