--Convolutional neural networks (CNNs) have succeeded remarkably in various computer vision tasks. However, they are not intrinsically explainable. While feature-level understanding of CNNs reveals where the models looked, concept-based explainability methods provide insights into what the models saw. However, their assumption of linear reconstructability of image activations fails to capture the intricate relationships within these activations. Their Fidelity-only approach to evaluating global explanations also presents a new concern. For the first time, we address these limitations with the novel Transformative Nonlinear Concept Explainer (TraNCE) for CNNs. Unlike linear reconstruction assumptions made by existing methods, TraNCE captures the intricate relationships within the activations. This study presents three original contributions to the CNN explain-ability literature: (i) An automatic concept discovery mechanism based on variational autoencoders (V AEs). This transformative concept discovery process enhances the identification of meaningful concepts from image activations. Based on the investigations on publicly available datasets, we prove that a valid decomposition of a high-dimensional image activation should follow a non-linear reconstruction, contributing to the explainer's efficiency. We also demonstrate quantitatively that, besides accuracy, consistency is crucial for the meaningfulness of concepts and human trust. The code is available at https://github.com/daslimo/TrANCE ONVOLUTIONAL neural networks (CNNs) are widely used in computer vision, achieving notable success in visual classification tasks [1], [2]. However, understanding them at a human level remains a major challenge in artificial intelligence (AI), raising significant concerns about their explainability, especially in promoting ethical AI [3]- [5].

--Federated Learning (FL) enables collaborative model training across decentralized devices without sharing raw data, but it remains vulnerable to poisoning attacks that compromise model integrity. Existing defenses often rely on external datasets or predefined heuristics (e.g. T o address these limitations, we propose a privacy-preserving defense framework that leverages a Conditional Generative Adversarial Network (cGAN) to generate synthetic data at the server for authenticating client updates, eliminating the need for external datasets. Our framework is scalable, adaptive, and seamlessly integrates into FL workflows. Extensive experiments on benchmark datasets demonstrate its robust performance against a variety of poisoning attacks, achieving high True Positive Rate (TPR) and True Negative Rate (TNR) of malicious and benign clients, respectively, while maintaining model accuracy. The proposed framework offers a practical and effective solution for securing federated learning systems. N an era of data-driven artificial intelligence, organizations increasingly rely on large-scale machine learning models trained on vast amounts of user data. From personalized recommendation systems to predictive healthcare analytics, the success of these models hinges on access to diverse and representative datasets [1]. However, collecting and centralizing user data raises serious privacy concerns, as evidenced by high-profile data breaches and regulatory actions. Notable incidents, such as the Facebook-Cambridge Analytica scandal [2] and the Equifax data breach [3], have underscored the risks of centralized data storage and processing. These incidents not only resulted in significant financial penalties and reputational damage but also eroded public trust in data-driven technologies. Companies such as Google and Facebook have faced substantial penalties for mishandling user data, with fines reaching billions of dollars under regulations like the General Data Protection Regulation (GDPR) [4] and the California Consumer Privacy Act (CCP A) [5]. The rising awareness of digital privacy has fueled the demand for decentralized learning paradigms that minimize data exposure while enabling collaborative model training. Usama Zafar, Andr e Teixeira, and Salman Toor are with Department of Information Technology, Uppsala University, 751 05 Uppsala, Sweden.

Network Intrusion Detection Systems (NIDS) are vital for ensuring enterprise security. Recently, Graph-based NIDS (GIDS) have attracted considerable attention because of their capability to effectively capture the complex relationships within the graph structures of data communications. Despite their promise, the reproducibility and replicability of these GIDS remain largely unexplored, posing challenges for developing reliable and robust detection systems. This study bridges this gap by designing a systematic approach to evaluate state-of-the-art GIDS, which includes critically assessing, extending, and clarifying the findings of these systems. We further assess the robustness of GIDS under adversarial attacks. Evaluations were conducted on three public datasets as well as a newly collected large-scale enterprise dataset. Our findings reveal significant performance discrepancies, highlighting challenges related to dataset scale, model inputs, and implementation settings. We demonstrate difficulties in reproducing and replicating results, particularly concerning false positive rates and robustness against adversarial attacks. This work provides valuable insights and recommendations for future research, emphasizing the importance of rigorous reproduction and replication studies in developing robust and generalizable GIDS solutions.

Time series anomaly detection (TSAD) is of widespread interest across many industries, including finance, healthcare, and manufacturing. Despite the development of numerous automatic methods for detecting anomalies, human oversight remains necessary to review and act upon detected anomalies, as well as verify their accuracy. We study the use of multimodal large language models (LLMs) to partially automate this process. We find that LLMs can effectively identify false alarms by integrating visual inspection of time series plots with text descriptions of the data-generating process. By leveraging the capabilities of LLMs, we aim to reduce the reliance on human effort required to maintain a TSAD system

We used contrastive neural networks to learn useful similarity scores between the 144 cartridge casings in the NBIDE dataset, under the common-but-unknown source paradigm. The common-but-unknown source problem is a problem archetype in forensics where the question is whether two objects share a common source (e.g. were two cartridge casings fired from the same firearm). Similarity scores are often used to interpret evidence under this paradigm. We directly compared our results to a state-of-the-art algorithm, Congruent Matching Cells (CMC). When trained on the E3 dataset of 2967 cartridge casings, contrastive learning achieved an ROC AUC of 0.892. The CMC algorithm achieved 0.867. We also conducted an ablation study where we varied the neural network architecture; specifically, the network's width or depth. The ablation study showed that contrastive network performance results are somewhat robust to the network architecture. This work was in part motivated by the use of similarity scores attained via contrastive learning for standard evidence interpretation methods such as score-based likelihood ratios.

While software requirements are often expressed in natural language, verifying the correctness of a program against natural language requirements is a hard and underexplored problem. Large language models (LLMs) are promising candidates for addressing this challenge, however our experience shows that they are ineffective in this task, often failing to detect even straightforward bugs. To address this gap, we introduce HoarePrompt, a novel approach that adapts fundamental ideas from program analysis and verification to natural language artifacts. Drawing inspiration from the strongest postcondition calculus, HoarePrompt employs a systematic, step-by-step process in which an LLM generates natural language descriptions of reachable program states at various points in the code. To manage loops, we propose few-shot-driven k-induction, an adaptation of the k-induction method widely used in model checking. Once program states are described, HoarePrompt leverages the LLM to assess whether the program, annotated with these state descriptions, conforms to the natural language requirements. For evaluating the quality of classifiers of program correctness with respect to natural language requirements, we constructed CoCoClaNeL, a challenging dataset of solutions to programming competition problems. Our experiments show that HoarePrompt improves the MCC by 62% compared to directly using Zero-shot-CoT prompts for correctness classification. Furthermore, HoarePrompt outperforms a classifier that assesses correctness via LLM-based test generation by increasing the MCC by 93%. The inductive reasoning mechanism contributes a 28% boost to MCC, underscoring its effectiveness in managing loops.

-- Understanding how scaffolding strategies influence human understanding in human-robot interaction is important for developing effective assistive systems. This empirical study investigates linguistic scaffolding strategies based on negation as an important means that de-biases the user from potential errors but increases processing costs and hesitations as a means to ameliorate processing costs. In an adaptive strategy, the user state with respect to the current state of understanding and processing capacity was estimated via a scoring scheme based on task performance, prior scaffolding strategy, and current eye gaze behavior . In the study, the adaptive strategy of providing negations and hesitations was compared with a nonadaptive strategy of providing only affirmations. The adaptive scaffolding strategy was generated using the computational model SHIFT . Our findings indicate that using adaptive scaffolding strategies with SHIFT tends to (1) increased processing costs, as reflected in longer reaction times, but (2) improved task understanding, evidenced by a lower error rate of almost 23%. We assessed the efficiency of SHIFT's selected scaffolding strategies across different cognitive states, finding that in three out of five states, the error rate was lower compared to the baseline condition. We discuss how these results align with the assumptions of the SHIFT model and highlight areas for refinement. Moreover, we demonstrate how scaffolding strategies, such as negation and hesitation, contribute to more effective human-robot explanatory dialogues. In the growing field of social robotics, robots are increasingly being designed to assist people in their everyday lives.

High Content Screening (HCS) microscopy datasets have transformed the ability to profile cellular responses to genetic and chemical perturbations, enabling cell-based inference of drug-target interactions (DTI). However, the adoption of representation learning methods for HCS data has been hindered by the lack of accessible datasets and robust benchmarks. To address this gap, we present RxRx3-core, a curated and compressed subset of the RxRx3 dataset, and an associated DTI benchmarking task. At just 18GB, RxRx3-core significantly reduces the size barrier associated with large-scale HCS datasets while preserving critical data necessary for benchmarking representation learning models against a zero-shot DTI prediction task. RxRx3-core includes 222,601 microscopy images spanning 736 CRISPR knockouts and 1,674 compounds at 8 concentrations. RxRx3-core is available on HuggingFace and Polaris, along with pre-trained embeddings and benchmarking code, ensuring accessibility for the research community. By providing a compact dataset and robust benchmarks, we aim to accelerate innovation in representation learning methods for HCS data and support the discovery of novel biological insights.

Depression disorder is a serious health condition that has affected the lives of millions of people around the world. Diagnosis of depression is a challenging practice that relies heavily on subjective studies and, in most cases, suffers from late findings. Electroencephalography (EEG) biomarkers have been suggested and investigated in recent years as a potential transformative objective practice. In this article, for the first time, a detailed systematic review of EEG-based depression diagnosis approaches is conducted using advanced machine learning techniques and statistical analyses. For this, 938 potentially relevant articles (since 1985) were initially detected and filtered into 139 relevant articles based on the review scheme 'preferred reporting items for systematic reviews and meta-analyses (PRISMA).' This article compares and discusses the selected articles and categorizes them according to the type of machine learning techniques and statistical analyses. Algorithms, preprocessing techniques, extracted features, and data acquisition systems are discussed and summarized. This review paper explains the existing challenges of the current algorithms and sheds light on the future direction of the field. This systematic review outlines the issues and challenges in machine intelligence for the diagnosis of EEG depression that can be addressed in future studies and possibly in future wearable technologies.

In this challenge, we explored text-based food hazard prediction with long tail distributed classes. The task was divided into two subtasks: (1) predicting whether a web text implies one of ten food-hazard categories and identifying the associated food category, and (2) providing a more fine-grained classification by assigning a specific label to both the hazard and the product. Our findings highlight that large language model-generated synthetic data can be highly effective for oversampling long-tail distributions. Furthermore, we find that fine-tuned encoder-only, encoder-decoder, and decoder-only systems achieve comparable maximum performance across both subtasks. During this challenge, we gradually released (under CC BY-NC-SA 4.0) a novel set of 6,644 manually labeled food-incident reports.