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Machine Learning and Knowledge Extraction, Volume 2, Issue 4

December 2020 - 16 articles

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Cover Story: Quantifying the extent to which Environmental, Social and Governance (ESG)-related conversations are carried out by companies is essential to objectively assess the impact of ESG on business operations. This research study detects historical trends in ESG discussions by analyzing the transcripts of corporate earning calls. It exploits recent advances in neural language modeling to understand the linguistic structure in ESG discourse. We develop a classification system that categorizes the relevance of a text sentence to ESG by fine-tuning a language model on sustainability reports. The semantic knowledge encoded in the classification model is then leveraged by applying it to the sentences in the conference transcripts using a novel distant-supervision approach. A trend analysis of earnings calls based on this transfer learning framework indicates that ESG factors are integral to business strategy. View this paper

Articles (16)

  • Review
  • Open Access
41 Citations
9,691 Views
33 Pages

Review on Learning and Extracting Graph Features for Link Prediction

  • Ece C. Mutlu,
  • Toktam Oghaz,
  • Amirarsalan Rajabi and
  • Ivan Garibay

Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and...

  • Article
  • Open Access
18 Citations
4,551 Views
25 Pages

Artificial Intelligence Analysis of the Gene Expression of Follicular Lymphoma Predicted the Overall Survival and Correlated with the Immune Microenvironment Response Signatures

  • Joaquim Carreras,
  • Yara Yukie Kikuti,
  • Masashi Miyaoka,
  • Shinichiro Hiraiwa,
  • Sakura Tomita,
  • Haruka Ikoma,
  • Yusuke Kondo,
  • Atsushi Ito,
  • Naoya Nakamura and
  • Rifat Hamoudi

Follicular lymphoma (FL) is the second most common lymphoma in Western countries. FL is characterized by being incurable, usually having an indolent clinical course with frequent relapses, and an eventual patient’s death or transformation to Di...

  • Article
  • Open Access
3 Citations
2,711 Views
17 Pages

SAC-NMF-Driven Graphical Feature Analysis and Applications

  • Nannan Li,
  • Shengfa Wang,
  • Haohao Li and
  • Zhiyang Li

Feature analysis is a fundamental research area in computer graphics; meanwhile, meaningful and part-aware feature bases are always demanding. This paper proposes a framework for conducting feature analysis on a three-dimensional (3D) model by introd...

  • Article
  • Open Access
20 Citations
10,352 Views
13 Pages

Automatic Electronic Invoice Classification Using Machine Learning Models

  • Chiara Bardelli,
  • Alessandro Rondinelli,
  • Ruggero Vecchio and
  • Silvia Figini

Electronic invoicing has been mandatory for Italian companies since January 2019. All the invoices are structured in a predefined xml template which facilitates the extraction of the information. The main aim of this paper is to exploit the informati...

  • Article
  • Open Access
19 Citations
5,607 Views
14 Pages

Quantifying the characteristics of public attention is an essential prerequisite for appropriate crisis management during severe events such as pandemics. For this purpose, we propose language-agnostic tweet representations to perform large-scale Twi...

  • Article
  • Open Access
62 Citations
9,482 Views
24 Pages

Machine Learning (ML) is increasingly applied for the control of safety-critical Cyber-Physical Systems (CPS) in application areas that cannot easily be mastered with traditional control approaches, such as autonomous driving. As a consequence, the s...

  • Article
  • Open Access
2 Citations
4,157 Views
25 Pages

Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine?

  • Joakim Linja,
  • Joonas Hämäläinen,
  • Paavo Nieminen and
  • Tommi Kärkkäinen

Minimal Learning Machine (MLM) is a recently popularized supervised learning method, which is composed of distance-regression and multilateration steps. The computational complexity of MLM is dominated by the solution of an ordinary least-squares pro...

  • Article
  • Open Access
19 Citations
5,623 Views
21 Pages

Probabilistic Jacobian-Based Saliency Maps Attacks

  • Théo Combey,
  • António Loison,
  • Maxime Faucher and
  • Hatem Hajri

Neural network classifiers (NNCs) are known to be vulnerable to malicious adversarial perturbations of inputs including those modifying a small fraction of the input features named sparse or L0 attacks. Effective and fast L0 attacks, such as the wide...

  • Article
  • Open Access
8 Citations
4,341 Views
28 Pages

Towards Knowledge Uncertainty Estimation for Open Set Recognition

  • Catarina Pires,
  • Marília Barandas,
  • Letícia Fernandes,
  • Duarte Folgado and
  • Hugo Gamboa

Uncertainty is ubiquitous and happens in every single prediction of Machine Learning models. The ability to estimate and quantify the uncertainty of individual predictions is arguably relevant, all the more in safety-critical applications. Real-world...

  • Article
  • Open Access
95 Citations
8,455 Views
15 Pages

COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities

  • Md Manjurul Ahsan,
  • Kishor Datta Gupta,
  • Mohammad Maminur Islam,
  • Sajib Sen,
  • Md. Lutfar Rahman and
  • Mohammad Shakhawat Hossain

The outbreak of COVID-19 has caused more than 200,000 deaths so far in the USA alone, which instigates the necessity of initial screening to control the spread of the onset of COVID-19. However, screening for the disease becomes laborious with the av...

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