Autism Spectrum Disorder (ASD) is characterized by challenges in social communication and restricted patterns, with motor abnormalities gaining traction for early detection. However, kinematic analysis in ASD is limited, often lacking robust validation and relying on hand-crafted features for single tasks, leading to inconsistencies across studies. Thus, end-to-end models have become promising methods to overcome the need for feature engineering. Our aim is to assess both approaches across various kinematic tasks to measure the efficacy of commonly used features in ASD assessment, while comparing them to end-to-end models. Specifically, we developed a virtual reality environment with multiple motor tasks and trained models using both classification approaches. We prioritized a reliable validation framework with repeated cross-validation. Our comparative analysis revealed that hand-crafted features outperformed our deep learning approach in specific tasks, achieving a state-of-the-art area under the curve (AUC) of 0.90$\pm$0.06. Conversely, end-to-end models provided more consistent results with less variability across all VR tasks, demonstrating domain generalization and reliability, with a maximum task AUC of 0.89$\pm$0.06. These findings show that end-to-end models enable less variable and context-independent ASD assessments without requiring domain knowledge or task specificity. However, they also recognize the effectiveness of hand-crafted features in specific task scenarios.

With the evolution of blockchain technology, the issue of transaction security, particularly on platforms like Ethereum, has become increasingly critical. Front-running attacks, a unique form of security threat, pose significant challenges to the integrity of blockchain transactions. In these attack scenarios, malicious actors monitor other users' transaction activities, then strategically submit their own transactions with higher fees. This ensures their transactions are executed before the monitored transactions are included in the block. The primary objective of this paper is to delve into a comprehensive classification of transactions associated with front-running attacks, which aims to equip developers with specific strategies to counter each type of attack. To achieve this, we introduce a novel detection method named FRAD (Front-Running Attacks Detection on Ethereum using Ternary Classification Model). This method is specifically tailored for transactions within decentralized applications (DApps) on Ethereum, enabling accurate classification of front-running attacks involving transaction displacement, insertion, and suppression. Our experimental validation reveals that the Multilayer Perceptron (MLP) classifier offers the best performance in detecting front-running attacks, achieving an impressive accuracy rate of 84.59% and F1-score of 84.60%.

Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. Previous decision support systems for appendicitis have focused on clinical, laboratory, scoring, and computed tomography data and have ignored abdominal ultrasound, despite its noninvasive nature and widespread availability. In this work, we present interpretable machine learning models for predicting the diagnosis, management and severity of suspected appendicitis using ultrasound images. Our approach utilizes concept bottleneck models (CBM) that facilitate interpretation and interaction with high-level concepts understandable to clinicians. Furthermore, we extend CBMs to prediction problems with multiple views and incomplete concept sets. Our models were trained on a dataset comprising 579 pediatric patients with 1709 ultrasound images accompanied by clinical and laboratory data. Results show that our proposed method enables clinicians to utilize a human-understandable and intervenable predictive model without compromising performance or requiring time-consuming image annotation when deployed. For predicting the diagnosis, the extended multiview CBM attained an AUROC of 0.80 and an AUPR of 0.92, performing comparably to similar black-box neural networks trained and tested on the same dataset.

This paper provides a comprehensive review of past and current advances in the early detection of bark beetle-induced tree mortality from three primary perspectives: bark beetle & host interactions, RS, and ML/DL. In contrast to prior efforts, this review encompasses all RS systems and emphasizes ML/DL methods to investigate their strengths and weaknesses. We parse existing literature based on multi- or hyper-spectral analyses and distill their knowledge based on: bark beetle species & attack phases with a primary emphasis on early stages of attacks, host trees, study regions, RS platforms & sensors, spectral/spatial/temporal resolutions, spectral signatures, spectral vegetation indices (SVIs), ML approaches, learning schemes, task categories, models, algorithms, classes/clusters, features, and DL networks & architectures. Although DL-based methods and the random forest (RF) algorithm showed promising results, highlighting their potential to detect subtle changes across visible, thermal, and short-wave infrared (SWIR) spectral regions, they still have limited effectiveness and high uncertainties. To inspire novel solutions to these shortcomings, we delve into the principal challenges & opportunities from different perspectives, enabling a deeper understanding of the current state of research and guiding future research directions.

In the last ten years, various automated machine learning (AutoM ) systems have been proposed to build end-to-end machine learning (ML) pipelines with minimal human interaction. Even though such automatically synthesized ML pipelines are able to achieve a competitive performance, recent studies have shown that users do not trust models constructed by AutoML due to missing transparency of AutoML systems and missing explanations for the constructed ML pipelines. In a requirements analysis study with 36 domain experts, data scientists, and AutoML researchers from different professions with vastly different expertise in ML, we collect detailed informational needs for AutoML. We propose XAutoML, an interactive visual analytics tool for explaining arbitrary AutoML optimization procedures and ML pipelines constructed by AutoML. XAutoML combines interactive visualizations with established techniques from explainable artificial intelligence (XAI) to make the complete AutoML procedure transparent and explainable. By integrating XAutoML with JupyterLab, experienced users can extend the visual analytics with ad-hoc visualizations based on information extracted from XAutoML. We validate our approach in a user study with the same diverse user group from the requirements analysis. All participants were able to extract useful information from XAutoML, leading to a significantly increased understanding of ML pipelines produced by AutoML and the AutoML optimization itself.

Mild Traumatic Brain Injury (mTBI) is a common and challenging condition to diagnose accurately. Timely and precise diagnosis is essential for effective treatment and improved patient outcomes. Traditional diagnostic methods for mTBI often have limitations in terms of accuracy and sensitivity. In this study, we introduce an innovative approach to enhance mTBI diagnosis using 3D Computed Tomography (CT) images and a metric learning technique trained with triplet loss. To address these challenges, we propose a Residual Triplet Convolutional Neural Network (RTCNN) model to distinguish between mTBI cases and healthy ones by embedding 3D CT scans into a feature space. The triplet loss function maximizes the margin between similar and dissimilar image pairs, optimizing feature representations. This facilitates better context placement of individual cases, aids informed decision-making, and has the potential to improve patient outcomes. Our RTCNN model shows promising performance in mTBI diagnosis, achieving an average accuracy of 94.3%, a sensitivity of 94.1%, and a specificity of 95.2%, as confirmed through a five-fold cross-validation. Importantly, when compared to the conventional Residual Convolutional Neural Network (RCNN) model, the RTCNN exhibits a significant improvement, showcasing a remarkable 22.5% increase in specificity, a notable 16.2% boost in accuracy, and an 11.3% enhancement in sensitivity. Moreover, RTCNN requires lower memory resources, making it not only highly effective but also resource-efficient in minimizing false positives while maximizing its diagnostic accuracy in distinguishing normal CT scans from mTBI cases. The quantitative performance metrics provided and utilization of occlusion sensitivity maps to visually explain the model's decision-making process further enhance the interpretability and transparency of our approach.

Biometrics is used to authenticate an individual based on physiological or behavioral traits. Mouse dynamics is an example of a behavioral biometric that can be used to perform continuous authentication as protection against security breaches. Recent research on mouse dynamics has shown promising results in identifying users; however, it has not yet reached an acceptable level of accuracy. In this paper, an empirical evaluation of different classification techniques is conducted on a mouse dynamics dataset, the Balabit Mouse Challenge dataset. User identification is carried out using three mouse actions: mouse move, point and click, and drag and drop. Verification and authentication methods are conducted using three machine-learning classifiers: the Decision Tree classifier, the K-Nearest Neighbors classifier, and the Random Forest classifier. The results show that the three classifiers can distinguish between a genuine user and an impostor with a relatively high degree of accuracy. In the verification mode, all the classifiers achieve a perfect accuracy of 100%. In authentication mode, all three classifiers achieved the highest accuracy (ACC) and Area Under Curve (AUC) from scenario B using the point and click action data: (Decision Tree ACC:87.6%,

The use of biometrics to authenticate users and control access to secure areas has become extremely popular in recent years, and biometric access control systems are frequently used by both governments and private corporations. However, these systems may represent risks to security when deployed without considering the possibility of biometric presentation attacks (also known as spoofing). Presentation attacks are a serious threat because they do not require significant time, expense, or skill to carry out while remaining effective against many biometric systems in use today. This research compares three different software-based methods for facial and iris presentation attack detection in images. The first method uses Inception-v3, a pre-trained deep Convolutional Neural Network (CNN) made by Google for the ImageNet challenge, which is retrained for this problem. The second uses a shallow CNN based on a modified Spoofnet architecture, which is trained normally. The third is a texture-based method using Local Binary Patterns (LBP). The datasets used are the ATVS-FIr dataset, which contains real and fake iris images, and the CASIA Face Anti-Spoofing Dataset, which contains real images as well as warped photos, cut photos, and video replay presentation attacks. We also present a third set of results, based on cropped versions of the CASIA images.

Biometric authentication is becoming more prevalent for secured authentication systems. However, the biometric substances can be deceived by the imposters in several ways. Among other imposter attacks, print attacks, mask attacks, and replay attacks fall under the presentation attack category. The bio-metric images, especially the iris and face, are vulnerable to different presentation attacks. This research applies deep learning approaches to mitigate presentation attacks in a biometric access control system. Our contribution in this paper is two-fold: First, we applied the wavelet transform to extract the features from the biometric images. Second, we modified the deep residual neural net and applied it to the spoof datasets in an attempt to detect the presentation attacks. This research applied the proposed approach to biometric spoof datasets, namely ATVS, CASIA two class, and CASIA cropped image sets. The datasets used in this research contain images that are captured in both a controlled and uncontrolled environment along with different resolutions and sizes. We obtained the best accuracy of 93% on the ATVS Iris datasets. For CASIA two class and CASIA cropped datasets, we achieved test accuracies of 91% and 82%, respectively.

The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). Three challenges categories are considered: (i) UAV-based Maritime Object Tracking with Re-identification, (ii) USV-based Maritime Obstacle Segmentation and Detection, (iii) USV-based Maritime Boat Tracking. The USV-based Maritime Obstacle Segmentation and Detection features three sub-challenges, including a new embedded challenge addressing efficicent inference on real-world embedded devices. This report offers a comprehensive overview of the findings from the challenges. We provide both statistical and qualitative analyses, evaluating trends from over 195 submissions. All datasets, evaluation code, and the leaderboard are available to the public at https://macvi.org/workshop/macvi24.