The continuous growth of scientific literature brings innovations and, at the same time, raises new challenges. One of them is related to the fact that its analysis has become difficult due to the high volume of published papers for which manual effort for annotations and management is required. Novel technological infrastructures are needed to help researchers, research policy makers, and companies to time-efficiently browse, analyse, and forecast scientific research. Knowledge graphs i.e., large networks of entities and relationships, have proved to be effective solution in this space. Scientific knowledge graphs focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. However, the current generation of knowledge graphs lacks of an explicit representation of the knowledge presented in the research papers. As such, in this paper, we present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications and integrates them in a large-scale knowledge graph. Within this research work, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) show the advantage of such an hybrid system over alternative approaches, and vi) as a chosen use case, we generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain. As our approach is general and can be applied to any domain, we expect that it can facilitate the management, analysis, dissemination, and processing of scientific knowledge.

Existing approaches for replay and synthetic speech detection still lack generalizability to unseen spoofing attacks. This work proposes to leverage a novel model structure, so-called Res2Net, to improve the anti-spoofing countermeasure's generalizability. Res2Net mainly modifies the ResNet block to enable multiple feature scales. Specifically, it splits the feature maps within one block into multiple channel groups and designs a residual-like connection across different channel groups. Such connection increases the possible receptive fields, resulting in multiple feature scales. This multiple scaling mechanism significantly improves the countermeasure's generalizability to unseen spoofing attacks. It also decreases the model size compared to ResNet-based models. Experimental results show that the Res2Net model consistently outperforms ResNet34 and ResNet50 by a large margin in both physical access (PA) and logical access (LA) of the ASVspoof 2019 corpus. Moreover, integration with the squeeze-and-excitation (SE) block can further enhance performance. For feature engineering, we investigate the generalizability of Res2Net combined with different acoustic features, and observe that the constant-Q transform (CQT) achieves the most promising performance in both PA and LA scenarios. Our best single system outperforms other state-of-the-art single systems in both PA and LA of the ASVspoof 2019 corpus.

Query-based document summarization aims to extract or generate a summary of a document which directly answers or is relevant to the search query. It is an important technique that can be beneficial to a variety of applications such as search engines, document-level machine reading comprehension, and chatbots. Currently, datasets designed for query-based summarization are short in numbers and existing datasets are also limited in both scale and quality. Moreover, to the best of our knowledge, there is no publicly available dataset for Chinese query-based document summarization. In this paper, we present QBSUM, a high-quality large-scale dataset consisting of 49,000+ data samples for the task of Chinese query-based document summarization. We also propose multiple unsupervised and supervised solutions to the task and demonstrate their high-speed inference and superior performance via both offline experiments and online A/B tests. The QBSUM dataset is released in order to facilitate future advancement of this research field.

Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science.

This post is co-written by Chiranjeev Ghai, ML Engineer at zomato. zomato is a global food-tech company based in India. Are you the kind of person who has very specific cravings? Maybe when the mood hits, you don’t want just any kind of Indian food—you want Chicken Chettinad with a side of paratha, and nothing […]

Logistic Regression is used for classification problems in machine learning. It is used to deal with binary classification and multiclass classification. In logistic regression, the target variable/dependent variable should be a discrete value or categorical value. Binary classification problems with two class values like male/female, yes/no, True/False, 0/1, pass/fail. Let's learn about logistic regression for binary classification in this story.

Self Taught Machine Learning Engineer and Data Scientist.Love Data driven problem and AI,ML and DS. Recognizing handwritten text is a problem that can be traced back to the first automatic machines that needed to recognize individual characters in handwritten documents. Think about, for example, the ZIP codes on letters at the post office and the automation needed to recognize these five digits. Perfect recognition of these codes is necessary in order to sort mail automatically and efficiently. To address this issue in Python, the scikit-learn library provides a good example to better understand this technique, the issues involved, and the possibility of making predictions.

In recent times, ensemble techniques have become popular among data scientists and enthusiasts. Until now Random Forest and Gradient Boosting algorithms were winning the data science competitions and hackathons, over the period of the last few years XGBoost has been performing better than other algorithms on problems involving structured data. Apart from its performance, XGBoost is also recognized for its speed, accuracy and scale. XGBoost is developed on the framework of Gradient Boosting. Just like other boosting algorithms XGBoost uses decision trees for its ensemble model.

But two main contradictions remain: First, AI/ML are very heterogeneous, and Services for Cyber-Physical Systems based on Artificial each AI/ML method comes with a specialized model Intelligence and Machine Learning require formalism to capture relevant aspects of the environment a virtual representation of the physical. To reduce and the application domain. Hence, the modeling efforts and to synchronize results, for each question is how a DigitalTwin can provide the correct system, a common and unique virtual representation model to each AI/ML method. The second used by all services during the whole system contradiction is that AI/ML requires explicit, i.e. life-cycle is needed--i.e. a DigitalTwin. In this paper by an algorithm processable knowledge, since compiled such a DigitalTwin, namely the AI reference knowledge in form of simulation libraries, raw model AITwin, is defined. This reference model is data or executables does not help. But most publications verified by using a running example from process refer to these kind of information.

Such examples include predicting input/output responses, design, and optimization [55]. These ODEs and their solutions often depend on a set of input parameters, and such ODEs are denoted as parameterized ODEs. Examples of such input parameters within the context of fluid dynamics include Reynolds number and Mach number. In many important scenarios, high-fidelity solutions of parameterized ODEs are required to be computed i) for many different input parameter instances (i.e., many-query scenario) or ii) in real time on a new input parameter instance. A single run of a high-fidelity simulation, however, often requires fine spatiotemporal resolutions. Consequently, performing real-time or multiple runs of a high-fidelity simulation can be computationally prohibitive. To mitigate this computational burden, many model-order reduction approaches have been proposed to replace costly high-fidelity simulations. The common goal of these approaches is to build a reduced-dynamical model with lower complexity than that of the high-fidelity model, and to use the reduced model to compute approximate solutions for any new input parameter instance. In general, model-order reduction approaches consist of two components: i) a low-dimensional latent-dynamics model, where the computational complexity is very low, and ii) a (non)linear mapping that constructs high-dimensional approximate states (i.e., solutions) from the low-dimensional states obtained from the latent-dynamics model.