This report investigates an unsupervised, feature-based image matching pipeline for the novel application of identifying individual k\=ak\=a. Applied with a similarity network for clustering, this addresses a weakness of current supervised approaches to identifying individual birds which struggle to handle the introduction of new individuals to the population. Our approach uses object localisation to locate k\=ak\=a within images and then extracts local features that are invariant to rotation and scale. These features are matched between images with nearest neighbour matching techniques and mismatch removal to produce a similarity score for image match comparison. The results show that matches obtained via the image matching pipeline achieve high accuracy of true matches. We conclude that feature-based image matching could be used with a similarity network to provide a viable alternative to existing supervised approaches.

Federated learning is a distributed machine learning paradigm that collaboratively trains a model with data on many clients. Unlike traditional distributed machine learning methods, which partition data into different clients to improve the efficiency of the learning algorithm, the goal of federated learning is to solve the learning problem without requiring the clients to reveal too much local information. With the increasing demand for data security and privacy protection, federated learning has received significant attention in both industry and academia. For example, banks want to collaboratively train a credit card scoring model without disclosing information about their customers, or hospitals want to carry out researches on a rare disease with each other due to the small number of sample cases, but they can't expose their patients' identity. For more on the progress of federated learning, see [1, 2]. The term federated learning was introduced by McMahan et al. [3], they also proposed the Federated Averaging(FedAvg) algorithm. FedAvg composes multiple rounds of local stochastic gradient descent updates and server-side averaging aggregation to train a centralized model.

An AI-based home screening test to detect oral and throat cancers from saliva samples is now available in the United States with the hope of transforming oral and throat cancer detection. Based on a technology approved by the US Food and Drug Administration (FDA) as a "breakthrough device," the saliva test can detect early symptoms of oral and throat cancer with more than 90 percent accuracy. Due to a lack of effective diagnostic tools, these cancers often go undiagnosed until they have reached an advanced stage, resulting in low survival rates. In a previous study, Maria Soledad Sosa from the Icahn School of Medicine at Mount Sinai and Julio A. Aguirre-Ghiso, now at Albert Einstein College of Medicine, discovered that the ability of cancer cells to remain dormant is controlled by a protein called NR2F1. This receptor protein can enter the cell nucleus and turn numerous genes on or off to activate a program that prevents the cancer cells from proliferating.

This work presents a new multimodal system for remote attention level estimation based on multimodal face analysis. Our multimodal approach uses different parameters and signals obtained from the behavior and physiological processes that have been related to modeling cognitive load such as faces gestures (e.g., blink rate, facial actions units) and user actions (e.g., head pose, distance to the camera). The multimodal system uses the following modules based on Convolutional Neural Networks (CNNs): Eye blink detection, head pose estimation, facial landmark detection, and facial expression features. First, we individually evaluate the proposed modules in the task of estimating the student's attention level captured during online e-learning sessions. For that we trained binary classifiers (high or low attention) based on Support Vector Machines (SVM) for each module. Secondly, we find out to what extent multimodal score level fusion improves the attention level estimation. The mEBAL database is used in the experimental framework, a public multi-modal database for attention level estimation obtained in an e-learning environment that contains data from 38 users while conducting several e-learning tasks of variable difficulty (creating changes in student cognitive loads).

Graph Neural Networks (GNNs) have received extensive research attention for their promising performance in graph machine learning. Despite their extraordinary predictive accuracy, existing approaches, such as GCN and GPRGNN, are not robust in the face of homophily changes on test graphs, rendering these models vulnerable to graph structural attacks and with limited capacity in generalizing to graphs of varied homophily levels. Although many methods have been proposed to improve the robustness of GNN models, the majority of these techniques are restricted to the spatial domain and employ complicated defense mechanisms, such as learning new graph structures or calculating edge attention. In this paper, we study the problem of designing simple and robust GNN models in the spectral domain. We propose EvenNet, a spectral GNN corresponding to an even-polynomial graph filter. Based on our theoretical analysis in both spatial and spectral domains, we demonstrate that EvenNet outperforms full-order models in generalizing across homophilic and heterophilic graphs, implying that ignoring odd-hop neighbors improves the robustness of GNNs. We conduct experiments on both synthetic and real-world datasets to demonstrate the effectiveness of EvenNet. Notably, EvenNet outperforms existing defense models against structural attacks without introducing additional computational costs and maintains competitiveness in traditional node classification tasks on homophilic and heterophilic graphs.

The modern industrial environment is equipping myriads of smart manufacturing machines where the state of each device can be monitored continuously. Such monitoring can help identify possible future failures and develop a cost-effective maintenance plan. However, it is a daunting task to perform early detection with low false positives and negatives from the huge volume of collected data. This requires developing a holistic machine learning framework to address the issues in condition monitoring of high-priority components and develop efficient techniques to detect anomalies that can detect and possibly localize the faulty components. This paper presents a comparative analysis of recent machine learning approaches for robust, cost-effective anomaly detection in cyber-physical systems. While detection has been extensively studied, very few researchers have analyzed the localization of the anomalies. We show that supervised learning outperforms unsupervised algorithms. For supervised cases, we achieve near-perfect accuracy of 98 percent (specifically for tree-based algorithms). In contrast, the best-case accuracy in the unsupervised cases was 63 percent :the area under the receiver operating characteristic curve (AUC) exhibits similar outcomes as an additional metric.

Determining the author of a text is a difficult task. Here we compare multiple AI techniques for classifying literary texts written by multiple authors by taking into account a limited number of speech parts (prepositions, adverbs, and conjunctions). We also introduce a new dataset composed of texts written in the Romanian language on which we have run the algorithms. The compared methods are Artificial Neural Networks, Support Vector Machines, Multi Expression Programming, Decision Trees with C5.0, and k-Nearest Neighbour. Numerical experiments show, first of all, that the problem is difficult, but some algorithms are able to generate decent errors on the test set.

Many algorithms are capable of underpinning decision systems. They vary in efficacy regarding properties such as accuracy, speed, and interpretability. In order to fulfill business requirements and objectives, companies are often torn about which algorithms to use. One of the most common yet thorniest issues is the tradeoff between accuracy and interpretability, especially when business goals require the algorithm to be both, but available methods outperform in one area while underperforming in the other. Logistic regression models, for one, are highly interpretable, but not necessarily accurate.

Shield helps people and organizations turn compliance into a competitive edge; moreover, it has revolutionized financial compliance. "Shield is a modern, agile, hybrid-cloud solution that enables organizations across highly regulated industries to'read between the lines'. It allows compliance teams to easily monitor all communication channels, such as Teams, WhatsApp and Zoom, from a single platform. Our end-to-end platform, equipped with extensive protection and transparency, offers greater flexibility, visibility, and control of alerts or triggers, especially when they could contain sensitive and confidential data." Center-stage startups may soon become game-changers.

Developing Hybrid Machine Learning Models to Assign Health Score to Railcar Fleets for Optimal Decision Making Mahyar Ejlali, Ebrahim Arian, Sajjad Taghiyeh, Kristina Chambers, Amir Hossein Sadeghi, Demet Cakdi, Robert B Handfield An expert hybrid predictive fault method is proposed based on fast-DBSCAN and PCA. Inspection data from 1986-2020 of North American Railcar Owner (NARO) is used. The model is able to predict future faults in the railcar fleet accurately. Abstract A large amount of data is generated during the operation of a railcar fleet, which can easily lead to dimensional disaster and reduce the resiliency of the railcar network. To solve these issues and offer predictive maintenance, this research introduces a hybrid fault diagnosis expert system method that combines density-based spatial clustering of applications with noise (DBSCAN) and principal component analysis (PCA). Firstly, the DBSCAN method is used to cluster categorical data that are similar to one another within the same group. Secondly, PCA algorithm is applied to reduce the dimensionality of the data and eliminate redundancy in order to improve the accuracy of fault diagnosis. Finally, we explain the engineered features and evaluate the selected models by using the Gain Chart and Area Under Curve (AUC) metrics. We use the hybrid expert system model to enhance maintenance planning decisions by assigning a health score to the railcar system of the North American Railcar Owner (NARO). According to the experimental results, our expert model can detect 96.4% of failures within 50% of the sample. This suggests that our method is effective at diagnosing failures in railcars fleet. Keywords: Expert system, Predictive maintenance, Railcar maintenance, Machine learning, Maintenance health score 1. Introduction Maintenance consists of activities that ensure the railcar assets continue to operate safely and reliably. These activities include inspection, repair, testing, and replacement of parts.