Keyword extraction is called identifying words or phrases that express the main concepts of texts in best. There is a huge amount of texts that are created every day and at all times through electronic infrastructure. So, it is practically impossible for humans to study and manage this volume of documents. However, the need for efficient and effective access to these documents is evident in various purposes. Weblogs, News, and technical notes are almost long texts, while the reader seeks to understand the concepts by topics or keywords to decide for reading the full text. To this aim, we use a combined approach that consists of two models of graph centrality features and textural features. In the following, graph centralities, such as degree, betweenness, eigenvector, and closeness centrality, have been used to optimally combine them to extract the best keyword among the candidate keywords extracted by the proposed method. Also, another approach has been introduced to distinguishing keywords among candidate phrases and considering them as a separate keyword. To evaluate the proposed method, seven datasets named, Semeval2010, SemEval2017, Inspec, fao30, Thesis100, pak2018 and WikiNews have been used, and results reported Precision, Recall, and F- measure.

International Conference on Learning Representations (ICLR) recently announced the ICLR 2021 Outstanding Paper Awards winners. It recognised eight papers out of the 860 submitted this year. The papers were evaluated for both technical quality and the potential to create a practical impact. The committee was chaired by Ivan Titov (U. This paper deals with parameterising hypercomplex multiplications using arbitrarily learnable parameters compared with the fully-connected layer counterpart.

Interpreting complex nonlinear machine-learning models is an inherently difficult task. A common approach is the post-hoc analysis of black-box models for dataset-level interpretation (Murdoch et al. 2019) using model-agnostic techniques such as the permutation-based variable importance, and graphical displays such as partial dependence plots that visualize main effects while integrating over the remaining dimensions (Molnar, Casalicchio, and Bischl 2020). These tools are so far limited to displaying the relationship between the response and one (or sometimes two) predictor(s), while attempting to control for the influence of the other predictors. This can be rather unsatisfactory when dealing with a large number of highly correlated predictors, which are often semantically grouped. While the literature on explainable machine learning has often focused on dealing with dependencies affecting individual features, e.g. by introducing conditional diagnostics (Strobl et al. 2008; Molnar, König, Bischl, et al. 2020), no practical solutions are available yet for dealing with model interpretation in highdimensional feature spaces with strongly dependent features (Molnar, Casalicchio, and Bischl 2020; Molnar, König, Herbinger, et al. 2020). These situations routinely occur in environmental remote sensing and other geographical and ecological analyses (Landgrebe 2002; Zortea, Haertel, and Clarke 2007), which motivated the present proposal to enhance existing model interpretation tools by offering a new, transformed perspective. For example, vegetation'greenness' as a measure of photosynthetic activity is often used to classify landcover or land use from satellite imagery acquired at multiple time points throughout the growing season (Peña and Brenning 2015; Peña, Liao, and Brenning 2017). Spectral reflectances of equivalent spectral bands (the features) are usually strongly correlated within the same phenological stage since vegetation characteristics vary gradually.

Extra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires the advanced equipment and chemical knowledge of certified laboratories, and has therefore a limited accessibility. In this work a minimalist, portable and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing classification of olive oil in the three mentioned classes with an accuracy of 100$\%$. These results confirm that this minimalist low-cost sensor has the potential of substituting expensive and complex chemical analysis.

Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for each triple. Knowledge bases are also often incomplete, prompting the use of embedding methods to generalize from known facts, however, existing embedding methods only model triple-level uncertainty, and reasoning results lack global consistency. To address these shortcomings, we propose BEUrRE, a novel uncertain knowledge graph embedding method with calibrated probabilistic semantics. BEUrRE models each entity as a box (i.e. axis-aligned hyperrectangle) and relations between two entities as affine transforms on the head and tail entity boxes. The geometry of the boxes allows for efficient calculation of intersections and volumes, endowing the model with calibrated probabilistic semantics and facilitating the incorporation of relational constraints. Extensive experiments on two benchmark datasets show that BEUrRE consistently outperforms baselines on confidence prediction and fact ranking due to its probabilistic calibration and ability to capture high-order dependencies among facts.

In a business context where an unconstrained real-world application were The potential risk of machine learning algorithms to unintentionally to be replaced with a fairer one, such extreme discrepancies embed and reproduce bias and therefore discriminating would not be viable because individuals who were substantially various sub populations in high-stakes decisionmaking negatively impacted would probably not accept applications has given rise to the new research the change and switch to a competitor. Based on our findings, field of fair machine learning (Kamiran and Calders 2009; we therefore propose algorithmic post-processing procedures Corbett-Davies et al. 2018; Barocas, Hardt, and Narayanan to adjust for unwanted, extreme discrepancies between 2019). Plenty of quantitative measures of fairness have been unconstrained and fair methods in order to enable a proposed (Dwork et al. 2011; Hardt, Price, and Srebro 2016; smooth transition from an "unfair" to a fairer model. Chouldechova 2017; Berk et al. 2018) which opened up The main contributions of this paper are: the way for three types of algorithms that seek to satisfy them: First, the pre-processing approach which modifies - We empirically examine the evolution of fair regression the data representation prior to using classical algorithms outputs compared to unconstrained predictors and demonstrate (Kamiran and Calders 2012; Zemel et al. 2013). Second, that some variations on the individual level may be the in-processing approach which intervenes during the unacceptable in practice. To the best of our knowledge we learning phase by adding a fairness constraint to the optimization offer the first investigation of this kind; objective (Kamishima et al. 2012; Zafar et al. - We propose a range of post-processing algorithms to mitigate 2017; Zhang, Lemoine, and Mitchell 2018). Third, the postprocessing this effect and therefore provide mechanisms to approach which adjusts the outputs of classical implement fair regression in practice.

This paper proposes an automatic Chinese text categorization method for solving the emergency event report classification problem. Since bidirectional encoder representations from transformers (BERT) has achieved great success in natural language processing domain, it is employed to derive emergency text features in this study. To overcome the data imbalance problem in the distribution of emergency event categories, a novel loss function is proposed to improve the performance of the BERT-based model. Meanwhile, to avoid the impact of the extreme learning rate, the Adabound optimization algorithm that achieves a gradual smooth transition from Adam to SGD is employed to learn parameters of the model. To verify the feasibility and effectiveness of the proposed method, a Chinese emergency text dataset collected from the Internet is employed. Compared with benchmarking methods, the proposed method has achieved the best performance in terms of accuracy, weighted-precision, weighted-recall, and weighted-F1 values. Therefore, it is promising to employ the proposed method for real applications in smart emergency management systems.

In 2019, AWS unveiled Amazon SageMaker Debugger, a SageMaker capability that enables you to automatically detect a variety of issues that may arise while a model is being trained. SageMaker Debugger captures model state data at specified intervals during a training job. With this data, SageMaker Debugger can detect training issues or anomalies by leveraging built-in or user-defined rules. In addition to detecting issues during the training job, you can analyze the captured state data afterwards to evaluate model performance and identify areas for improvement. This task is made easier with the newly launched XGBoost training report feature.

We are going to do binary classification, so the value of y (true/target) is going to be either 0 or 1. For example, suppose we have a breast cancer dataset with X being the tumor size and y being whether the lump is malignant(cancerous) or benign(non-cancerous). Whenever a patient visits, your job is to tell him/her whether the lump is malignant(0) or benign(1) given the size of the tumor. There are only two classes in this case. So, y is going to be either 0 or 1. Let's use the following randomly generated data as a motivating example to understand Logistic Regression.

Register for a free or VIP pass today. The past several years have made it clear that AI and machine learning are not a panacea when it comes to fair outcomes. Applying algorithmic solutions to social problems can magnify biases against marginalized peoples; undersampling populations always results in worse predictive accuracy. But bias in AI doesn't arise from the datasets alone. Problem formulation, or the way researchers fit tasks to AI techniques, can contribute.