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"Topic": models, code, and papers

Choice-Aware User Engagement Modeling andOptimization on Social Media

Apr 01, 2021
Saketh Reddy Karra, Theja Tulabandhula

We address the problem of maximizing user engagement with content (in the form of like, reply, retweet, and retweet with comments)on the Twitter platform. We formulate the engagement forecasting task as a multi-label classification problem that captures choice behavior on an unsupervised clustering of tweet-topics. We propose a neural network architecture that incorporates user engagement history and predicts choice conditional on this context. We study the impact of recommend-ing tweets on engagement outcomes by solving an appropriately defined sweet optimization problem based on the proposed model using a large dataset obtained from Twitter.

* 11 pages, 1 figure 

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Inferring Symbolic Automata

Nov 12, 2020
Dana Fisman, Hadar Frenkel, Sandra Zilles

We study the learnability of {symbolic finite state automata}, a model shown useful in many applications in software verification. The state-of-the-art literature on this topic follows the {query learning} paradigm, and so far all obtained results are positive. We provide a necessary condition for efficient learnability of SFAs in this paradigm, from which we obtain the first negative result. Most of this work studies learnability of SFAs under the paradigm of {identification in the limit using polynomial time and data}. We provide a sufficient condition for efficient learnability of SFAs in this paradigm, as well as a necessary condition, and provide several positive and negative results.

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Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters

Sep 29, 2019
Vitor Cerqueira, Luis Torgo, Carlos Soares

Time series forecasting is one of the most active research topics. Machine learning methods have been increasingly adopted to solve these predictive tasks. However, in a recent work, these were shown to systematically present a lower predictive performance relative to simple statistical methods. In this work, we counter these results. We show that these are only valid under an extremely low sample size. Using a learning curve method, our results suggest that machine learning methods improve their relative predictive performance as the sample size grows. The code to reproduce the experiments is available at

* 9 pages 

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Dual Supervised Learning for Natural Language Understanding and Generation

May 28, 2019
Shang-Yu Su, Chao-Wei Huang, Yun-Nung Chen

Natural language understanding (NLU) and natural language generation (NLG) are both critical research topics in the NLP field. Natural language understanding is to extract the core semantic meaning from the given utterances, while natural language generation is opposite, of which the goal is to construct corresponding sentences based on the given semantics. However, such dual relationship has not been investigated in the literature. This paper proposes a new learning framework for language understanding and generation on top of dual supervised learning, providing a way to exploit the duality. The preliminary experiments show that the proposed approach boosts the performance for both tasks.

* Accepted by ACL 2019 

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On Inertial Navigation and Attitude Initialization in Polar Areas

Mar 29, 2019
Yuanxin Wu, Chao He, Gang Liu

Inertial navigation and attitude initialization in polar areas become a hot topic in recent years in the navigation community, as the widely-used navigation mechanization of the local level frame encounters the inherent singularity when the latitude approaches 90 degrees. Great endeavors have been devoted to devising novel navigation mechanizations such as the grid or transversal frames. This paper highlights the fact that the common Earth-frame mechanization is sufficiently good to well handle the singularity problem in polar areas. Simulation results are reported to demonstrate the singularity problem and the effectiveness of the Earth-frame mechanization.

* 10 pages, 4 figures 

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Day-Ahead Hourly Forecasting of Power Generation from Photovoltaic Plants

Feb 26, 2019
Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, Alessandro Franco, Mauro Tucci, Fabrizio Bizzarri, Debora Mucci

The ability to accurately forecast power generation from renewable sources is nowadays recognised as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the impact of single components in providing accurate predictions. In this paper we extensively compare simple forecasting methodologies with more sophisticated ones over 32 photovoltaic plants of different size and technology over a whole year. Also, we try to evaluate the impact of weather conditions and weather forecasts on the prediction of PV power generation.

* IEEE Transactions of Sustainable Energy, Vol. 9, Issue 2, pp. 831 - 842 (2018) 
* Preprint of IEEE Transactions of Sustainable Energy, Vol. 9, Issue 2, pp. 831 - 842 (2018) 

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Proceedings 3rd Workshop on formal reasoning about Causation, Responsibility, and Explanations in Science and Technology

Jan 01, 2019
Bernd Finkbeiner, Samantha Kleinberg

The CREST 2018 workshop is the third in a series of workshops addressing formal approaches to reasoning about causation in systems engineering. The topic of formally identifying the cause(s) of specific events - usually some form of failures -, and explaining why they occurred, are increasingly in the focus of several, disjoint communities. The main objective of CREST is to bring together researchers and practitioners from industry and academia in order to enable discussions how explicit and implicit reasoning about causation is performed. A further objective is to link to the foundations of causal reasoning in the philosophy of sciences and to causal reasoning performed in other areas of computer science, engineering, and beyond.

* EPTCS 286, 2019 

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A New Framework for Machine Intelligence: Concepts and Prototype

Jun 06, 2018
Abel Torres Montoya

Machine learning (ML) and artificial intelligence (AI) have become hot topics in many information processing areas, from chatbots to scientific data analysis. At the same time, there is uncertainty about the possibility of extending predominant ML technologies to become general solutions with continuous learning capabilities. Here, a simple, yet comprehensive, theoretical framework for intelligent systems is presented. A combination of Mirror Compositional Representations (MCR) and a Solution-Critic Loop (SCL) is proposed as a generic approach for different types of problems. A prototype implementation is presented for document comparison using English Wikipedia corpus.

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