SEMIOTICS OF SOCIAL MEMORY IN URBAN SPACE: THE CASE OF VOLGOGRAD (STALINGRAD)

Social memory as a kind of collective memory is connected with the strategies and practices of perpetuating the memory about important events, and city as a commemorative space can be viewed as a sign and as a text. The semiotic means encoding social phenomena and events represent the system of denotation, while the ways of place naming represent the culturally conditioned system of connotation operating behind the denotation code. The semiotics of social memory was examined by the example of the city of Volgograd (Stalingrad), the landscape of which appeals to a most significant historical event – the Great Patriotic War (World War II) – and can be conveniently described by means of Ch. S. Peirce’s classification of signs in which icons include signs denoting war heroes and represented by their sculptural images; indices include signs denoting artifacts associated with the war events; symbols are represented by toponymy signs characterized by the connotations of heroic deeds; all these signs representing cultural and political values specific for the Volgograd society. The semiotic density of social memory representation may be considered a ground for shaping the city’s ‘imagined community’ (the term suggested by B. Anderson, 1983) of a particular kind

Event-by-event Simulation of Quantum Cryptography Protocols

We present a new approach to simulate quantum cryptography protocols using event-based processes. The method is validated by simulating the BB84 protocol and the Ekert protocol, both without and with the presence of an eavesdropper

Phenomenological aspects of Russian verbal culture: the book in the creative mind of V. A. Zhukovsky

The paper focuses on the ‘the writer and the book’ problem, which is studied from the viewpoint of the phenomenology of the Russian culture, and is a case study of the Russian poet V. A. Zhukovsky's personal library stored in the Scientific Library of Tomsk State University. The study is based on the idea that the active interaction between the creative process and the author's range of reading is crucial because the poet's library and his preferences as a reader function as an essential system modelling his outlook and works

Causal Models Applied to the Patterns of Human Migration due to Climate Change

The impacts of mass migration, such as crisis induced by climate change, extend beyond environmental concerns and can greatly affect social infrastructure and public services, such as education, healthcare, and security. These crises exacerbate certain elements like cultural barriers, and discrimination by amplifying the challenges faced by these affected communities. This paper proposes an innovative approach to address migration crises in the context of crisis management through a combination of modeling and imbalance assessment tools. By employing deep learning for forecasting and integrating causal reasoning via Bayesian networks, this methodology enables the evaluation of imbalances and risks in the socio-technological landscape, providing crucial insights for informed decision-making. Through this framework, critical systems can be analyzed to understand how fluctuations in migration levels may impact them, facilitating effective crisis governance strategies.Comment: submitted to IEEE Symposium Series on Computational Intelligenc

An Ensemble of Knowledge Sharing Models for Dynamic Hand Gesture Recognition

The focus of this paper is dynamic gesture recognition in the context of the interaction between humans and machines. We propose a model consisting of two sub-networks, a transformer and an ordered-neuron long-short-term-memory (ON-LSTM) based recurrent neural network (RNN). Each sub-network is trained to perform the task of gesture recognition using only skeleton joints. Since each sub-network extracts different types of features due to the difference in architecture, the knowledge can be shared between the sub-networks. Through knowledge distillation, the features and predictions from each sub-network are fused together into a new fusion classifier. In addition, a cyclical learning rate can be used to generate a series of models that are combined in an ensemble, in order to yield a more generalizable prediction. The proposed ensemble of knowledge-sharing models exhibits an overall accuracy of 86.11% using only skeleton information, as tested using the Dynamic Hand Gesture-14/28 datasetComment: Accepted at International Joint Conference on Neural Networ

Multi-Metric Evaluation of Thermal-to-Visual Face Recognition

In this paper, we aim to address the problem of heterogeneous or cross-spectral face recognition using machine learning to synthesize visual spectrum face from infrared images. The synthesis of visual-band face images allows for more optimal extraction of facial features to be used for face identification and/or verification. We explore the ability to use Generative Adversarial Networks (GANs) for face image synthesis, and examine the performance of these images using pre-trained Convolutional Neural Networks (CNNs). The features extracted using CNNs are applied in face identification and verification. We explore the performance in terms of acceptance rate when using various similarity measures for face verification