Guilt, shame, and antiwar action in an authoritarian country at war

Feeling guilt and shame for the harm done to others by the ingroup can facilitate intergroup reconciliation. Most of the studies showing this effect are conducted in democratic countries and on historical, not current, conflicts. We investigated the role of group‐based guilt and shame in collective action in an authoritarian country at war. We asked more than 1000 Russians living in Russia, in a sample representative of the country's population by gender and age, about their experiences of group‐based guilt and shame regarding Russia's invasion of Ukraine and their past and future antiwar political actions. We tested whether political efficacy is necessary for experiencing group‐based guilt and shame, and whether these emotions are predictive of antiwar action over and above other emotions and attitudes. Democratic values, not political efficacy, were the most robust predictor of group‐based guilt and shame. Only moral shame, but not image shame or guilt, predicted past and future antiwar action. Whereas attitude towards the war and moral shame predicted engagement in antiwar action (vs none), other negative dominant emotions like anger predicted the degree of this engagement. We highlight the gaps in the study of collective action and the need for more evidence from nondemocratic contexts

PUBLIC KEY ENCRYPTION AND ENCRYPTION EMULATION ATTACKS

Abstract. The main purpose of this paper is to show that public key encryption can be secure against the “encryption emulation ” attack (on the sender’s encryption) by computationally unbounded adversary, with one reservation: a legitimate receiver decrypts correctly with probability that can be made arbitrarily close to 1, but not equal to 1. 1. Summary of our claims We thought it would make sense to summarize, for the reader’s convenience, our two main claims in a separate section, before proceeding to a narrative introduction. In Section 3, we describe a public-key encryption protocol that allows Bob (the sender) to send secret information to Alice (the receiver), encrypted one bit at a time, so that: (1) Assuming that Eve (the adversary): (a) is computationally unbounded, (b) knows everything about Bob’s encryption algorithm and hardware, (c) does not know Alice’s algorithm for creating public key, then: she cannot decrypt any single bit correctly with probability> 3

Anomaly detection for visual quality control of 3D-printed products

We present a method for detection of surface defects in images of 3D-printed products that enables automated visual quality control. The data characterising this problem is typically high-dimensional (high-resolution images), imbalanced (defects are relatively rare), and has few labelled examples. We approach these challenges by formulating the problem as probabilistic anomaly detection, where we use Variational Autoencoders (VAE) to estimate the probability density of non-faulty products. We train the VAE in an unsupervised manner on images of non-faulty products only. A successful model will then assign high likelihood to unseen images of non-faulty products, and lower likelihood to images displaying defects.We test this method on anomaly detection scenarios using the MNIST dataset, as well as on images of 3D-printed products. The demonstrated performance is related to the capability of the model to closely estimate the density distribution of the non-faulty (expected) data. For both datasets we present empirical results that the likelihood estimated with a convolutional VAE can separate the normal and anomalous data. Moreover we show how the reconstruction capabilities of VAEs are highly informative for human observers towards localising potential anomalies, which can aid the quality control process

Political behavior in an authoritarian country at war

This project contains materials, data, and code for a study conducted in Russia in August 2022 measuring Russians' attitudes towards the war in Ukraine and their anti- and pro-war behaviors