Reviewerly/exHarmony huggingface dataset:
Comparing the performance of various Encoder Models and Vectorization Techniques on Text Classification

This paper presents a comprehensive comparison of the performance of various encoder models and vectorization techniques for text classification tasks. The study includes an evaluation of various pre-trained Autoencoding Transformers such as BERT, DistilBERT and Roberta, as well as traditional machine learning models used along with vectorization techniques such as BOW and TF-IDF. The authors conducted experiments on a diverse set of benchmark datasets from the Kaggle problem statement to classify disaster tweets as catastrophic and non-catastrophic and compared the accuracies of different models, computational efficiency, and scalability. The results of this study provide a valuable reference for researchers and practitioners looking to choose the best encoder models and vectorization techniques for text classification tasks.
Experimental Comparison of Autoencoder Variants in Content-Based Image Retrieval

Content based image retrieval (CBIR) refers to a form of lookup that is performed on a set of images. It is an attempt to find images from a dataset that are similar to a given input image. One of the most popular free products where CBIR is visibly used as the core idea is Google's Reverse Image Search Engine. Traditionally, CBIR was done by extracting specific types of features using specialized algorithms after manually analyzing the images and then comparing the similarity of those patterns to obtain relevant results. In recent times, these specialized algorithms that were quite image dataset specific have been commonly replaced by relatively more generic autoencoder models that tend to provide better accuracy for CBIR. All this makes it important to understand the different existing autoencoder models in the context of CBIR.This paper discusses and compares various types of autoencoders while primarily focusing on basic, variational and denoising autoencoders for the purpose of CBIR. The performance of the autoencoders that are being primarily described in this paper have been implemented and compared based on their label ranking average precision values on the Fashion MNIST dataset. In its evaluation, it has been observed that the denoising autoencoder performs better than the other models being proposed in this paper.
Nonparametric Bayes Differential Analysis of Multigroup DNA Methylation Data

DNA methylation datasets in cancer studies are comprised of measurements on a large number of genomic locations called cytosine-phosphate-guanine (CpG) sites with complex correlation structures. A fundamental goal of these studies is the development of statistical techniques that can identify disease genomic signatures across multiple patient groups defined by different experimental or biological conditions. We propose BayesDiff, a nonparametric Bayesian approach for differential analysis relying on a novel class of first order mixture models called the Sticky Pitman-Yor process or two-restaurant two-cuisine franchise (2R2CF). The BayesDiff methodology flexibly utilizes information from all CpG sites or biomarker probes, adaptively accommodates any serial dependence due to the widely varying inter-probe distances, and makes posterior inferences about the differential genomic signature of patient groups. Using simulation studies, we demonstrate the effectiveness of the BayesDiff procedure relative to existing statistical techniques for differential DNA methylation. The methodology is applied to analyze a gastrointestinal (GI) cancer dataset exhibiting serial correlation and complex interaction patterns. The results support and complement known aspects of DNA methylation and gene association in upper GI cancers.
Structural Synthesis of the Computer Vision and Its Parametric Identification with Statistical Estimation of Variational Parameters

The article presents the results of a study of the problem on structural synthesis of a computer vision and its parametric identification. We present the main classes of procedural transformations of images and describe a method for constructing a model of a vision system for measuring a deterministic feature using procedural and parametric transformations of modified descriptive image algebras. The function of measuring a deterministic quantitative characteristic of an object of observation is defined as a superposition of a procedural and parametric descriptive algebraic image transformation scheme. The parametric identification problem is formulated in the form of a nonlinear optimization problem. We give examples of the use of the described method in measuring the area of objects of various nature. Recommendations are formulated for the statistical processing of the values of the measured characteristic in order to clarify its value.
The Role of Game Practices in the Model University of the Future

The article is devoted to the analysis of cognitive strategies within the framework of the ecosystem model of the university of the future, focused in its development on the coordination of the goals and results of the activities of different professional communities in the process of training a specialist. In this regard, it is important to study the potential of gamification to involve people in joint educational activities. Considering gamification as an innovative pedagogical technology, the authors emphasize the problem of motivation of the student's cognitive activity, highlight the role of game practices in the context of cognitive and metacognitive educational strategies. This study is based on a systematic methodology that allows us to analyze interactive gamification techniques as flexible ways to involve students in the cognitive process. In the university model of the future, the use of digital platforms involves the provision of flexible, personalized learning that connects different interactive technologies in a wide range of interactions between humans and technical systems. Game practices are represented by groups of methods that contribute to the activation of the solution of educational problems in the formation of the cognitive and communicative potential of the individual, which is necessary in the future profession. An assessment of the role of gamification in the educational practice of university is presented in the article on the basis of data from a survey of students’ and teachers’ opinions. It is noted that the role of game practices is associated with a variety of ways of concentrating attention and forms of involving the student in the learning process. From the point of view of psychology, the influence of gamification on cognitive activity is associated with motivation that maintains the state of experiencing information, both in the mind of the student and in the mind of the teacher.
“The Best City Left”: City 17 in the Half Life 2 Game Universe and the Transformation of the Image of Cities of the Future

The article is devoted to the rethinking of the visual image of the city in popular culture and the role in this process of one of the most iconic and memorable cities ever created in video games. The novelty lies in the activation of the classic category “image of the city” in the analysis of the aesthetic and narrative foundations of the virtual urban space of the Half Life 2 series of games in the broad context of the visualization of future urbanism in popular culture. On the example of City 17, which is the spatial center of the entire game universe of Half Life 2, the mechanisms of figurative influence on the player, the reflection in virtual urbanism of general trends in the representation of urban space, the enormous opportunities in the implementation of fictional worlds, relaying specific images of virtual reality and its meaningful parameters are studied. All images of fantastic megacities are constructing a future with the prospects of transforming the urban environment, man and the world as a whole. And City 17 is no exception, but rather a contrasting and original illustration of the rethinking and re-evaluation of not only the visual image, but also the very nature of the representation of the city and its role in modern society. The authors come to the conclusion that the dystopian city created in the Half Life 2 video game continues and develops the direction of discrediting the city as a symbol of human civilization and its future living space, which has been established in science fiction since the middle of the 20th century. City 17 is another urban nightmare with a specific variation on an inhospitable, repulsive space that is both familiar due to the familiar elements of dystopian fantasy cities, and at the same time palpably different due to the original visualization of integration with alien technologies. Such an image of the city coincides with the general trend, which is only emerging today, of crowding out urban landscapes from the center to the periphery of the proposed future. The city ceases to be a symbol of the future, whatever that future may be.
The Recursive Paradigm and Semiotic Models of Games in Third-Order Technical Cybernetics

This article addresses the issue, which, in general, can be defined as “absent (model-independent) semantics” of language. The influence of results of cyber technological evolution on the development of formal languages of technological cybernetics is investigated in the article. Research is carried out in the context of modern transformations of recursive paradigm of scientific research, originating from the concepts of Plato’s noumena and Kant’s “thing-in-itself”. Development of recursive paradigm in epistemology determined the emergence of the concept of “third-order cybernetics” according to classification of Vladimir Lepsky, the concept contains a reflexive feedback function and a mechanism for implementing a poly-subject environment (autopoiesis). A broad interpretation of sign, including dynamic objects, allows the use of semiotic models with automatically realized semantics and autopoiesis as a pragmatic component in third-order technological cybernetics, which determines methodological component of the article. Third–order poly-subject environment of third-order technological cybernetics with a realized autopoiesis mechanism is a powerful tool for realizing virtually unlimited strategies within the framework of the basic model - “a game playing games”. Recursive nature of poly-subject environment of “technological cybernetics 3.0.“ makes it possible to implement an almost unlimited number of game models, determined in general by the criteria of accuracy (analytical criterion of the model) and scope (synthetic criterion of model). The purpose of the article is formation of philosophical prerequisites associated with acceptance by the scientific community of the procedural nature of semantics; automatically realized semantics; cyber technological semantics as a subject of study within the framework of technological cybernetics; reflexive autopoietic semiotic models of “technological cybernetics 3.0.”. The expected result is the development of basic provisions of third-order technological cybernetics language concept.
The Immersive Approach and Gamification: New Forms of Educational Technologies Through Games

This paper examines problems related to implementing digital technologies in the educational process, integration and updating foreign language teaching methods, as well as the development of lexical skills in non-language students when learning foreign languages. The article is focused on the topic of adopting an immersive approach to teach vocabulary to first-year students, using the gamification method as an illustration. The key benefits and drawbacks of using the most recent computer and digital technologies in educational activities, as well as their features and operating principles, are analyzed. Additionally, collections of gamified and immersive platforms for teaching foreign languages are created, along with a detailed description of their functionality. The practical value of the research comes from the development of a digital product, an educational game for teaching English vocabulary that complies with both the theoretical requirements of an immersive approach and the fundamental tenets of gamification.
Compatibility of Edutainment and Traditional Methods in the University’s Educational Environment

The introduction of digital game-based learning, focused on the main professional goals, allows stimulating the active cognitive activity of modern students and goes in line with the psychological portrait of generation Z. The article provides an overview of modern publications on the application of edutainment in higher education and offers methodological recommendations of the authors on this issue based on the experience of teaching at the Financial University. The possibilities of implementing edutainment with a profile focus in the study of non-major disciplines at a University with economic direction of instruction. In particular, foreign language and mathematics are considered. The compatibility of edutainment technology and traditional methods of teaching these subjects is substantiated and demonstrated by examples. The authors expand on creative, competitive effort on generating interactive forms in Excel, designed for first-year bachelors in Digital Mathematics. The feedback of students on this task is analyzed. The article describes the results of a survey of first- to fourth-year students and their lecturers and instructors regarding their attitude to game-based learning. Trends have been identified that show a shift in emphasis from full acceptance of the game at any time and in any situation (playing for the sake of playing) in the first year, to the perception of the game as an opportunity to make the learning process interesting (learning in the game) in subsequent years. The interest in obtaining professional skills in the game edges highest among freshmen and graduates. The authors draw the conclusion that the use of edutainment in higher education should be based on a professional context.
Gradient-Boosted Tree Regression with Pandas, Scikit-Learn, and PySpark

In this chapter, we continue with supervised learning and tree-based regression. Specifically, we develop a gradient-boosted tree (GBT) regression model using the same housing dataset we used for decision tree and random forest regression in the preceding chapters. This way, we can have a better idea about which tree type performs better by comparing their performance metrics.
A Secure Data Sharing System for 6G Networks

The advent of 6G networks with their capabilities for massive communication and low latency presents opportunities for a more intelligent way of life. This paper proposes a secure, flexible, and decentralized data sharing approach, tailor-made for the complexities of 6G networks. Building upon a famous data sharing framework called Droplet, the proposed approach furnishes users with fine-grained access to time-series data, while maintaining low communication overhead and minimizing potential data leakage. We spotlight an underlying vulnerability within the Droplet scheme, prompting the development of an advanced scheme that amalgamates segmented dual-key regression technology and binary hash trees. Further, we devise an algorithm for a minimal key set to reduce communication overhead for data owners during the data sharing process, thereby enhancing the operational efficiency of our proposed scheme. Extensive simulations are executed to authenticate the successful realization of our design objectives through the proposed scheme. This research aims to address the pressing issues of data sharing in the context of 6G networks, providing insights and solutions for secure and flexible data management.grammatically correct.
Double-Blind Proof of Existence for Decentralized Identities

Decentralized identities return control of identities to the identity owners. Although current work enhances the privacy of these publicly stored identities using encryption and zero-knowledge proofs, decentralized identities can still be abused due to the following problems: identity holders, e.g., blockchain peers, can profile identity owners by looking at “who is reading which identity data”, and identity verifiers, e.g., applications and websites, learn private data about owners, like their monetary values and previous transactions during the identity linking. In the worst case scenario, the identity holders and verifiers collaboratively profile users to learn more information. As a practical solution, we introduce the notion of Double Blind Proofs of Existence (DBPoE), which shows that an opened DID is committed in one of the constant-sized multi-generator Pedersen commitments (33 Bytes at 128-bit security), and nothing else. Hence, our DBPoE double-blinds identity holders and identity verifiers to mitigate private information leakage. Equally importantly, our multi-generator commitment-based DBPoE is more resistant to graph analysis than other one-of-many proofs, e.g., ring signatures, which we show mathematically using the maximal flow problem. Our DBPoE protocol has a size complexity of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> ) + <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> ) when the real commitment is hidden in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> commitments and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> generators are used, e.g., when <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> = 4, a DBPoE of 1000 commitments is only 3 KB.
Z-Number-Based Fuzzy Logic Approach for Mobile Robot Navigation

The primary objective of this study is to investigate the effects of mobile robot navigation using a fuzzy logic framework based on Z-numbers implemented within the Robot Operating System (ROS) Noetic. The methodology addresses uncertainty and imprecise information in robot navigation using extensive simulations performed using the TurtleBot3 robot in the ROS framework. Our unique approach enables the autonomous navigation of mobile robots in unknown environments, utilizing fuzzy rules with multiple inputs and outputs. The navigation strategy relies on the laser scan sensor, the Adaptive Monte Carlo Localization (AMCL) algorithm, and particle filter mapping, enabling real-time localization and mapping capabilities. Path planning incorporates local and global planners, while obstacle avoidance generates collision-free paths by dynamically detecting and circumventing obstacles in the robot’s proximity. We employ Simultaneous Localization and Mapping (SLAM) techniques to estimate the robot’s position and create a map of the environment. Our integration of these methods presents a promising solution for autonomous mobile robot navigation in real-world applications, thereby advancing the capabilities of robot systems in complex environments. Our results demonstrate the suitability and effectiveness of using a Z-number-based system in the navigation scenarios of mobile robots.
Manipulation of Age Variation using StyleGAN inversion and fine-tuning

Recent advancements in deep learning have yielded significant developments in age manipulation techniques in the field of computer vision. To handle this task, recent approaches using generative adversarial networks latent space transformation or image-to-image based techniques have been developed. However, such methods are limited in terms of preserving the facial identity of the subject and recovering background details during lifelong age variation. To address these limitations, this paper presents a novel framework to manipulate the age of subjects in photos. The proposed framework involves two main steps, i.e., age manipulation and StyleGAN fine-tuning. In the first step, the iterative ReStyle StyleGAN inversion technique discovers a latent vector that is most similar to the input image, and which is used to train an age manipulation encoder. In the second step, a StyleGAN fine-tuning process is used to reconstruct the details lost in the images synthesized using the StyleGAN generator during the age manipulation. To preserve substructures, e.g., backgrounds, we optimize the loss function using facial masks generated from the original and age-manipulated images. The proposed framework is compatible with various StyleGAN-based techniques, e.g., stylization and view synthesis. Compared with state-of-the-art methods, the proposed framework achieves reasonable manipulation and variation of the target age for real-world input images. The results demonstrate the effectiveness of the proposed method in preserving the facial identity and background details during lifelong age variation.
Enhancing Pedestrian Group Detection and Tracking through Zone-Based Clustering

Advancements in self-driving car technology have the potential to revolutionize transportation by enhancing safety, efficiency, and accessibility. Nonetheless, the successful integration of autonomous vehicles into our urban landscapes necessitates robust and reliable pedestrian detection and tracking systems. As we frequently observe pedestrians moving together, tracking them as a group becomes a beneficial approach, mitigating occlusion and enhancing both the accuracy and speed of object detection and tracking. However, utilizing a human-view camera in an autonomous vehicle presents challenges as pedestrians occupy varied fields of view. In some instances, pedestrians closer to the camera may overlap with those farther away, as seen from the camera’s viewpoint, which causes the mis-groupings to happen. To address these challenges, we proposed a strategy to divide the image into distinct zones and perform grouping within each, significantly minimizing mis-groupings. First, an object detection method was utilized to extract pedestrians and their bounding boxes from an image. Second, zone detection was applied to separate the image into several zones. Third, clustering methods were applied to detect pedestrian groups within each zone. Last, the object tracking method was utilized to track pedestrian groups. We repeated the process over a ten-frame sequence to achieve better performance, with object detection executed in the first frame and object tracking in the remaining nine frames. The comparison of processing times of different group detection methods indicated that tracking pedestrian groups is more time-efficient than tracking individuals and achieved a 4.5% to 14.1% improvement. Furthermore, according to the Adjusted Rand Index (ARI) evaluation metric, our proposed zone-based group detection method outperforms the other commonly used approaches by achieving scores of 0.635 on the MOT17 dataset and 0.781 on the KITTI dataset. In addition, the proposed approach surpasses the other approaches in addressing scenarios where individuals from different fields of view intersect with each other.