Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework

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The objective of this study was to evaluate the effect of Motor Imagery (MI) training on language comprehension. In line with literature suggesting an intimate relationship between the language and the motor system, we proposed that a MI-training could imp ...

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Natural Language Processing (NLP) has become increasingly utilized to provide adaptivity in educational applications. However, recent research has highlighted a variety of biases in pre-trained language models. While existing studies investigate bias in di ...

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Cognitive twin construction for system of systems operation based on semantic integration and high-level architecture

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With the increasing complexity of engineered systems, digital twins (DTs) have been widely used to support integrated modeling, simulation, and decision-making of the system of systems (SoS). However, when integrating DTs of each constituent system, it is ...

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The Role of Compromised Accounts in Social Media Manipulation

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In recent years we have seen a marked increase in disinformation including as part of a strategy of so-called hybrid warfare. Adversaries not only directly spread misleading content but manipulate social media by employing sophisticated techniques that exp ...

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Further results on latent discourse models and word embeddings

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We discuss some properties of generative models for word embeddings. Namely, (Arora et al., 2016) proposed a latent discourse model implying the concentration of the partition function of the word vectors. This concentration phenomenon led to an asymptotic ...

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Learning computationally efficient static word and sentence representations

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With the current exponential growth of video-based social networks, video retrieval using natural language is receiving ever-increasing attention. Most existing approaches tackle this task by extracting individual frame-level spatial features to represent ...

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Low-Rank Subspaces for Unsupervised Entity Linking

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Entity linking is an important problem with many applications. Most previous solutions were designed for settings where annotated training data is available, which is, however, not the case in numerous domains. We propose a light-weight and scalable entity ...

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The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing

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Enabling autonomous driving (AD) can be considered one of the biggest challenges in today?s technology. AD is a complex task accomplished by several functionalities, with environment perception being one of its core functions. Environment perception is usu ...

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Lightweight Cross-Lingual Sentence Representation Learning

Martin Jaggi, Prakhar Gupta, Zhuoyuan Mao

Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks. However, further increases and modifications based on such ...

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The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing

Peter Clemens Schlicht

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021