Real Images Research Articles

Behavioural shortcomings to avoid in asset valuation using comparable method – insights from a survey experiment

PurposeThe study aims to identify behavioural shortcomings made by appraisers while conducting valuation (in an experimental setting) of a commercial property in Indonesia. The research attempts to provide insights regarding the valuers’ behaviour, that is logic, judgment and decision particularly during the selection of comparables and the adjustment process.Design/methodology/approachThe research mimics the setting of a real valuation assignment by applying survey experiment. To conduct the survey experiment, the researcher first creates a standardised working paper to be electronically sent to and then filled in by individually/personally approached valuers who have confirmed their willingness to participate in the research. The respondents are valuers from various institutions such as appraisal firm, bank, financial institution and multinational corporation in property research and agency, who have passed Basic Professional Education in Property Valuation (which makes them entitled registered valuers from the Indonesian Ministry of Finance). The respondents were asked to be involved in the experiment as an appraiser valuing a real object by filling in a working paper based on a given valuation case. The researcher (who is also a chartered/licensed valuer) carefully observed what actually has happened in the process of adjustment determined by the appraisers according to their judgments and decisions. The researcher and the respondents are able to interact (when necessary) in order to clarify, to confirm or if there are comments as well as questions from the respondents regarding the research. The interactions enable the researcher to capture richer behavioural insights.FindingsThe following behavioural shortcomings are identified from the respondents’ submitted working papers and reactions during their participation in the research: (a) information avoidance, (b) bias and fallacy and (c) inaccuracies. The first two are related to how valuers address and react on a certain issue such as information, while the last is related to their judgement and decision making. It is also found that inaccuracies can appear in the following modes: inconsistency, misplacement, assumed equality (assumption of insignificant difference) and unjustified allocation.Practical implicationsThe results of this research provide insights of real examples regarding what to avoid when conducting a professional valuation work, that is unnecessary, incorrect perspectives and actions.Originality/valueThe selected research method and the applied data-collection technique allow a closer look at how valuers conducting valuation work and/or a deeper observation inside valuation process therefore enables to identify what inappropriate uses during anchoring and adjustments might be. This research contributes to scarce literature on behavioural valuation research based on own/direct experience.

Examining the mechanism of disavowal and its two forms: Cynical disavowal and fetishistic disavowal

This paper posits the existence of two forms of disavowal: cynical and fetishistic. It explores how cynical disavowal involves maintaining a manipulative distance by obscuring the gap between belief and action, allowing the cynic to disavow their investment in an unattainable object and their knowledge of the Other’s lack. In contrast, fetishistic disavowal acknowledges both the objective reality of things and their subjective appearance to the fetishist. Unlike cynicism, fetishism does not rely on obscuring the gap between belief and action; instead, it recognizes and embraces both aspects. In doing so, this paper highlights significant differences between the manipulative distance of the cynic and the self-awareness of the fetishist. It acknowledges the role of cynicism in maintaining the limits and prohibitions within ideology and authority, through examples of banality, while also emphasizing the unique qualities of fetishistic disavowal.

‘They Only Get the Feed That Grows on Our Farm’: A Survey Experiment on Government-Subsidized Greenwashing of Swiss Meat and Agricultural Policy

Increased consumer concern about environmental issues has led to a surge in green advertising, including misleading forms known as greenwashing. Definitions of greenwashing have mostly focused on companies misleading consumers about products, and existing empirical analyses of greenwashing effects have mostly used fabricated ad materials referring to fictitious products. The objective of the present paper is to use ad material from an actual ad campaign to examine joint greenwashing, by an industry organization and a national government, of consumer products and public policies. Using an actual government-subsidized video commercial for ‘Swiss meat’ as an example, the present paper examines greenwashing targeting both consumers and citizens to influence their product choices and policy opinions. Based on an online survey of an age- and gender-representative sample (n = 637), we measured aspects of consumer knowledge that would enable the consumers to put the ad’s message in perspective, perceptions of the ad, and beliefs about Swiss meat production. Furthermore, we used a two-factorial experimental design to examine how the video commercial and objective product information affected policy opinions relative to control treatments. We find that the consumers strongly overestimated the true percentage of Swiss farms that used the environmentally friendly feeding system featured in the ad. Regarding perceptions, 52% of the respondents ‘rather’ or ‘fully’ agreed with the statement ‘the ad conveys a realistic image of Swiss meat production’, and 41% ‘rather’ or ‘fully’ agreed with the statement ‘the ad shows the production of Swiss beef as it is’. Regarding beliefs, 51% of the respondents ‘partly’, ‘rather’, or ‘fully’ agreed that ‘more meat production in Switzerland is good for the environment’. The video commercial did not influence beliefs about Swiss meat but changed policy opinions in the favor of government subsidies for the advertisement of Swiss meat. The information treatment influenced policy opinions in the opposite direction but did not decrease the effect of the misleading ad. Other significant predictors of support for increased meat subsidies included frequent meat consumption, low factual knowledge, and positive product beliefs. The results of the survey indicate that the ad campaign may have unduly influenced perceptions of Swiss meat production and public opinion about agricultural policy. The findings raise concerns about the current self-regulation of the communication industry and joint private and public advertising of Swiss agricultural products and policies.

Face Image Generation for Anime Characters based on Generative Adversarial Network

With the increasing demand for digital art, animation, and games, facial generation for anime characters has attracted growing research interest in recent years, which aims to build models to automatically generate unique and high-quality character images. Thanks to the rapid advancement of deep learning techniques, particularly generative adversarial networks, GAN-based image generation methods have continuously achieved breakthroughs in generation effectiveness and speed. Focusing on generating realistic anime face images, this paper proposes an anime character face image generation model based on GANs, which integrates Bath Normalization and Dropout to maintain strong stability and avoid overfitting. Comprehensive experiments show the efficacy of the proposed method, which can achieve high diversity in facial features and styles while maintaining visual coherence

Illustration image style transfer method design based on improved cyclic consistent adversarial network.

To improve the expressiveness and realism of illustration images, the experiment innovatively combines the attention mechanism with the cycle consistency adversarial network and proposes an efficient style transfer method for illustration images. The model comprehensively utilizes the image restoration and style transfer capabilities of the attention mechanism and the cycle consistency adversarial network, and introduces an improved attention module, which can adaptively highlight the key visual elements in the illustration, thereby maintaining artistic integrity during the style transfer process. Through a series of quantitative and qualitative experiments, high-quality style transfer is achieved, especially while retaining the original features of the illustration. The results show that when running on the Monet2photo dataset, when the system iterates to 72 times, the loss function value of the research method approaches the target value of 0.00. On the Horse2zebra dataset, as the sample size increases, the research method has the smallest FID value, and the value approaches 40.00 infinitely. With the change of peak signal-to-noise ratio, the accuracy of the research algorithm has been greater than 95.00%. Practical application found that the color of the image obtained by the research method is more gorgeous and the line features are more obvious. The above results all show that the research method has achieved more satisfactory results in the task of style transfer of illustration images, especially in terms of the accuracy of style transfer and the retention of image details.

Advancing haptic realism: modelling grasp contact vibrations for enhanced virtual environment interaction

Abstract In haptic technology, achieving realistic tactile feedback is crucial for enhancing the user experience in virtual environments. Previous studies lack effective methods for transmitting high-frequency vibrations crucial for realistic tactile feedback in haptic interfaces, highlighting the need for our research to address this gap. This paper explores the application of a tactile gripping interface to transmit the high-frequency vibrations produced when contacting a hard object's surface. These short vibrations improve the tactile sensation of hard virtual surfaces when overlaid on traditional position-based force feedback within a haptic environment. The enhanced realism of virtual objects is achieved by effectively estimating the vibration composition from user-induced parameters. This work presents a prototype grasping interface and empirically demonstrates this device's utility. We examine empirical grasp contact data, recorded and interpreted, to recognise the relationship between dynamic user-controlled parameters and the resulting vibration transients. This relationship effectively incorporates these changing dynamics to model the grasp impact and estimate the essential system parameters to understand the influence of the user's grasp force. Through our multi-point grasping interface design, this work demonstrates a mathematical relationship between the user's grasp force and the high-frequency vibrations from contact with hard surfaces. The study found that the proposed haptic interface had an RMSE of 0.05, demonstrating the system's high level of accuracy. This low RMSE value indicates that the predicted vibrations closely matched the actual measured vibrations, proving the effectiveness of our methods through objective validation.

Retrieval of Phase Information from Low-Dose Electron Microscopy Experiments: Are We at the Limit Yet?

The challenge of imaging low-density objects in an electron microscope without causing beam damage is significant in modern transmission electron microscopy. This is especially true for life science imaging, where the sample, rather than the instrument, still determines the resolution limit. Here, we explore whether we have to accept this or can progress further in this area. To do this, we use numerical simulations to see how much information we can obtain from a weak phase object at different electron doses. Starting from a model with four phase values, we compare Zernike phase contrast with measuring diffracted intensity under multiple random phase illuminations to solve the inverse problem. Our simulations have shown that diffraction-based methods perform better than the Zernike method, as we have found and addressed a normalization issue that, in some other studies, led to an overly optimistic representation of the Zernike setup. We further validated this using more realistic 2D objects and found that random phase illuminated diffraction can be up to five times more efficient than an ideal Zernike implementation. These findings suggest that diffraction-based methods could be a promising approach for imaging beam-sensitive materials and that current low-dose imaging methods are not yet at the quantum limit.

Accurate shear estimation with fourth-order moments

Abstract As imaging surveys progress in exploring the large-scale structure of the Universe through the use of weak gravitational lensing, achieving subpercent accuracy in estimating shape distortions caused by lensing, or shear, is imperative for precision cosmology. In this paper, we extend the Fourier Power Function Shapelets (FPFS) shear estimator using fourth-order shapelet moments and combine it with the original second-order shear estimator to reduce galaxy shape noise. We calibrate this novel shear estimator analytically to a subpercent level accuracy using the AnaCal framework. This higher-order shear estimator is tested with realistic image simulations, and after analytical correction for the detection/selection bias and noise bias, the multiplicative shear bias |m| is below 3 × 10−3 (99.7 % confidence interval) for both isolated and blended galaxies. Once combined with the second-order FPFS shear estimator, the shape noise is reduced by ∼35 % for isolated galaxies in simulations with Hyper Suprime-Cam (HSC) and Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) observational conditions. However, for blended galaxies, the effective number density does not significantly improve with the combination of the two estimators. Based on these results, we recommend exploration of how this framework can further reduce the systematic uncertainties in shear due to PSF leakage and modeling error, and potentially provide improved precision in shear inference in high-resolution space-based images.

Being as the Unity of Substance and Accident in the Metaphysics of the Peripatetic

Representatives of “Falsafa” rationalistically interpreted the ontological and logical-epistemological nature of the categories. They tried to highlight the most general concepts about the world and ways of knowing it. Objective reality appears to Peripatetic thinkers, primarily as the unity of substance and accident. Therefore, the categorical table of the Eastern Peripatetics was built according to a certain system and, to a certain extent, corresponds to the principle of substantial connection of categories. The basis of the substrate of categorical statements is substance. Separate categories such as quality, quantity, relation, place, time, action, possession and others concretize from various sides and reveal its content and essence. Representatives of medieval Arabian philosophy on the basis of the teachings of ancient philosophers proposed number of philosophical concepts in their treatises. The separate categories put forward by Peripatetics as time, space, place, quality, quantity are revealed in their essential characteristics.

Concept of Linguistic Personality Conceptosphere

The main feature of modern linguistics is the close study of the language with a person's consciousness, worldview and thought process, as well as with his practical activities. For this reason, the article emphasizes the importance of taking into account, first of all, the main symptoms of the phenomenon of the linguistic personality in the study of the language. In this sense, it is shown that truth is not limited to the perception of being and one's environment. Since a working linguistic person lives in the same world, the language, along with objective reality, is analyzed to determine whether the person himself is a world-recognizing entity. In this regard, it is clear that the issue of language and thinking, which is considered in the context of the cognitive-conceptual orientation of modern linguistics, will become increasingly important. Because the solution to this problem allows us to define the basic laws of the emergence of abstract, abstract concepts that occur in semantic spaces of different languages. Moreover, it is known that a person's knowledge of objective reality is organized in the form of abstract mental structures, concepts that define various spheres of human activity. That is, a person thinks through concepts, comparing them with each other and sometimes combining them to form new concepts in his thinking. Therefore, based on the ideas presented in the scientific linguistic literature, the author understands the concept as a global unit of thought as a structured quantum of knowledge.

The role and place of federal state unitary enterprises in the sphere of state property management.

The article is devoted to the problem of the organization of professional management of objects of the treasury of the Russian Federation in the light of the goals and objectives defined by the State Program “Federal Property Management” approved by the Government of the Russian Federation, in terms of securing property on the right of economic management for federal state unitary enterprises (FSUE) under the jurisdiction of the Federal Agency for State Property Management (Rosimushchestvo), in order to maintain the objects in proper condition technical condition, commercial use, reduction of facilities, not involved in economic turnover, replenishment of the federal budget. The purpose of the work is to propose ways to organize a management mechanism at the federal level aimed at using extra-budgetary mechanisms for maintaining federally owned facilities or properties and involving them in economic turnover with the involvement of commercial organisations —FGUP. The proposed ways and methods of management may allow using a more flexible mechanism for the extra-budgetary maintenance of federally owned real estate objects, their commercial use, involvement in economic turnover, with the transfer of part of the income received to the federal budget or their use for the purchase of both real estate objects for state needs based on a decision of the Government of the Russian Federation and movable property with subsequent transfer to the Treasury of the Russian Federation.

Hybrid Models of Atmospheric Block Columns of Primary Oil Refining Unit Under Conditions of Initial Information Deficiency

Hybrid Models of Atmospheric Block Columns of Primary Oil Refining Unit Under Conditions of Initial Information Deficiency

Brain Glial Cell Tumor Classification through Ensemble Deep Learning with APCGAN Augmentation

Classification of brain tumor plays a vital role in medical imaging for accurate diagnosis, treatment, and monitoring. Deep learning approaches have gained significant traction in this industry because of their ability to extract relevant features from medical images. The research suggests employing an ensemble classifier with a weighted voting mechanism to categorize glial cell brain malignancies such as Astrocytoma, Glioblastoma multiforme, Oligodendroglioma, and Ependymoma. The proposed ensemble technique employs three main classifiers: Convolutional Neural Network (CNN), Deep Convolutional Long Short Term Memory (C-LSTM), and Deep Convolutional Neural Network + Conditional Random Fields (DCNN+CRF). Deep learning algorithms require a huge amount of input data to avoid overfitting. The Adaptive Progressive Convolutional Generative Adversarial Networks (APCGANs) are used to produce realistic artificial images to efficiently train the proposed methodology. Overall, the proposed ensemble method with weighted voting strategy consistently outperforms the other tested algorithms (CNN, C-LSTM, and DCNN+CRF). Ensemble method attained an accuracy of 99.4 %, recall - 99.1%, precision- 98.0%, and F1-score of 99.2%. Ensemble method consistently demonstrates superior performance in accurately classifying brain tumors, making it a promising algorithm for brain tumor analysis tasks.

The Visual Integration of Semantic and Spatial Information of Objects in Naturalistic Scenes (VISIONS) database: attentional, conceptual, and perceptual norms.

The complex interplay between low- and high-level mechanisms governing our visual system can only be fully understood within ecologically valid naturalistic contexts. For this reason, in recent years, substantial efforts have been devoted to equipping the scientific community with datasets of realistic images normed on semantic or spatial features. Here, we introduce VISIONS, an extensive database of 1136 naturalistic scenes normed on a wide range of perceptual and conceptual norms by 185 English speakers across three levels of granularity: isolated object, whole scene, and object-in-scene. Each naturalistic scene contains a critical object systematically manipulated and normed regarding its semantic consistency (e.g., a toothbrush vs. a flashlight in a bathroom) and spatial position (i.e., left, right). Normative data are also available for low- (i.e., clarity, visual complexity) and high-level (i.e., name agreement, confidence, familiarity, prototypicality, manipulability) features of the critical object and its embedding scene context. Eye-tracking data during a free-viewing task further confirms the experimental validity of our manipulations while theoretically demonstrating that object semantics is acquired in extra-foveal vision and used to guide early overt attention. To our knowledge, VISIONS is the first database exhaustively covering norms about integrating objects in scenes and providing several perceptual and conceptual norms of the two as independently taken. We expect VISIONS to become an invaluable image dataset to examine and answer timely questions above and beyond vision science, where a diversity of perceptual, attentive, mnemonic, or linguistic processes could be explored as they develop, age, or become neuropathological.

Exploring the Power of Generative Adversarial Networks (GANs) for Image Generation: A Case Study on the MNIST Dataset

This paper provides a comprehensive examination of Generative Adversarial Networks (GANs), a groundbreaking deep learning architecture that has transformed the field of image generation. GANs consist of two neural networks—the generator and the discriminator—that engage in a competitive process to produce highly realistic images by learning patterns from existing data. This study highlights several key applications of GANs, including text-to-image generation, superresolution, neural style transfer, andimage completion. These applications have significant implications for various industries, such as medical imaging, creative content generation, and data augmentation for machinelearning models. The practical implementation section utilizes the MNIST dataset to illustrate how GANs can generate new, realistic images of handwritten digits. This implementation underscores the importance of data preprocessing, such as normalizationandshuffling, in improving model accuracy and performance. The paper also explores the critical role of GPU accelerationin speeding up the training process and the selection of optimal loss functions, particularly the cross-entropy loss, to enhance the discriminator's ability to differentiate between real and generated data. Moreover, the paper addresses the inherent challenges in training GANs, such as mode collapse and the extensive computational resources required. Solutions and best practices, including advanced optimization techniques and checkpointing, are discussed to ensure stable and efficient model training. The results demonstrate the remarkable ability of GANs to generate images that closely resemble real data, with promising applications in the fields of imaging systems, healthcare, and beyond. Future directions for research and emerging trends in GAN technology are also outlined.

Transforming Education with Photogrammetry: Creating Realistic 3D Objects for Augmented Reality Applications

Transforming Education with Photogrammetry: Creating Realistic 3D Objects for Augmented Reality Applications

Novel Framework for Artificial Bubble Image Generation and Boundary Detection Using Superformula Regression and Computer Vision Techniques

Bubble multiphase systems are crucial in industries such as biotechnology, medicine, oil and gas, and water treatment. Optical data analysis provides critical insights into bubble characteristics, such as the shape and size, complementing physical sensor data. Existing detection techniques rely on classical computer vision algorithms and neural network models. While neural networks achieve a higher accuracy, they require extensive annotated datasets, and classical methods often struggle with complex systems due to their lower accuracy. This study proposes a novel framework to address these limitations. Using Superformula parameter regression, we introduce an advanced border detection method for accurately identifying gas inclusions and complex-shaped objects in multiphase environments. The framework also includes a new approach for generating realistic artificial bubble images based on physical flow conditions, leveraging the Superformula to create extensive, labeled datasets without manual annotation. Tested on real bubble flows in mass transfer equipment, the algorithms enable bubble classification by shape and size, enhance detection accuracy, and reduce development time for neural network solutions. This work provides a robust method for object detection and dataset generation in multiphase systems, paving the way for more precise modeling and analysis.

WGAN-GP for Synthetic Retinal Image Generation: Enhancing Sensor-Based Medical Imaging for Classification Models.

Accurate synthetic image generation is crucial for addressing data scarcity challenges in medical image classification tasks, particularly in sensor-derived medical imaging. In this work, we propose a novel method using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and nearest-neighbor interpolation to generate high-quality synthetic images for diabetic retinopathy classification. Our approach enhances training datasets by generating realistic retinal images that retain critical pathological features. We evaluated the method across multiple retinal image datasets, including Retinal-Lesions, Fine-Grained Annotated Diabetic Retinopathy (FGADR), Indian Diabetic Retinopathy Image Dataset (IDRiD), and the Kaggle Diabetic Retinopathy dataset. The proposed method outperformed traditional generative models, such as conditional GANs and PathoGAN, achieving the best performance on key metrics: a Fréchet Inception Distance (FID) of 15.21, a Mean Squared Error (MSE) of 0.002025, and a Structural Similarity Index (SSIM) of 0.89 in the Kaggle dataset. Additionally, expert evaluations revealed that only 56.66% of synthetic images could be distinguished from real ones, demonstrating the high fidelity and clinical relevance of the generated data. These results highlight the effectiveness of our approach in improving medical image classification by generating realistic and diverse synthetic datasets.

MAIN FACTORS OF THE FORMATION OF HUMAN BEHAVIOR IN SOCIETY

The article identifies the main factors influencing human behavior in society, which interact with each other and, in aggregate, constitute the internal content of the environment of human existence – objective reality. These factors include everyday household, consciousness, needs, interests, values, ideology, goals, motivation and human behavior. The authors establish that the primary element of the mechanism of human behavior formation is life, which is a set of conditions, means, ways and forms of satisfaction of material and spiritual needs of individuals in the process of their joint activity, communication, and various forms of interaction. It is found that life as an integral element of everyday life has a significant impact on people's consciousness, but the reverse nature of interaction is not excluded. The components of everyday life, which exert an influence on human consciousness, are given. Typical interests and needs of a person are presented, and the interaction between them is investigated. The authors summarize the list of the universal human values, that reflects the change of priorities in the life values of Ukrainians in connection with the military aggression of the russian federation against Ukraine, identifies the most important ones and establishes why they differ in different regions of the country. The role of ideology in shaping human consciousness and behavior is established, the reasons for the lack of a national ideology in Ukraine and the consequences of this for society are substantiated. The main reasons for the divergence of the state and individual interests are clarified, as this affects the motivation and behavior of a person in society in order to achieve their own goals. It is established that each individual has a so-called «motivational mix», so in order to foresee and predict his or her behavior in society, it is necessary to understand the internal motives and external incentives that motivate him or her to behave in a certain way. The factors of the formation of human behavior in the society, the features of which can be manifested in everyday life can change under the influence of social transformations, are substantiated.

The language of manipulation: A multimodal discourse analysis of mainstream news media and fake news websites

In the contemporary digital media landscape marked by the presence of disinformation and fake news, the aim of this study is to perform a comparative analysis of mainstream news media and fake news websites, and to identify the multimodal resources characteristic of them. In particular, the study focuses on newsbits and newsbites, which comprise clusters of headlines, leads, hyperlinks, and images (Knox 2007), because they arguably represent the most salient features of any news websites. A representative corpus of newsbits and newsbites was compiled from mainstream news media and fake news websites that meet the criteria of such websites defined in the study. In total, 8 newsbites and newsbits were collected from 2 mainstream news media and 2 fake news websites. The study primarily draws on Bednarek and Caple’s (2012) approach to news discourse, which affords ways to perform a complex multimodal analysis of text and image, as well as explores the concepts of news values, parameters of evaluation, text-image relations, and communicative functions of images. As the primary objective of newsbits and newsbites is to entice readers to read the full article, the creation of compelling newsbits and newsbites is the shared goal of both mainstream news media and fake news websites, even though their broader objectives or ethical standards may not align. The manipulative impact of linguistic modes is more pronounced in the case of fake news websites because they rely on attracting clicks through controversial evaluations or alternate perspectives of some existing news. On the other hand, mainstream news media create more engaging and clickable newsbits and newsbites with higher emotional pull via the interplay of text and real-life images.