Spain

ECAI 2020

Machine Learning for Gas and Oil Exploration

hydrocarbon exploration

petroleum industry

supervised machine learning

regression

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

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NAML 2021

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Point-Of-Interest Semantic Tag Completion in a Global Crowdsourced Search-and-Discovery Database

Intelligent Recommendation & Decision Technologies for Community-Driven Requirements Engineering

Cross-Border Medical Research using Multi-Layered and Distributed Knowledge

Remaining Useful Life Curve Prediction of Rolling Bearings under Defect Progression Based on Hierarchical Bayesian Regression

KnowledgeCheckeR: Intelligent Techniques for Counteracting Forgetting

FishNet: A Unified Embedding for Salmon Recognition

In this work we consider optimization problems that require to make interdependent offline and online decisions under uncertainty. We broadly refer to long-term strategic decisions as offline and to short-term operational decisions as online. For example, in Distributed Energy Management Systems we may need to define (offline) a daily production schedule for an industrial plant, and then manage (online) its power supply on a hour by hour basis. Traditionally offline and online phases are tackled in isolation, leading to some drawbacks: offline decisions are taken without regard for the capabilities of the downstream online solver; while the applicability of the best approaches for online decisions (e.g. anticipatory algorithms) is limited by the need to provide high responsiveness. Starting from a (literature-based) baseline, we define general methods for leading to significant quality improvements, at the cost of an increased computation effort either in the offline or the online phase. All our methods have broad applicability, and provide multiple options to balance the solution quality/time trade-off, suiting a variety of practical application scenarios with both offline and online decisions and featuring continuous and discrete decisions. An extensive analysis of the experimental results shows that offline/online integration may lead to substantial benefits.

Hybrid Offline/Online Optimization Under Uncertainty

In the field of Information Retrieval, word embedding models have shown to be effective in several tasks. In this paper, we show how one of these neural embedding techniques can be adapted to the recommendation task. This adaptation only makes use of collaborative filtering information, and the results show that it is able to produce effective recommendations efficiently.

Shallow Neural Models for Top-N Recommendation

Sketches have been employed since the ancient era of cave paintings for simple illustrations to represent real-world entities. The abstract nature and varied artistic styling make automatic recognition of drawings more challenging than other areas of image classification. Moreover, the representation of sketches as a sequence of strokes instead of raster images introduces them at the correct abstract level. However, dealing with images as a sequence of small information makes it challenging. In this paper, we propose a Transformer-based network, dubbed as TransSketchNet, for sketch recognition. This architecture incorporates ordinal information to perform the classification task in real-time through vector images.

TransSketchNet: Attention-based Sketch Recognition using Transformers

The next technological revolution will be interwoven to the proliferation of intelligent systems. As we bridge the gap between physical and cyber worlds, we will give rise to large-scale, multi-agent based technologies. A key challenge that cities of the future will have to face is coordination in the use of limited resources, central to which is finding an optimal allocation between agents. To truly allow for scalable solutions, we need to shift from traditional approaches, to multi-agent solutions, ideally run on-device. We highlight a novel heuristic (ALMA), which exhibits such properties, for solving the assignment problem. ALMA is decentralized, requires only partial feedback, and has constant in the total problem size running time, under reasonable assumptions on the preference domain of the agents, making it ideal for an on-device implementation. We have evaluated ALMA in a variety of scenarios including synthetic and real data, time constraints, and on-line settings. In all of the cases, ALMA was able to reach high social welfare, while being orders of magnitude faster than the centralized, optimal algorithm.

Machine Learning for Gas and Oil Exploration

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Machine Learning for Gas and Oil Exploration

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Point-Of-Interest Semantic Tag Completion in a Global Crowdsourced Search-and-Discovery Database

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Using Machine Learning to Predict CASREP Occurrence and Trouble Ticket Duration

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