Hasil untuk "Art"

Abhiraam Eranti, Yogesh Tewari, Rafael Palacios et al.

Deep-learning methods have boosted the analytical power of Raman spectroscopy, yet they still require large, task-specific, labeled datasets and often fail to transfer across application domains. The study explores pre-trained encoders as a solution. Pre-trained encoders have significantly impacted Natural Language Processing and Computer Vision with their ability to learn transferable representations that can be applied to a variety of datasets, significantly reducing the amount of time and data required to create capable models. The following work puts forward a new approach that applies these benefits to Raman Spectroscopy. The proposed approach, RSPTE (Raman Spectroscopy Pre-Trained Encoder), is designed to learn generalizable spectral representations without labels. RSPTE employs a novel domain adaptation strategy using unsupervised Barlow Twins decorrelation objectives to learn fundamental spectral patterns from multi-domain Raman Spectroscopy datasets containing samples from medicine, biology, and mineralogy. Transferability is demonstrated through evaluation on several models created by fine-tuning RSPTE for different application domains: Medicine (detection of Melanoma and COVID), Biology (Pathogen Identification), and Agriculture. As an example, using only 20% of the dataset, models trained with RSPTE achieve accuracies ranging 50%–86% (depending on the dataset used) while without RSPTE the range is 9%–57%. Using the full dataset, accuracies with RSPTE range 81%–97%, and without pre-training 51%–97%. Current methods and state-of-the-art models in Raman Spectroscopy are compared to RSPTE for context, and RSPTE exhibits competitive results, especially with less data as well. These results provide evidence that the proposed RSPTE model can effectively learn and transfer generalizable spectral features across different domains, achieving accurate results with less data in less time (both data collection time and training time).

Jianfu Li

This article presents a discussion about Xiangtong Xi (香童戏), a traditional musical theatrical form associated with the Baoshan area of China’s Yunnan province. Xiangtong Xi drama originated from the folk religious and mystical rites of the southwestern regions of China. It organically combines elements such as singing, recitation, acting and martial arts techniques that are characteristic of the musical culture of the region. This genre has its own cult music and traditional performance style. At the same time, supporting and preserving the traditions of their art, Xiangtong Xi artists throughout the history of its existence have developed and continue to develop Xiangtong Xi music by studying the singing melodies and musical styles of other cultures, musical genres and movements and introducing their elements into their performances. The basis for such borrowings is primarily local folk music and songs, as well as other traditional musical genres of the region. Keywords: Xiangtong Xi, musical drama, ritual music, religious music, Prince’s Chant, Even Chant, Universal Chant, Chant of the Black God, plague god’s chant, percussion instruments For citation: Li Jianfu (2025). Traditional Regional Features in Xiangtong Xi Musical Drama. Contemporary Musicology, 9(2), 134–150. https://doi.org/10.56620/2587-9731-2025-2-134-150

Muhammad Attique Khan, Usama Shafiq, Ameer Hamza et al.

Abstract Deep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification and diagnosis. However, challenges such as inter- and intra-class similarities, class imbalance, and computational inefficiencies due to numerous hyperparameters persist. This study aims to address these challenges by presenting a novel deep-learning framework for classifying and localizing gastrointestinal (GI) diseases from wireless capsule endoscopy (WCE) images. The proposed framework begins with dataset augmentation to enhance training robustness. Two novel architectures, Sparse Convolutional DenseNet201 with Self-Attention (SC-DSAN) and CNN-GRU, are fused at the network level using a depth concatenation layer, avoiding the computational costs of feature-level fusion. Bayesian Optimization (BO) is employed for dynamic hyperparameter tuning, and an Entropy-controlled Marine Predators Algorithm (EMPA) selects optimal features. These features are classified using a Shallow Wide Neural Network (SWNN) and traditional classifiers. Experimental evaluations on the Kvasir-V1 and Kvasir-V2 datasets demonstrate superior performance, achieving accuracies of 99.60% and 95.10%, respectively. The proposed framework offers improved accuracy, precision, and computational efficiency compared to state-of-the-art models. The proposed framework addresses key challenges in GI disease diagnosis, demonstrating its potential for accurate and efficient clinical applications. Future work will explore its adaptability to additional datasets and optimize its computational complexity for broader deployment.

Hassan Haji Amiri, Arash Seghfi Asl, Mehdi Ashjaie

The question of resource constraints is an issue for all industrialized, developed and developing countries. Therefore, saving on fossil energy consumption and sustainable development have become very important and common issues internationally. So as to conserve energy resources, prevent contamination of the land and the environment, reduce fossil energy use and co-exist with natural and state-of-the-art environments, one of the most important measures in architecture and urban planning, and the architects and urban planners have to adhere to the principles and rules. Special in the field of construction. Over the years, various guidelines, standards, and standards have been developed to optimize energy consumption in buildings, including the most noteworthy metrics today (LEED). The purpose of the present study is to identify the causal pattern of environmental sustainability principles based on the Leid's Code. The present study is of applied purpose and of descriptive-analytical method. The statistical population of this study consisted of Qom architects and experts. The sample size was 25 individuals. In order to achieve the purpose of the study, fuzzy DEMATEL model was used. The results of this study showed that amongst the biodiversity sustainability criteria based on the Leading Model of the Sustainable Site Criterion was identified as the most influential criterion and the Regional Priority and Innovation Criteria in the design as the most influential criterion. Energy, climate and water efficiency and indoor air quality of materials and materials were also identified as intermediate criteria.

Олег Степанович Смоляк, Богдан Остапович Водяний, Ірина Вікторівна Романюк

У статті представлено новітні аналітичні алгоритми щодо реального музичного тексту як Тексту культури, що розглядається в ролі його структурно-семантичного інваріанту як засобу комунікації. Методи дослідження – структурно-герменевтичний, моделювання, функціональний. Результати дослідження формулюються на основі доведення того, що конструкти цього інваріанту володіють синергічною природою – тісною взаємодією та наскрізністю вияву. Задля унаочнення цієї взаємодії пропонується модель архітектоніки Світу музики як звукоінтонаційного середовища та мистецтва інтонованого смислу. Це – концентрично розташовані площини, які є рівнями його побудови. А саме: музичні лексеми (осереддя моделі); інтонаційний рельєф тематичного матеріалу; тип композиційно-драматургічної логіки; інваріант жанрової форми; інваріант стилю; історичний тип культури у вимірах «духовної ситуації часу»; історично актуальна світоглядна парадигма; інтонаційний континуум. Наголосимо, що поміж названими конструктами моделі існують взаємозумовлюючі звʼязки, у яких щоразу «просвічує» слід інших конструктів. Тому велике значення має як доцентровий, так і відцентровий рух від осереддя моделі до її замикаючого «поля», який фактично пронизує усю модель ідеєю щодо інтонаційної природи Світу музики в цілому. Практичну доцільність розробленої моделі забезпечено новітніми поняттями та категоріями сучасного систематичного музикознавства зразка «інтонаційна модель» (В. Москаленко), «інтонаційний образ світу» (Ю. Чекан), «типологія композиційно-драматургічного структурування» (Н. Горюхіна), «семантика жанрової форми» (М. Арановський, Т. Симонова); «металогіка стилю» (О. Завʼялова, М. Ярко), «психоаналітичні виміри індивідуального композиторського стилю» (М. Ярко), «національно-культурна ідентичність у матрицях етнічної та національної форм ідентичності» (М. Ярко), «алюзійний спосіб розвитку національного стилю» (М. Ярко), «українська академічна пісня» (Б. Водяний, Т. Задорожна) та ін. Висновкові судження щодо проведеного дослідження та його практичне значення тісно повʼязані з музикознавчою та музично-освітньою практикою: відомості щодо архітектоніки Світу музики та структурно-семантичного інваріанту музичного тексту як Тексту культури здатні забезпечувати високу міру праксеологічного (раціонально-логічного й ефективного) та евристичного (творчого) потенціалу як дослідницької, так і навчально-освітньої практики. Зокрема, перспективи подальших досліджень в обраному напрямі бачаться у звʼязку з проекціями розвитку когнітивної експансії свідомості в намірі глибокого пізнання духовного світу мистецької творчості.

Adri De Ridder, Steffen Eriksen, Bert Scholtens

This study seeks to address the difficulty of pricing art and the limitations of conventional valuation models by using visual analysis to determine the price of paintings. We examine a large hand-collected sample of classical paintings by Swedish Masters, categorize them based on various dimensions, and reduce measurement error by visually examining and classifying each painting into a theme. We compare this 'visual' approach with the conventional 'terminological' approach. We find that the technique, theme, and auction house all have a substantial impact on the price. We argue that a visual inspection should take precedence over analysis based on the artwork's title. This is because the latter leaves many artworks unclassified and results in a systematic bias. The study demonstrates the importance of using art-informed characteristics to reduce measurement error in pricing paintings.

Sadettin Y. Ugurlu, David McDonald, Huangshu Lei et al.

Abstract Probing the surface of proteins to predict the binding site and binding affinity for a given small molecule is a critical but challenging task in drug discovery. Blind docking addresses this issue by performing docking on binding regions randomly sampled from the entire protein surface. However, compared with local docking, blind docking is less accurate and reliable because the docking space is too largetly sampled. Cavity detection-guided blind docking methods improved the accuracy by using cavity detection (also known as binding site detection) tools to guide the docking procedure. However, it is worth noting that the performance of these methods heavily relies on the quality of the cavity detection tool. This constraint, namely the dependence on a single cavity detection tool, significantly impacts the overall performance of cavity detection-guided methods. To overcome this limitation, we proposed Consensus Blind Dock (CoBDock), a novel blind, parallel docking method that uses machine learning algorithms to integrate docking and cavity detection results to improve not only binding site identification but also pose prediction accuracy. Our experiments on several datasets, including PDBBind 2020, ADS, MTi, DUD-E, and CASF-2016, showed that CoBDock has better binding site and binding mode performance than other state-of-the-art cavity detector tools and blind docking methods.

Ali Aytac Seymen, Ezgi Gulten, Erol Ozgur et al.

Abstract Coronavirus Disease-19 (COVID-19) is a highly contagious infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The development of rapid antigen tests has contributed to easing the burden on healthcare and lifting restrictions by detecting infected individuals to help prevent further transmission of the virus. We developed a state-of-art rapid antigen testing system, named DIAGNOVIR, based on immune-fluorescence analysis, which can process and give the results in a minute. In our study, we assessed the performance of the DIAGNOVIR and compared the results with those of the qRT-PCR test. Our results demonstrated that the sensitivity and specificity of the DIAGNOVIR were 94% and 99.2%, respectively, with a 100% sensitivity and 96.97% specificity, among asymptomatic patients. In addition, DIAGNOVIR can detect SARS‑CoV‑2 with 100% sensitivity up to 5 days after symptom onset. We observed that the DIAGNOVIR Rapid Antigen Test’s limit of detection (LoD) was not significantly affected by the SARS‑CoV‑2 variants including Wuhan, alpha (B1.1.7), beta (B.1.351), delta (B.1.617.2) and omicron (B.1.1.529) variants, and LoD was calculated as 8 × 102, 6.81 × 101.5, 3.2 × 101.5, 1 × 103, and 1 × 103.5 TCID50/mL, respectively. Our results indicated that DIAGNOVIR can detect all SARS-CoV-2 variants in just seconds with higher sensitivity and specificity lower testing costs and decreased turnover time.

Zhenbao Wang, Shuyue Liu, Yuchen Zhang et al.

The impact of the built environment on the ridership of ride-hailing results depends on the spatial grid scale. The existing research on the demand model of ride-hailing ignores the modifiable areal unit problem (MAUP). Taking Chengdu as an example, and taking the density of pick-ups and drop-offs as dependent variables, 12 explanatory variables were selected as independent variables according to the “5D” built environment theory. The nugget–sill ratio (NSR) method and optimal parameter-based geographical detector (OPGD) model were used to determine the optimal grid scale for the aggregation of the built environment variables and the ridership of ride-hailing. Based on the optimal grid scale, the optimal data discretization method of the explanatory variables was determined by comparing the results of the geographic detector under different discretization methods (such as the natural break method, k-means clustering method, equidistant method, and quantile method); we utilized the geographic detector model to explore the relative importance and the interactive impacts of the explanatory variables on the ridership of ride-hailing under the optimal grid scale and optimal data discretization method. The results indicated that: (1) the suggested grid scale for the aggregation of the built environment and ride-hailing ridership in Chengdu is 1100 m; (2) the optimal data discretization method is the quantile method; (3) the floor area ratio (FAR), distance from the nearest subway station, and residential POI (point of interest) density resulted in a relatively high importance of the explanatory variable that affects the ridership of ride-hailing; and (4) the interactions of the diversity index of mixed land use ∩ FAR, distance to the nearest subway station ∩ FAR, transportation POI density ∩ FAR, and distance to the central business district (CBD) ∩ FAR made a higher contribution to ride-hailing ridership than the single-factor effect of FAR, which had the highest contribution compared with the other explanatory variables. The proposed grid scale can provide the basis for the partitioning management and scheduling optimization of ride-hailing. In the process of adjusting the ride-hailing demand, the ranking results of the importance and interaction of the built-environment explanatory variables offer valuable references for formulating the priority renewal order and proposing a scientific combination scheme of the built-environment factors.

Yihan Chen, Xingyu Gu, Zhen Liu et al.

Traditional automatic pavement distress detection methods using convolutional neural networks (CNNs) require a great deal of time and resources for computing and are poor in terms of interpretability. Therefore, inspired by the successful application of Transformer architecture in natural language processing (NLP) tasks, a novel Transformer method called LeViT was introduced for automatic asphalt pavement image classification. LeViT consists of convolutional layers, transformer stages where Multi-layer Perception (MLP) and multi-head self-attention blocks alternate using the residual connection, and two classifier heads. To conduct the proposed methods, three different sources of pavement image datasets and pre-trained weights based on ImageNet were attained. The performance of the proposed model was compared with six state-of-the-art (SOTA) deep learning models. All of them were trained based on transfer learning strategy. Compared to the tested SOTA methods, LeViT has less than 1/8 of the parameters of the original Vision Transformer (ViT) and 1/2 of ResNet and InceptionNet. Experimental results show that after training for 100 epochs with a 16 batch-size, the proposed method acquired 91.56% accuracy, 91.72% precision, 91.56% recall, and 91.45% F1-score in the Chinese asphalt pavement dataset and 99.17% accuracy, 99.19% precision, 99.17% recall, and 99.17% F1-score in the German asphalt pavement dataset, which is the best performance among all the tested SOTA models. Moreover, it shows superiority in inference speed (86 ms/step), which is approximately 25% of the original ViT method and 80% of some prevailing CNN-based models, including DenseNet, VGG, and ResNet. Overall, the proposed method can achieve competitive performance with fewer computation costs. In addition, a visualization method combining Grad-CAM and Attention Rollout was proposed to analyze the classification results and explore what has been learned in every MLP and attention block of LeViT, which improved the interpretability of the proposed pavement image classification model.

Azim Keshavarzian Nodehi, Nasrollah Moghadam Charkari

Word Sense Disambiguation (WSD) is one of the earliest problems in natural language processing which aims to determine the correct sense of words in context. The semantic information provided by WSD systems is highly beneficial to many tasks such as machine translation, information extraction, and semantic parsing. In this work, a new approach for WSD is proposed which uses a neural network as a surrogate fitness function in a metaheuristic algorithm. Also, a new method for simultaneous training of word and sense embeddings is proposed in this work. Accordingly, the node2vec algorithm is employed on the WordNet graph to generate sequences containing both words and senses. These sequences are then used along with paragraphs from Wikipedia in the word2vec algorithm to generate embeddings for words and senses at the same time. In order to address data imbalance in this task, sense probability distribution data extracted from the training corpus is used in the search process of the proposed simulated annealing algorithm. Furthermore, we introduce a new approach for clustering and mapping senses in the WordNet graph, which considerably improves the accuracy of the proposed method. In this approach, nodes in the WordNet graph are clustered on the condition that no two senses of the same word be present in one cluster. Then, repeatedly, all nodes in each cluster are mapped to a randomly selected node from that cluster, meaning that the representative node can take advantage of the training instances of all the other nodes in the cluster. Training the proposed method in this work is done using the SemCor dataset and the SemEval-2015 dataset has been used as the validation set. The final evaluation of the system is performed on SensEval-2, SensEval-3, SemEval-2007, SemEval-2013, SemEval-2015, and the concatenation of all five mentioned datasets. The performance of the system is also evaluated on the four content word categories, namely, nouns, verbs, adjectives, and adverbs. Experimental results show that the proposed method achieves accuracies in the range of 74.8 to 84.6 percent in the ten aforementioned evaluation categories which are close to and in some cases better than the state of the art in this task.

Bernard Ngara, Simbarashe Zvada, Tariro Dianah Chawana et al.

Abstract Background Drug potency is a pharmacological parameter defining dose or concentration of drug required to obtain 50% of the drug’s maximal effect. Pharmacokinetic-pharmacodynamic modelling and simulation allows estimation of potency and evaluate strategies improving treatment outcome. The objective of our study is to determine potency of atazanavir in hair, defined as atazanavir level in hair associated with 50% probability of failing to achieve viral load below 1000 copies/ml among adolescents, and explore the effect of participant specific variables on potency. Methods A secondary analysis was performed on data from a previous study conducted in HIV-infected adolescents failing 2nd line ART from Harare central hospital, Zimbabwe, between 2015 and 2016. We simulated atazanavir concentrations in hair using NONMEM (version 7.3) ADVAN 13, based on a previously established pharmacokinetic model. Logistic regression methods were used for PKPD analysis. Simulations utilising PKPD model focused on estimation of potency and exploring the effect of covariates. Results The potency of atazanavir in hair was found to be 4.5 ng/mg hair before adjusting for covariate effects. Participants at three months follow-up, reporting adequate adherence, having normal BMI-for-age, and cared for by mature guardians had increased potency of atazanavir in hair of 2.6 ng/mg, however the follow-up event was the only statistically significant factor at 5% level. Conclusion Atazanavir in hair in the range 2.6 to 4.5 ng/mg is associated with above 50% probability of early viral load suppression. Adherence monitoring to adolescents with lower potency of atazanavir is recommended. The effect self-reported adherence level, BMI-for-age, and caregiver status require further evaluation.

Despoina Spyropoulou

European museums (of ethnography) and the material culture under their custody — a large portion of which was collected by the soldiers, explorers, and professional looters of the colonial era — are increasingly confronted by formerly colonized countries and Indigenous communities demanding the repatriation of their cultural patrimony. In this context, more and more ancestral human remains become the protagonists of their descendants’ concerted efforts to bring them back home and offer them a reburial. Recognized as having been brought to Europe and its museums primarily as specimens for the racial theories that scientifically abetted the colonial agendas of power and control, these bones now find themselves at the center of the contemporary scenario of Europe’s — delayed — reckoning with its colonial past. From an anthropological point of view, the current potential for repatriation to their native lands (and their capacity to acquire a ‘repatriatable’ status) should not be pinned down to singular meanings. Indeed, from their long museum sojourns and their unfolding repatriation adventures to their troubling stories of colonial acquisition, the reclaimed remains seem to condense diverse temporalities. Analytically speaking, this paper suggests that the bones’ ‘repatriatable status’ does not entail their entrapment within a discursive system of binary oppositions, but their emergence as social persons that could be paralleled to other classical person-like ‘things’ in anthropology: the art objects of Alfred Gell, or the Maussian gift. Through such a theorization, the repatriatable remains are empowered to teach us that the social dramas around their potential return are not necessarily about the infliction of closure, but the activation of incessant cycles of reciprocity. Repatriation then, can be narrated otherwise: not as a story of resolution, but as one of irreducible ambiguity.

Marco Antonio Contreras-Cruz, Juan Pablo Ramirez-Paredes, Uriel Haile Hernandez-Belmonte et al.

One of the essential abilities in animals is to detect novelties within their environment. From the computational point of view, novelty detection consists of finding data that are different in some aspect to the known data. In robotics, researchers have incorporated novelty modules in robots to develop automatic exploration and inspection tasks. The visual sensor is one of the preferred sensors to perform this task. However, there exist problems as illumination changes, occlusion, and scale, among others. Besides, novelty detectors vary their performance depending on the specific application scenario. In this work, we propose a visual novelty detection framework for specific exploration and inspection tasks based on evolved novelty detectors. The system uses deep features to represent the visual information captured by the robots and applies a global optimization technique to design novelty detectors for specific robotics applications. We verified the performance of the proposed system against well-established state-of-the-art methods in a challenging scenario. This scenario was an outdoor environment covering typical problems in computer vision such as illumination changes, occlusion, and geometric transformations. The proposed framework presented high-novelty detection accuracy with competitive or even better results than the baseline methods.

Tong He, Yihui Zeng, Zhuangli Hu

With the rapid development of the economy, the scale of transmission networks has been expanding, bring higher demands and challenges on transmission line operation and maintenance. In this paper, the multi-rotor unmanned aerial vehicles (UAVs) safety inspection rules and automatic detailed inspection methods for transmission towers are studied. The theoretical model of multi-rotor UAVs of transmission lines inspection is established. The imaging calculation range of multi-rotor UAV-equipped cameras is determined. According to the inspection theory model, the flight safety judgment standard is designed, and the key parts of the transmission lines that need to be inspected are determined according to the accurate model of the transmission tower. Then the inspection waypoints are manually operated, and the photographing position and angle of each waypoint from the flight control are recorded through the waypoint planning function. Finally, the waypoints are connected in order, and the inspection route is automatically generated to achieve automatic detailed inspection. The inspection efficiency, error analysis and position accuracy comparing the proposed method with some state-of-the-art methods have been evaluated. The results show that the position error of UAVs automatic detailed inspection is less than 10 cm. The error of height is between 1.26 and 1.76 meter. Compared with the traditional manual inspection, the efficiency of multi-rotor UAVs automatic detailed inspection can be increased by 57.98%~62.88% and can be applied to large-scale inspection of transmission lines.

Tim Gustafson

Artist's Statement for the cover art of IJPS volume 5, issue 2: Watershed, 2018. Music video.

Umerenkova Olga V.

The article considers issues related to the principals of scientific approach, methods and procedure of costume reconstruction on the basis of archaeological materials dating back to the Bronze Age discovered in the territory of Western Siberia. The costume is considered by researchers as one of the brightest manifestations of material culture. Its decoration provides multidisciplinary information containing elements of ideology and aesthetic norms together with traditions and social relationships. Reconstruction of clothing and headwear adornments in archaeological literature related to the Bronze Age is one of the understudied topics. Researchers use various sources for its recreation: archaeological materials, written historical, literature and folklore sources, and fine art items. A significant amount of source items has accumulated over the last decades, although the analysis and principles of processing thereof have not been sufficiently covered in special literature. In order to increase the informative capabilities of adornments as sources for the reconstruction of the Bronze Age costume, the author suggested a scheme of accounting for the location of adornments with respect to the remains of the buried when the excavations are documented. The article features the results of the author's reconstruction of women's headwear decoration with metal articles executed on the basis of Bronze Age materials.

Melanie Turgeon

The art song genre began in Ukraine with Mykola Lysenko. Lysenko’s student, Kyrylo Stetsenko, followed his teacher’s example and composed 42 art songs, that are marked by desolation, anguish, and repression, yet with occasional strong glimpses of hope and love. Repressive political circumstances, which Stetsenko desperately fought to change, and various life events as a composer and Orthodox priest truly resulted in the heartfelt music that he wrote. Subsequent Ukrainian composers also wrote art songs despite prohibition of the Ukrainian language in print, in performance, and in scores. Over the past decade, thanks to the diligent efforts of the Ukrainian Art Song Project, the world stage is being introduced to hundreds of forbidden art songs by Ukrainian composers. Founded in 2004 by world-renowned bass-baritone, Pavlo Hunka, the Ukrainian Art Song Project aims to record, publish, promote and perform the art songs of more than 26 eminent Ukrainian composers by 2025.

VIORICA BLÎNDA

This study will provide a brief look into the numerous aspects of description as a unit of discourse and into the/as well as into those/distinctive discourse methods. The perspectives of the proposed analysis emphasize that description as a unit of discourse is no longer denigrated and that it has regained its well-defined place within the discourse (especially within the discourse of geography as a primary unit of discourse). The analysis is based on a corpus of studies represented by texts of geography available in geography textbooks. Through this study there will be outlined a number of methods and strategies of the discursive process through description.

Roxana Sosa Sánchez

RESUMEN: El presente trabajo consiste en un breve análisis sobre cómo los movimientos artísticos contemporáneos han dejado un lugar muy limitado para el desarrollo de un espacio creativo donde tengan cabida las manifestaciones de las mujeres artistas. Las creadoras han ido abandonando la pintura clásica, reducto por excelencia acotado a la heroicidad masculina, y, desde los años sesenta del siglo XX, las artistas han buscado en otros soportes y técnicas, como la fotografía, las instalaciones, la performance o el vídeo, espacios donde expresar su historia. ABSTRACT: This paper is a brief analysis of how contemporary art movements have left a very limited area for the development of a creative space for production by female artists. These artists have given up classical painting, the stronghold par excellence of male heroism, and since the 1960s, female artists have sought spaces to tell their story in other media and techniques such as photography, installations, and video and performance.