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From Apocalyptic Demonization to Theological Responsibility

As the effects of climate change, political upheaval and ravaging war are palpable, apocalyptic images as well as the concept ‘apocalyptic’ are today regularly invoked in politics, popular culture and mass media. This essay ponders the potential dangers of the apocalyptic imaginary, especially its tendency to encourage idealization of one’s own community and demonization of the other. As carriers

SNOW-Vi : An extreme performance variant of SNOW-V for lower grade CPUs

SNOW 3G is a stream cipher used as one of the standard algorithms for data confidentiality and integrity protection over the air interface in the 3G and 4G mobile communication systems. SNOW-V is a recent new version that was proposed as a candidate for inclusion in the 5G standard. In this paper, we propose a faster variant of SNOW-V, called SNOW-Vi, that can reach the targeted speeds for 5G in a

Faster Dual Lattice Attacks for Solving LWE with Applications to CRYSTALS

Cryptosystems based on the learning with errors (LWE) problem are assigned a security level that relates to the cost of generic algorithms for solving the LWE problem. This includes at least the so-called primal and dual lattice attacks. In this paper, we present an improvement of the dual lattice attack using an idea that can be traced back to work by Bleichenbacher. We present an improved distin

Breaking Masked and Shuffled CCA Secure Saber KEM by Power Analysis

In this paper, we show that a software implementation of CCA secure Saber KEM protected by first-order masking and shuffling can be broken by deep learning-based power analysis. Using an ensemble of deep neural networks created at the profiling stage, we can recover the session key and the long-term secret key from 257xN and 24x257xN traces, respectively, where N is the number of repetitions of th

Beyond Gröbner Bases : Basis Selection for Minimal Solvers

Many computer vision applications require robust estimation of the underlying geometry, in terms of camera motion and 3D structure of the scene. These robust methods often rely on running minimal solvers in a RANSAC framework. In this paper we show how we can make polynomial solvers based on the action matrix method faster, by careful selection of the monomial bases. These monomial bases have trad

Camera Pose Estimation with Unknown Principal Point

To estimate the 6-DoF extrinsic pose of a pinhole camera with partially unknown intrinsic parameters is a critical sub-problem in structure-from-motion and camera localization. In most of existing camera pose estimation solvers, the principal point is assumed to be in the image center. Unfortunately, this assumption is not always true, especially for asymmetrically cropped images. In this paper, w

Radially-Distorted Conjugate Translations

This paper introduces the first minimal solvers that jointly solve for affine-rectification and radial lens distortion from coplanar repeated patterns. Even with imagery from moderately distorted lenses, plane rectification using the pinhole camera model is inaccurate or invalid. The proposed solvers incorporate lens distortion into the camera model and extend accurate rectification to wide-angle

Deep Learning of Graph Matching

The problem of graph matching under node and pairwise constraints is fundamental in areas as diverse as combinatorial optimization, machine learning or computer vision, where representing both the relations between nodes and their neighborhood structure is essential. We present an end-to-end model that makes it possible to learn all parameters of the graph matching process, including the unary and

3D Human Sensing, Action and Emotion Recognition in Robot Assisted Therapy of Children with Autism

We introduce new, fine-grained action and emotion recognition tasks defined on non-staged videos, recorded during robot-assisted therapy sessions of children with autism. The tasks present several challenges: a large dataset with long videos, a large number of highly variable actions, children that are only partially visible, have different ages and may show unpredictable behaviour, as well as non

Deep Reinforcement Learning of Region Proposal Networks for Object Detection

We propose drl-RPN, a deep reinforcement learning-based visual recognition model consisting of a sequential region proposal network (RPN) and an object detector. In contrast to typical RPNs, where candidate object regions (RoIs) are selected greedily via class-agnostic NMS, drl-RPN optimizes an objective closer to the final detection task. This is achieved by replacing the greedy RoI selection pro

Rotation Averaging and Strong Duality

In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of computer vision applications. In its conventional form, rotation averaging is stated as a minimization over multiple rotation constraints. As these constraints are non-convex, this problem is generally considered challenging to solve globally. We show how to circu

Improving a real-time object detector with compact temporal information

Neural networks designed for real-time object detectionhave recently improved significantly, but in practice, look-ing at only a single RGB image at the time may not be ideal.For example, when detecting objects in videos, a foregrounddetection algorithm can be used to obtain compact temporaldata, which can be fed into a neural network alongside RGBimages. We propose an approach for doing this, bas

Secure ownership transfer for the Internet of Things

With the increasing number of IoT devices deployed, the problem of switching ownership of devices is becoming more apparent. Especially, there is a need for transfer protocols not only addressing a single unit ownership transfer but secure transfer of a complete infrastructure of IoT units including also resource constraint devices. In this paper we present our novel ownership transfer protocol fo

A Decentralized Dynamic PKI based on Blockchain

The central role of the certificate authority (CA) in traditional public key infrastructure (PKI) makes it fragile and prone to compromises and operational failures. Maintaining CAs and revocation lists is demanding especially in loosely-connected and large systems. Log-based PKIs have been proposed as a remedy but they do not solve the problem effectively. We provide a general model and a solutio

Public Key Compression and Fast Polynomial Multiplication for NTRU using the Corrected Hybridized NTT-Karatsuba Method

NTRU is a lattice-based public-key cryptosystem that has been selected as one of the Round III finalists at the NIST Post-Quantum Cryptography Standardization. Compressing the key sizes to increase efficiency has been a long-standing open question for lattice-based cryptosystems. In this paper we provide a solution to three seemingly opposite demands for NTRU cryptosystem: compress the key size, i

AppArmor Profile Generator as a Cloud Service

Along with the rapid development of containerization technology, remarkable benefits have been created for developers and operation teams, and overall software infrastructure. Although lots of effort has been devoted to enhancing containerization security, containerized environments still have a huge attack surface. This paper proposes a secure cloud service for generating a Linux security module,

Radical Incarnation : The Dangers and Promises of Christian Universalism in the Wake of Badiou’s Saint Paul

In his 1997 pamphlet Saint Paul: The Foundation of Universalism, Alain Badiou pointed to the cynical interaction between the burgeoning identitarian movements and neoliberal capitalism. As a bulwark against these tendencies, he proposed a creative reinterpretation of Christian universalism inspired by the Pauline letters. This article revisits Badiou’s argument in light of recent debates on the li