Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers

Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to black-box attacks, which are more realistic scenarios. In this paper, we present a novel algorithm, DeepWordBug, to effectively generate small text perturbations in a black-box setting that forces a deep-learning classifier to misclassify a text input. We employ novel scoring strategies to identify the critical tokens that, if modified, cause the classifier to make an incorrect prediction. Simple character-level transformations are applied to the highest-ranked tokens in order to minimize the edit distance of the perturbation, yet change the original classification. We evaluated DeepWordBug on eight real-world text datasets, including text classification, sentiment analysis, and spam detection. We compare the result of DeepWordBug with two baselines: Random (Black-box) and Gradient (White-box). Our experimental results indicate that DeepWordBug reduces the prediction accuracy of current state-of-the-art deep-learning models, including a decrease of 68\% on average for a Word-LSTM model and 48\% on average for a Char-CNN model.Comment: This is an extended version of the 6page Workshop version appearing in 1st Deep Learning and Security Workshop colocated with IEEE S&

Методичні вказівки з розвитку навичок читання спеціальної літератури за фахом

Дані методичні вказівки призначені для самостійної та аудиторної роботи студентів І-ІІI курсів факультету КІТ. Вони мають за мету розвиток навичок читання літератури за фахом, з використанням типових завдань з технік анотування та реферування, спрямованих на краще оволодіння змістом. Матеріал вказівок містить 15 текстів, присвячених різним галузям інформатики та обчислювальної техніки, а також інформаційних технологій. Дані методичні вказівки складено на базі матеріалів автентичного курсу, розробленого Оксфордським університетом, а також з використанням матеріалів Інтернету

Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance

We investigate the use of Twitter data to deliver signals for syndromic surveillance in order to assess its ability to augment existing syndromic surveillance efforts and give a better understanding of symptomatic people who do not seek healthcare advice directly. We focus on a specific syndrome—asthma/difficulty breathing. We outline data collection using the Twitter streaming API as well as analysis and pre-processing of the collected data. Even with keyword-based data collection, many of the tweets collected are not be relevant because they represent chatter, or talk of awareness instead of an individual suffering a particular condition. In light of this, we set out to identify relevant tweets to collect a strong and reliable signal. For this, we investigate text classification techniques, and in particular we focus on semi-supervised classification techniques since they enable us to use more of the Twitter data collected while only doing very minimal labelling. In this paper, we propose a semi-supervised approach to symptomatic tweet classification and relevance filtering. We also propose alternative techniques to popular deep learning approaches. Additionally, we highlight the use of emojis and other special features capturing the tweet’s tone to improve the classification performance. Our results show that negative emojis and those that denote laughter provide the best classification performance in conjunction with a simple word-level n-gram approach. We obtain good performance in classifying symptomatic tweets with both supervised and semi-supervised algorithms and found that the proposed semi-supervised algorithms preserve more of the relevant tweets and may be advantageous in the context of a weak signal. Finally, we found some correlation (r = 0.414, p = 0.0004) between the Twitter signal generated with the semi-supervised system and data from consultations for related health conditions

Disinformation and propaganda – impact on the functioning of the rule of law in the EU and its Member States. Study Requested by the LIBE committee. CEPS Special Report, February 2019

This study, commissioned by the European Parliament’s Policy Department for Citizens’ Rights and Constitutional Affairs and requested by the European Parliament’s Committee on Civil Liberties, Justice and Home Affairs, assesses the impact of disinformation and strategic political propaganda disseminated through online social media sites. It examines effects on the functioning of the rule of law, democracy and fundamental rights in the EU and its Member States. The study formulates recommendations on how to tackle this threat to human rights, democracy and the rule of law. It specifically addresses the role of social media platform providers in this regard

Newman v. Google

3rd amended complain

Tech Giants, Artificial Intelligence and the Future of Journalism

This book examines the impact of the "Big Five" technology companies – Apple, Alphabet/Google, Amazon, Facebook and Microsoft – on journalism and the media industries. It looks at the current role of algorithms and artificial intelligence in curating how we consume media and their increasing influence on the production of the news. Exploring the changes that the technology industry and automation have made in the past decade to the production, distribution and consumption of news globally, the book considers what happens to journalism once it is produced and enters the media ecosystems of the internet tech giants – and the impact of social media and AI on such things as fake news in the post-truth age

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Pandemic Media: Preliminary Notes Toward an Inventory

With its unprecedented scale and consequences the COVID-19 pandemic has generated a variety of new configurations of media. Responding to demands for information, synchronization, regulation, and containment, these "pandemic media" reorder social interactions, spaces, and temporalities, thus contributing to a reconfiguration of media technologies and the cultures and polities with which they are entangled. Highlighting media’s adaptability, malleability, and scalability under the conditions of a pandemic, the contributions to this volume track and analyze how media emerge, operate, and change in response to the global crisis and provide elements toward an understanding of the post-pandemic world to come