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"Topic": models, code, and papers

Hate versus Politics: Detection of Hate against Policy makers in Italian tweets

Jul 12, 2021
Armend Duzha, Cristiano Casadei, Michael Tosi, Fabio Celli

Accurate detection of hate speech against politicians, policy making and political ideas is crucial to maintain democracy and free speech. Unfortunately, the amount of labelled data necessary for training models to detect hate speech are limited and domain-dependent. In this paper, we address the issue of classification of hate speech against policy makers from Twitter in Italian, producing the first resource of this type in this language. We collected and annotated 1264 tweets, examined the cases of disagreements between annotators, and performed in-domain and cross-domain hate speech classifications with different features and algorithms. We achieved a performance of ROC AUC 0.83 and analyzed the most predictive attributes, also finding the different language features in the anti-policymakers and anti-immigration domains. Finally, we visualized networks of hashtags to capture the topics used in hateful and normal tweets.

* to appear in SN social sciences - special issue on hate speech 

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QoS Prediction for 5G Connected and Automated Driving

Jul 11, 2021
Apostolos Kousaridas, Ramya Panthangi Manjunath, Jose Mauricio Perdomo, Chan Zhou, Ernst Zielinski, Steffen Schmitz, Andreas Pfadler

5G communication system can support the demanding quality-of-service (QoS) requirements of many advanced vehicle-to-everything (V2X) use cases. However, the safe and efficient driving, especially of automated vehicles, may be affected by sudden changes of the provided QoS. For that reason, the prediction of the QoS changes and the early notification of these predicted changes to the vehicles have been recently enabled by 5G communication systems. This solution enables the vehicles to avoid or mitigate the effect of sudden QoS changes at the application level. This article describes how QoS prediction could be generated by a 5G communication system and delivered to a V2X application. The tele-operated driving use case is used as an example to analyze the feasibility of a QoS prediction scheme. Useful recommendations for the development of a QoS prediction solution are provided, while open research topics are identified.

* 7 pages, 5 figures, accepted for publication in the IEEE Communications Magazine 

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Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?

Jul 01, 2021
Xiaolong Ma, Geng Yuan, Xuan Shen, Tianlong Chen, Xuxi Chen, Xiaohan Chen, Ning Liu, Minghai Qin, Sijia Liu, Zhangyang Wang, Yanzhi Wang

There have been long-standing controversies and inconsistencies over the experiment setup and criteria for identifying the "winning ticket" in literature. To reconcile such, we revisit the definition of lottery ticket hypothesis, with comprehensive and more rigorous conditions. Under our new definition, we show concrete evidence to clarify whether the winning ticket exists across the major DNN architectures and/or applications. Through extensive experiments, we perform quantitative analysis on the correlations between winning tickets and various experimental factors, and empirically study the patterns of our observations. We find that the key training hyperparameters, such as learning rate and training epochs, as well as the architecture characteristics such as capacities and residual connections, are all highly correlated with whether and when the winning tickets can be identified. Based on our analysis, we summarize a guideline for parameter settings in regards of specific architecture characteristics, which we hope to catalyze the research progress on the topic of lottery ticket hypothesis.

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Pretrained Language Models for Text Generation: A Survey

May 25, 2021
Junyi Li, Tianyi Tang, Wayne Xin Zhao, Ji-Rong Wen

Text generation has become one of the most important yet challenging tasks in natural language processing (NLP). The resurgence of deep learning has greatly advanced this field by neural generation models, especially the paradigm of pretrained language models (PLMs). In this paper, we present an overview of the major advances achieved in the topic of PLMs for text generation. As the preliminaries, we present the general task definition and briefly describe the mainstream architectures of PLMs for text generation. As the core content, we discuss how to adapt existing PLMs to model different input data and satisfy special properties in the generated text. We further summarize several important fine-tuning strategies for text generation. Finally, we present several future directions and conclude this paper. Our survey aims to provide text generation researchers a synthesis and pointer to related research.

* Accepted by IJCAI 2021 Survey Track 

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Training Quantized Neural Networks to Global Optimality via Semidefinite Programming

May 05, 2021
Burak Bartan, Mert Pilanci

Neural networks (NNs) have been extremely successful across many tasks in machine learning. Quantization of NN weights has become an important topic due to its impact on their energy efficiency, inference time and deployment on hardware. Although post-training quantization is well-studied, training optimal quantized NNs involves combinatorial non-convex optimization problems which appear intractable. In this work, we introduce a convex optimization strategy to train quantized NNs with polynomial activations. Our method leverages hidden convexity in two-layer neural networks from the recent literature, semidefinite lifting, and Grothendieck's identity. Surprisingly, we show that certain quantized NN problems can be solved to global optimality in polynomial-time in all relevant parameters via semidefinite relaxations. We present numerical examples to illustrate the effectiveness of our method.

* v2: Minor edits in the text. The results are unchanged 

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DeepStyle: User Style Embedding for Authorship Attribution of Short Texts

Mar 14, 2021
Zhiqiang Hu, Roy Ka-Wei Lee, Lei Wang, Ee-Peng Lim, Bo Dai

Authorship attribution (AA), which is the task of finding the owner of a given text, is an important and widely studied research topic with many applications. Recent works have shown that deep learning methods could achieve significant accuracy improvement for the AA task. Nevertheless, most of these proposed methods represent user posts using a single type of feature (e.g., word bi-grams) and adopt a text classification approach to address the task. Furthermore, these methods offer very limited explainability of the AA results. In this paper, we address these limitations by proposing DeepStyle, a novel embedding-based framework that learns the representations of users' salient writing styles. We conduct extensive experiments on two real-world datasets from Twitter and Weibo. Our experiment results show that DeepStyle outperforms the state-of-the-art baselines on the AA task.

* Paper accepted for 4th APWeb-WAIM Joint Conference on Web and Big Data 

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Exploring the social influence of Kaggle virtual community on the M5 competition

Feb 28, 2021
Xixi Li, Yun Bai, Yanfei Kang

One of the most significant differences of M5 over previous forecasting competitions is that it was held on Kaggle, an online community of data scientists and machine learning practitioners. On the Kaggle platform, people can form virtual communities such as online notebooks and discussions to discuss their models, choice of features, loss functions, etc. This paper aims to study the social influence of virtual communities on the competition. We first study the content of the M5 virtual community by topic modeling and trend analysis. Further, we perform social media analysis to identify the potential relationship network of the virtual community. We find some key roles in the network and study their roles in spreading the LightGBM related information within the network. Overall, this study provides in-depth insights into the dynamic mechanism of the virtual community influence on the participants and has potential implications for future online competitions.

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Expression Recognition Analysis in the Wild

Jan 22, 2021
Donato Cafarelli, Fabio Valerio Massoli, Fabrizio Falchi, Claudio Gennaro, Giuseppe Amato

Facial Expression Recognition(FER) is one of the most important topic in Human-Computer interactions(HCI). In this work we report details and experimental results about a facial expression recognition method based on state-of-the-art methods. We fine-tuned a SeNet deep learning architecture pre-trained on the well-known VGGFace2 dataset, on the AffWild2 facial expression recognition dataset. The main goal of this work is to define a baseline for a novel method we are going to propose in the near future. This paper is also required by the Affective Behavior Analysis in-the-wild (ABAW) competition in order to evaluate on the test set this approach. The results reported here are on the validation set and are related on the Expression Challenge part (seven basic emotion recognition) of the competition. We will update them as soon as the actual results on the test set will be published on the leaderboard.

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