bluuebunny/crossref_metadata_2025 huggingface dataset:
Training on making environmentally friendly soap from soap nuts and bengle essential oil in Gajahmungkur District
Authors: Januarti Ika Buana , Rahmawati Laily Mega
Published: October 2024

The UNISULLA Pharmacy service team collaborated with the PAC GP Ansor of Gajahmungkur District to conduct community service activities aimed at enhancing public awareness of making environmentally friendly laundry soap from soap nuts and bengle essential oil. The activity was conducted in several stages, including a pre-test, education, training, and a post-test. This activity successfully increased participants knowledge and skills in making natural soap significantly by 100%. This initiative benefits the environment and has the potential to improve community welfare through the development of small and medium enterprises based on local resources.
Fortaleciendo la participación comunitaria: el rol del trabajo social en la promoción del asociativismo municipal y la contraloría social
Authors: Sanhueza Cisterna Adriana , García Moreno Cristina
Published: October 2024

Introducción: La participación ciudadana es esencial para una sociedad democrática y equitativa. En Chile, aunque se reconoce la importancia de promoverla, el país enfrenta una profunda crisis de participación. El trabajo social juega un papel crucial en el fortalecimiento de las capacidades individuales y colectivas para una ciudadanía activa. Este estudio examina el papel del Trabajo Social en la promoción de la participación ciudadana en Chile, cuestionando los actuales liderazgos locales y las formas limitadas de participación que se proponen tanto en lo local como en lo nacional. Metodología: La investigación se basó en un enfoque cualitativo y etnográfico, realizando 15 entrevistas en profundidad a trabajadoras/es sociales. Resultados: En el ámbito local, se identifican oportunidades especiales para fortalecer la participación ciudadana. Discusión: Aunque existe una relativa escasez de estudios sobre el desarrollo de estos temas, esta investigación propone, a partir de lo existente, un punto de partida para la implementación de métodos renovados de participación. Conclusiones: El estudio sugiere dos metodologías estratégicas para mejorar la participación y, por ende, la democracia: el asociativismo municipal y la contraloría social, ambas buscan revitalizar la participación ciudadana y promover una mayor equidad y un potenciamiento a la democracia en la sociedad chilena.
Шляхи попередження та виправлення помилок учнів при розв’язуванні рівнянь в курсі алгебри 7-9 класів
Authors: Наконечна Людмила , Наконечний Ярослав , Поліщук Катерина
Published: October 2024

The article substantiates the need for the activities of mathematics teachers aimed at preventing and correcting typical student errors, identifies typical student errors in the process of solving equations in the elementary school algebra course, analyzes the ways to prevent and eliminate these errors, considers the possibilities of modern online platforms for increasing preventive activities the teacher, examples of the use of specific online platforms in the process of studying quadratic equations are demonstrated.
Явные численно реализуемые формулы для операторов Пуанкаре–Стеклова
Authors: Демидов А. С. , Самохин А. С.
Published: October 2024

Представлены явные численно реализуемые формулы для операторов Пуанкаре–Стеклова, таких как операторы Дирихле–Неймана, Дирихле–Робена, Робена1–Робена2, Гринберга–Майергойза, относящихся к двумерному уравнению Лапласа. Эти формулы базируются на лемме об однолистном изометрическом отображении замкнутой аналитической кривой на окружность. Численные результаты для областей с весьма сложной геометрией получены для нескольких тестовых гармонических функций для операторов Дирихле–Неймана и Дирихле–Робена. Библ. 9. Фиг. 9.
Implementasi Pembelajaran PAI Melalui Aplikasi Zoom Meeting (Studi Kasus Kelas 6 SDN 2 Rancasari Indramayu)
Authors: Faozie Rakhman , Rusydi Ibnu , Romdhoni Abdul Aziz
Published: October 2024

Penelitian ini bertujuan untuk menginvestigasi implementasi pembelajaran Pendidikan Agama Islam (PAI) melalui aplikasi Zoom Meeting pada kelas 6 SDN 2 Rancasari Indramayu. Metode yang digunakan dalam penelitian ini adalah studi kasus kualitatif deskriptif. Peneliti menggunakan aplikasi Zoom Meeting sebagai platform untuk melaksanakan pembelajaran PAI secara online. Hasil penelitian menunjukkan bahwa implementasi pembelajaran PAI melalui aplikasi Zoom Meeting memberikan beberapa manfaat. Pertama, aplikasi ini memungkinkan guru untuk berinteraksi secara langsung dengan siswa, meskipun dalam bentuk virtual. Guru dapat memberikan penjelasan, menjawab pertanyaan, dan memberikan umpan balik secara real-time. Kedua, aplikasi ini memfasilitasi kolaborasi antara siswa. Mereka dapat berdiskusi, berbagi ide, dan bekerja sama dalam tugas-tugas kelompok. Ketiga, aplikasi ini memungkinkan rekaman pembelajaran, sehingga siswa dapat mengakses kembali materi pembelajaran jika diperlukan. Namun, penelitian ini juga mengidentifikasi beberapa tantangan dalam implementasi pembelajaran PAI melalui aplikasi Zoom Meeting. Beberapa siswa mengalami kesulitan dalam mengakses internet atau memiliki keterbatasan perangkat yang memadai. Selain itu, interaksi langsung antara guru dan siswa dapat terhambat oleh kualitas koneksi internet yang buruk atau masalah teknis lainnya. Dalam kesimpulannya, implementasi pembelajaran PAI melalui aplikasi Zoom Meeting pada kelas 6 SDN 2 Rancasari Indramayu memiliki potensi untuk meningkatkan interaksi antara guru dan siswa, memfasilitasi kolaborasi siswa, dan memungkinkan akses materi pembelajaran yang fleksibel. Namun, tantangan terkait akses internet dan masalah teknis perlu diatasi agar pembelajaran online dapat berjalan dengan lancar.
TimeMachine: A Time Series is Worth 4 Mambas for Long-Term Forecasting
Authors: Ahamed Md Atik , Cheng Qiang
Published: October 2024

Long-term time-series forecasting remains challenging due to the difficulty in capturing long-term dependencies, achieving linear scalability, and maintaining computational efficiency. We introduce TimeMachine, an innovative model that leverages Mamba, a state-space model, to capture long-term dependencies in multivariate time series data while maintaining linear scalability and small memory footprints. TimeMachine exploits the unique properties of time series data to produce salient contextual cues at multi-scales and leverage an innovative integrated quadruple-Mamba architecture to unify the handling of channel-mixing and channel-independence situations, thus enabling effective selection of contents for prediction against global and local contexts at different scales. Experimentally, TimeMachine achieves superior performance in prediction accuracy, scalability, and memory efficiency, as extensively validated using benchmark datasets. Code availability: https://github.com/Atik-Ahamed/TimeMachine
Individual Fairness with Group Constraints in Graph Neural Networks
Authors: Wang Zichong , Ulloa David , Yu Tongjia , Rangaswami Raju , Yap Roland , Zhang Wenbin
Published: October 2024

Graph Neural Networks (GNNs) have demonstrated remarkable capabilities across various domains. Despite the successes of GNN deployment, their utilization often reflects societal biases, which critically hinder their adoption in high-stake decision-making scenarios such as online clinical diagnosis, financial crediting, etc. Numerous efforts have been made to develop fair GNNs but they typically concentrate on either individual or group fairness, overlooking the intricate interplay between the two, resulting in the enhancement of one, usually at the cost of the other. In addition, existing individual fairness approaches using a ranking perspective fail to identify discrimination in the ranking. This paper introduces two innovative notions dealing with individual graph fairness and group-aware individual graph fairness, aiming to more accurately measure individual and group biases. Our Group Equality Individual Fairness (GEIF) framework is designed to achieve individual fairness while equalizing the level of individual fairness among subgroups. Preliminary experiments on several real-world graph datasets demonstrate that GEIF outperforms state-of-the-art methods by a significant margin in terms of individual fairness, group fairness, and utility performance.
FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph Neural Network
Authors: Du Boyu , Zhou Jingya , Liu Ling , She Xiaolong
Published: October 2024

Graph Neural Networks (GNNs) have become an important graph feature learning paradigm that is extensively applied to graph inference tasks. However, GNNs still have limitations in some aspects such as representation capability, interpretability, etc. To this end, this paper introduces a novel hybrid Fuzzy-Logic Graph Neural Network (FL-GNN) that synergistically combines Fuzzy Neural Network (FNN) and GNN to effectively capture and aggregate local information flows within the graph. FL-GNN exhibits three distinctive features. First, we incorporate a specialized structure fuzzy rule to enhance FL-GNN’s graph inference capability, surpassing representative GNN models. Second, we augment the interpretability of FL-GNN by integrating analytical exploration methods from two perspectives: Fuzzy Inference System and Message Passing Algorithm (MPA). Lastly, we refine the structure of FL-GNN based on MPA to optimize its calculation complexity and consequently improve learning efficiency. Experimental results show that FL-GNN outperforms existing representative graph neural networks for graph inference tasks.
Interpretable Graph Neural Networks for Tabular Data
Authors: Alkhatib Amr , Ennadir Sofiane , Boström Henrik , Vazirgiannis Michalis
Published: October 2024

Data in tabular format is frequently occurring in real-world applications. Graph Neural Networks (GNNs) have recently been extended to effectively handle such data, allowing feature interactions to be captured through representation learning. However, these approaches essentially produce black-box models, in the form of deep neural networks, precluding users from following the logic behind the model predictions. We propose an approach, called IGNNet (Interpretable Graph Neural Network for tabular data), which constrains the learning algorithm to produce an interpretable model, where the model shows how the predictions are exactly computed from the original input features. A large-scale empirical investigation is presented, showing that IGNNet is performing on par with state-of-the-art machine-learning algorithms that target tabular data, including XGBoost, Random Forests, and TabNet. At the same time, the results show that the explanations obtained from IGNNet are aligned with the true Shapley values of the features without incurring any additional computational overhead.
Pessimistic Off-Policy Optimization for Learning to Rank
Authors: Cief Matej , Kveton Branislav , Kompan Michal
Published: October 2024

Off-policy learning is a framework for optimizing policies without deploying them, using data collected by another policy. In recommender systems, this is especially challenging due to the imbalance in logged data: some items are recommended and thus logged more frequently than others. This is further perpetuated when recommending a list of items, as the action space is combinatorial. To address this challenge, we study pessimistic off-policy optimization for learning to rank. The key idea is to compute lower confidence bounds on parameters of click models and then return the list with the highest pessimistic estimate of its value. This approach is computationally efficient, and we analyze it. We study its Bayesian and frequentist variants and overcome the limitation of unknown prior by incorporating empirical Bayes. To show the empirical effectiveness of our approach, we compare it to off-policy optimizers that use inverse propensity scores or neglect uncertainty. Our approach outperforms all baselines and is both robust and general.
Merit-Based Fair Combinatorial Semi-Bandit with Unrestricted Feedback Delays
Authors: Chen Ziqun , Cai Kechao , Chen Zhuoyue , Zhang Jinbei , Lui John C.S.
Published: October 2024

We study the stochastic combinatorial semi-bandit problem with unrestricted feedback delays under merit-based fairness constraints. This is motivated by applications such as crowdsourcing, and online advertising, where immediate feedback is not immediately available and fairness among different choices (or arms) is crucial. We consider two types of unrestricted feedback delays: reward-independent delays where the feedback delays are independent of the rewards, and reward-dependent delays where the feedback delays are correlated with the rewards. Furthermore, we introduce merit-based fairness constraints to ensure a fair selection of the arms. We define the reward regret and the fairness regret and present new bandit algorithms to select arms under unrestricted feedback delays based on their merits. We prove that our algorithms all achieve sublinear expected reward regret and expected fairness regret, with a dependence on the quantiles of the delay distribution. We also conduct extensive experiments using synthetic and real-world data and show that our algorithms can fairly select arms with different feedback delays.
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection
Authors: Yang Hui-Yue , Chen Hui , Liu Lihao , Lin Zijia , Chen Kai , Wang Liejun , Han Jungong , Ding Guiguang
Published: October 2024

Unsupervised anomaly detection (AD) aims to train robust detection models using only normal samples, while can generalize well to unseen anomalies. Recent research focuses on a unified unsupervised AD setting in which only one model is trained for all classes, i.e., n-class-one-model paradigm. Feature-reconstruction-based methods achieve state-of-the-art performance in this scenario. However, existing methods often suffer from a lack of sufficient contextual awareness, thereby compromising the quality of the reconstruction. To address this issue, we introduce a novel Reconstruction as Sequence (RAS) method, which enhances the contextual correspondence during feature reconstruction from a sequence modeling perspective. In particular, based on the transformer technique, we integrate a specialized RASFormer block into RAS. This block enables the capture of spatial relationships among different image regions and enhances sequential dependencies throughout the reconstruction process. By incorporating the RASFormer block, our RAS method achieves superior contextual awareness capabilities, leading to remarkable performance. Experimental results show that our RAS significantly outperforms competing methods, well demonstrating the effectiveness and superiority of our method. Our code is available at https://github.com/Nothingtolose9979/RAS
A Data-Driven Defense Against Edge-Case Model Poisoning Attacks on Federated Learning
Authors: Purohit Kiran , Das Soumi , Bhattacharya Sourangshu , Rana Santu
Published: October 2024

Federated Learning systems are increasingly subjected to a multitude of model poisoning attacks from clients. Among these, edge-case attacks that target a small fraction of the input space are nearly impossible to detect using existing defenses, leading to a high attack success rate. We propose an effective defense using an external defense dataset, which provides information about the attack target. The defense dataset contains a mix of poisoned and clean examples, with only a few known to be clean. The proposed method, DataDefense, uses this dataset to learn a poisoned data detector model which marks each example in the defense dataset as poisoned or clean. It also learns a client importance model that estimates the probability of a client update being malicious. The global model is then updated as a weighted average of the client models’ updates. The poisoned data detector and the client importance model parameters are updated using an alternating minimization strategy over the Federated Learning rounds. Extensive experiments on standard attack scenarios demonstrate that DataDefense can defend against model poisoning attacks where other state-of-the-art defenses fail. In particular, DataDefense is able to reduce the attack success rate by at least ∼ 40% on standard attack setups and by more than 80% on some setups. Furthermore, DataDefense requires very few defense examples (as few as five) to achieve a near-optimal reduction in attack success rate.
Classifier Guidance Enhances Diffusion-Based Adversarial Purification by Preserving Predictive Information
Authors: Zhang Mingkun , Li Jianing , Chen Wei , Guo Jiafeng , Cheng Xueqi
Published: October 2024

Adversarial purification is one of the promising approaches to defend neural networks against adversarial attacks. Recently, methods utilizing diffusion probabilistic models have achieved great success for adversarial purification in image classification tasks. However, such methods fall into the dilemma of balancing the needs for noise removal and information preservation. This paper points out that existing adversarial purification methods based on diffusion models gradually lose sample information during the core denoising process, causing occasional label shift in subsequent classification tasks. As a remedy, we suggest to suppress such information loss by introducing guidance from the classifier confidence. Specifically, we propose Classifier-cOnfidence gUided Purification (COUP) algorithm, which purifies adversarial examples while keeping away from the classifier decision boundary. Experimental results show that COUP can achieve better adversarial robustness under strong attack methods.
TrustFed: Navigating Trade-offs Between Performance, Fairness, and Privacy in Federated Learning
Authors: Badar Maryam , Sikdar Sandipan , Nejdl Wolfgang , Fisichella Marco
Published: October 2024

As Federated Learning (FL) gains prominence in secure machine learning applications, achieving trustworthy predictions without compromising predictive performance becomes paramount. While Differential Privacy (DP) is extensively used for its effective privacy protection, yet its application as a lossy protection method can lower the predictive performance of the machine learning model. Also, the data being gathered from distributed clients in an FL environment often leads to class imbalance making traditional accuracy measure less reflective of the true performance of prediction model. In this context, we introduce a fairness-aware FL framework (TrustFed) based on Gaussian differential privacy and Multi-Objective Optimization (MOO), which effectively protects privacy while providing fair and accurate predictions. To the best of our knowledge, this is the first attempt towards achieving Pareto-optimal trade-offs between balanced accuracy and fairness in a federated environment while safeguarding the privacy of individual clients. The framework’s flexible design adeptly accommodates both statistical parity and equal opportunity fairness notions, ensuring its applicability in various FL scenarios. We demonstrate our framework’s effectiveness through comprehensive experiments on five real-world datasets. TrustFed consistently achieves comparable performance fairness tradeoff to the state-of-the-art (SoTA) baseline models while preserving the anonymization rights of users in FL applications.
Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension
Authors: Herde Marek , Lührs Lukas , Huseljic Denis , Sick Bernhard
Published: October 2024

Training with noisy class labels impairs neural networks’ generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by making memorizing false class labels more difficult. However, mixup neglects that multiple annotators, e.g., crowdworkers, typically provide class labels. Therefore, we propose an extension of mixup, which handles multiple class labels per instance while considering which class label originates from which annotator. Integrated into our multi-annotator classification framework annot-mix, it performs superiorly to eleven (mostly state-of-the-art) approaches in an evaluation study with eleven datasets comprising noisy class labels from either human or simulated annotators. Our code is publicly available through our GitHub repository at https://github.com/ies-research/multi-annotator-machine-learning/tree/annot-mix.
FenGe–An Interactive Framework for Improving the Utility of Deep Dune Segmentation in Geographical Tasks
Authors: Jiang Zheng , Lu Anqi , Wu Zifeng , Wang Wei , Zhang Gaowei , Hasi Eerdun , Wang Yi
Published: October 2024

Segmenting dunes from remote sensing landforms images with deep vision models is promising by freeing geographers from manual visual interpretation tasks, making them more concentrated on the essential tasks in solving desertification challenges. However, geographers have reported that automated segmentation results may be not satisfactory though achieving high accuracy, implying there are potential gaps between pixel-level metrics and the utility in downstream geographic tasks. Therefore, pixel-wise metrics may be not proper in evaluating the deep dune segmentation in the geography domain, arising the necessity to develop domain-specific, human-centered measurements for deep dune segmentation. This paper first proposes a novel measurement based on geographers’ subjective judgments, which allows the evaluation of the alignment between deep dune segmentation models and geographical utility. We design an interactive framework integrating multiagent reinforcement learning (MARL) with geographers’ domain knowledge to improve models’ utility in the domain of geography. Our extensive experiments show that (1) our framework enables the interactive domain knowledge integration in the model-building process, and thus (2) the dune segmentation model better aligns with geographical utility, which ultimately improves the effectiveness of dune segmentation. We have deployed the framework with a number of geographers to support their various tasks including dune segmentation as a component. The results demonstrate our framework’s capabilities.
Learning a Mini-Batch Graph Transformer via Two-Stage Interaction Augmentation
Authors: Li Wenda , Chen Kaixuan , Liu Shunyu , Zheng Tongya , Huang Wenjie , Song Mingli
Published: October 2024

Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computational efficiency and enhanced model robustness. However, existing methods for processing local information either rely on sampling or simple aggregation, which respectively result in the loss and squashing of critical neighbor information. Moreover, the limited number of nodes in each mini-batch restricts the model’s capacity to capture the global characteristic of the graph. In this paper, we propose LGMformer, a novel MGT model that employs a two-stage augmented interaction strategy, transitioning from local to global perspectives, to address the aforementioned bottlenecks. The local interaction augmentation (LIA) presents a neighbor-target interaction Transformer (NTIformer) to acquire an insightful understanding of the co-interaction patterns between neighbors and the target node, resulting in a locally effective token list that serves as input for the MGT. In contrast, global interaction augmentation (GIA) adopts a cross-attention mechanism to incorporate entire graph prototypes into the target node representation, thereby compensating for the global graph information to ensure a more comprehensive perception. To this end, LGMformer achieves the enhancement of node representations under the MGT paradigm. Experimental results related to node classification on the ten benchmark datasets demonstrate the effectiveness of the proposed method. Our code is available at https://github.com/l-wd/LGMformer.
Informed Spectral Normalized Gaussian Processes for Trajectory Prediction
Authors: Schlauch Christian , Wirth Christian , Klein Nadja
Published: October 2024

Prior parameter distributions provide an elegant way to represent prior expert knowledge for informed learning. Previous work has shown that using such informative priors to regularize probabilistic deep learning (DL) models increases their performance and data efficiency. However, commonly used sampling-based approximations for probabilistic DL models can be computationally expensive, requiring multiple forward passes and longer training times. Promising alternatives are compute efficient last layer kernel approximations like spectral normalized Gaussian processes (SNGPs). We propose a novel regularization-based continual learning method for SNGPs, which enables the use of informative priors that represent prior knowledge learned from previous tasks. Our proposal builds upon well-established methods and requires no rehearsal memory or parameter expansion. We apply our informed SNGP model to the trajectory prediction problem in autonomous driving by integrating prior drivability knowledge. On two public datasets, we investigate its performance under diminishing training data and across locations, and thereby demonstrate an increase in data efficiency and robustness to location-transfers over non-informed and informed baselines.
HBIC: A Biclustering Algorithm for Heterogeneous Datasets
Authors: José-García Adán , Jacques Julie , Chauvet Clément , Sobanski Vincent , Dhaenens Clarisse
Published: October 2024

Biclustering is an unsupervised machine-learning approach aiming to cluster rows and columns simultaneously in a data matrix. Several biclustering algorithms have been proposed for handling numeric datasets. However, real-world data mining problems often involve heterogeneous datasets with mixed attributes. To address this challenge, we introduce a biclustering approach called HBIC, capable of discovering meaningful biclusters in complex heterogeneous data, including numeric, binary, and categorical data. The approach comprises two stages: bicluster generation and bicluster model selection. In the initial stage, several candidate biclusters are generated iteratively by adding and removing rows and columns based on the frequency of values in the original matrix. In the second stage, we introduce two approaches for selecting the most suitable biclusters by considering their size and homogeneity. Through a series of experiments, we investigated the suitability of our approach on a synthetic benchmark and in a biomedical application involving clinical data of systemic sclerosis patients. The evaluation comparing our method to existing approaches demonstrates its ability to discover high-quality biclusters from heterogeneous data. Our biclustering approach is a starting point for heterogeneous bicluster discovery, leading to a better understanding of complex underlying data structures.
A Neural Rewriting System to Solve Algorithmic Problems
Authors: Petruzzellis Flavio , Testolin Alberto , Sperduti Alessandro
Published: October 2024

Modern neural network architectures still struggle to learn algorithmic procedures that require to systematically apply compositional rules to solve out-of-distribution problem instances. In this work, we focus on formula simplification problems, a class of synthetic benchmarks used to study the systematic generalization capabilities of neural architectures. We propose a modular architecture designed to learn a general procedure for solving nested mathematical formulas by only relying on a minimal set of training examples. Inspired by rewriting systems, a classic framework in symbolic artificial intelligence, we include in the architecture three specialized and interacting modules: the Selector, trained to identify solvable sub-expressions; the Solver, mapping sub-expressions to their values; and the Combiner, replacing sub-expressions in the original formula with the solution provided by the Solver. We benchmark our system against the Neural Data Router, a recent model specialized for systematic generalization, and a state-of-the-art large language model (GPT-4) probed with advanced prompting strategies. We demonstrate that our approach achieves a higher degree of out-of-distribution generalization compared to these alternative approaches on three different types of formula simplification problems, and we discuss its limitations by analyzing its failures.
Quantifying a Causal Effect from a CPDAG with Targeted Exogenous Causal Knowledge
Authors: Hadj Ali Mahdi , Le Biannic Yann , Wuillemin Pierre-Henri
Published: October 2024

Machine Learning models are getting more accurate, yet their complexity and opacity are also increasing. Explainable AI improved their general interpretability by quantifying the contributions of input features to predictions. Despite these advancements, practitioners still seek to gain causal insights into the underlying data-generating mechanisms. To this end, a possible solution is to rely on classical probabilistic causal analysis, which offers tools to quantify causal effects. However, causal analysis assumes a sufficient knowledge of causal structure, which is often unreachable from data alone. Indeed, causal discovery algorithms produce, at most, partial causal structures, namely Completed Partially Directed Acyclic Graphs (CPDAG). The conventional approach involves fully orienting the structure with exogenous causal knowledge through expert interaction or real-world experiments. In this paper, we focus on quantifying a specific total causal effect. Within this context, we emphasize that a partial structure can be sufficient to answer the query, and can be reached by different sequences of additional causal knowledge. Whether coming from an expert or an experiment, each addition has a cost difficult to assess a priori. The contribution of this paper is twofold: given a CPDAG and a specific query, we identify a set of irrelevant edges, and we introduce an algorithm for ranking the remaining informative edges, providing a guide to iteratively obtain a partial structure sufficient for resolving the query. Simulations show that these two contributions significantly reduce the number of requests for exogenous causal information, corroborating the feasibility of a causal impact quantification with very limited exogenous information.
Exploring Large Language Models Text Style Transfer Capabilities
Authors: Li Weijie , Gu Zhentao , Fan Xiaochao , Deng Wenjun , Yang Yong , Zhao Xinyuan , Diao Yufeng , Yang Liang
Published: October 2024

The emergence of Large Language Models (LLMs) provides a new solution to text generation tasks that involve high complexity, such as text style transfer (TST) tasks. However, previous studies have not fully explored the TST capabilities of different LLMs, and have faced issues with a lack of uniform standards in the human evaluation stage. This makes the results of human evaluation difficult to reproduce and less credible. To address this, this paper designs a prompt template to guide the cutting-edge LLMs to perform effective text style transfer and carries out an in-depth comparative analysis of various small-scale language models. In the stage of human evaluation, this paper eschews the conventional rating system, opting instead for a comparative human assessment methodology, which we refer to as duel-ranking. This method determines the relative ranking of models through mutual comparison, serving as an alternative to direct scoring. Detailed evaluation instructions are provided herein, to enhance the reproducibility of this method and ensure consistency throughout the evaluation process. This manual evaluation process reveals that GPT-3.5 and GPT-4 exhibit excellent performance in the TST tasks.
Temporal Elections: Welfare, Strategyproofness, and Proportionality
Authors: Elkind Edith , Neoh Tzeh Yuan , Teh Nicholas
Published: October 2024

We investigate a model of sequential decision-making where a single alternative is chosen at each round. We focus on two objectives—utilitarian welfare (UTIL) and egalitarian welfare (EGAL)—and consider the computational complexity of the associated maximization problems, as well as their compatibility with strategyproofness and proportionality. We observe that maximizing UTIL is easy, but the corresponding decision problem for EGAL is NP-complete even in restricted cases. We complement this hardness result for EGAL with parameterized complexity analysis and an approximation algorithm. Additionally, we show that, while a mechanism that outputs a UTIL outcome is strategyproof, all deterministic mechanisms for computing EGAL outcomes fail a very weak variant of strategyproofness, called non-obvious manipulability (NOM). However, we show that when agents have non-empty approval sets at each timestep, choosing an EGAL-maximizing outcome while breaking ties lexicographically satisfies NOM. Regarding proportionality, we prove that a proportional (PROP) outcome can be computed efficiently, but finding an outcome that maximizes UTIL while guaranteeing PROP is NP-hard. We also derive upper and lower bounds on the price of proportionality with respect to UTIL and EGAL.
Evolutionary Reinforcement Learning via Cooperative Coevolution
Authors: Hu Chengpeng , Liu Jialin , Yao Xin
Published: October 2024

Recently, evolutionary reinforcement learning has obtained much attention in various domains. Maintaining a population of actors, evolutionary reinforcement learning utilises the collected experiences to improve the behaviour policy through efficient exploration. However, the poor scalability of genetic operators limits the efficiency of optimising high-dimensional neural networks. To address this issue, this paper proposes a novel cooperative coevolutionary reinforcement learning (CoERL) algorithm. Inspired by cooperative coevolution, CoERL periodically and adaptively decomposes the policy optimisation problem into multiple subproblems and evolves a population of neural networks for each of the subproblems. Instead of using genetic operators, CoERL directly searches for partial gradients to update the policy. Updating policy with partial gradients maintains consistency between the behaviour spaces of parents and offspring across generations. The experiences collected by the population are then used to improve the entire policy, which enhances the sampling efficiency. Experiments on six benchmark locomotion tasks demonstrate that CoERL outperforms seven state-of-the-art algorithms and baselines. Ablation study verifies the unique contribution of CoERL’s core ingredients.
Complexity and Approximation Schemes for Social Welfare Maximization in the High-Multiplicity Setting
Authors: Nguyen Trung Thanh , Elbassioni Khaled , Rothe Jörg
Published: October 2024

We study the social welfare maximization problem in the high-multiplicity setting where agents and/or items are available in multiple types, provided that the numbers of types are small. We focus on the egalitarian and Nash social welfare maximization problems, and show that they are NP-hard even when the number of item types is a constant. Furthermore, we present two polynomial-time approximation schemes (PTAS), one for egalitarian social welfare with two item types, and one for Nash social welfare with any constant number of agent types. The first PTAS can be applied to the unrelated machine scheduling problem, thus partially solving an open question raised by Jansen and Maack in 2019. The second PTAS significantly improves upon the existing PTAS for identical agents.
Learning Brave Assumption-Based Argumentation Frameworks via ASP
Authors: De Angelis Emanuele , Proietti Maurizio , Toni Francesca
Published: October 2024

Assumption-based Argumentation (ABA) is advocated as a unifying formalism for various forms of non-monotonic reasoning, including logic programming. It allows capturing defeasible knowledge, subject to argumentative debate. While, in much existing work, ABA frameworks are given up-front, in this paper we focus on the problem of automating their learning from background knowledge and positive/negative examples. Unlike prior work, we newly frame the problem in terms of brave reasoning under stable extensions for ABA. We present a novel algorithm based on transformation rules (such as Rote Learning, Folding, Assumption Introduction and Fact Subsumption) and an implementation thereof that makes use of Answer Set Programming. Finally, we compare our technique to state-of-the-art ILP systems that learn defeasible knowledge.
Fairness in Repeated House Allocation
Authors: Micheel Karl Jochen , Wilczynski Anaëlle
Published: October 2024

This article considers a house allocation setting–where exactly one object has to be assigned to each agent–in a repeated context, where the same allocation problem is decided a fixed given number of times, while taking previous decisions into account. Since fairness can be rarely achieved in a one-shot decision, we study whether fairness over time can be reached. In particular, we use several fairness criteria adapted to this particular repeated house allocation setting and investigate whether they can be satisfied. While we show that most related decision problems are computationally hard in general, we identify restricted positive cases.
Complexity Results and Algorithms for Manipulation and Bribery in Judgment Aggregation
Authors: Conati Ari , Niskanen Andreas , de Haan Ronald , Järvisalo Matti
Published: October 2024

The study of limits of strategic behavior in collective decision making is a central topic in computational social choice. Focusing on judgment aggregation, we provide complexity results and algorithms for manipulation and bribery under various aggregation rules. Specifically, we show that manipulation and bribery are complete for the second level of the Polynomial Hierarchy and detail aggregation-rule-specific strong refinements for effective counterexample-guided abstraction refinement algorithms based on iterative calls to a maximum satisfiability solver for both manipulation and bribery. We provide an open-source implementation of the approach and empirically evaluate its performance on standard PrefLib datasets, showing that the strong refinement strategies developed in this work enable scaling up to solving more instances.
QFMTS: Generating Query-Focused Summaries over Multi-Table Inputs
Authors: Zhang Weijia , Pal Vaishali , Huang Jia-Hong , Kanoulas Evangelos , de Rijke Maarten
Published: October 2024

Table summarization is a crucial task aimed at condensing information from tabular data into concise and comprehensible textual summaries. However, existing approaches often fall short of adequately meeting users’ information and quality requirements and tend to overlook the complexities of real-world queries. In this paper, we propose a novel method to address these limitations by introducing query-focused multi-table summarization. Our approach, which comprises a table serialization module, a summarization controller, and a large language model (LLM), utilizes textual queries and multiple tables to generate query-dependent table summaries tailored to users’ information needs. To facilitate research in this area, we present a comprehensive dataset specifically tailored for this task, consisting of 4,909 query-summary pairs, each associated with multiple tables. Through extensive experiments using our curated dataset, we demonstrate the effectiveness of our proposed method compared to baseline approaches. Our findings offer insights into the challenges of complex table reasoning for precise summarization, contributing to the advancement of research in query-focused multi-table summarization.
Machine Learning for Quantifier Selection in cvc5
Authors: Jakubův Jan , Janota Mikoláš , Piepenbrock Jelle , Urban Josef
Published: October 2024

In this work we considerably improve the state-of-the-art SMT solving on first-order quantified problems by efficient machine learning guidance of quantifier selection. Quantifiers represent a significant challenge for SMT and are technically a source of undecidability. In our approach, we train an efficient machine learning model that informs the solver which quantifiers should be instantiated and which not. Each quantifier may be instantiated multiple times and the set of the active quantifiers changes as the solving progresses. Therefore, we invoke the ML predictor many times, during the whole run of the solver. To make this efficient, we use fast ML models based on gradient boosted decision trees. We integrate our approach into the state-of-the-art cvc5 SMT solver and show a considerable increase of the system’s holdout-set performance after training it on a large set of first-order problems collected from the Mizar Mathematical Library.
EEG-Based fMRI Digital Twin: Towards a Cheap and Ecological Approach to Measure Subcortical Brain Activity
Authors: Dagaev Nikolay , Semenkov Ilia , Ossadtchi Alexei
Published: October 2024

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are the two most commonly used non-invasive methods for studying brain function, having different but complementary strengths: high temporal resolution of the former and high spatial resolution of the latter. Crucially, fMRI is vital for studying subcortical areas, as those are practically out of reach for EEG. At the same time, EEG is cost-effective and, thus, preferable to fMRI if comparable information can be extracted. Here we present an EEG-to-fMRI neural network with an interpretable module for feature extraction. Using the EEG-fMRI dataset, we show that our model allows us to predict the detailed resting state Blood Oxygenation Level Dependent (BOLD) activity of seven bilaterally symmetric subcortical structures solely from multichannel EEG data. Preliminary results reported here show a performance level significantly above chance and exceeding the state-of-the-art accuracy typically reported for a single structure such as the amygdala or striatum. These findings pave the road toward the creation of low-cost mobile scanners of subcortical activity with improved usability, EEG-based fMRI digital twin technology, with a broad range of applications – from fundamental neuroscience through diagnostics to neurorehabilitation and affective neurointerfaces. The demo video is presented in https://youtu.be/IOOwb7Wt2sY.
EquinorQA: Large Language Models for Question Answering Over Proprietary Data
Authors: Garigliotti Darío , Johansen Bjarte , Vigerust Kallestad Jakob , Cho Seong-Eun , Ferri Cèsar
Published: October 2024

Large Language Models (LLMs) have become the state-of-the-art technology in a variety of language understanding tasks. Accordingly, many commercial organizations have been increasingly trying to integrate LLMs in multiple areas of their production and analytics. A typical scenario is the need for answering questions over a domain-specific, private collection of documents, such that the answer is supported by evidence clearly referenced from those documents. The Retrieval-Augmented Generation (RAG) framework has been recently used by many applications for this kind of scenarios, as it intuitively bridges dedicated data collections and state-of-the-art generative models. Yet, LLMs are known to present data contamination, a phenomenon in which their performance on evaluation data relevant to a task is influenced by said data being already incorporated to the LLM during training phase. In this paper, we assess the performance of LLMs within the domain of Equinor, the largest energy company in Norway. Specifically, we address question answering with a RAG-based approach over a novel data collection not available for well-established LLMs during training, in order to study the effect of data contamination for this task. Beyond shedding light on LLM performance for a highly-demanded, realistic industrial scenario, we also analyze its potential impact for an ensemble of personas in Equinor with particular information needs and contexts.
A Uniformitarian Solution to the Appearance of Small-Bodied Hominins, Dwarfs, Pathologies, and Self-Domestication: Theories of New Discoveries
Authors: Caldararo Niccolo
Published: October 2024

A variety of new finds has produced a new set of species classified to the _Homo_ clade. Some of these appear to represent very small hominins with a variety of traits that are often seen in pathologies. The group of traits, however, do not associate with any known suite or any single condition, either genetic or induced by chemicals, radiation or other means. The first find of a hominin, the Neandertal, was considered a pathological modern human at first. Some claimed hominin finds were fakes that displayed so unusual traits, but whose status was supported by prominent scientists, that only a few questioned their authenticity. The lack of fit into the evolutionary sequence known at a given time has not been considered a single factor in rejecting a find, as the consideration of the Taung fossil eventually proved. Nevertheless, repeated finds of the same kind of fossil in different parts of the world, in different contexts by the same investigator does seem remarkable. Placing these finds in a phylogenetic setting is in order, but the distance and time frames are as challenging as is the problem of coincidence. Reference to pathological conditions producing several types of dwarfism are discussed in the context of these small-bodied finds. This paper proposes a hypothesis that a unique infection in several _Homo erectus _populations could offer a potential explanation for the developmental anomalies seen in small-bodied hominins.
The Evolution and Future of Virtual Reality in Geographic Information Systems
Authors: Arun Kumar Epuri
Published: October 2024

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