Modern data analytics underpinned by machine learning techniques has become a key enabler to the automation of data-led decision making. As an important branch of state-of-the-art data analytics, business process predictions are also faced with a challenge in regard to the lack of explanation to the reasoning and decision by the underlying `black-box' prediction models. With the development of interpretable machine learning techniques, explanations can be generated for a black-box model, making it possible for (human) users to access the reasoning behind machine learned predictions. In this paper, we aim to present an approach that allows us to use model explanations to investigate certain reasoning applied by machine learned predictions and detect potential issues with the underlying methods thus enhancing trust in business process prediction models. A novel contribution of our approach is the proposal of model inspection that leverages both the explanations generated by interpretable machine learning mechanisms and the contextual or domain knowledge extracted from event logs that record historical process execution. Findings drawn from this work are expected to serve as a key input to developing model reliability metrics and evaluation in the context of business process predictions.

Natural Language Processing (NLP) systems have been subjected to a Cambrian explosion of architectural paradigms in the past few years. The scale on the number of contributions and its exponential growth, bring challenges in understanding how NLP architectural patterns evolve and consolidate in different sub-areas and tasks. This paper aims to provide the methodological support for the interpretation of NLP architectural patterns at scale by applying statistical corpus analysis methods over large-scale NLP corpora. We analyse the use of corpus statistics to compute large-scale collocation patterns jointly with graph visualisation methods as a device to interpret architectural patterns at scale. The proposed methods aims to address questions such as: - What is the complete list of architectural patterns present in NLP? - What are the prevailing architectural patterns (classifiers, layers, regularisation, linguistic resources) for each NLP task? - How these patterns are evolving over time and what are the emerging consolidated/canonical architectural motifs?

We propose in this work a new method for estimating the main mode of multivariate distributions, with application to eye-tracking calibrations. When performing eye-tracking experiments with poorly cooperative subjects, such as infants or monkeys, the calibration data generally suffer from high contamination. Outliers are typically organized in clusters, corresponding to the time intervals when subjects were not looking at the calibration points. In this type of multimodal distributions, most central tendency measures fail at estimating the principal fixation coordinates (the first mode), resulting in errors and inaccuracies when mapping the gaze to the screen coordinates. Here, we developed a new algorithm to identify the first mode of multivariate distributions, named BRIL, which rely on recursive depth-based filtering. This novel approach was tested on artificial mixtures of Gaussian and Uniform distributions, and compared to existing methods (conventional depth medians, robust estimators of location and scatter, and clustering-based approaches). We obtained outstanding performances, even for distributions containing very high proportions of outliers, both grouped in clusters and randomly distributed. Finally, we demonstrate the strength of our method in a real-world scenario using experimental data from eye-tracking calibrations with Capuchin monkeys, especially for distributions where other algorithms typically lack accuracy.

With the constant advancements of genetic engineering, a common concern is to be able to identify the lab-of-origin of genetically engineered DNA sequences. For that reason, AltLabs has hosted the genetic Engineering Attribution Challenge to gather many teams to propose new tools to solve this problem. Here we show our proposed method to rank the most likely labs-of-origin and generate embeddings for DNA sequences and labs. These embeddings can also perform various other tasks, like clustering both DNA sequences and labs and using them as features for Machine Learning models applied to solve other problems. This work demonstrates that our method outperforms the classic training method for this task while generating other helpful information.

As an essential element for the diagnosis and rehabilitation of psychiatric disorders, the electroencephalogram (EEG) based emotion recognition has achieved significant progress due to its high precision and reliability. However, one obstacle to practicality lies in the variability between subjects and sessions. Although several studies have adopted domain adaptation (DA) approaches to tackle this problem, most of them treat multiple EEG data from different subjects and sessions together as a single source domain for transfer, which either fails to satisfy the assumption of domain adaptation that the source has a certain marginal distribution, or increases the difficulty of adaptation. We therefore propose the multi-source marginal distribution adaptation (MS-MDA) for EEG emotion recognition, which takes both domain-invariant and domain-specific features into consideration. First, we assume that different EEG data share the same low-level features, then we construct independent branches for multiple EEG data source domains to adopt one-to-one domain adaptation and extract domain-specific features. Finally, the inference is made by multiple branches. We evaluate our method on SEED and SEED-IV for recognizing three and four emotions, respectively. Experimental results show that the MS-MDA outperforms the comparison methods and state-of-the-art models in cross-session and cross-subject transfer scenarios in our settings. Codes at https://github.com/VoiceBeer/MS-MDA.

By 2035 AI could boost average profitability rates by 38 percent and lead to an economic increase of $14 Trillion. The words Artificial Intelligence (AI), and algorithms are most often misused and misunderstood. There are often used interchangeably when they shouldn't be. This leads to unnecessary confusion. In this article, let's understand what AI and algorithms are, and what the difference between them is.

Imagine you are being productive creating an AI/ML model for fun or task at hand, and one day a child comes asking you, what you are doing, you:- i am doing machine learning. Then the child asks, OK! What is it, and here you get confused as to what to tell the child so he understands, obviously you know the technical definition but what about the explain-ability of it. There is saying if you can't explain it to someone, you don't really understand it. It is hard to read from a book because it take your precious time, which obviously you don't have and this information is heavily inspired by hands-on machine learning with scikit learn and tensorflow book credit to the book Arthur . Machine learning is the way of programming computers to learn from data.

In the case of XGBoost, it is more useful to discuss hyperparameter tuning than the underlying mathematics because hyperparameter tuning is unusually complex, time-consuming, and necessary for deployment, whereas the mathematics are already embedded in the code libraries. While manual hyperparameter tuning is essential and time-consuming in many machine learning algorithms or models, it is especially so in XGBoost. Therefore, while this section focuses on identifying a key element to deploying XGBoost -- in our case study and example here to predict new fashions ("fast fashion") to gain competitive advantage in online apparel sales -- these hyperparameter tuning lessons are valid for all applications of XGBoost, and many other machine learning model applications herein also. The distinction and roles of parameters and hyperparameters is critical to affordable, timely, and accurate machine learning deployments. A core benefit to machine learning is its ability to discover and identify patterns and regularities in Big Data by automatically tuning many thousands or millions of "learnable" parameters. For example, in tree-based models like XGBoost (and decision trees and random forests), these learnable parameters are how many decision variables are at each node.

As a Data Scientist, you are employed because of your skill in data analysis and machine learning. One of the analyses often requested by the business is to do a business forecast, especially the time-related forecast.

Preprocessing data is an often overlooked key step in Machine Learning. In fact - it's as important as the shiny model you want to fit with it. You can have the best model crafted for any sort of problem - if you feed it garbage, it'll spew out garbage. It's worth noting that "garbage" doesn't refer to random data. It's a harsh label we attach to any data that doesn't allow the model to do its best - some more so than other.