237 research outputs found
Improving the resilience of post-disaster water distribution systems using a dynamic optimization framework
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Improving the resilience of water distribution systems (WDSs) to handle natural disasters (e.g., earthquakes) is a critical step towards sustainable urban water management. This requires the water utility to be able to respond quickly to such disaster events and in an organized manner, to prioritize the use of available resources to restore service rapidly whilst minimizing the negative impacts. Many methods have been developed to evaluate the WDS resilience, but few efforts are made so far to improve resilience of a post-disaster WDS through identifying optimal sequencing of recovery actions. To address this gap, a new dynamic optimization framework is proposed here where the resilience of a post-disaster WDS is evaluated using six different metrics. A tailored Genetic Algorithm is developed to solve the complex optimization problem driven by these metrics. The proposed framework is demonstrated using a real-world WDS with 6,064 pipes. Results obtained show that the proposed framework successfully identifies near-optimal sequencing of recovery actions for this complex WDS. The gained insights, conditional on the specific attributes of the case study, include: (i) the near-optimal sequencing of recovery strategy heavily depends on the damage properties of the WDS, (ii) replacements of damaged elements tend to be scheduled at the intermediate-late stages of the recovery process due to their long operation time, and (iii) interventions to damaged pipe elements near critical facilities (e.g., hospitals) should not be necessarily the first priority to recover due to complex hydraulic interactions within the WDS
Self-Supervised Learning for Recommender Systems: A Survey
In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with
highly sparse data. Self-supervised learning (SSL), as an emerging technique
for learning from unlabeled data, has attracted considerable attention as a
potential solution to this issue. This survey paper presents a systematic and
timely review of research efforts on self-supervised recommendation (SSR).
Specifically, we propose an exclusive definition of SSR, on top of which we
develop a comprehensive taxonomy to divide existing SSR methods into four
categories: contrastive, generative, predictive, and hybrid. For each category,
we elucidate its concept and formulation, the involved methods, as well as its
pros and cons. Furthermore, to facilitate empirical comparison, we release an
open-source library SELFRec (https://github.com/Coder-Yu/SELFRec), which
incorporates a wide range of SSR models and benchmark datasets. Through
rigorous experiments using this library, we derive and report some significant
findings regarding the selection of self-supervised signals for enhancing
recommendation. Finally, we shed light on the limitations in the current
research and outline the future research directions.Comment: 20 pages. Accepted by TKD
Artificial intelligence research community and associations in Poland
In last years Artificial Intelligence presented a tremendous progress by offering a variety of novel methods, tools and their spectacular applications. Besides showing scientific breakthroughs it attracted interest both of the general public and industry. It also opened heated debates on the impact of Artificial Intelligence on changing the economy and society. Having in mind this international landscape, in this short paper we discuss the Polish AI research community, some of its main achievements, opportunities and limitations. We put this discussion in the context of the current developments in the international AI community. Moreover, we refer to activities of Polish scientific associations and their initiative of founding Polish Alliance for the Development of Artificial Intelligence (PP-RAI). Finally two last editions of PP-RAI joint conferences are summarized
Adversarial Training Towards Robust Multimedia Recommender System
With the prevalence of multimedia content on the Web, developing recommender
solutions that can effectively leverage the rich signal in multimedia data is
in urgent need. Owing to the success of deep neural networks in representation
learning, recent advance on multimedia recommendation has largely focused on
exploring deep learning methods to improve the recommendation accuracy. To
date, however, there has been little effort to investigate the robustness of
multimedia representation and its impact on the performance of multimedia
recommendation.
In this paper, we shed light on the robustness of multimedia recommender
system. Using the state-of-the-art recommendation framework and deep image
features, we demonstrate that the overall system is not robust, such that a
small (but purposeful) perturbation on the input image will severely decrease
the recommendation accuracy. This implies the possible weakness of multimedia
recommender system in predicting user preference, and more importantly, the
potential of improvement by enhancing its robustness. To this end, we propose a
novel solution named Adversarial Multimedia Recommendation (AMR), which can
lead to a more robust multimedia recommender model by using adversarial
learning. The idea is to train the model to defend an adversary, which adds
perturbations to the target image with the purpose of decreasing the model's
accuracy. We conduct experiments on two representative multimedia
recommendation tasks, namely, image recommendation and visually-aware product
recommendation. Extensive results verify the positive effect of adversarial
learning and demonstrate the effectiveness of our AMR method. Source codes are
available in https://github.com/duxy-me/AMR.Comment: TKD
Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak
Epidemic is a rapid and wide spread of infectious disease threatening many lives and economy damages. It is important to fore-tell the epidemic lifetime so to decide on timely and remedic actions. These measures include closing borders, schools, suspending community services and commuters. Resuming such curfews depends on the momentum of the outbreak and its rate of decay. Being able to accurately forecast the fate of an epidemic is an extremely important but difficult task. Due to limited knowledge of the novel disease, the high uncertainty involved and the complex societal-political factors that influence the widespread of the new virus, any forecast is anything but reliable. Another factor is the insufficient amount of available data. Data samples are often scarce when an epidemic just started. With only few training samples on hand, finding a forecasting model which offers forecast at the best efforts is a big challenge in machine learning. In the past, three popular methods have been proposed, they include 1) augmenting the existing little data, 2) using a panel selection to pick the best forecasting model from several models, and 3) fine-tuning the parameters of an individual forecasting model for the highest possible accuracy. In this paper, a methodology that embraces these three virtues of data mining from a small dataset is proposed. An experiment that is based on the recent coronavirus outbreak originated from Wuhan is conducted by applying this methodology. It is shown that an optimized forecasting model that is constructed from a new algorithm, namely polynomial neural network with corrective feedback (PNN+cf) is able to make a forecast that has relatively the lowest prediction error. The results showcase that the newly proposed methodology and PNN+cf are useful in generating acceptable forecast upon the critical time of disease outbreak when the samples are far from abundant
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