Considering the growing global demand for machine learning training data, synthetic data generation is a reasonable way to address the versatile challenges in data acquisition. Conditional Tabular Generative Adversarial Network (CTGAN), an extension of the widely used Generative Adversarial Network (GAN), is considered one of the most promising techniques in the field of tabular data generation. Despite numerous successes of CTGAN, a lack of preserving categorical dependencies within the data has been identified. In prior work, the Cramer’s V (CV) as a natural metric for representing the correlation of categorical dependencies was proposed for hyperparameter tuning of CTGAN models. In this paper, we explore two novel strategies to directly integrate CV statistics of data batches within CTGAN training. The first approach is a generator loss term that penalizes differences between the CV statistics of the original and generated data. The second innovation is the extraction of the CV matrix as an additional feature for the critic. By applying our proposed methods to three benchmark datasets, we improve the averaged accuracy of supervised learning models trained on synthesized data by 11 % compared to the legacy CTGAN. We also outline the impact of CV statistics on preserving dependencies between categorical data columns in terms of integrity and contingency similarity, discuss existing challenges, and identify potential improvements.

We study the underpricing phenomenon in the U.S. Initial Public Offering (IPO) market using an interpretative machine learning approach. Inadequate conventional modelling of IPOs efficiency can lead to suboptimal investment decisions and a poor understanding of underlying factors that drive IPO underpricing. We employ interpretative machine learning for both predicting the numeric underpricing levels and classifying IPOs into underpriced and overpriced categories. We use the SHapley Additive exPlanations method to provide concise insights into the underlying factors that contribute to underpricing. Our numerical study reveals that offer price has the most predictive power for the models’ output, followed by equity retained and assets. Additionally, underpricing is more pronounced in technology-based sectors, and a higher dispersion in quality among firms during IPO surges results in higher levels of underpricing. Our analyses emphasize the importance of considering both the industry sector and market conditions when evaluating the firms going public.

The process of belief revision is impacted by trust relationships between agents. In the simplest case, information is reported from a single source and the belief change that occurs is dependent on the extent to which that source is trusted over a particular domain. In this paper, we are concerned with the more complicated case where the new information is reported by a set of agents. We first introduce a simple model of trust in an agent, and show how it influences the process of belief revision. We then define an joint notion of trust that is built by combining the trust held in each individual agent in the reporting set. We use this formal framework to define precisely when a collection of agents can be seen as a trusted authority over a particular formula for revision. While our framework is based on a particular model of trust, we argue that this approach can be used to define a suitable notion of joint trust in a wide range of settings.

Shear Wave Elastography (SWE) is a non-invasive ultrasound method that evaluates changes in liver stiffness, serving as a useful biomarker for liver fibrosis. The proper placement of a region of interest (ROI) on the liver in the B-mode image is imperative for obtaining accurate and dependable results in SWE. In order to develop an automated system for liver fibrosis measurement utilizing SWE, the initial crucial phase involves the segmentation of the liver capsule. This paper presents a novel approach for liver segmentation in ultrasound images using a contrastive self-supervised learning approach. The proposed method leverages a large dataset of unannotated abdominal ultrasound images to learn the feature representations, which are then fine-tuned on the downstream task of liver segmentation. The algorithm is trained in two stages: in the first stage a SimCLR model is trained to learn the feature representations from non-labeled data, and in the second stage these representations are fine-tuned with a smaller annotated dataset of liver segmentation masks. Finally, this is followed by a refinement step using CascadePSP. The study also investigates the use of physics-inspired augmentations, such as sector angle and penetration to improve the performance of the deep learning model on ultrasound images. The proposed approach of SimCLR+ENet was compared against the state-of-the-art method U-Net. Evaluation of the average Dice similarity showed that SimCLR+ENet outperformed U-Net with a result of 90.58% compared to 89.77%. Similarly, the average Huasdorff distance evaluation demonstrated that SimCLR+ENet achieved superior performance with a value of 21.71 compared to U-Net’s 29.53. This highlights the effectiveness of the proposed approach, with performance improvements of 0.9% and 26.5% for the average Dice coefficient and average Hausdorff distance, respectively. The study provides insights into the use of physics-inspired augmentations in the medical ultrasound imaging field and highlights the potential for self-supervised learning in improving segmentation results.

The .Net Framework has made writing windows applications easier than ever. Several programming languages can be used to write software using the .Net Framework, the most common one being C#. Due to the abundance of modules and pre-built functionalities that allow programmers to easily manipulate the windows operating system with high abstraction and no need for low-level coding, the .Net framework has also become a desirable environment for malicious actors to write their malware. To best of our knowledge, researchers have been treating .NET malware and other malware the same way by utilizing features from the PE header to classify the files. This is not possible for.Net files because their PE headers are nearly identical. In this paper, we tackle the problem of detecting malicious .Net files by extracting features from the CLR header. As far as we know, we are the first ones to explore this approach. Furthermore, we create a new dataset comprised of.Net malware and benign files, which we freely distribute to the research community. Finally, we assess the performance of several machine learning algorithms to detect malicious .NET files. The random forest model was the best solution among the set of algorithms tested, exhibiting a performance of 92% for this predictive task.

In gas metal arc welding, a weld quality and performance depends on many parameters. Selecting the right ones can be complex, even for an expert. One generally proceeds through trial and error to find a good set of parameters. Therefore, the current experts’ method is not optimized and can require a lot of time and materials. We propose using supervised learning techniques to help experts in their decision-making. To that extent, a two-part recommendation system is proposed. The first step is dedicated to identify, through classification, the number of weld passes. The second one suggests the seven remaining parameter values for each pass: layer, amperage, voltage, wire feed rate, frequency offset, trimming and welding speed. After extracting data from historical Welding Procedure Specification forms, we tested 11 different supervised learning algorithms. The recommendation system is able to provide good results for all the different settings mentioned above even if the data is noisy due to the heuristic nature of the experts’ process. The best classification model is CatBoost with 82.22% average F1 Weighted-Score and the best regression models are Extra Trees or a boosting algorithm with a reduced mean absolute percentage error compared to our baseline.

Vision-based Human Activity Recognition (HAR) aims to recognize human activities based on the analysis of video data, and has extensive applications in modern industry and human life. Inflated 3D (I3D) is a deep learning architecture that is commonly used for action recognition by using two-stream video data: RGB stream and optical flow stream. I3D achieved great success on a variety of action recognition benchmarks. However, the use of optical flow incurs high computational cost, making the approach unsuitable for real-time applications. We propose an alternative simple motion information extractor to replace the optical flow branch and reduce the computational cost. It is a modified I3D that uses 128 frames of 112x112 images as input. The low spatial resolution and long temporal range of the proposed I3D RGB stream can reduce the spatial information and enhance the motion information. Experiments show that this simple motion stream can increase the accuracy of the original I3D spatial stream by 4.09% on the Kinetics 400 dataset.

We present the Radar de Parité, an automated Natural Language Processing (NLP) system that measures the proportion of women and men quoted daily in six Canadian French-language media outlets. We outline the system’s architecture and detail the challenges we overcame to address French-specific issues, in particular regarding coreference resolution, a new contribution to the NLP literature on French. We also showcase statistics covering over one year’s worth of data (282,512 news articles). Our results highlight the underrepresentation of women in news stories, while also illustrating the application of modern NLP methods to measure gender representation and address societal issues.