Real Images Research Articles

이미지 자동 생성 인공지능의 원리와 현황

This study identifies the principles of image generation artificial intelligence, examines its performance and limitations, and examines the innovative changes that have occurred in image production methods. Specifically, we reveal the structure of the Generative Adversarial Network (GAN), trace its technical improvement process in detail, and classify the types of subsequent models developed later. The tasks performed by GAN’s successor models are diverse. GAN is steadily evolving, completing amazing tasks that were impossible with existing generative models, such as high-resolution image generation, image substitution such as drawings and photos, and face synthesis. The principles of GAN are, first, data learning through adversarial competition between generator and discriminator, second, calculation of accurate probability distribution through Maximum Likelihood Estimation, and third, calculation of fine features of images through deep convolution structure. The most important characteristic of GAN as a generative model is its ability to create realistic images similar to photographs. Humans have been producing images using hands and tools, but with the invention of photography, humans began producing images using camera devices. Now, generative artificial intelligence is becoming another means of image production as technology continues to improve rapidly.

Color constancy in real-world settings.

Color constancy denotes the ability to assign a particular and stable color percept to an object, irrespective of its surroundings and illumination. The light reaching the eye confounds illumination and spectral reflectance of the object, making the recovery of constant object color an ill-posed problem. How good the visual system is at accomplishing this task is still a matter of heated debate, despite more than a 100 years of research. Depending on the laboratory task and the specific cues available to observers, color constancy was found to be at levels ranging between 15% and 80%, which seems incompatible with the relatively stable color appearance of objects around us and the consistent usage of color names in real life. Here, we show close-to-perfect color constancy using real objects in a natural task and natural environmental conditions, chosen to mimic the role of color constancy in everyday life. Participants had to identify the color of a (non-present) item familiar to them in an office room under five different experimental illuminations. They mostly selected the same colored Munsell chip as their match to the absent object, even though the light reaching the eye in each case differed substantially. Our results demonstrate that color constancy under ideal conditions in the real world can indeed be exceptionally good. We found it to be as good as visual memory permits and not generally compromised by sensory uncertainty.

Contextual reinforcement learning for supply chain management

Efficient generalisation in supply chain inventory management is challenging due to a potential mismatch between the model optimised and objective reality. It is hard to know how the real world is configured and, thus, hard to train an agent optimally for it. We address this problem by combining offline training and online adaptation. Agents were trained offline using data from all possible environmental configurations, termed contexts. During an online adaptation phase, agents search for the context maximising rewards. Agents adapted online rapidly and achieved performance close to knowing the context a-priori. In particular, they acted optimally without inferring the correct context, but by finding a suitable one for reward maximisation. By enabling agents to leverage off-line training and online adaptation, we improve their efficiency and effectiveness in unknown environments. The methodology has broader potential applications and contributes to making RL algorithms useful in practical scenarios. We have released the code for this paper under https://github.com/abatsis/supply_chain_few_shot_RL.

Impact of focus cue presentation on perceived realism of 3-D scene structure: Implications for scene perception and for display technology.

Stereoscopic imagery often aims to evoke three-dimensional (3-D) percepts that are accurate and realistic-looking. The "gap" between 3-D imagery and real scenes is small, but focus cues typically remain incorrect because images are displayed on a single focal plane. Research has concentrated on the resulting vergence-accommodation conflicts. Yet, incorrect focus cues may also affect the appearance of 3-D imagery. We investigated whether incorrect focus cues reduce perceived realism of 3-D structure ("depth realism"). Experiment1 used a multiple-focal-planes display to compare depth realism with correct focus cues vs. conventional stereo presentation. The stimuli were random-dot stereograms, which isolated the role of focus cues. Depth realism was consistently lower with incorrect focus cues, providing proof-of-principle evidence that they contribute to perceptual realism. Experiments2 and3 examined whether focus cues play a similar role with realistic objects, presented with an almost complete set of visual cues using a high-resolution, high-dynamic-range multiple-focal-planes display. We also examined the efficacy of approximating correct focus cues via gaze-contingent depth-of-field rendering. Improvements in depth realism with correct focus cues were less clear in more realistic scenes, indicating that the role of focus cues in depth realism depends on scene content. Rendering-based approaches, if anything, reduced depth realism, which we attribute to their inability to present higher-order aspects of blur correctly. Our findings suggest future general 3-D display solutions may need to present focus cues correctly to maximise perceptual realism.

Correcting Optical Aberration via Depth-Aware Point Spread Functions.

Optical aberration is a ubiquitous degeneration in realistic lens-based imaging systems. Optical aberrations are caused by the differences in the optical path length when light travels through different regions of the camera lens with different incident angles. The blur and chromatic aberrations manifest significant discrepancies when the optical system changes. This work designs a transferable and effective image simulation system of simple lenses via multi-wavelength, depth-aware, spatially-variant four-dimensional point spread functions (4D-PSFs) estimation by changing a small amount of lens-dependent parameters. The image simulation system can alleviate the overhead of dataset collecting and exploiting the principle of computational imaging for effective optical aberration correction. With the guidance of domain knowledge about the image formation model provided by the 4D-PSFs, we establish a multi-scale optical aberration correction network for degraded image reconstruction, which consists of a scene depth estimation branch and an image restoration branch. Specifically, we propose to predict adaptive filters with the depth-aware PSFs and carry out dynamic convolutions, which facilitate the model's generalization in various scenes. We also employ convolution and self-attention mechanisms for global and local feature extraction and realize a spatially-variant restoration. The multi-scale feature extraction complements the features across different scales and provides fine details and contextual features. Extensive experiments demonstrate that our proposed algorithm performs favorably against state-of-the-art restoration methods. The source code and trained models are available to the public.

SYSTEM OF COST INDICATORS FOR ANALOGOUS OBJECTS

The military aggression of the Russian Federation in Ukraine has resulted in a significant number of damaged and destroyed buildings and structures. The challenge of determining the extent of material losses and damage, within tight timelines and under the conditions of active combat in the country, becomes more pressing than ever. To restore normal life in areas that were under occupation or in conflict zones, a series of tasks must be undertaken, including the initial assessment of the extent of destruction, conducting surveys of damaged buildings, and subsequently determining the corresponding requirements for the restoration of real estate. The purpose of the work. To demonstrate the feasibility and appropriateness of applying the Handbook System of cost indicators for analogous objects to determine the reproduction cost (replacement) of objects, with the aim of assessing damages and determining the needs for the restoration of real estate. The Handbook System of cost indicators is developed based on actual cost indicators of analogous objects and consolidated cost estimates or consolidated indicators of construction costs (types of work, structural elements, etc.), adjusted to current price levels. This involves analyzing all possible sources containing accumulated cost information from project and estimate documentation, determining necessary corrective coefficients, and developing models to ascertain the cost of all types of real estate, including specialized properties. Methodology. The statistical analysis methodology was employed, specifically using the method of grouping and consolidating indicators to assess the costs of restoring residential real estate in one of the districts of Dnipro city. This involves the application of the developed Handbook System of cost indicators for analogous objects, determining the structure of the real estate object as a construction object in a two-dimensional system of physical/cost indicators. Scientific novelty. A model for determining restoration costs using the Handbook System of cost indicators for analogous objects has been developed and implemented in the Sobornyi district of Dnipro city. Practical significance. The determination of the structure of any real estate object in the system of physical/cost indicators, based on the results of using the Handbook System of cost indicators for analogous objects, addresses a range of crucial economic, managerial, and legal issues. Specifically, it opens the way for the application of rapid screening of investment projects, assessment of actual damages, standardized and analytical damage assessments. This contributes to making effective managerial decisions in the country's recovery process, ensuring the efficient use of resources in society, and can be applied for other purposes in case of open access to this information, following the experience of leading countries worldwide. Results. A Handbook System of cost indicators for analogous objects has been developed and tested to determine the reproduction cost (replacement) of objects, aiming to assess damages and needs for the restoration of real estate. The results obtained from testing the Handbook System provide grounds for practical application, enabling the acquisition of logically comprehensible outcomes. It can be implemented not only for determining the needs for the restoration of real estate and assessing damages but also for various other purposes.

New Real Estate Legislation

The article is devoted to the study of certain provisions of the Federal Law № 430-FZ “On Amendments to Part One of the Civil Code of the Russian Federation” which is expected to come into force on September 1, 2022. Disputes about the types, order of formation and acquisition of the right of ownership of immovable property are among the most common in judicial practice. In addition, the process of improving the legislation on immovable property cannot be effective without a deep scientific understanding of the problems of the relevant legal institute. As a consequence, the study of new legisla tion and law enforcement practice in the field of real estate turnover is relevant and timely. The purpose of this work is to analyze the new provisions of the Civil Code of the Russian Federation on the legal regime of real estate. The study is based on general scientific methods: logical, analysis, synthesis, which made it possible to structure the material, to reveal relationships in the study of individual real estate objects. Also, private scientific methods were used in the work: formal-legal, comparative-legal, method of interpretation of legal norms. The author notes the positive nature of innovations in the field of legal regulation of such real estate objects as a land plot, a parking place, an object of construction in progress, a single real estate complex. At the same time, problematic issues that have not found legislative resolution in the new chapter 6.1 of the Civil Code of the Russian Federation “Real Estate” are identified in the work, ways to solve them are proposed.

A Domain Adaptive Semantic Segmentation Method Using Contrastive Learning and Data Augmentation

For semantic segmentation tasks, it is expensive to get pixel-level annotations on real images. Domain adaptation eliminates this process by transferring networks trained on synthetic images to real-world images. As one of the mainstream approaches to domain adaptation, most of the self-training based domain adaptive methods focus on how to select high confidence pseudo-labels, i.e., to obtain domain invariant knowledge indirectly. A more direct means to explicitly align the data of the source and target domains globally and locally is lacking. Meanwhile, the target features obtained by traditional self-training methods are relatively scattered and cannot be aggregated in a relatively compact space. We offer an approach that utilizes data augmentation and contrastive learning in this paper to perform more effective knowledge migration with the basis of self-training. Specifically, the style migration and image mixing modules are first introduced for data augmentation to cope with the problem of large domain gaps in the source and target domains. To assure the aggregation of features from the same class and the discriminability of features from other classes during the training process, we propose a multi-scale pixel-level contrastive learning module. What’s more, a cross-scale contrastive learning module is proposed to help each level of the model gain the capability to obtain more information on the basis of its own original task. Experiments show that our final trained model can effectively classify the images from target domain.

Quantifying trade-offs in satellite hardware configurations using a super-resolution framework with realistic image degradation

ABSTRACT When designing and operating Earth observation satellites, trade-offs must often be made between different hardware components, operational considerations, etc… For example, considerations include how much of the mass budget should be spent on imaging lenses?, or how long should image exposure times be?. Resulting limitations in image resolution and quality can be partially compensated for using super-resolution (SR) techniques. However, deep SR networks recently applied to satellite imagery are often trained and tested on data that lacks the typical image degrading noise present in real satellite images. In this work, we combine a method for generating realistically degraded satellite images with a deep SR network, in the context of different satellite hardware configurations and geographical types. We use this framework to assess deep SR performance given realistic remote sensing payloads across different terrain types, by evaluating payload- and terrain-dependent SR performance in reconstructing realistically degraded images. The framework allows us to model the effect of alternative satellite hardware configurations on resulting SR image quality, providing insight into optimal satellite operations and payload design in the context of SR-based image quality enhancements.

Ensemble CART surrogate-assisted automatic multi-objective rough fuzzy clustering algorithm for unsupervised image segmentation

Multi-objective clustering algorithms (MOCAs) are popular in unsupervised image segmentation due to their merit of meeting multiple segmentation requirements and the prospect of automatically estimating the number of clusters. However, most of them suffer from high time costs and are easily to be influenced by the uncertainty when handling real complex images. To address these issues, we propose an ensemble classification and regression tree (CART) surrogate-assisted automatic multi-objective rough fuzzy clustering (ECS-AMRFC) algorithm for unsupervised image segmentation. Firstly, a cluster medoid-based encoding scheme is employed to represent solutions with different number of clusters and meanwhile lessen the length of encoding. Then, we design an ensemble CART as the surrogate model to significantly reduce the computational burden. Moreover, a surrogate model management strategy is proposed to accelerate the optimization and enhance the quality of surrogate modeling. To handle the uncertainty in data, we extend the rough fuzzy clustering into MOCAs and construct three complementary objective functions to seek proper cluster medoids from multiple perspectives. In addition, the Gaussian kernel is introduced into the objective functions to handle image pixels that cannot separate linearly in the feature space. Finally, a kernelized rough fuzzy clustering validity index is defined to automatically select the optimal solution with no requirements of any prior knowledge. Experiments show that ECS-AMRFC not only identifies appropriate number of clusters on different kinds of images, but also obtains better segmentation results than state-of-the-art rough fuzzy clustering algorithms and automatic MOCAs.

Foreclosure on a Share in the Ownership of Real Estate if the Creditor and Co-Owner Coincide in one Person

This study is devoted to the specifics of foreclosure on a share in the ownership of real estate in situations where the creditor is simultaneously a co-owner of a share in the right to this property, and accordingly has a preferential right to purchase the debtor's share. Some issues still remain unresolved at the legislative level, which causes difficulties in considering cases of this category. In judicial practice, there is no unified approach to the methodology for determining the market value of the share in the ownership right to which foreclosure is taking place. It is unclear how the plaintiff's demands for foreclosure on the debtor's share should sound with recognition of ownership of the share for the recoverer in terms of payment of funds by the plaintiff to the defendant. The authors propose to change the procedure for foreclosing on a share in the ownership of a real estate object if the co-owner and the recoverer coincide in one person: foreclosure will be carried out through bidding, after determining the winner, co-owners will be offered to purchase a share at a price determined as a result of bidding. This procedure will allow you to determine the actual market value of the share for which foreclosure is taking place, while respecting the pre-emptive right of purchase.

Benchmarking the influence of pre-training on explanation performance in MR image classification.

Convolutional Neural Networks (CNNs) are frequently and successfully used in medical prediction tasks. They are often used in combination with transfer learning, leading to improved performance when training data for the task are scarce. The resulting models are highly complex and typically do not provide any insight into their predictive mechanisms, motivating the field of "explainable" artificial intelligence (XAI). However, previous studies have rarely quantitatively evaluated the "explanation performance" of XAI methods against ground-truth data, and transfer learning and its influence on objective measures of explanation performance has not been investigated. Here, we propose a benchmark dataset that allows for quantifying explanation performance in a realistic magnetic resonance imaging (MRI) classification task. We employ this benchmark to understand the influence of transfer learning on the quality of explanations. Experimental results show that popular XAI methods applied to the same underlying model differ vastly in performance, even when considering only correctly classified examples. We further observe that explanation performance strongly depends on the task used for pre-training and the number of CNN layers pre-trained. These results hold after correcting for a substantial correlation between explanation and classification performance.

Deep learning-based augmented reality work instruction assistance system for complex manual assembly

The manual assembly process of complex products is lengthy, the assembly requirements are difficult to recall, and the assembly quality requirements are high. The separation of the operation guidance information from the real physical object in the traditional paper manual easily distracts the operator, increasing their cognitive burden. To address this issue, we integrate augmented reality (AR) and artificial intelligence (AI) technologies for manual assembly assistance. A novel encoding and decoding convolutional neural network (CNN) is built to realize accurate AR registration under mark-less assembly environment. An assembly quality inspection method integrating a neural network and virtual model matching was proposed for AR systems. The specific process of the proposed method includes two stages: offline and online. In the first stage, a monocular RGB image dataset was built for assembling object keypoints and assembly object detection. CNN models were designed and trained for deployment in an AR-assisted assembly system. Second, the deployed CNN models were used to perform AR registration and assembly quality inspection. Experimental results demonstrate that the proposed method can accurately present AR work instruction guidance and assembly quality inspection for manual assembly. The proposed AR registration method can achieve a frame rate of 25FPS, which satisfies the timeliness requirements of the AR system. The accuracy rate of the proposed assembly quality inspection method for the MONA component assembly reached 92.5%.

A One-Class Classifier for the Detection of GAN Manipulated Multi-Spectral Satellite Images

The current image generative models have achieved a remarkably realistic image quality, offering numerous academic and industrial applications. However, to ensure these models are used for benign purposes, it is essential to develop tools that definitively detect whether an image has been synthetically generated. Consequently, several detectors with excellent performance in computer vision applications have been developed. However, these detectors cannot be directly applied as they areto multi-spectral satellite images, necessitating the training of new models. While two-class classifiers generally achieve high detection accuracies, they struggle to generalize to image domains and generative architectures different from those encountered during training. In this paper, we propose a one-class classifier based on Vector Quantized Variational Autoencoder 2 (VQ-VAE 2) features to overcome the limitations of two-class classifiers. We start by highlighting the generalization problem faced by binary classifiers. This was demonstrated by training and testing an EfficientNet-B4 architecture on multiple multi-spectral datasets. We then illustrate that the VQ-VAE 2-based classifier, which was trained exclusively on pristine images, could detect images from different domains and generated by architectures not encountered during training. Finally, we conducted a head-to-head comparison between the two classifiers on the same generated datasets, emphasizing the superior generalization capabilities of the VQ-VAE 2-based detector, wherewe obtained a probability of detection at a 0.05 false alarm rate of 1 for the blue and red channels when using the VQ-VAE 2-based detector, and 0.72 when we used the EfficientNet-B4 classifier.

Digital transformation of cities. Future plans and risks

The essay expresses an opinion about the inevitable risks of the process of digital transformation of cities. Digitalization of the living environment and lifestyle affects all spheres of human activity not only in a positive way. This inevitable process is an objective reality, and the speed with which it occurs does not allow us to realize all the risks and prepare for their neutralization. It is assumed that a dialectical understanding of this process with an analysis of the negative consequences will help determine the acceptable framework for digital transformation.

Brain-like illusion produced by Skye's Oblique Grating in deep neural networks.

The analogy between the brain and deep neural networks (DNNs) has sparked interest in neuroscience. Although DNNs have limitations, they remain valuable for modeling specific brain characteristics. This study used Skye's Oblique Grating illusion to assess DNNs' relevance to brain neural networks. We collected data on human perceptual responses to a series of visual illusions. This data was then used to assess how DNN responses to these illusions paralleled or differed from human behavior. We performed two analyses:(1) We trained DNNs to perform horizontal vs. non-horizontal classification on images with bars tilted different degrees (non-illusory images) and tested them on images with horizontal bars with different illusory strengths measured by human behavior (illusory images), finding that DNNs showed human-like illusions; (2) We performed representational similarity analysis to assess whether illusory representation existed in different layers within DNNs, finding that DNNs showed illusion-like responses to illusory images. The representational similarity between real tilted images and illusory images was calculated, which showed the highest values in the early layers and decreased layer-by-layer. Our findings suggest that DNNs could serve as potential models for explaining the mechanism of visual illusions in human brain, particularly those that may originate in early visual areas like the primary visual cortex (V1). While promising, further research is necessary to understand the nuanced differences between DNNs and human visual pathways.

Semi-supervised low-dose SPECT restoration using sinogram inner-structure aware graph neural network

Objective. To mitigate the potential radiation risk, low-dose single photon emission computed tomography (SPECT) is of increasing interest. Numerous deep learning-based methods have been developed to perform low-dose imaging while maintaining image quality. However, most existing methods seldom explore the unique inner-structure inherent within sinograms. In addition, traditional supervised learning methods require large-scale labeled data, where the normal-dose data serves as annotation and is intractable to acquire in low-dose imaging. In this study, we aim to develop a novel sinogram inner-structure-aware semi-supervised framework for the task of low-dose SPECT sinogram restoration. Approach. The proposed framework retains the strengths of UNet, meanwhile introducing a sinogram-structure-based non-local neighbors graph neural network (SSN-GNN) module and a window-based K-nearest neighbors GNN (W-KNN-GNN) module to effectively exploit the inherent inner-structure within SPECT sinograms. Moreover, the proposed framework employs the mean teacher semi-supervised learning approach to leverage the information available in abundant unlabeled low-dose sinograms. Main results. The datasets exploited in this study were acquired from the (Extended Cardiac-Torso) XCAT anthropomorphic digital phantoms, which provide realistic images for imaging research of various modalities. Quantitative as well as qualitative results demonstrate that the proposed framework achieves superior performance compared to several state-of-the-art reconstruction methods. To further validate the effectiveness of the proposed framework, ablation and robustness experiments were also performed. The experimental results show that each component of the proposed framework effectively improves the model performance, and the framework exhibits superior robustness with respect to various noise levels. Besides, the proposed semi-supervised paradigm showcases the efficacy of incorporating supplementary unlabeled low-dose sinograms. Significance. The proposed framework improves the quality of low-dose SPECT reconstructed images by utilizing sinogram inner-structure and incorporating supplementary unlabeled data, which provides an important tool for dose reduction without sacrificing the image quality.

Sensor-based characterization of construction and demolition waste at high occupancy densities using synthetic training data and deep learning.

Sensor-based monitoring of construction and demolition waste (CDW) streams plays an important role in recycling (RC). Extracted knowledge about the composition of a material stream helps identifying RC paths, optimizing processing plants and form the basis for sorting. To enable economical use, it is necessary to ensure robust detection of individual objects even with high material throughput. Conventional algorithms struggle with resulting high occupancy densities and object overlap, making deep learning object detection methods more promising. In this study, different deep learning architectures for object detection (Region-based CNN/Region-based Convolutional Neural Network (Faster R-CNN), You only look once (YOLOv3), Single Shot MultiBox Detector (SSD)) are investigated with respect to their suitability for CDW characterization. A mixture of brick and sand-lime brick is considered as an exemplary waste stream. Particular attention is paid to detection performance with increasing occupancy density and particle overlap. A method for the generation of synthetic training images is presented, which avoids time-consuming manual labelling. By testing the models trained on synthetic data on real images, the success of the method is demonstrated. Requirements for synthetic training data composition, potential improvements and simplifications of different architecture approaches are discussed based on the characteristic of the detection task. In addition, the required inference time of the presented models is investigated to ensure their suitability for use under real-time conditions.

Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy

Over the last decade, supervised denoising models, trained on extensive datasets, have exhibited remarkable performance in image denoising, owing to their superior denoising effects. However, these models exhibit limited flexibility and manifest varying degrees of degradation in noise reduction capability when applied in practical scenarios, particularly when the noise distribution of a given noisy image deviates from that of the training images. To tackle this problem, we put forward a two-stage denoising model that is actualized by attaching an unsupervised fine-tuning phase after a supervised denoising model processes the input noisy image and secures a denoised image (regarded as a preprocessed image). More specifically, in the first stage we replace the convolution block adopted by the U-shaped network framework (utilized in the deep image prior method) with the Transformer module, and the resultant model is referred to as a U-Transformer. The U-Transformer model is trained to preprocess the input noisy images using noisy images and their labels. As for the second stage, we condense the supervised U-Transformer model into a simplified version, incorporating only one Transformer module with fewer parameters. Additionally, we shift its training mode to unsupervised training, following a similar approach as employed in the deep image prior method. This stage aims to further eliminate minor residual noise and artifacts present in the preprocessed image, resulting in clearer and more realistic output images. Experimental results illustrate that the proposed method achieves significant noise reduction in both synthetic and real images, surpassing state-of-the-art methods. This superiority stems from the supervised model’s ability to rapidly process given noisy images, while the unsupervised model leverages its flexibility to generate a fine-tuned network, enhancing noise reduction capability. Moreover, with support from the supervised model providing higher-quality preprocessed images, the proposed unsupervised fine-tuning model requires fewer parameters, facilitating rapid training and convergence, resulting in overall high execution efficiency.

Ethics: What is the Brazilian Software Engineering Research Scenario?

Background: Ethics is the theory or science of the moral behavior of humans in society. Traditionally, we value “unethical” actions that go against determining morality in a specific context. One of the sub-domains of Ethics is Computational Ethics, which deals with ethical dilemmas that are strictly related to computational issues. Dilemmas in this area involve privacy, improper access, intellectual property, digital norms and laws, power, socio-technical aspects (such as gender discrimination), and robotics, among others. In this context, “Software Engineering” and “Software” are different objects. Engineering is an act, a practice, as also coding, programming, and software reuse. As with any act, moral and subsequent ethical considerations are appropriate. We characterize software as an object of concrete reality, as a sociotechnical system formed by a technical artifact, human aspect, and procedural aspect. This assumption will form the main base discussion of ethics and morals in Software Engineering in this paper. Objective: The goal of this paper is to unveil the Brazilian Software Engineering ethics panorama. Method: We follow the rigor of, and inspired by, a Systematic Literature Review (SLR) protocol to answer the question: how does ethics explicitly permeate the Brazilian Software Engineering publications between the last thirteen years (2010 and 2022)? Results: After analyzing 1529 papers through the research protocol, 175 (≈11%) presented some explicit occurrence of ethical aspects. The occurrence was relevant in only 7 papers (≈0.4%), exposing a shallow scenario on ethical or moral aspects. Conclusions: If Ethics is a topic considered important to deliberate, research or discuss, this did not occur significantly in the Brazilian Software Engineering research scenario since 2010. With this result in mind, we discussed parallel terms and concepts to enrich the contribution of the qualitative synthesis.