Learning a Policy for Opportunistic Active Learning

Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions. Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in an interactive object retrieval task. In this work, we use reinforcement learning for such an object retrieval task, to learn a policy that effectively trades off task completion with model improvement that would benefit future tasks.Comment: EMNLP 2018 Camera Read

循证理论在科技文献推荐中的可行性研究

为提高科技文献推荐系统的精准性，打破目前推荐系统普遍存在的用户针对性低、依据性差的问题， 引入在医学上证明普遍有效的循证理论，其通过构建治疗证据体系，将自然科学方法理论引入到以经验为主的 诊疗过程。类似的，将其用于科技文献推荐模型，能够构建层次化的推荐依据，使推荐结果更具有说服力和精 准性。将该方法应用于真实开放数据集上，通过线性回归的机器学习方法给证据打分并分层，从而构建层次化 的推荐证据体系，证明该方法具有有效性和可实施性。</p

Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation

A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets

A Comparative Survey Data Analysis of Declining Oil Revenue Implications on the Economic Performance of Oil-Exporting Countries: Nigeria, Venezuela and Norway

The global oil markets have witnessed different episodes of oil price fluctuations at different intervals. The effect of the fluctuations differs across nations and partly depending on the direction of the shocks. Declining oil revenue also has both direct and indirect impact on the economic performance of the countries where the heavy dependency on crude oil prevails.  This study is a comparative survey analysis of declining oil revenue implications on the economic performance of oil-exporting countries; the case of highly oil-dependent nations: Nigeria (West Africa), Venezuela (South America) and Norway (Europe).  Purposive sampling was used in selecting our samples, while the Survey Monkey cloud-based tool was employed to administer and collect the questionnaires from the targeted audience.  Survey data analysis was carried out using SPSS Version 25 and the Survey Monkey platform.  The results reveal that both increasing and decreasing oil price affect the oil revenues and the external reserves of Nigeria, Venezuela and Norway proportionately.  The outcome also shows that during periods of declining oil revenues, Nigeria and Venezuela attain their budgetary needs through borrowing and seigniorage.  On the other hand, Norway utilises its savings with the Sovereign Wealth Fund (SWF) and Pension Funds in financing its fiscal needs, thereby exonerating Norway from the resource curse syndrome.  It is recommended that these nations explore other sources of revenue through diversification and the development of other natural resources.   Nigeria and Venezuela should also restore security which would help in attracting further foreign investors.  They should also ensure effective management of government funds and pay attention to its human capital development.  However, an economic model is proposed to aid in closing the revenue gaps in these highly oil-dependent nations, given that the “power of oil” is gradually fizzling out as other alternative forms of energy that are assumed to be environmentally friendly are widely embraced. Keywords:Oil Revenue, Dwindling Oil Price, Comparative Analysis, Survey Data, Economic Model, Oil-Exporters DOI: 10.7176/JRDM/76-03 Publication date:June 30th 202

ALEC: Active learning with ensemble of classifiers for clinical diagnosis of coronary artery disease

Invasive angiography is the reference standard for coronary artery disease (CAD) diagnosis but is expensive and associated with certain risks. Machine learning (ML) using clinical and noninvasive imaging parameters can be used for CAD diagnosis to avoid the side effects and cost of angiography. However, ML methods require labeled samples for efficient training. The labeled data scarcity and high labeling costs can be mitigated by active learning. This is achieved through selective query of challenging samples for labeling. To the best of our knowledge, active learning has not been used for CAD diagnosis yet. An Active Learning with Ensemble of Classifiers (ALEC) method is proposed for CAD diagnosis, consisting of four classifiers. Three of these classifiers determine whether a patient’s three main coronary arteries are stenotic or not. The fourth classifier predicts whether the patient has CAD or not. ALEC is first trained using labeled samples. For each unlabeled sample, if the outputs of the classifiers are consistent, the sample along with its predicted label is added to the pool of labeled samples. Inconsistent samples are manually labeled by medical experts before being added to the pool. The training is performed once more using the samples labeled so far. The interleaved phases of labeling and training are repeated until all samples are labeled. Compared with 19 other active learning algorithms, ALEC combined with a support vector machine classifier attained superior performance with 97.01% accuracy. Our method is justified mathematically as well. We also comprehensively analyze the CAD dataset used in this paper. As part of dataset analysis, features pairwise correlation is computed. The top 15 features contributing to CAD and stenosis of the three main coronary arteries are determined. The relationship between stenosis of the main arteries is presented using conditional probabilities. The effect of considering the number of stenotic arteries on sample discrimination is investigated. The discrimination power over dataset samples is visualized, assuming each of the three main coronary arteries as a sample label and considering the two remaining arteries as sample features

Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review

This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives-for application-based opportunities, with emphasis on those that address big data with geospatial components