DECONSTRUCTION AND RECONSTRUCTION OF THE KACU CONCEPT OF BATIK AS INSPIRATION FOR ARTWORKS

Batik is one of the arts that contains a whole descriptive belief about how Javanese interpret their lives. It is found inclassical batiks, which are still committed to the standard elements from Keraton as a center for preserving Javaneseculture. Through studying classical batik, the writer finds a concept of Kacu ratio used in making batik. For thewriter, the kacu is a local form of genius that belongs to the Javanese community. The consciousness of the emptinesspermeates all material forms that exist in the universe. This belief is also applied to the process of designing batikfabrics. For the Javanese, beauty lies in the balance of macro and microcosmos. The actual balance lies at the pointof paradox. Through this research, similarities in the numbers of the Kacu ratio, which is believed to be the goldenratio of the Javanese people in ancient times were sought. Then the equation would be applied to the artwork. Thisstudy uses a qualitative method with several literature sources to dissect the Kacu concept and apply it to works ofart. The writer uses the Kacu concept to arrange visual objects as a scale and balance composition through a formalistapproach. Finally, the writer presents batik and screen-printing techniques on textile. Therefore, the writer needsto study the Kacu ratio further to understand what this ancient ratio means in the beliefs of the Javanese people.

Subjective and Objective User Behavior Disparity: Towards Balanced Visual Design and Color Adjustment

Interactive environments create endless possibilities for the design of websites, games, online platforms, and mobile applications. Their visual aspects and functional characteristics influence the user experience. Depending on the project, the purpose of the environment can be oriented toward marketing targets, user experience, or accessibility. Often, these conflicting aspects should be integrated within a single project, and a search for trade-offs is needed. One of these conflicts involves a disparity in user behavior concerning declared preferences and real observed activity in terms of visual attention. Taking into account accessibility guidelines (WCAG) further complicates the problem. In our study, we focused on the analysis of color combinations and their contrast in terms of user-friendliness; visual intensity, which is important for attracting user attention; and recommendations from the Web Accessibility Guidelines (WCAG). We took up the challenge to reduce the disparity between user preferences and WCAG contrast, on one hand, and user natural behavior registered with an eye-tracker, on the other. However, we left the choice of what is more important—human conscious reaction or objective user behavior results—to the designer. The former corresponds to user-friendliness, while the latter, visual intensity, is consistent with marketing expectations. The results show that the ranking of visual objects characterized by different levels of contrast differs when considering the perspectives of user experience, commercial goals, and objective recording. We also propose an interactive tool with the possibility of assigning weights to each criterion to generate a ranking of objects.

Multisensory integration of contextual boundaries affects temporal order memory, but not encoding or recognition

We memorize our daily life experiences, which are often multisensory in nature, by segmenting them into distinct event models, in accordance with perceived contextual or situational changes. However, very little is known about how multisensory integration affects segmentation, as most studies have focused on unisensory (visual or audio) segmentation. In three experiments, we investigated the effect of multisensory integration on segmentation in memory and perception. In Experiment 1, participants encoded lists of visual objects while audio and visual contexts changed synchronously or asynchronously. After each list, we tested recognition and temporal associative memory for pictures that were encoded in the same audio-visual context or that crossed a synchronous or an asynchronous multisensory change. We found no effect of multisensory integration for recognition memory: Synchronous and asynchronous changes similarly impaired recognition for pictures encoded at those changes, compared to pictures encoded further away from those changes. Multisensory integration did affect temporal associative memory, which was worse for pictures encoded at synchronous than at asynchronous changes. Follow up experiments showed that this effect was not due to the higher complexity of multisensory over unisensory contexts (Experiment 2), nor that it was due to the temporal unpredictability of contextual changes inherent to Experiment 1 (Experiment 3). We argue that participants formed situational expectations through multisensory integration, such that synchronous multisensory changes deviated more strongly from those expectations than asynchronous changes. We discuss our findings in light of supportive and conflicting findings of uni- and multisensory segmentation.

Recognition and calculation of objects in images using YOLOv3 architecture

Program that searches for five types of fruits in the images of fruit trees, classifies them and counts their quantity is presented. Its creation took into account the requirement to be able to work both in the background and in real time and to identify the desired objects at a sufficiently high speed. The program should also be able to learn from available computers (including laptops) and within a reasonable time. In carrying out this task, the possibilities of several existing approaches to the recognition and identification of visual objects based on the use of convolutional neural networks were analyzed. Among the considered network archi-tectures were R-CNN, Fast R-CNN, Faster R-CNN, SSD, YOLO and some modifications based on them. Based on the analysis of the peculiarities of their work, the YOLO architecture was used to perform the task, which allows the analy-sis of visual objects in real time with high speed and reliability. The software product was implemented by modifying the YOLOv3 architecture implemented in TensorFlow 2.1. Object recognition in this architecture is performed using a trained Darknet-53 network, the parameters of which are freely available. The modification of the network was to replace its original classification layer. The training of the network modified in this way was carried out on the basis of Transfer learning technology using the Agrilfruit Dataset. There was also a study of the peculiarities of the learning process of the network under the use of different types of gradient descent (stochastic and with the value of the batch 4 and 8), as a result of which the optimal version of the trained network weights was selected for further use. Tests of the modified and trained network have shown that the system based on it with high reliability distin-guishes objects of the corresponding classes of different sizes in the image (even with their significant masking) and counts their number. The ability of the program to distinguish and count the number of individual fruits in the analyzed image can be used to visually assess the yield of fruit trees

Cross-modal Semantic Relationships Guide Spontaneous Orienting in Real-life Scenes

In real-world scenes, the different objects and events available to our senses are interconnected within a rich web of semantic associations. These semantic links help parse information and make sense of the environment. For example, during goal-directed attention, characteristic everyday life object sounds help speed up visual search for these objects in natural and dynamic environments. However, it is not known whether semantic correspondences also play a role under spontaneous observation. Here, we investigated this question addressing whether crossmodal semantic congruence can drive spontaneous, overt visual attention in free-viewing conditions. We used eye-tracking whilst participants (N=45) viewed video clips of realistic complex scenes presented alongside sounds of varying semantic congruency with objects within the videos. We found that characteristic sounds increased the probability of looking, the number of fixations, and the total dwell time on the semantically corresponding visual objects, in comparison to when the same scenes were presented with semantically neutral sounds or just with background noise only. Our results suggest that crossmodal semantic congruence has an impact on spontaneous gaze and eye movements, and therefore on how attention samples information in a free viewing paradigm. Our findings extend beyond known effects of object-based crossmodal interactions with simple stimuli and shed new light upon how audio-visual semantically congruent relationships play out in everyday life scenarios.

The Applicability of Augmented Reality Technologies for Evaluating Learning Activities

a new trend in the development of immersive technologies has become augmented reality (AR), which is in demand due to its property to implement visual objects to enrich the learning content. The paper is devoted to the study of the applicability of AR technologies for evaluating learning activi-ties since there is a problem of inconsistency of teaching approaches with tools that lead to biased results. This led to the development of the “AR Quiz” application that contains interaction types such as touch-based, voice, input field, gaze and gesture that stimulate activities. In combination with 10 other forms of assessment materials, its application field has expanded and the tasks for students have diversified. The present study provides the calculation of validity and reliability coefficients of the assessment materials contained in the “AR Quiz” application that reflects the suitability of indicators for the purpose, accuracy and stability of measurements. The paper reveals positive attitudes of expert teachers and students towards the use of AR when evaluating learning activities. Along with integration map of compliance of AR interaction types with assessment materials, the paper provides recommendations for teachers on evaluating learning activities based on AR.

Faces and words are both associated and dissociated as evidenced by visual problems in dyslexia

Faces and words are traditionally assumed to be independently processed. Dyslexia is also traditionally thought to be a non-visual deficit. Counter to both ideas, face perception deficits in dyslexia have been reported. Others report no such deficits. We sought to resolve this discrepancy. 60 adults participated in the study (24 dyslexic, 36 typical readers). Feature-based processing and configural or global form processing of faces was measured with a face matching task. Opposite laterality effects in these tasks, dependent on left–right orientation of faces, supported that they tapped into separable visual mechanisms. Dyslexic readers tended to be poorer than typical readers at feature-based face matching while no differences were found for global form face matching. We conclude that word and face perception are associated when the latter requires the processing of visual features of a face, while processing the global form of faces apparently shares minimal—if any—resources with visual word processing. The current results indicate that visual word and face processing are both associated and dissociated—but this depends on what visual mechanisms are task-relevant. We suggest that reading deficits could stem from multiple factors, and that one such factor is a problem with feature-based processing of visual objects.

Visual Entity Linking via Multi-modal Learning

Existing visual scene understanding methods mainly focus on identifying coarse-grained concepts about the visual objects and their relationships, largely neglecting fine-grained scene understanding. In fact, many data-driven applications on the web (e.g. newsreading and e-shopping) require to accurately recognize much less coarse concepts as entities and properly link to a knowledge graph, which can take their performance to the next level. In light of this, in this paper, we identify a new research task: visual entity linking for fine-grained scene understanding. To accomplish the task, we first extract features of candidate entities from different modalities, i.e., visual features, textual features, and KG features. Then, we design a deep modal-attention neural network-based learning-to-rank method aggregates all features and map visual objects to the entities in KG. Extensive experimental results on the newly constructed dataset show that our proposed method is effective as it significantly improves the accuracy performance from 66.46% to 83.16% comparing with baselines.