Reviewerly/exHarmony huggingface dataset:
Semantic-Aware Modular Capsule Routing for Visual Question Answering

Visual Question Answering (VQA) is fundamentally compositional in nature, and many questions are simply answered by decomposing them into modular sub-problems. The recent proposed Neural Module Network (NMN) employ this strategy to question answering, whereas heavily rest with off-the-shelf layout parser or additional expert policy regarding the network architecture design instead of learning from the data. These strategies result in the unsatisfactory adaptability to the semantically-complicated variance of the inputs, thereby hindering the representational capacity and generalizability of the model. To tackle this problem, we propose a Semantic-aware modUlar caPsulE Routing framework, termed as SUPER, to better capture the instance-specific vision-semantic characteristics and refine the discriminative representations for prediction. Particularly, five powerful specialized modules as well as dynamic routers are tailored in each layer of the SUPER network, and the compact routing spaces are constructed such that a variety of customizable routes can be sufficiently exploited and the vision-semantic representations can be explicitly calibrated. We comparatively justify the effectiveness and generalization ability of our proposed SUPER scheme over five benchmark datasets, as well as the parametric-efficient advantage. It is worth emphasizing that this work is not to pursue the state-of-the-art results in VQA. Instead, we expect that our model is responsible to provide a novel perspective towards architecture learning and representation calibration for VQA.
Living Capsules: reflections on an ongoing art-sociology collaboration

Abstract Living Capsules is the umbrella name we give to our art pieces that are being born out of an ongoing collaboration, between an artist and a sociologist, who share interests in the relations between senses, bodies, and technologies. This reflective paper tells the story of their co-creation. We introduce the notion of biohybrid systems as our sociotechnical inspiration. Second, we mark out the conceptual space in which we began to prototype Living Capsules. Third, we reflect on how and why we blend our disciplinary practices. And finally, we share and discuss some prototype pieces, sketching future directions for our continued collaboration.
Statistical models for identifying missing and unclear signs of the Indus script

A writing system was developed between 2500 and 1800 BCE in the Indus Valley civilization in the Indian subcontinent and remains undeciphered. Indus script texts found so far in the archeological digs from this civilization are limited in number and include a lot of damaged artifacts with missing and unclear signs. Identifying the missing and unclear signs and extending the Indus text corpus will aid the researchers in deciphering this script. This work aimed to predict the missing and unclear signs using n-gram Markov chain models using the Interactive Corpus of Indus Texts (ICIT) text corpus. First, we analyzed patterns and concordances of the signs, pairs, triplets, and other n-grams and discovered the positional behavior of signs in the Indus texts. With that understanding, we built Markov chain language models based on n-grams, augmented with sign positional probabilities. Since signs could be missing in any location of the texts, we devised and implemented effective sign fill-in algorithms on top of these Markov chain models. Using the language models and the sign fill-in algorithms, we tuned our models and predicted single signs that were deliberately removed from complete texts with about 63% accuracy. Then we used the best model and our tuned parameters to predict missing and unclear single signs in about 100 texts. The statistical methods we described here improve our understanding of the Indus script. Filling in the missing signs makes the corpus more complete and helps contribute to the broader decipherment effort.
Joint intent detection and slot filling for Turkish natural language understanding

Intent detection and slot filling are two crucial subtasks of a text-based goal-oriented dialogue system. In a goal-oriented dialogue system, users interact with the system to complete a goal (or to fulfill their intent) and provide the necessary information (slot values) to achieve that goal. Therefore, a user?s text input includes information about the user?s intent and contains required slot values. Recently, joint models that simultaneously detect the intent and extract the slots are proposed to benefit from the interaction between the two tasks. The proposed methods are usually tested using benchmark data sets in English such as ATIS and SNIPS. Intent detection and slot filling problems are much less studied for the Turkish language mainly due to the lack of publicly available Turkish data sets. In this paper, we translate ATIS in English to Turkish and report intent detection and slot filling accuracies of several different joint models for the translated data set. We publicly share the Turkish ATIS data set to accelerate the research on the tasks. In our experiments, the best performance is obtained with the state-of-the-art bidirectional encoder representations from a transformers (BERT) based model. The BERT model is trained using a combination of intent detection and slot filling losses to jointly optimize a single model for both tasks. We achieved 96.54% intent detection accuracy and 91.56% slot filling F1 for the Turkish language. These accuracies significantly improve (7% absolute in slot filling F1) previously reported results for the same tasks in Turkish. On the other hand, we observe that the accuracy in Turkish is still slightly lower compared to the accuracy in English counterparts. This observation indicates that there is still room for improvement in the results for Turkish.
Stepwise dynamic nearest neighbor (SDNN): a new algorithm for classification

Although the standard k-nearest neighbor (KNN) algorithm has been used widely for classification in many different fields, it suffers from various limitations that abate its classification ability, such as being influenced by the distribution of instances, ignoring distances between the test instance and its neighbors during classification, and building a single/weak learner. This paper proposes a novel algorithm, called stepwise dynamic nearest neighbor (SDNN), which can effectively handle these problems. Instead of using a fixed parameter k like KNN, it uses a dynamic neighborhood strategy according to the data distribution and implements a new voting mechanism, called stepwise voting. Experimental results were conducted on 50 benchmark datasets. The results showed that the proposed SDNN method outperformed the KNN method, KNN variants, and the state-of-the-art methods in terms of accuracy.
Performance optimization of web applications using Qwik

This article analyzes the performance of three frameworks - React.js, Next.js and Qwik - that offer different methods of rendering application views. The purpose of the study was to show whether the new Qwik framework allows for better application load times compared to the other frameworks. The study was conducted using 3 applications representing the same research content, referring to cases occurring in production environments. In order to assess the performance, the Google Lighthouse tool was used, thanks to which it was proved that it is impossible to unequivocally say that Qwik allows for better optimization of the application compared to other frameworks.
A TWO-STEP CLUSTER FOR CLASSIFYING PROVINCES IN INDONESIA BASED ON ENVIRONMENTAL QUALITY

The main objective of this study was to conduct a cluster analysis of the environmental health index in Indonesia for all the provinces. Clustering the environmental health index was important to reveal regional disparities, target and intervention policies, monitor progress over time, and allocate resources more effectively for improved environmental health outcomes. In this study, a sample of 34 units was utilized, encompassing all provinces in Indonesia. The environmental health index was clustered based on five indicators, namely Water Quality Index, Air Quality Index, Soil Quality Index, Marine Quality Index, and Land Cover Quality Index. This research used the two-stage clustering method, which was unique in combining both hierarchical and non-hierarchical clustering methods to produce a more accurate and reliable solution. Four clusters were determined to group provinces in Indonesia based on the environmental health index. The analysis found that the quality of clustering was in the fair but close to good category. The clustering results showed that 32% of the provinces were in cluster 4 and 26.5% of the provinces were in cluster 1. Then, 23.5% and 17.6% of the provinces were in clusters 2 and 3, respectively. In addition, two indicators were found to be the most predictive of the overall clustering solution, namely the Soil Quality Index and the Land Cover Quality Index. The results also implied that provinces in cluster 3 had the lowest environmental quality so they must improve it by looking at provinces in cluster 4, which was the group with the best environmental quality index.
Involving Particle Technology in Computational Fluid Dynamics Research: A Bibliometric Analysis

This research was conducted to determine (i) the growth in the number of scientific publications in the field of particle technology in computational fluid dynamics (CFD), (ii) top citations based on the number of citations, publisher, and country, (iii) visualization of the most productive author, and (iv) publication development map based on keywords. This research method is a descriptive quantitative with bibliometric analysis using Publish or Perish software to harvest data and VOSviewer to visualize related research terms. Data was collected by searching using the Google Scholar database with the keywords “particle technology” and “Computational Fluid Dynamics” from 2019 to 2023. The results showed the growth of scientific publications in the field of particle technology in CFD. The most researched subjects in CFD are “technology,” “CFD analysis,” “fine particles,” and “experimental validation,” which are based on overlay and density visualization. The interrelationships between topics are grouped into four clusters with excellent network visualization. This research is expected to assist researchers in determining the topics to be studied and can also be a reference for further research.
A Systematic Stereo Camera Calibration Strategy: Leveraging Latin Hypercube Sampling and 2k Full-Factorial Design of Experiment Methods

This research aimed to optimize the camera calibration process by identifying the optimal distance and angle for capturing checkered board images, with a specific focus on understanding the factors that influence the reprojection error (ϵRP). The objective was to improve calibration efficiency by exploring the impacts of distance and orientation factors and the feasibility of independently manipulating these factors. The study employed Zhang’s camera calibration method, along with the 2k full-factorial analysis method and the Latin Hypercube Sampling (LHS) method, to identify the optimal calibration parameters. Three calibration methods were devised: calibration with distance factors (D, H, V), orientation factors (R, P, Y), and the combined two influential factors from both sets of factors. The calibration study was carried out with three different stereo cameras. The results indicate that D is the most influential factor, while H and V are nearly equally influential for method A; P and R are the two most influential orientation factors for method B. Compared to Zhang’s method alone, on average, methods A, B, and C reduce ϵRP by 25%, 24%, and 34%, respectively. However, method C requires about 10% more calibration images than methods A and B combined. For applications where lower value of ϵRP is required, method C is recommended. This study provides valuable insights into the factors affecting ϵRP in calibration processes. The proposed methods can be used to improve the calibration accuracy for stereo cameras for the applications in object detection and ranging. The findings expand our understanding of camera calibration, particularly the influence of distance and orientation factors, making significant contributions to camera calibration procedures.
CIPp Model In Utilizing Interactive Multimedia-Based Teaching Materials (Evaluation Study On Pertiwi 1)

The purpose of this study was to determine the use of interactive multimedia-based teaching materials in early childhood with the application of the CIPP evaluation model (Context, Input, Process, Product). This study used the CIPP method which has four evaluation attributes: 1) Context, 2) Input, 3) Process 4) Product. And the rating weight category with a Likert Scale with a questionnaire used in the form of 5 answer choices. Objects in product evaluation of interactive multimedia-based teaching materials that will be developed. The research was conducted by testing the evaluation model. By testing the quality of the model developed is truly tested empirically. The validation instrument is to determine the feasibility of interactive multimedia-based teaching materials developed for use by Early Childhood Children to achieve an effective learning process. Collecting data in this study using validation instruments. The data analysis technique in this study used the CIPP model to evaluate the use of interactive multimedia-based teaching materials with 12 indicators and 53 questions related to the use of interactive multimedia-based teaching materials for early childhood with an average score of 72%. Declared relevant and can be utilized in learning in early childhood. The revisions desired by the evaluator are: 1) instructions for using teaching materials are made, because in the manufacture of interactive multimedia-based teaching materials there are no guidelines for operating these interactive multimedia teaching materials. With the revision of this evaluator, the researcher made instructions or guidelines for the operation of this interactive multimedia-based teaching material. 2) suitability of the material with the daily learning implementation plan (RPPH) on interactive multimedia-based teaching materials
ELECTRICAL, ELECTRONICS AND INFORMATION TECHNOLOGY SCIENCE MAP

The map of science for the Omsk State Technical University in the field of microelectronics and information technology is presented in the work. The map was built on the basis of data from the SciVal scientometric system based on university publications in Scopus. Also, the results of registration of intellectual activity, registered in the Unified State Information System of Accounting. research, development and technological work for civil purposes are used.
A Novel Feature Fusion for the Classification of Histopathological Carcinoma Images

Breast cancer is a significant global health concern, demanding advanced diagnostic approaches. Although traditional imaging and manual examinations are common, the potential of artificial intelligence (AI) and machine learning (ML) in breast cancer detection remains underexplored. This study proposes a hybrid approach combining image processing and ML methods to address breast cancer diagnosis challenges. The method utilizes feature fusion with gray-level co-occurrence matrix (GLCM), local binary patterns (LBP), and histogram features, alongside an ensemble learning technique for improved classification. Results demonstrate the approach's effectiveness in accurately classifying three carcinoma classes (ductal, lobular, and papillary). The Voting Classifier, an ensemble learning model, achieves the highest accuracy, precision, recall, and F1-scores across carcinoma classes. By harnessing feature extraction and ensemble learning, the proposed approach offers advantages such as early detection, improved accuracy, personalized medicine recommendations, and efficient analysis. Integration of AI and ML in breast cancer diagnosis shows promise for enhancing accuracy, effectiveness, and personalized patient care, supporting informed decision-making by healthcare professionals. Future research and technological advancements can refine AI-ML algorithms, contributing to earlier detection, better treatment outcomes, and higher survival rates for breast cancer patients. Validation and scalability studies are needed to confirm the effectiveness of the proposed hybrid approach. In conclusion, leveraging AI and ML techniques has the potential to revolutionize breast cancer diagnosis, leading to more accurate and personalized detection and treatment. Technology-driven advances can significantly impact breast cancer care and management.
Deep Neural Network-based Detection of Road Traffic Objects from Drone-Captured Imagery Focusing on Road Regions

This paper presents a novel deep learning approach for the detection of traffic objects from drone-based imagery, focusing predominantly on the rapid and accurate detection of vehicles within road sections. The proposed method consists of two primary components: a road segmentation module and a vehicle detection network. The former leverages a residual unit with skip-connections to effectively extract road areas, while the latter employs a modified version of the YOLOv3 architecture, tailored for high-accuracy and high-speed vehicle detection. To address the issue of data imbalance, which is a pervasive challenge in drone images, this paper utilizes a range of data augmentation techniques to improve the robustness of the proposed model. Experimental results on the UAVDT and UAVid datasets exhibit that the proposed model attains a substantial boost in accuracy and inference speed of vehicle detection in comparison to the existing methods. These findings underscore the potential of the proposed approach for real-world traffic monitoring applications, where rapid and reliable vehicle detection is paramount.
Social consequences of application artificial intelligence system in education

The influence of innovative technologies, and especially artificial intelligence programs on the educational process, is currently increasing and causing uncertain estimates and consequences. Despite the retention of leadership by students and teachers of the classical format of the educational process, the introduction of digital distance technologies has significantly affected the education market, and projects related to the integration of artificial intelligence through the use of neural networks and machine learning technologies have serious capitalization. The phenomenon of using programs with artificial intelligence affects scientific activity and a qualitative change in education. The well-known cases of the application of this technology have social consequences and ambiguity in the regulation of these programs, as well as initiate a discussion about the advantages and disadvantages of this technology.
Philosophical Futurism and the Evolution of Education: Analyzing Personality Consciousness, ICT, and Forward-Thinking Pedagogical Strategies

The purpose of the article was to identify promising educational strategies in the context of the subject of the educational space personal potential correlation and the capabilities of innovative educational elements. The philosophical and futuristic guidelines of educational development are focused on the worldview and mental transformation of consciousness associated with the active use of ICT elements. The tasks of the article were focused on the philosophical and ideological understanding of the educational space of the future – the positioning of the subjects of educational activity and a new balance between the traditional and innovative components of the educational system. Methodology. The study used general scientific methods: analysis, comparison, and systematisation. A special place in the methodological arsenal of scientific research is occupied by forecasting and modelling since they help achieving the goals of the article, namely - to identify promising educational directions of development. The philosophical and scientific methodological approaches, especially those of synergistic content, have also been updated. Interdisciplinary connections provided an opportunity to study innovative elements and their impact on improving the educational paradigm. The results of the study indicated the need for a balance between the potential of the subject of the educational space and the capabilities of the information technology cluster in the educational process. Prospects for the study are the development of philosophical and axiological guidelines that will become a guide for the feasibility of using innovative educational formats. Conclusion. Thus, the evolution of education is occurring at a rapid pace and is causing a transformation of the value components of this sphere of social activity. Such perspectives define a new understanding of the content of the educational environment, pedagogical format, and activity of all subjects and stakeholders of the educational process.
The Influence of Servant Leadership Style and Ethical Leadership on Organizational Change

The significance of human resources in determining a company's success renders them a pivotal component in the everyday operations of the organization. Furthermore, the status of this entity is unaffected by external factors, including advanced technological advancements, so rendering it immune to prohibition. This may be attributed to the primary goals of enhancing regulatory frameworks, optimizing decision-making processes, and promoting policy facilitation. The primary objective of this study is to investigate and evaluate the impact of servant leadership and ethical leadership on organizational transformation within the context of Travel An-Namira Madura. All individuals included in this study were employed by the same business. The present study employs a quantitative methodology, using questionnaires sent to a sample of 52 individuals employed in the workforce. The data was processed using the IBM SPSS 25.0 software. The study results indicate that the implementation of servant leadership has a positive influence on the effectiveness of organizational transformation
Innovative Trends in Educational Management in a Globalized World: Organizational Neuroscience

The article discusses the problems and prospects of personally centric and, partly, technological reforms in the management of Ukrainian educational institutions in the context of innovation and leading global trends.&#x0D; The main idea of the article is to deploy the concept that institutionalized education is self-regulated by local actors and relies on the human proactive motivated resource of teachers and students with minimal implementation from above.&#x0D; The purpose of the article was to investigate three aspects of education management: innovative global trends, problems of current education management in Ukraine and recommendations and methodological advice for such management.&#x0D; The authors used mainly theoretical methods of literature selection by keywords, system analysis of this literature, methods of summarizing - generalizing - classifying principles, methods and forms of education management, as well as the method of design and educational modeling to provide specific recommendations and advice.&#x0D; The result of the article was a synthesis of the latest global trends in educational management, which we summarized in 12 key points; argumentation of challenges and obstacles to innovative management of educational institutions in Ukraine, identification of destructive factors in the way of this management and provision of outline recommendations for reform.