bluuebunny/crossref_metadata_2025 huggingface dataset:
Uncertainty-Guided Dual Task Framework for Semi-Supervised Segmentation of Thyroid Nodules
Authors: Ying Xiang , Zhang Jizhe , Gao Jie , Yu Mei , Zhang Ruixuan , Zhu Jialin , Li Xuewei , Yu Ruiguo
Published: October 2024

Since ultrasound imaging technique is convenient and real-time, it plays a crucial role in diagnosing thyroid nodules. With the development of deep learning, computer-aided diagnosis models have been widely applied to diagnose thyroid nodules, in which thyroid nodule segmentation is a basic yet essential task. Semi-supervised learning is a popular topic for thyroid ultrasound images segmentation under limited annotations. However, current mainstream semi-supervised methods (e.g., for nature image scenes) commonly produce poor segmentation results when facing thyroid ultrasound images. We analyze that there are two main reasons: First, since these methods lack the targeted learning for the ambiguous regions of images that may contain complementary clues for segmentation, the model will likely be over-fitting in the regions that are easy to predict, leading to fail to make full use of the unlabeled data. Second, these methods lack the shape constraint on thyroid nodules, resulting in incomplete segmentation shape on the nodule boundary. To address the above issues, we propose a novel Uncertainty-guided Dual Task Framework (UDTF). Concretely, we propose an Uncertainty Region Selection Module (URSM) to guide the segmentation task to learn from the ambiguous regions calculated by prototype under the constraint of consistency regularization. Additionally, to further keep the integrity of the nodules, we propose a Dual Task Module (DTM) to impose the shape constraint on thyroid nodules by exploring the task-level consistency between the segmentation and auxiliary reconstruction task. Extensive experiments are conducted on two thyroid ultrasound image datasets, including a private Philips Thyroid Ultrasound dataset and a public TN3K dataset. The results show that UDTF achieves superior performance compared to several other state-of-the-art methods.
Spatial and Frequency-Based Feature Reconstruction for Cross-Database Micro-Expression Recognition
Authors: Feng Zhi , Chen C. L. Philip , Xu Shiting , Zhang Tong
Published: October 2024

The vulnerability of individual-database learned model for Micro-Expression Recognition (MER) has significantly hindered their performance in real-world scenarios. Cross-Database Micro-Expression Recognition (CDMER) aims to enhance the robustness and generalization performance for more complicated situations. Most existing CDMER methods typically attempt to leverage spatial information to eliminate the inevitable domain shift between source and target domains. In this paper, we propose a novel feature space reconstruction model that provides feature space with better generalization for CDMER. Specifically, we introduce the Spatial and Frequency Domain Co-learning (SFDC), a three-branch module, that adaptively exploits the spatial and frequency characteristics of intermediate feature representations, capturing the global and local features of micro-expression adequately. Furthermore, to facilitate the synchronization of two domains, Source and Target Domain Synchronization (STDS) module is employed to guide the alignment of different subspaces simultaneously. Extensive experimental results on the SMIC and CASME II databases demonstrate the effectiveness of the reconstruction and the superiority of our proposed method over state-of-the-art (SOTA) methods.
Contribution of V1 Receptive Field Properties to Corruption Robustness in CNNs
Authors: Barbulescu Ruxandra , Marques Tiago , Oliveira Arlindo L.
Published: October 2024

Recently, it has been shown that simulating computations in early primate visual areas, up to the primary visual cortex (V1), at the front of convolutional neural networks (CNNs) leads to improvements in robustness to image corruptions. However, it remains unclear whether this improvement requires precisely matching the receptive field (RF) properties of V1 neurons or if some aspects are sufficient. Here, we explore this question by building several variants of a CNN model with a front-end modeling the primate V1 using a classical neuroscientific model, a Gabor Filter Bank (GFB) followed by simple- and complex- cell nonlinearities. Each model variant had varying levels of biological detail according to how the RF properties were sampled. The model variant sampling these parameters according to empirical biological distributions was considerably more robust to image corruptions than the variant sampling the parameters uniformly and independently (relative difference of 8.72%). However, a uniform variant capturing correlations between some GFB parameters obtained the same performance as the biological sampling variant. Our results show that it is not sufficient to approximate V1 with only the right class of function and parameter range, as it is also required to include the observed correlations between RF properties. However, it is not necessary to fully replicate the empirical distributions of V1 RF properties to obtain the desired improvement in robustness.
CLIP-based Cross-Level Semantic Interaction and Recombination Network for Composed Image Retrieval
Authors: Liu Zhihang , Huang Qiang , Zhu Yingying
Published: October 2024

Composed image retrieval seeks to retrieve a target image that fulfills both modalities based on the user’s query, which comprises a modified text and a reference image. Although existing studies introduce novel multi-modal feature fusion techniques at the global or local level, they ignore exploring the cross-level semantic correspondence and recombining the original and target features in the reference image and the modified text, and the problems of modal inconsistency. To address these issues, we propose a CLIP-based Cross-Level Semantic Interaction and Recombination Network (SeIR). Specifically, we first resort the image and text encoders of the CLIP pre-trained model to narrow the modal gap between image and text, and extract both global and local features. We also introduce a cross-modal attention mechanism to screen out original and target features by exploring the semantic correlation between the cross-level image and text features. Subsequently, to alleviate the modal difference between the generated composed query representation and the target image, we utilize an affine transformation technique to recombine the original and target features from the image and text. Extensive experiments on the FashionIQ and CIRR benchmark datasets demonstrate the competitive performance of the proposed SeIR compared to the state-of-the-art methods.
ActivityGen: Extracting Enabled Activities from Screenshots
Authors: Beyel Harry H. , Manuel Sovin , van der Aalst Wil M. P.
Published: October 2024

Many tasks in organizations are performed in a desktop environment. It is possible to record users’ interactions in a desktop environment by taking screenshots when an action happens. The result is an interaction log. By considering the associated images of a record, it is possible to detect which activity was performed and which activities were enabled. This information can be extracted, resulting in a translucent event log. Such a translucent event log is valuable and can be used as input for dedicated process-mining techniques. The results can be used to analyze human-computer interactions or create bots for robotic process automation. However, current techniques for extracting information on enabled activities rely on template matching, which is rigid and sensitive to variations. To solve this issue, we present our modular framework, ActivityGen. ActivityGen detects and labels graphical user interface elements by also considering additional information. ActivityGen uses more advanced techniques to overcome the limitations of previous approaches and can extract information without a user’s input. Furthermore, it can be adjusted to a user’s needs. It detects graphical user interface elements more accurately than state-of-the-art techniques and labels them faster, more robust, and more domain-oriented than state-of-the-art techniques.
GABInsight: Exploring Gender-Activity Binding Bias in Vision-Language Models
Authors: Abdollahi Ali , Ghaznavi Mahdi , Karimi Nejad Mohammad Reza , Mari Oriyad Arash , Abbasi Reza , Salesi Ali , Behjati Melika , Rohban Mohammad Hossein , Soleymani Baghshah Mahdieh
Published: October 2024

Vision-language models (VLMs) are intensively used in many downstream tasks, including those requiring assessments of individuals appearing in the images. While VLMs perform well in simple single-person scenarios, in real-world applications, we often face complex situations in which there are persons of different genders doing different activities. We show that in such cases, VLMs are biased towards identifying the individual with the expected gender (according to ingrained gender stereotypes in the model or other forms of sample selection bias) as the performer of the activity. We refer to this bias in associating an activity with the gender of its actual performer in an image or text as the Gender-Activity Binding (GAB) bias and analyze how this bias is internalized in VLMs. To assess this bias, we have introduced the GAB dataset with approximately 5500 AI-generated images that represent a variety of activities, addressing the scarcity of real-world images for some scenarios. To have extensive quality control, the generated images are evaluated for their diversity, quality, and realism. We have tested 12 renowned pre-trained VLMs on this dataset in the context of text-to-image and image-to-text retrieval to measure the effect of this bias on their predictions. Additionally, we have carried out supplementary experiments to quantify the bias in VLMs’ text encoders and to evaluate VLMs’ capability to recognize activities. Our experiments indicate that VLMs experience an average performance decline of about 13.2% when confronted with gender-activity binding bias.
Few-Shot Object Detection with Instance Feature Generation and Hybrid Contrastive Learning
Authors: Wang Yuhui , Peng Bo , Qin Tianyi , Song Jiahui , Zhang Xu
Published: October 2024

Few-shot object detection aims to effectively detect novel classes with limited annotated samples. Due to the low-quality instance features obtained by deep learning in data-scarce scenarios, few-shot object detection remains a significant challenge. In this paper, a novel method is proposed to enhance the few-shot object detection performance by focusing on the generalizability and discriminability of instance features. In detail, by synthesizing auxiliary instances that embed diverse attributes, a mask-guided instance feature generation module is presented to alleviate the overfitting on sample-specific characteristics, thereby facilitating the acquisition of generalizable object-relevant knowledge. Then, to learn discriminative object-relevant attributes, a hybrid cross-layer and intra-layer contrastive learning mechanism is designed to enhance the discriminability of instance features by building contrastive constraints between instances within and across layers. Experimental results on two widely used benchmarks demonstrate the effectiveness of the proposed method.
Axiomatic Characterisations of Sample-based Explainers
Authors: Amgoud Leila , Cooper Martin , Debbaoui Salim
Published: October 2024

Explaining decisions of black-box classifiers is both important and computationally challenging. In this paper, we scrutinize explainers that generate feature-based explanations from samples or datasets. We start by presenting a set of desirable properties that explainers would ideally satisfy, delve into their relationships, and highlight incompatibilities of some of them. We identify the entire family of explainers that satisfy two key properties which are compatible with all the others. Its instances provide sufficient reasons, called weak abductive explanations. We then unravel its various sub-families that satisfy subsets of compatible properties. Indeed, we fully characterize all the explainers that satisfy any subset of compatible properties. In particular, we introduce the first (broad family of) explainers that guarantee the existence of explanations and their global consistency. We discuss some of its instances including the irrefutable explainer and the surrogate explainer whose explanations can be found in polynomial time.
Sanitizing Hidden Information with Diffusion Models
Authors: Robinette Preston K. , Moyer Daniel , Johnson Taylor T.
Published: October 2024

Information hiding is the process of embedding data within another form of data, often to conceal its existence or prevent unauthorized access. This process is commonly used in various forms of secure communications (steganography) that can be used by bad actors to propagate malware, exfiltrate victim data, and discreetly communicate. Recent work has utilized deep neural networks to remove this hidden information in a defense mechanism known as sanitization. Previous deep learning works, however, are unable to scale efficiently beyond the MNIST dataset. In this work, we present a novel sanitization method called DM-SUDS that utilizes a diffusion model framework to sanitize/remove hidden information from image-into-image universal and dependent steganography from CIFAR-10 and ImageNet datasets. We evaluate DM-SUDS against three different baselines using MSE, PSNR, SSIM, and NCC metrics and provide further detailed analysis through an ablation study. DM-SUDS outperforms all three baselines and significantly improves image preservation MSE by 50.44%, PSNR by 12.69%, SSIM by 11.49%, and NCC by 3.26% compared to previous deep learning approaches. Additionally, we introduce a novel evaluation specification that considers the successful removal of hidden information (safety) as well as the resulting quality of the sanitized image (utility). We further demonstrate the versatility of this method with an application in an audio case study, demonstrating its broad applicability to additional domains.
Resistance Against Manipulative AI: Key Factors and Possible Actions
Authors: Wilczyński Piotr , Mieleszczenko-Kowszewicz Wiktoria , Biecek Przemysław
Published: October 2024

If AI is the new electricity, what should we do to keep ourselves from getting electrocuted? In this work, we explore factors related to the potential of large language models (LLMs) to manipulate human decisions. We describe the results of two experiments designed to determine what characteristics of humans are associated with their susceptibility to LLM manipulation, and what characteristics of LLMs are associated with their manipulativeness potential. We explore human factors by conducting user studies in which participants answer general knowledge questions using LLM-generated hints, whereas LLM factors by provoking language models to create manipulative statements. Then, we analyze their obedience, the persuasion strategies used, and the choice of vocabulary. Based on these experiments, we discuss two actions that can protect us from LLM manipulation. In the long term, we put AI literacy at the forefront, arguing that educating society would minimize the risk of manipulation and its consequences. We also propose an ad hoc solution, a classifier that detects LLM manipulation – a Manipulation Fuse.
Aligning XAI with EU Regulations for Smart Biomedical Devices: A Methodology for Compliance Analysis
Authors: Sovrano Francesco , Lognoul Michael , Vilone Giulia
Published: October 2024

Significant investment and development have gone into integrating Artificial Intelligence (AI) in medical and healthcare applications, leading to advanced control systems in medical technology. However, the opacity of AI systems raises concerns about essential characteristics needed in such sensitive applications, like transparency and trustworthiness. Our study addresses these concerns by investigating a process for selecting the most adequate Explainable AI (XAI) methods to comply with the explanation requirements of key EU regulations in the context of smart bioelectronics for medical devices. The adopted methodology starts with categorising smart devices by their control mechanisms (open-loop, closed-loop, and semi-closed-loop systems) and delving into their technology. Then, we analyse these regulations to define their explainability requirements for the various devices and related goals. Simultaneously, we classify XAI methods by their explanatory objectives. This allows for matching legal explainability requirements with XAI explanatory goals and determining the suitable XAI algorithms for achieving them. Our findings provide a nuanced understanding of which XAI algorithms align better with EU regulations for different types of medical devices. We demonstrate this through practical case studies on different neural implants, from chronic disease management to advanced prosthetics. This study fills a crucial gap in aligning XAI applications in bioelectronics with stringent provisions of EU regulations. It provides a practical framework for developers and researchers, ensuring their AI innovations advance healthcare technology and adhere to legal and ethical standards.
Explicit Modelling of Theory of Mind for Belief Prediction in Nonverbal Social Interactions
Authors: Bortoletto Matteo , Ruhdorfer Constantin , Shi Lei , Bulling Andreas
Published: October 2024

We propose MToMnet – a Theory of Mind (ToM) neural network for predicting beliefs and their dynamics during human social interactions from multimodal input. ToM is key for effective nonverbal human communication and collaboration, yet existing methods for belief modelling have not included explicit ToM modelling or have typically been limited to one or two modalities. MToMnet encodes contextual cues (scene videos and object locations) and integrates them with person-specific cues (human gaze and body language) in a separate MindNet for each person. Inspired by prior research on social cognition and computational ToM, we propose three different MToMnet variants: two involving the fusion of latent representations and one involving the re-ranking of classification scores. We evaluate our approach on two challenging real-world datasets, one focusing on belief prediction while the other examining belief dynamics prediction. Our results demonstrate that MToMnet surpasses existing methods by a large margin while at the same time requiring a significantly smaller number of parameters. Taken together, our method opens up a highly promising direction for future work on artificial intelligent systems that can robustly predict human beliefs from their non-verbal behaviour and, as such, more effectively collaborate with humans.
Synthetically Augmented Self-Supervised Fine-Tuning for Diverse Text OCR Correction
Authors: Guan Shuhao , Greene Derek
Published: October 2024

The adoption of Optical Character Recognition (OCR) tools has been central to the increased digitization of historical documents. However, the errors introduced during OCR, particularly in texts with a specialized vocabulary (SV), necessitate effective post-OCR correction methodologies. This study introduces a novel approach that leverages weak supervision and self-supervised fine-tuning to enhance post-OCR correction without the need for substantial manual annotations. By using multi-noise-level synthetic data, generated through automatically-extracted OCR errors and applied to clean texts, we can train robust models tailored for post-OCR tasks. Furthermore, we propose a unique self-supervised fine-tuning strategy, applied specifically to long texts, enables models to adeptly handle out-of-vocabulary problems and SV. Additionally, we tested the performance of the GPT model on post-OCR tasks.
Group Fairness with Individual and Censorship Constraints
Authors: Wang Zichong , Zhang Wenbin
Published: October 2024

The widespread use of Artificial Intelligence (AI) based decision-making systems has raised a lot of concerns regarding potential discrimination, particularly in domains with high societal impact. Most existing fairness research focused on tackling bias relies heavily on the presence of class labels, an assumption that often mismatches real-world scenarios, which ignores the ubiquity of censored data. Further, existing works regard group fairness and individual fairness as two disparate goals, overlooking their inherent interconnection, i.e., addressing one can degrade the other. This paper proposes a novel unified method that aims to mitigate group unfairness under censorship while curbing the amplification of individual unfairness when enforcing group fairness constraints. Specifically, our introduced ranking algorithm optimizes individual fairness within the bounds of group fairness, uniquely accounting for censored information. Evaluation across four benchmark tasks confirms the effectiveness of our method in quantifying and mitigating both fairness dimensions in the face of censored data.
PLACO: A Multi-Stage Framework for Cost-Effective Performance in Human-AI Teams
Authors: Mallela Pranavkumar , Kumar Vinay , Shekhar Jha Shashi , Jain Shweta
Published: October 2024

Human-AI teams have a pervasive impact in various fields including healthcare diagnosis, robotics in manufacturing, cyber-security, autonomous vehicles, and many more. The effectiveness of Human-AI teams highly depends on the set of humans that interact with the AI model for determining the final output. In this paper, we tackle the practical setting where taking the human input is of considerable cost and even expert humans can make mistakes. This paper proposes Probabilistic Labeler Assisted Cost Optimization (PLACO), a two-step framework to find cost-effective subsets of humans for multi-way classification tasks. The inputs from the subset of humans are then combined with the AI model’s output resulting in the most accurate output. For cost-effective human selection given an input task, we estimate human labels by maximizing the posterior probability of a true human label given the AI model’s output on the task. We further derive a value function that determines the value of a given human subset to maximize the lower bound on the overall accuracy of the Human-AI team. We present the theoretical foundations of our human label estimation method and human subset value function. We also empirically demonstrate the effectiveness of PLACO in terms of the Human-AI team’s performance and cost-effectiveness against state-of-art methods on the CIFAR-10H and Imagenet-16H datasets having human annotations.
Probabilistically Plausible Counterfactual Explanations with Normalizing Flows
Authors: Wielopolski Patryk , Furman Oleksii , Stefanowski Jerzy , Zięba Maciej
Published: October 2024

We present PPCEF, a novel method for generating probabilistically plausible counterfactual explanations (CFs). PPCEF advances beyond existing methods by combining a probabilistic formulation that leverages the data distribution with the optimization of plausibility within a unified framework. Compared to reference approaches, our method enforces plausibility by directly optimizing the explicit density function without assuming a particular family of parametrized distributions. This ensures CFs are not only valid (i.e., achieve class change) but also align with the underlying data’s probability density. For that purpose, our approach leverages normalizing flows as powerful density estimators to capture the complex high-dimensional data distribution. Furthermore, we introduce a novel loss function that balances the trade-off between achieving class change and maintaining closeness to the original instance while also incorporating a probabilistic plausibility term. PPCEF’s unconstrained formulation allows for an efficient gradient-based optimization with batch processing, leading to orders of magnitude faster computation compared to prior methods. Moreover, the unconstrained formulation of PPCEF allows for the seamless integration of future constraints tailored to specific counterfactual properties. Finally, extensive evaluations demonstrate PPCEF’s superiority in generating high-quality, probabilistically plausible counterfactual explanations in high-dimensional tabular settings.
Backward Compatibility in Attributive Explanation and Enhanced Model Training Method
Authors: Matsuno Ryuta
Published: October 2024

Model update is a crucial process in the operation of ML/AI systems. While updating a model generally enhances the average prediction performance, it also significantly impacts the explanations of predictions. In real-world applications, even minor changes in explanations can have detrimental consequences. To tackle this issue, this paper introduces BCX, a quantitative metric that evaluates the backward compatibility of attributive explanations between pre- and post-update models. BCX utilizes practical agreement metrics to calculate the average agreement between the explanations of pre- and post-update models, specifically among samples on which both models accurately predict. In addition, we propose BCXR, a BCX-aware model training method by designing surrogate losses which theoretically lower bounds agreement scores. Furthermore, we present a universal variant of BCXR that improves all agreement metrics, utilizing L2 distance among the explanations of the models. To validate our approach, we conducted experiments on eight real-world data sets, demonstrating that BCXR achieves superior trade-offs between predictive performances and BCX scores, showcasing the effectiveness of our BCXR methods.
Model Provenance via Model DNA
Authors: Mu Xin , Wang Yu , Zhang Yehong , Zhang Jiaqi , Wang Hui , Xiang Yang , Yu Yue
Published: October 2024

Understanding the life cycle of the machine learning (ML) model is an intriguing area of research (e.g., understanding where the model comes from, how it is trained, and how it is used). Our focus is on a novel problem within this domain, namely Model Provenance (MP). MP concerns the relationship between a target model and its pre-training model and aims to determine whether a source model serves as the provenance for a target model. In this paper, we formulate this new challenge as a learning problem, supplementing our exploration with empirical discussions on its connections to existing works. Following that, we introduce “Model DNA”, an interesting concept encoding the model’s training data and input-output information to create a compact machine-learning model representation. Capitalizing on this model DNA, we establish an efficient framework consisting of three key components: DNA generation, DNA similarity loss, and a provenance classifier, aimed at identifying model provenance. We conduct evaluations on both computer vision and natural language processing tasks using various models, datasets, and scenarios to demonstrate the effectiveness of our approach.
Locally-Minimal Probabilistic Explanations
Authors: Izza Yacine , Meel Kuldeep S. , Marques-Silva Joao
Published: October 2024

Explainable Artificial Intelligence (XAI) is widely regarded as a cornerstone of trustworthy AI. Unfortunately, most work on XAI offers no guarantees of rigor. In high-stakes domains, e.g. uses of AI that impact humans, the lack of rigor of explanations can have disastrous consequences. Formal abductive explanations offer crucial guarantees of rigor and so are of interest in high-stakes uses of machine learning (ML). One drawback of abductive explanations is explanation size, justified by the cognitive limits of human decision-makers. Probabilistic abductive explanations (PAXps) address this limitation, but their theoretical and practical complexity makes their exact computation most often unrealistic. This paper proposes novel efficient algorithms for the computation of locally-minimal PAXps, which offer high-quality approximations of PXAps in practice. The experimental results demonstrate the practical efficiency of the proposed solutions.
Covered for Life: Lifelong Area Coverage under Spatiotemporal Uncertainty
Authors: Street Charlie , Mansouri Masoumeh
Published: October 2024

To efficiently cover an environment, robots must be able to handle changes in occupancy during execution. These occupancy dynamics are often stochastic, and so we cannot deterministically predict when a location will be occupied. Existing coverage solutions either assume static environments or react to changes as they occur, limiting performance. In this paper we present a framework for lifelong area coverage under spatiotemporal uncertainty, where a robot repeats coverage over multiple episodes. The stochastic occupancy dynamics are a priori unknown and learned using the observations received during coverage. For each coverage episode, we build and solve a partially observable Markov decision process which exploits our learned spatiotemporal dynamics model to improve performance. We demonstrate the efficacy of our framework across extensive experiments in synthetic environments.
Computational Complexity of Standpoint LTL
Authors: Demri Stéphane , Wałęga Przemysław Andrzej
Published: October 2024

Standpoint linear temporal logic SLTL is a recent formalism able to model possibly conflicting commitments made by distinct agents, taking into account aspects of temporal reasoning. In this paper, we analyse the computational properties of SLTL. First, we establish logarithmic-space reductions between the satisfiability problems for the multi-dimensional modal logic PTL×S5 and SLTL. This leads to the ExpSpace-completeness of the satisfiability problem in SLTL, which is a surprising result in view of previous investigations. Next, we present a method of restricting SLTL so that the obtained fragment is a strict extension of both the (non-temporal) standpoint logic and linear-time temporal logic LTL, but the satisfiability problem is PSpace-complete in this fragment. Thus, we show how to combine standpoint logic with LTL so that the worst-case complexity of the obtained combination is not higher than of pure LTL.
Hierarchical Planning for Resource-Constrained Long-Term Monitoring Missions in Time-Varying Environments
Authors: Stephens Alex , Lacerda Bruno , Hawes Nick
Published: October 2024

We consider autonomous robots deployed on long-term monitoring missions in unknown environments. The planning objective is to maximise the value of observations obtained over the course of a mission, subject to resource constraints which demand periodic visits to depots where resources can be replenished. Effective planning in this setting requires reasoning over long horizons based on sparse observational data, and flexible management of the constrained resources. We present a hierarchical planning approach to this problem, using a spatiotemporal Gaussian process environment model at different levels of abstraction for short- and long-horizon planning. We empirically evaluate our approach on a series of synthetic domains, and a wildfire monitoring scenario based on real data.
A Neuro-symbolic Approach for Faceted Search in Digital Libraries
Authors: Khalid Mutahira , Auer Sören , Stocker Markus
Published: October 2024

Academic Search Engines (ASEs) are crucial for navigating the vast landscape of scholarly literature. Traditionally, these engines rely on keyword-based search, supplemented by predefined facets encompassing metadata such as research field, publication year, type, authors, and language. However, ASEs are limited in their ability to generate dynamic facets in real-time based on article contents. This limitation impedes the efficient exploration and navigation of large article collections. We propose an approach that addresses this limitation by dynamically generating facets using article abstracts. We introduce three distinct methods for dynamic facet generation: (1) KB2 (based on Knowledge Bases) utilizes two knowledge bases (KB) to extract facet values and their associated facets; (2) KBLLM (based on a Knowledge Base and a Large Language Model) utilizes a KB for extracting facet values and a large language model (LLM) to categorize these values by predicting facets; finally, (3) KBLLMKA (based on a Knowledge Base and a Large Language Model with Knowledge Augmentation) combines KB-spotting with facet-value pair extraction and adds this information as auxiliary data to enhance LLM’s facet prediction capabilities. We evaluated the effectiveness of these methods with a user study, performance evaluation, and comparative analyses, which showed the effectiveness of the approach.
A Novel View of Analogical Proportion Between Formulas
Authors: Herzig Andreas , Lorini Emiliano , Prade Henri
Published: October 2024

Analogical proportions are statements of the form “α is to β as γ is to δ”, noted α:β::γ:δ, and can be understood as “α differs from β as γ differs from δ” and conversely “β differs from α as δ differs from γ”. In this paper, α, β, γ, δ are supposed to be propositional logic formulas, which are appropriate for representing concepts. There exists one approach, developed over the last 15 years, where “α differs from β” is understood in terms of the negation of the material implication α → β. The paper investigates another view where “α differs from β” is interpreted in terms of transformations where some variables become false, some variables become true, and some variables become irrelevant. Both approaches satisfy the three basic postulates of analogical proportions (reflexivity, symmetry, and stability under central permutation), as well as other interesting properties such as transitivity and unicity of δ such that α:β::γ:δ. However, the two approaches depart from each other since they do not validate the same analogical proportions. In particular, when p,q,r are atoms the proportion p:(p∧r)::q:(q∧r) holds in the new approach, while it fails to do so for the other. The new approach exhibits also a good behaviour with respect to integrity constraints. It is advocated that this makes it appropriate for handling analogy between concepts, while the other approach has proved to be fruitful for Boolean features-based representations. The paper provides a thorough analysis of the differences between the two approaches.
Hierarchical Average-Reward Linearly-Solvable Markov Decision Processes
Authors: Infante Guillermo , Jonsson Anders , Gómez Vicenç
Published: October 2024

We introduce a novel approach to hierarchical reinforcement learning for Linearly-solvable Markov Decision Processes (LMDPs) in the infinite-horizon average-reward setting. Unlike previous work, our approach allows learning low-level and high-level tasks simultaneously, without imposing limiting restrictions on the low-level tasks. Our method relies on partitions of the state space that create smaller subtasks that are easier to solve, and the equivalence between such partitions to learn more efficiently. We then exploit the compositionality of low-level tasks to exactly represent the value function of the high-level task. Experiments show that our approach can outperform flat average-reward reinforcement learning by one or several orders of magnitude.
Complex Multi-Ontology Alignment Through Geometric Operations on Language Embeddings
Authors: Silva Marta Contreiras , Faria Daniel , Pesquita Catia
Published: October 2024

With knowledge graphs increasing in popularity, aligning and integrating them is paramount to ensure their usefulness and reusability. A key step in this process is ontology matching, whereby the semantic models of KGs are aligned into a single cohesive semantic backbone. While finding simple pairwise equivalences between entities in two ontologies is well addressed by state-of-the-art algorithms, finding more complex mappings that can include multiple entities from different ontologies is far from solved, despite their importance in ensuring a deep and meaningful integration of KGs. We propose a novel complex ontology matching approach that explores geometric operations over the shared semantic space afforded by large language models, enabling the discovery of complex mappings that are missed by purely lexical approaches. We evaluate our approach on several biomedical ontologies using partial reference alignments and manual expert validation. Our approach improves on the performance of a purely lexical approach while also increasing the coverage of complex multi-ontology alignments by 20 to 80%, which translates to a 97% coverage of the source ontologies. Moreover, the manual evaluation of the mappings produced by LLM shows that it achieves a high level of precision. This work demonstrates that the use of LLMs can improve on the performance of traditional lexical strategies.
A Practical Operational Semantics for Classical Planning in BDI Agents
Authors: Xu Mengwei , Lumley Tom , Fraga Pereira Ramon , Meneguzzi Felipe
Published: October 2024

Implementations of the Belief-Desire-Intention (BDI) architecture have a long tradition in the development of autonomous agent systems. However, most practical implementations of the BDI framework rely on a pre-defined plan library for decision-making, which places a significant burden on programmers, and still yields systems that may be brittle, struggling to achieve their goals in dynamic environments. This paper overcomes this limitation by introducing an operational semantics for BDI systems that rely on Classical Planning at run time to both cope with failures that were unforeseeable and synthesise new plans that were unspecified at design time. This semantics places particular emphasis on the interaction of the reasoning cycle and an underlying planning algorithm. We empirically demonstrate the practical feasibility and generality of such an approach in an implementation of this semantics within two popular BDI platforms together with in-depth computational evaluation.
Enhancing Node Representations for Real-World Complex Networks with Topological Augmentation
Authors: Zhao Xiangyu , Li Zehui , Shen Mingzhu , Stan Guy-Bart , Liò Pietro , Zhao Yiren
Published: October 2024

Graph augmentation methods play a crucial role in improving the performance and enhancing generalisation capabilities in Graph Neural Networks (GNNs). Existing graph augmentation methods mainly perturb the graph structures, and are usually limited to pairwise node relations. These methods cannot fully address the complexities of real-world large-scale networks, which often involve higher-order node relations beyond only being pairwise. Meanwhile, real-world graph datasets are predominantly modelled as simple graphs, due to the scarcity of data that can be used to form higher-order edges. Therefore, reconfiguring the higher-order edges as an integration into graph augmentation strategies lights up a promising research path to address the aforementioned issues. In this paper, we present Topological Augmentation (TopoAug), a novel graph augmentation method that builds a combinatorial complex from the original graph by constructing virtual hyperedges directly from the raw data. TopoAug then produces auxiliary node features by extracting information from the combinatorial complex, which are used for enhancing GNN performances on downstream tasks. We design three diverse virtual hyperedge construction strategies to accompany the construction of combinatorial complexes: (1) via graph statistics, (2) from multiple data perspectives, and (3) utilising multi-modality. Furthermore, to facilitate TopoAug evaluation, we provide 23 novel real-world graph datasets across various domains including social media, biology, and e-commerce. Our empirical study shows that TopoAug consistently and significantly outperforms GNN baselines and other graph augmentation methods, across a variety of application contexts, which clearly indicates that it can effectively incorporate higher-order node relations into the graph augmentation for real-world complex networks.
Cautious Calibration in Binary Classification
Authors: Allikivi Mari-Liis , Järve Joonas , Kull Meelis
Published: October 2024

Being cautious is crucial for enhancing the trustworthiness of machine learning systems integrated into decision-making pipelines. Although calibrated probabilities help in optimal decision-making, perfect calibration remains unattainable, leading to estimates that fluctuate between under- and overconfidence. This becomes a critical issue in high-risk scenarios, where even occasional overestimation can lead to extreme expected costs. In these scenarios, it is important for each predicted probability to lean towards underconfidence, rather than just achieving an average balance. In this study, we introduce the novel concept of cautious calibration in binary classification. This approach aims to produce probability estimates that are intentionally underconfident for each predicted probability. We highlight the importance of this approach in a high-risk scenario and propose a theoretically grounded method for learning cautious calibration maps. Through experiments, we explore and compare our method to various approaches, including methods originally not devised for cautious calibration but applicable in this context. We show that our approach is the most consistent in providing cautious estimates. Our work establishes a strong baseline for further developments in this novel framework.
Rethinking Domain Generalization from Perspective of Gradient Granularity
Authors: Zhou Yujie , Hu Haigen , Zhou Qianwei , Guan Qiu , Jiang Mingfeng
Published: October 2024

Domain generalization (DG) aims to enhance the ability of model learning from source domains to generalize to other unseen domains. Existing gradient-based methods focus on learning better domain-invariant features using gradients from multiple source domains, but do not consider the impact of gradient granularity on model training. In this paper, we rethink how to mitigate the gradient conflicting problem from an optimization perspective. The limitations of existing gradient-based methods are theoretically analyzed in terms of modification ratio and modification frequency, showing that gradient granularity is a key factor in ensuring correct modification of the gradient. To address this issue, a gradient modification method, called CorGrad, is proposed by layering and slicing refinement operations to increase the modification frequency and the modification ratio. It can better reduce domain-specific information so that the model can learn better domain-invariant features. Finally, extensive experiments are conducted to verify the effectiveness of the proposed CorGrad, and the results show that the proposed CorGrad can obtain competitive performance in five DG benchmarks, and an average performance of 60.4% can be obtained on the DomainBed when using ResNet18 as the backbone. The code is publicly available at https://github.com/libzwo/CorGrad.
Explorative Imitation Learning: A Path Signature Approach for Continuous Environments
Authors: Gavenski Nathan , Monteiro Juarez , Meneguzzi Felipe , Luck Michael , Rodrigues Odinaldo
Published: October 2024

Some imitation learning methods combine behavioural cloning with self-supervision to infer actions from state pairs. However, most rely on a large number of expert trajectories to increase generalisation and human intervention to capture key aspects of the problem, such as domain constraints. In this paper, we propose Continuous Imitation Learning from Observation (CILO), a new method augmenting imitation learning with two important features: (i) exploration, allowing for more diverse state transitions, requiring less expert trajectories and resulting in fewer training iterations; and (ii) path signatures, allowing for automatic encoding of constraints, through the creation of non-parametric representations of agents and expert trajectories. We compared CILO with a baseline and two leading imitation learning methods in five environments. It had the best overall performance of all methods in all environments, outperforming the expert in two of them.
RADAr: A Transformer-Based Autoregressive Decoder Architecture for Hierarchical Text Classification
Authors: Younes Yousef , Galke Lukas , Scherp Ansgar
Published: October 2024

Recent approaches in hierarchical text classification (HTC) rely on the capabilities of a pre-trained transformer model and exploit the label semantics and a graph encoder for the label hierarchy. In this paper, we introduce an effective hierarchical text classifier RADAr (A Transformer-based Autoregressive Decoder Architecture) that is based only on an off-the-shelf RoBERTa transformer to process the input and a custom autoregressive decoder with two decoder layers for generating the classification output. Thus, unlike existing approaches for HTC, the encoder of RADAr has no explicit encoding of the label hierarchy and the decoder solely relies on the samples’ label sequences observed during training. We demonstrate on three benchmark datasets that RADAr achieves results competitive to the state of the art with less training and inference time. Our model consistently performs better when organizing the label sequences from children to parents versus the inverse, as done in existing HTC approaches. Our experiments show that neither the label semantics nor an explicit graph encoder for the hierarchy is needed. This has strong practical implications for HTC as the architecture has fewer requirements and provides a speed-up by a factor of 2 at inference time. Moreover, training a separate decoder from scratch in conjunction with fine-tuning the encoder allows future researchers and practitioners to exchange the encoder part as new models arise. The source code is available at https://github.com/yousef-younes/RADAr.
Resilient Graph Neural Networks: A Coupled Dynamical Systems Approach
Authors: Eliasof Moshe , Murari Davide , Sherry Ferdia , Schönlieb Carola-Bibiane
Published: October 2024

Graph Neural Networks (GNNs) have established themselves as a key component in addressing diverse graph-based tasks. Despite their notable successes, GNNs remain susceptible to input perturbations in the form of adversarial attacks. This paper introduces an innovative approach to fortify GNNs against adversarial perturbations through the lens of coupled dynamical systems. Our method introduces graph neural layers based on differential equations with contractive properties, which, as we show, improve the robustness of GNNs. A distinctive feature of the proposed approach is the simultaneous learned evolution of both the node features and the adjacency matrix, yielding an intrinsic enhancement of model robustness to perturbations in the input features and the connectivity of the graph. We mathematically derive the underpinnings of our novel architecture and provide theoretical insights to reason about its expected behavior. We demonstrate the efficacy of our method through numerous real-world benchmarks, reading on par or improved performance compared to existing methods.
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning
Authors: Corbucci Luca , Heikkilä Mikko A. , Noguero David Solans , Monreale Anna , Kourtellis Nicolas
Published: October 2024

Training and deploying Machine Learning models that simultaneously adhere to principles of fairness and privacy while ensuring good utility poses a significant challenge. The interplay between these three factors of trustworthiness is frequently underestimated and remains insufficiently explored. Consequently, many efforts focus on ensuring only two of these factors, neglecting one in the process. The decentralization of the datasets and the variations in distributions among the clients exacerbate the complexity of achieving this ethical trade-off in the context of Federated Learning (FL). For the first time in FL literature, we address these three factors of trustworthiness. We introduce PUFFLE, a high-level parameterised approach that can help in the exploration of the balance between utility, privacy, and fairness in FL scenarios. We prove that PUFFLE can be effective across diverse datasets, models, and data distributions, reducing the model unfairness up to 75%, with a maximum reduction in the utility of 17% in the worst-case scenario, while maintaining strict privacy guarantees during the FL training.