Art Methods Research Articles

MBFusion: Multi-modal balanced fusion and multi-task learning for cancer diagnosis and prognosis

Pathological images and molecular omics are important information for predicting diagnosis and prognosis. The two kinds of heterogeneous modal data contain complementary information, and the effective fusion of the two modals can better reveal the complex mechanisms of cancer. However, due to the different representation learning methods, the expression strength of different modals in different tasks varies greatly, so that many multimodal fusions do not achieve the best results. In this paper, MBFusion is proposed, to achieve multiple tasks such as prediction of diagnosis and prognosis through multi-modal balanced fusion. The MBFusion framework uses two kinds of specially constructed graph convolutional network to extract the features of molecular omics data, and uses ResNet to extract the features of pathological image data and retain important deep features by using attention and clustering, which effectively improves both kinds of the features representation, making their expressive ability balanced and comparable. The features of these two modal data are then fused through cross-attention Transformer, and the fused features are used to learn both tasks of cancer subtype classification and survival analysis by using multi-task learning. In this paper, MBFusion and other state of the art methods are compared on two public cancer datasets, and MBFusion shows an improvement of up to 10.1% by three kinds of evaluation metrics. In the ablation experiment, MBFusion explores the contribution of each modal data and each framework module to the performance. Furthermore, the interpretability of MBFusion is explained in detail to show the value of application.

Explaining Deep Learning Models Applied in Histopathology: Current Developments and the Path to Sustainability.

The digital pathology landscape is in continuous expansion. The digitalization of slides using WSIs (Whole Slide Images) fueled the capacity of automatic support for diagnostics. The paper presents an overview of the current state of the art methods used in histopathological practice for explaining CNN classification useful for histopathological experts. Following the study we observed that histopathological deep learning models are still underused and that the pathologists do not trust them. Also we need to point out that in order to get a sustainable use of deep learning we need to get the experts to trust the models. In order to do that, they need to understand how the results are generated and how this information correlates with their prior knowledge and for obtaining this they can use the methods highlighted in this study.

Reckoning with landscape tensions, reflecting on creative methodologies

In this paper landscape painting is developed as a mode of enquiry through which to explore the tensions inherent to landscape. Set on the high street in a small market town and undertaken by individuals with varying artistic experience, this methodological experiment explores the tensions between the non-representational and representational realms of landscape. By using Elizabeth Grosz’s writings on art, I develop this landscape painting methodology through an understanding of art and artmaking as intensifying sensation. Here, Grosz’s ideas become the foundation for a participatory arts method that decentres the human, whilst remaining focused on the presence of pleasure within landscape. It is by drawing together methodological experimentation and feminist theory that I revisit feminist geographies’ already existing interest in pleasure in/through landscapes through a more-than-representational lens.

Geospatialization of Sporothrix spp: spreading knowledge in scientific circles

The Sporothrix fungus exists in different species, S. schenckii, S. americana, S. luriei , S. globosa, S. chilensis, S. pallida and S. brasiliensis . It can be found in soil and putrefactive plant matter, occurring in tropical and subtropical regions. It is observed that infections are the result of the traumatic inoculation of fungal filaments through the skin, affecting humans who have contact with contaminated soil, plants and domestic animals. It is recorded that occurrences of contamination of people with domestic animals have been increasing in Brazil, in the southeast and south regions. The objective was to reflect on research carried out in Brazilian universities, optionally considering doctoral theses studies. State of the Art and Bibliographic methods were used, considering ten years, with a time frame from 2011 to 2020, using two databases, CAPES and BDBTD platforms. For data analysis, statistical analysis was used through ANOVA and geospatialization with the QGIS software, version QGIS-OSGeo4W-3.24.0-2 (18.02.2022). With the results, 49 productions (theses) were retrieved, 18 from the CAPES platform and 31 from the BDBTD platform and analyzed from the perspective of the thematic axes public health, infection and treatment, pharmacology and veterinary sciences. It is concluded that there are records showing the presence of the fungus in the country since the 1980s, and the treatment of the disease caused by the pathogen is possible and dependent on therapy and effective drugs, considering that it is a zoonosis and, therefore, public health issue in some states by mandatory notification.

HiSEG: Human assisted instance segmentation

Instance segmentation is a form of image detection which has a range of applications, such as object refinement, medical image analysis, and image/video editing, all of which demand a high degree of accuracy. However, this precision is often beyond the reach of what even state-of-the-art, fully automated instance segmentation algorithms can deliver. The performance gap becomes particularly prohibitive for small and complex objects. Practitioners typically resort to fully manual annotation, which can be a laborious process. In order to overcome this problem, we propose a novel approach to enable more precise predictions and generate higher-quality segmentation masks for high-curvature, complex and small-scale objects. Our human-assisted segmentation method, HiSEG, augments the existing Strong Mask R-CNN network to incorporate human-specified partial boundaries. We also present a dataset of hand-drawn partial object boundaries, which we refer to as “human attention maps”. In addition, the Partial Sketch Object Boundaries (PSOB) dataset contains hand-drawn partial object boundaries which represent curvatures of an object’s ground truth mask with several pixels. Through extensive evaluation using the PSOB dataset, we show that HiSEG outperforms state-of-the art methods such as Mask R-CNN, Strong Mask R-CNN, Mask2Former, and Segment Anything, achieving respective increases of +42.0, +34.9, +29.9, and +13.4 points in APMask metrics for these four models. We hope that our novel approach will set a baseline for future human-aided deep learning models by combining fully automated and interactive instance segmentation architectures.

FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources

Recommendation in settings such as e-recruitment and online dating involves distributing limited opportunities, which differs from recommending practically unlimited goods such as in e-commerce or music recommendation. This setting calls for novel approaches to quantify and enforce fairness. Indeed, typical recommender systems recommend each user their top relevant items, such that desirable items may be recommended simultaneously to more and to less qualified individuals. This is arguably unfair to the latter. Indeed, when they pursue such a desirable recommendation (e.g., by applying for a job), they are unlikely to be successful. To quantify fairness in such settings, we introduce inferiority : a novel (un)fairness measure that quantifies the competitive disadvantage of a user for their recommended items. Inferiority is complementary to envy : a previously-proposed fairness notion that quantifies the extent to which a user prefers other users’ recommendations over their own. We propose to use both inferiority and envy in combination with an accuracy-related measure called utility : the aggregated relevancy scores of the recommended items. Unfortunately, none of these three measures are differentiable, making it hard to optimize them, and restricting their immediate use to evaluation only. To remedy this, we reformulate them in the context of a probabilistic interpretation of recommender systems, resulting in differentiable versions. We show how these loss functions can be combined in a multi-objective optimization problem that we call FEIR (Fairness through Envy and Inferiority Reduction), used as a post-processing of the scores from any standard recommender system. Experiments on synthetic and real-world data show that the proposed approach effectively improves the trade-offs between inferiority, envy and utility, compared to the naive recommendation and the state-of-the-art method for the related problem of congestion alleviation in job recommendation. We discuss and enhance the practical impact of our findings on a wide range of real-world recommendation scenarios, and we offer implementations of visualization tools to render the envy and inferiority metrics more accessible.

Intergenerational Methods for Processing Art to Understand Challenging Topics (The IMPACT Study): Addiction

ABSTRACT Little is known about generational differences and similarities in beliefs about addiction. Science Gallery Atlanta (SGA) integrates science and art to approach challenging topics, like addiction, with empathy. Intergenerational communication about addiction could positively impact older and younger adults. This study investigated perceptions of addiction among older (60+ years) and younger (18–26 years) adults through intergenerational dialogue on addiction-focused art, offered through SGA’s HOOKED exhibition. This exploratory pilot study recruited ten randomized older-younger dyads who received a mediated tour of HOOKED. Addiction attitudes were assessed pre- and post-tour with the Belief in a Just World and Addiction Belief Scale questionnaires, concurrent interviews, and focus groups. Qualitative data were recorded, transcribed and analyzed thematically. Thematic analysis revealed younger and older adults were equally concerned with addiction-produced waste and technology addiction. Generational differences emerged regarding social media and drug-induced euphoria. Across age groups, participants scored similarly in their beliefs about justice for others and self. Post-tour, young adults were more likely to view addiction as a disease rather than a choice (p = 0.05). Art-based intergenerational communication may increase empathy. Intergenerational bridging may help young adults see addiction as a disease and older adults understand behavioral addictions. Emergent themes illuminate generational differences and similarities in addiction beliefs.

Research on the Path of Folk Art

With the development of social economy, the globalization of the world has become an inevitable trend. However, the cultures of various countries and nationalities in the world are constantly communicating and merging with each other; the folk art in China has been greatly impacted, and the traditional culture with distinctive national characteristics has been rejected. Strengthening folk art education is needed not only to improve people's personality and cultivate people's aesthetic taste, but also to protect traditional culture and impart national spirit. With the rapid development of information technology relying on hand-held learning equipment, mobile internet technology, and network multimedia technology, life-long learning is possible. Using smart mobile devices to study the art teaching of folk art is conducive to in-depth exploration, applying the learning methods of folk art to our own study and life, and protecting and inheriting the infringed folk art.

A two-stage importance-aware subgraph convolutional network based on multi-source sensors for cross-domain fault diagnosis

Graph convolutional networks (GCNs) as the emerging neural networks have shown great success in Prognostics and Health Management because they can not only extract node features but can also mine relationship between nodes in the graph data. However, the most existing GCNs-based methods are still limited by graph quality, variable working conditions, and limited data, making them difficult to obtain remarkable performance. Therefore, it is proposed in this paper a two stage importance-aware subgraph convolutional network based on multi-source sensors named I2SGCN to address the above-mentioned limitations. In the real-world scenarios, it is found that the diagnostic performance of the most existing GCNs is commonly bounded by the graph quality because it is hard to get high quality through a single sensor. Therefore, we leveraged multi-source sensors to construct graphs that contain more fault-based information of mechanical equipment. Then, we discovered that unsupervised domain adaptation (UDA) methods only use single stage to achieve cross-domain fault diagnosis and ignore more refined feature extraction, which can make the representations contained in the features inadequate. Hence, it is proposed the two-stage fault diagnosis in the whole framework to achieve UDA. In the first stage, the multiple-instance learning is adopted to obtain the importance factor of each sensor towards preliminary fault diagnosis. In the second stage, it is proposed I2SGCN to achieve refined cross-domain fault diagnosis. Moreover, we observed that deficient and limited data may cause label bias and biased training, leading to reduced generalization capacity of the proposed method. Therefore, we constructed the feature-based graph and importance-based graph to jointly mine more effective relationship and then presented a subgraph learning strategy, which not only enriches sufficient and complementary features but also regularizes the training. Comprehensive experiments conducted on four case studies demonstrate the effectiveness and superiority of the proposed method for cross-domain fault diagnosis, which outperforms the state-of-the art methods.

Psychological Dimensions in Sayed Qutb's Novel 'Ashwak' (Thorns)

This study examines the novel "Thorns" by Sayed Qutb, published in 1947, as a personal narrative reflecting a real experience that Sayed lived in his private life, particularly focusing on the emotional dimensions stemming from this experience. The study investigates the significance of the title, which faithfully reflects the text, the events of the novel and its structure in light of psychological methodology, the nature of the characters and their internal conflicts, the psychological trajectory of the protagonist and the heroine, the structure of place and time, analysis of artistic elements, and the artistic methods and techniques that often reveal a refined narrative sensibility and a complete awareness of the creative process.

The Study of Pigments in Cultural Heritage: A Review Using Machine Learning

In this review, topic modeling—an unsupervised machine learning tool—is employed to analyze research on pigments in cultural heritage published from 1999–2023. The review answers the following question: What are topics and time trends in the past three decades in the analytical study of pigments within cultural heritage (CH) assets? In total, 932 articles are reviewed, ten topics are identified and time trends in the share of these topics are revealed. Each topic is discussed in-depth to elucidate the community, purpose and tools involved in the topic. The time trend analysis shows that dominant topics over time include T1 (the spectroscopic and microscopic study of the stratigraphy of painted CH assets) and T5 (X-ray based techniques for CH, conservation science and archaeometry). However, both topics have experienced a decrease in attention in favor of other topics that more than doubled their topic share, enabled by new technologies and methods for imaging spectroscopy and imaging processing. These topics include T6 (spectral imaging techniques for chemical mapping of painting surfaces) and T10 (the technical study of the pigments and painting methods of historical and contemporary artists). Implications for the field are discussed in conclusion.

The impact of commercial advertising on the public

Throughout the world, commercial advertising’s have become an art and effective method of communication. It is a recent necessity for all countries, nations, classes, and races. All of them are working on commercial advertising and commercial advertising are the work of all. It is crucial that the Commercial advertisements are used properly and must at a high level, Kurdish satellite channels can live and manage business in this way. Advertisements can provide facilities for citizens and consumers especially when The commercial advertisings were used properly and correctly of the direction and goals of the right and bright way. In Kurdistan Region, commercial advertisements have passed through a short period of time. In the early stages they were at a very low level, until they were within technology development and the emergence of necessary instructions to organize advertising ,becoming more advanced and developed.The significance of this research which is a scientific attempt to demonstrate and determine the effect of the commercial advertisements of The Kurdish Channels in encouraging behavior of transaction by a pattern of This research ambition is to certify the emphasis extent of advertising Commercial advertisements on Kurdish channels. To what extent Kurdish satellite channels encourage consumer’s behavior to acquisitions Goods? Which are the business rhetoric that attracts more consumer attention to buy equipments. This research is a described research, we have used the method of surveying that is the most appropriate The method is set because the most accurate data and information can be obtained in this way. This research in which the theory of relying on practical media tools.It is one of the most significant theories in the domain of media. The society of this research consists of the population of Kurdistan region which consists of 500 individuals of male and female in all four provinces of Region (Erbil ,Sulaymaniyah ,Duhok and Halabja) participated in it. the researcher to collect data and Knowledge and achievement of research studies survey formused it, and in this study, the statistical text known as the SPSS system It's used.The most important results of the research are: the relationship between commercial advertising and Kurdish channels which Are a strong and effective direct connection, and a connection between commercial advertising and buying, it is a strong and effective direct relationship, in addition to commercial advertisements on Kurdish-sourced satellite channels.the importance of user information in selecting the necessary goods and services for the election It's the best choice. In addition, commercial advertising on Kurdish airlines has an important role to play in building trust and agreement with the Kurdistan Regional Government, particularly on topics that have been advertised commercially.

Detection of Credit Card Fraud with Optimized Deep Neural Network in Balanced Data Condition

Due to the huge number of financial transactions, it is almost impossible for humans to manually detect fraudulent transactions. In previous work, the datasets are not balanced and the models suffer from overfitting problems. In this paper, we tried to overcome the problems by tuning hyperparameters and balancing the dataset by hybrid approach using under-sampling and over-sampling techniques. In this study, we have observed that these modifications are effective to get better performance in comparison to the existing models. The MCC score is considered an important parameter in binary classification since it ensures the correct prediction of the majority of positive data instances and negative data instances. So, we emphasize on MCC score and our method achieved MCC score of 97.09%, which is far more (16 % approx.) than other state of art methods. In terms of other performance metrics, the result of our proposed model is also improved significantly.

Analysing social media with an Improved K-Means Clustering Algorithm

Nowadays, sharing human social behavior and growing a multi-user network requires the use of social media. Analysis of social media provides a valuable opportunity to study human social activity at scale. People often bring and talk about different kind of topics on social media platforms. depending on which, discussions are made to show the positive as well dis positives ways. This is an interesting point about using social media. There is a great deal of information about the type and substance of these discussion holography’s that informs us more broadly around patterns in social media interactions, and how content flows between individuals. Data mining techniques exists which can be utilized to crunch out user information from social media and relationships existing within the network. However, state of the art methods often fail to model user communities and their behaviors at a correct level. To solve this problem and help successfully do the grouping of related information, in our work here we present an enhanced fuzzy means clustering approach. Using this technique reduces the creation and time complexity of clusters, resulting in higher quality group outcomes. Our proposed system aims at mitigating the problems as described above by introducing a scalable and effective solution for real-time cluster social media data.

НОВЫЙ ГЕРОЙ В КЫРГЫЗСКОЙ ЛИТЕРАТУРЕ (ПО РОМАНУ К. ДЖУСУБАЛИЕВА «ХОЛОДНЫЕ СТЕНЫ»)

K. Dzhusubaliev's novel "Cold Walls" (1969) is a pioneering work in Kyrgyz literature, employing the “stream of consciousness” technique and introducing a new type of hero. This article examines Arykov's character and the artistic methods used to reveal his inner world. The author creates a multilayered psychological portrait of a hero living in his memories and reflections, representing a departure from traditional Kyrgyz literary characters. “Cold Walls” significantly influenced the development of Kyrgyz literature by showcasing the potential of modernist techniques and profound psychologism, opening new avenues for portraying the human inner world.

Expert Consensus Methods In The Humanities - An Exploration of their Potential

Background Expert consensus methods are regularly used in natural, social, and life sciences. This article explores the potential of applying these methods more frequently in humanities research. Methods The authors reviewed literature and applied the philosophical methods of conceptual analysis and conceptual engineering. Results This article identifies and describes six main elements of expert consensus methods. It also provides an overview of the different types of expert consensus methods regularly used in the natural, social, and life sciences: Delphi studies, nominal groups, consensus conferences, and Glaser’s state of the art method. Subsequently, each of these types is illustrated by an example from the sciences. The article also presents the potential of and objections to the application of expert consensus methods there. It gives four examples of expert consensus methods that were applied in humanities research, also presented in line with the six elements. Conclusions The comparisons and categorization show that, as in the natural, social, and life sciences, expert consensus methods in the humanities can in some instances potentially accelerate the epistemic process and enhance transparency, replicability, diversity, and fair processes. Nevertheless, expert consensus methods need to be fine-tuned to do justice to the unique nature and approaches of the humanities and therefore further research is needed.

Cocreating a sonopoetic analysis

This article presents an artful analysis method coauthors created with more-than-human collaborators to make sense of a collaborative inquiry into their long-time creative sound practice. They discuss how Heron's whole person theory, posthuman concepts, and a multimodal data assemblage of sonic and textual, extant and researcher-created materials informed their methodological process and led to a sonopoetic collaging analysis-presentation. Mapping this inquiry's methodological trajectory, this article highlights key impasses researchers encountered, how decision-making at these specific junctures related to theories they were thinking with, and what each of these decisions produced. Following transcript excerpts from the audio collage created from this sonopoetic method we improvised, the article considers some wider significances of employing sonic methods in qualitative research.

The Digital Media Art and Design System under the Integration of Computer Big Data ChatGPT Technology

Traditional digital media art and design systems take a long time to run and cannot handle a large number of objects simultaneously. Therefore, the author proposes a digital media art and design method based on ChatGPT technology. ChatGPT technology is an important part of networking.Prepare to provide conference support for various applications such as customer service, personal assistant, robotic interview, and so on. ChatGPT can not only answer the question that is set, but also solve the question openly.The comprehensiveness and intelligence of ChatGPT have attracted widespread attention since its launch. The hardware system adopts dual core TMS320C6657 and DDR3Thel1333 series processors as the core circuit, together with high speed external storage, to expand the image transmission line of multi-data; System software uses module architecture to design image modules, video modules, and voice modules, all of which are integrated into the core of the system as modules. In this experiment, 10 images were transferred and the running time was tested. Experimental results showed that, the traditional digital media art and design system had three errors, which took more time to run.

On the Eastern style of thinking in A. Bely’s theory of symbolism

RELEVANCE. The study is devoted to understanding and identifying the significance of the eastern style of philosophy and aesthetics of Taoism in the work of the prominent representative of Russian symbolism Andrey Bely (Boris Nikolaevich Bugaev), who determined the development of the literary process (modernist and post-modernist movements) and art of the first third of the 20th century and influenced the further development Russian literature. The purpose of the study is to show the conceptual content of the aesthetic thought of the Russian philosopher and symbolist poet through the prism of Taoism and to clarify the presence of Eastern traditional wisdom (“Tao Te Ching”) in the construction of A. Bely’s theory of symbolism.MATERIALS AND METHODS. The research material was the collected works of A. Bely. The translation of the book “Tao Te Ching” written by the great Chinese thinker Lao Tzu, presented for the first time for the Russian-speaking reader, was used.” When solving research problems, a description method with the interpretive function of language was used, that is, a method of studying the interpretation of knowledge about the world. In our case, the interpretation of Taoism is focused on the interpretive activity of an individual creative personality, which always presupposes the subjective selectivity of the word.RESULT AND DISCUSSION. An analysis of the work of A. Bely showed that in reflecting the problems of the symbol, the philosophical principles of symbolism, in addition to two sources, they participate: on the one hand, the ideas of the Slavophiles, arising from the philosophy of V. Solovyov, on the other hand, the system of Western philosophy (the ideas of A. Schopenhauer and F. Nietzsche), a reflection of the creative borrowing of Eastern wisdom, ideas and functions of Chinese literature. The originality of the imaginative thinking of the Russian symbolist poet, who, in accordance with his spiritual nature, followed his bright creative path, is reflected in the development of the artistic method – symbolism.CONCLUSION. The idea of A. Bely’s symbolism is based on the compatibility of the concepts of various thinkers, including philosophizing in the Taoist style. A. Bely created a unified symbolic picture of the world - the unification of artistic, aesthetic, religious, mystical and everyday ideas and knowledge about the world into one whole. The connecting sign of this unification is the Symbol, which has a pronounced authorial character, has an infinity of boundaries of value, and not a semantic individual interpretation of the word and broadcasts the manifestation of systemic pluralism, revealing the essence of the world and being.

Map Cards: Creative Mapping for Place Dialogue

Creative mapping is a type of critical observational drawing that uses cartographic art to negotiate personal visions, social constructions, and objective representations of the world. Geographers have been experimenting with this method to produce their own creative maps and in the process have begun to explore the effects and implications of these works for engaging with the places and relationships of research in professional and personal life. This essay puts theoretical work on researcher vulnerability into conversation with scholarship on place dialogue to explore how creative mapping facilitates relationship accountability in research. The transformation in the author’s creative-mapping practice illustrates the agency of place to communicate the realities and responsibilities of relationship through art, and suggests how creative mapping enhances our capacity for place dialogue through artistic methods that negotiate the more-than-human entanglements of research.