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"Topic": models, code, and papers

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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RDF2Vec-based Classification of Ontology Alignment Changes

May 23, 2018
Matthias Jurisch, Bodo Igler

When ontologies cover overlapping topics, the overlap can be represented using ontology alignments. These alignments need to be continuously adapted to changing ontologies. Especially for large ontologies this is a costly task often consisting of manual work. Finding changes that do not lead to an adaption of the alignment can potentially make this process significantly easier. This work presents an approach to finding these changes based on RDF embeddings and common classification techniques. To examine the feasibility of this approach, an evaluation on a real-world dataset is presented. In this evaluation, the best classifiers reached a precision of 0.8.

* 6 pages, accepted at Workshop on Deep Learning for Knowledge Graphs and Semantic Technologies (DL4KGS) 

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Thread Reconstruction in Conversational Data using Neural Coherence Models

Jul 25, 2017
Dat Tien Nguyen, Shafiq Joty, Basma El Amel Boussaha, Maarten de Rijke

Discussion forums are an important source of information. They are often used to answer specific questions a user might have and to discover more about a topic of interest. Discussions in these forums may evolve in intricate ways, making it difficult for users to follow the flow of ideas. We propose a novel approach for automatically identifying the underlying thread structure of a forum discussion. Our approach is based on a neural model that computes coherence scores of possible reconstructions and then selects the highest scoring, i.e., the most coherent one. Preliminary experiments demonstrate promising results outperforming a number of strong baseline methods.

* Neu-IR: Workshop on Neural Information Retrieval 2017 

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How to merge three different methods for information filtering ?

Oct 26, 2015
Jean-Valère Cossu, Ludovic Bonnefoy, Xavier Bost, Marc El Bèze

Twitter is now a gold marketing tool for entities concerned with online reputation. To automatically monitor online reputation of entities , systems have to deal with ambiguous entity names, polarity detection and topic detection. We propose three approaches to tackle the first issue: monitoring Twitter in order to find relevant tweets about a given entity. Evaluated within the framework of the RepLab-2013 Filtering task, each of them has been shown competitive with state-of-the-art approaches. Mainly we investigate on how much merging strategies may impact performances on a filtering task according to the evaluation measure.


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Estimating complex causal effects from incomplete observational data

Jul 02, 2014
Juha Karvanen

Despite the major advances taken in causal modeling, causality is still an unfamiliar topic for many statisticians. In this paper, it is demonstrated from the beginning to the end how causal effects can be estimated from observational data assuming that the causal structure is known. To make the problem more challenging, the causal effects are highly nonlinear and the data are missing at random. The tools used in the estimation include causal models with design, causal calculus, multiple imputation and generalized additive models. The main message is that a trained statistician can estimate causal effects by judiciously combining existing tools.


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Practical Collapsed Stochastic Variational Inference for the HDP

Dec 02, 2013
Arnim Bleier

Recent advances have made it feasible to apply the stochastic variational paradigm to a collapsed representation of latent Dirichlet allocation (LDA). While the stochastic variational paradigm has successfully been applied to an uncollapsed representation of the hierarchical Dirichlet process (HDP), no attempts to apply this type of inference in a collapsed setting of non-parametric topic modeling have been put forward so far. In this paper we explore such a collapsed stochastic variational Bayes inference for the HDP. The proposed online algorithm is easy to implement and accounts for the inference of hyper-parameters. First experiments show a promising improvement in predictive performance.

* NIPS Workshop; Topic Models: Computation, Application, and Evaluation 

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A Brief Introduction to Temporality and Causality

Jul 14, 2010
Kamran Karimi

Causality is a non-obvious concept that is often considered to be related to temporality. In this paper we present a number of past and present approaches to the definition of temporality and causality from philosophical, physical, and computational points of view. We note that time is an important ingredient in many relationships and phenomena. The topic is then divided into the two main areas of temporal discovery, which is concerned with finding relations that are stretched over time, and causal discovery, where a claim is made as to the causal influence of certain events on others. We present a number of computational tools used for attempting to automatically discover temporal and causal relations in data.


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