GenAI4ELab/papercli-papers huggingface dataset:
Expanding the Reach of Social Choice Theory
Authors: Warut Suksompong
Venue: IJCAI 2024 (main)

Abstract: The field of social choice theory investigates how individual preferences are aggregated to reach collective decisions. While traditional social choice addresses problems such as choosing a winning candidate based on voter rankings or fairly allocating resources among individuals with the same entitlement, the wide range of decision-making scenarios in real-world applications calls for an extension beyond these basic frameworks. In this paper, I present an overview of my efforts to expand the reach of social choice theory in the domains of fair division, voting, and tournaments. Furthermore, I discuss avenues and challenges of bringing the developed theory closer to practice.
Formal Argumentation in Symbolic AI
Authors: Markus Ulbricht
Venue: IJCAI 2024 (main)

Abstract: In the area of symbolic AI, researchers strive to develop techniques to teach machines (commonsense) reasoning. Human reasoning is often argumentative in its nature, and consequently, computational models of argumentation constitute a vibrant research area in symbolic AI. In this paper I describe my most significant contributions to the field spanning from general non-monotonic logics to formal argumentation.
Computational Argumentation: Reasoning, Dynamics, and Supporting Explainability
Authors: Johannes P. Wallner
Venue: IJCAI 2024 (main)

Abstract: This overview accompanies the author's Early Career Track presentation. We survey recent research and research agenda of the author, focusing on contributions in the area of computational argumentation. Contributions span from foundations of static and dynamic forms of argumentative reasoning and approaches to support explainability, e.g., analysis of the computational complexity of argumentative reasoning and algorithmic approaches.
Machine Unlearning: Challenges in Data Quality and Access
Authors: Miao Xu
Venue: IJCAI 2024 (main)

Abstract: Machine unlearning aims to remove specific knowledge from a well-trained machine learning model. This topic has gained significant attention recently due to the widespread adoption of machine learning models across various applications and the accompanying privacy, legal, and ethical considerations. During the unlearning process, models are typically presented with data that specifies which information should be erased and which should be retained. Nonetheless, practical challenges arise due to prevalent issues of data quality issues and access restrictions. This paper explores these challenges and introduces strategies to address problems related to unsupervised data, weakly supervised data, and scenarios characterized by zero-shot and federated data availability. Finally, we discuss related open questions, particularly concerning evaluation metrics, how the forgetting information is represented and delivered, and the unique challenges posed by large generative models.
REAVER: Real-time Earthquake Prediction with Attention-based Sliding-Window Spectrograms
Authors: Lotfy Abdel Khaliq; Sabine Janzen; Wolfgang Maass
Venue: IJCAI 2024 (main)

Abstract: Predicting earthquakes with precision remains an ongoing challenge in earthquake early warning systems (EEWS), that struggle with accuracy and fail to provide timely warnings for impending earthquakes. Recent efforts employing deep learning techniques have shown promise in overcoming these limitations. However, current methods lack the ability to capture subtle frequency changes indicative of seismic activity in real-time, limiting their effectiveness in EEWS. To address this gap, we propose REAVER, a novel approach for real-time prediction of P- and S-waves of earthquakes using attention-based sliding-window spectrograms. REAVER leverages Mel-Spectrogram signal representations to capture temporal frequency changes in seismic signals effectively. By employing an encoder-decoder architecture with attention mechanisms, REAVER accurately predicts the onset of P- and S-waves moments when an earthquake occurs. We benchmark the effectiveness of REAVER, showing its performance in terms of both accuracy and real-time prediction capabilities compared to existing methods. Additionally, we provide a web-based implementation of REAVER, allowing users to monitor seismic activity in real-time and analyze historical earthquake waveforms.
PyXAI: An XAI Library for Tree-Based Models
Authors: Gilles Audemard; Jean-Marie Lagniez; Pierre Marquis; Nicolas Szczepanski
Venue: IJCAI 2024 (main)

Abstract: PyXAI (Python eXplainable AI) is a Python library designed for providing explanations and cor- recting tree-based Machine Learning (ML) models. It is suited to decision trees, random forests, and boosted trees, when used for regression or classification tasks. In contrast to many model-agnostic approaches to XAI, PyXAI exploits the model it- self to generate explanations, ensuring them to be faithful. PyXAI includes several algorithms for the generation of explanations, which can be abductive or contrastive. PyXAI also includes algorithms for correcting tree-based models when their predictions conflict with pieces of user knowledge.
Design of a Data-driven Intervention Dashboard for SDG Localization
Authors: Pooja Bassin; Abraham G K; Srinath Srinivasa
Venue: IJCAI 2024 (main)

Abstract: The localization problem of the United Nations Sustainable Development Goals (SDGs) involves adopting strategies that are in tune with local conditions, to achieve a given SDG target. However, even within a given region, localized conditions may vary drastically. With increasing amounts of Open Government Data (OGD) being available, there is an opportunity to systematically address the localization problem by using predictive and prescriptive modeling techniques. This work presents a predictive and prescriptive modeling dashboard for the SDG indicator maternal deaths (MD) for the Indian state of Karnataka. The dashboard was created by examining a vast set of data points to focus on four factors that showed high correlations with that of MD. We then construct a multivariate linear regression model to showcase the differential impact that a given factor has on the indicator and identify prescribed values for different factors to achieve a given target value of the indicator. Finally, a budget allocation dashboard is also provided that helps policymakers allocate budgets to specific schemes to help operationalize these changes. This dashboard was built by combining data coming from five different OGD sources.
PyClause - Simple and Efficient Rule Handling for Knowledge Graphs
Authors: Patrick Betz; Luis Galárraga; Simon Ott; Christian Meilicke; Fabian Suchanek; Heiner Stuckenschmidt
Venue: IJCAI 2024 (main)

Abstract: Rule mining finds patterns in structured data such as knowledge graphs. Rules can predict facts, help correct errors, and yield explainable insights about the data. However, existing rule mining implementations focus exclusively on mining rules -- and not on their application. The PyClause library offers a rich toolkit for the application of the mined rules: from explaining facts to predicting links, scoring rules, and deducing query results. The library is easy to use and can handle substantial data loads.
GigaPevt: Multimodal Medical Assistant
Authors: Pavel Blinov; Konstantin Egorov; Ivan Sviridov; Nikolay Ivanov; Stepan Botman; Evgeniy Tagin; Stepan Kudin; Galina Zubkova; Andrey V. Savchenko
Venue: IJCAI 2024 (main)

Abstract: Building an intelligent and efficient medical assistant is still a challenging AI problem. The major limitation comes from the data modality scarceness, which reduces comprehensive patient perception. This demo paper presents GigaPevt, the first multimodal medical assistant that combines the dialog capabilities of large language models with specialized medical models. Such an approach shows immediate advantages in dialog quality and metric performance, with a 1.18% accuracy improvement in the question-answering task.
FD-UAD: Unsupervised Anomaly Detection Platform Based on Defect Autonomous Imaging and Enhancement
Authors: Yang Chang; Yuxuan Lin; Boyang Wang; Qing Zhao; Yan Wang; Wenqiang Zhang
Venue: IJCAI 2024 (main)

Abstract: In industrial quality control, detecting defects is essential. However, manual checks and machine vision encounter challenges in complex conditions, as defects vary among products made of different materials and shapes. We create FD-UAD, Unsupervised Anomaly Detection Platform Based on Defect Autonomous Imaging and Enhancement. It uses multi-sensor technology, combining RGB and infrared imaging, liquid lenses for adjustable focal lengths, and uses image fusion to capture multidimensional features. The system incorporates image restoration techniques such as enhancement, deblurring, denoising, and super-resolution, alongside unsupervised anomaly detection model for enhanced accuracy. FD-UAD is successfully used in a top diesel engine manufacturer, demonstrating its value in AI-enhanced industrial applications.
IntEr-HRI Competition: Intrinsic Error Evaluation during Human - Robot Interaction
Authors: Kartik Chari; Niklas Kueper; Su Kyoung Kim; Frank Kirchner; Elsa Andrea Kirchner
Venue: IJCAI 2024 (main)

Abstract: Reliable detection of human intentions from electroencephalogram (EEG) to improve human-robot interaction (HRI) has recently gained significant importance. To ensure safe and satisfactory interactions, implicit detection of erroneous behavior of robotic systems, particularly assistive devices, is essential. This can be achieved by detecting error-related potentials (ErrPs) in EEG, evoked by visual, tactile, or visuo-tactile stimuli. Of these, the ErrPs evoked tactilely with the help of a robot remains unexplored and has been the main focus of this competition. The task for participating teams was to develop robust AI models for continuous real-time classification of erroneous behavior of assistive robotic devices from the human EEG. Even though the competition results prove its feasibility, a performance gap (balanced accuracy and computation time) of more than 10% was observed between the offline and online classification of errors in real-world scenarios. In addition to the competitive AI models developed by the participating teams, this competition also contributed towards a one-of-its-kind open-access EEG and EMG dataset, a lossless live streaming solution for EEG data, and a novel quantitative metric for benchmarking online asynchronous EEG detection solutions.
Integrating LLM, VLM, and Text-to-Image Models for Enhanced Information Graphics: A Methodology for Accurate and Visually Engaging Visualizations
Authors: Chao-Ting Chen; Hen-Hsen Huang
Venue: IJCAI 2024 (main)

Abstract: This study presents an innovative approach to the creation of information graphics, where the accuracy of content and aesthetic appeal are of paramount importance. Traditional methods often struggle to balance these two aspects, particularly in complex visualizations like phylogenetic trees. Our methodology integrates the strengths of Large Language Models (LLMs), Vision Language Models (VLMs), and advanced text-to-image models to address this challenge. Initially, an LLM plans the layout and structure, employing Mermaid—a JavaScript-based tool that uses Markdown-like scripts for diagramming—to establish a precise and structured foundation. This structured script is crucial for ensuring data accuracy in the graphical representation. Following this, text-to-image models are employed to enhance the vector graphic generated by Mermaid, adding rich visual elements and enhancing overall aesthetic appeal. The integration of text-to-image models is a key innovation, enabling the creation of graphics that are not only informative but also visually captivating. Finally, a VLM performs quality control, ensuring that the visual enhancements align with the informational accuracy. This comprehensive approach effectively combines the accuracy of structured data representation, the creative potential of text-to-image models, and the validation capabilities of VLMs. The result is a new standard in information graphic creation, suitable for diverse applications ranging from education to scientific communication, where both information integrity and visual engagement are essential.
E-QGen: Educational Lecture Abstract-based Question Generation System
Authors: Mao-Siang Chen; An-Zi Yen
Venue: IJCAI 2024 (main)

Abstract: To optimize the preparation process for educators in academic lectures and associated question-and-answer sessions, this paper presents E-QGen, a lecture abstract-based question generation system. Given a lecture abstract, E-QGen generates potential student inquiries. The questions suggested by our system are expected to not only facilitate teachers in preparing answers in advance but also enable them to supply additional resources when necessary.
3D-FuM: Benchmarking 3D Molecule Learning with Functional Groups
Authors: Tingwei Chen; Jianpeng Chen; Dawei Zhou
Venue: IJCAI 2024 (main)

Abstract: Molecular graph representation learning plays a crucial role in various domains, such as drug discovery and chemical reaction prediction, where molecular graphs are typically depicted as 2D topological structures. However, recent insights highlight the critical role of 3D geometric information and functional groups in accurately predicting molecular properties, aspects often neglected in existing molecular graph benchmark datasets. To bridge the research gap, we introduce a comprehensive molecular learning benchmark named 3D-FUM, which incorporates both 3D geometric information and functional groups of a large number of molecules. 3D-FUM integrates 18 state-of-the-art algorithms and 19 evaluation metrics on three molecular learning tasks, including general molecule generation, conditional molecule generation, and property predictions. 3D-FUM, for the first time, take into consideration both 3D geometric information and molecular functional groups, which enables researchers and practitioners to effectively and impartially evaluate newly proposed methods in comparison to existing baselines across diverse datasets. Furthermore, we design a user interface for user-friendly interaction and development with the benchmark for evaluation metrics selection, parameter adjustment, and leaderboard comparison. To ensure accessibility and reproducibility, we opensource our benchmark 3D-FUM and experimental results at https://3dfunctiongroupmoleculedataset.github.io/3D-FuM/#/Home.
NegoLog: An Integrated Python-based Automated Negotiation Framework with Enhanced Assessment Components
Authors: Anıl Doğru; Mehmet Onur Keskin; Catholijn M. Jonker; Tim Baarslag; Reyhan Aydoğan
Venue: IJCAI 2024 (main)

Abstract: The complexity of automated negotiation research calls for dedicated, user-friendly research frameworks that facilitate advanced analytics, comprehensive loggers, visualization tools, and auto-generated domains and preference profiles. This paper introduces NegoLog, a platform that provides advanced and customizable analysis modules to agent developers for exhaustive performance evaluation. NegoLog introduces an automated scenario and tournament generation tool in its Web-based user interface so that the agent developers can adjust the competitiveness and complexity of the negotiations. One of the key novelties of the NegoLog is an individual assessment of preference estimation models independent of the strategies.
An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotations
Authors: Jonathan Erskine; Matt Clifford; Alexander Hepburn; Raul Santos Rodriguez
Venue: IJCAI 2024 (main)

Abstract: Human-Computer Interaction has been shown to lead to improvements in machine learning systems by boosting model performance, accelerating learning and building user confidence. In this work, we aim to alleviate the expectation that human annotators adapt to the constraints imposed by traditional labels by allowing for extra flexibility in the form that supervision information is collected. For this, we propose a human-machine learning interface for binary classification tasks which enables human annotators to utilise counterfactual examples to complement standard binary labels as annotations for a dataset. Finally we discuss the challenges in future extensions of this work.
Probabilistic Feature Matching for Fast Scalable Visual Prompting
Authors: Thomas Frick; Cezary Skura; Filip M. Janicki; Roy Assaf; Niccolo Avogaro; Daniel Caraballo; Yagmur G. Cinar; Brown Ebouky; Ioana Giurgiu; Takayuki Katsuki; Piotr Kluska; Cristiano Malossi; Haoxiang Qiu; Tomoya Sakai; Florian Scheidegger; Andrej Simeski; Daniel Yang; Andrea Bartezzaghi; Mattia Rigotti
Venue: IJCAI 2024 (main)

Abstract: In this work, we propose a novel framework for image segmentation guided by visual prompting which leverages the power of vision foundation models. Inspired by recent advancements in computer vision, our approach integrates multiple large-scale pretrained models to address the challenges of segmentation tasks with limited and sparsely annotated data interactively provided by a user. Our method combines a frozen feature extraction backbone with a scalable and efficient probabilistic feature correspondence (soft matching) procedure derived from Optimal Transport to couple pixels between reference and target images. Moreover, a pretrained segmentation model is harnessed to translate user scribbles into reference masks and matched target pixels into output target segmentation masks. This results in a framework that we name Softmatcher, a versatile and fast training-free architecture for image segmentation by visual prompting. We demonstrate the efficiency and scalability of Softmatcher for real-time interactive image segmentation by visual prompting and showcase it in diverse visual domains including technical visual inspection use cases.
Do You Remember the Future? Weak-to-Strong Generalization in 3D Object Detection
Authors: Alexander Gambashidze; Aleksandr Dadukin; Maxim Golyadkin; Maria Razzhivina; Ilya Makarov
Venue: IJCAI 2024 (main)

Abstract: This paper demonstrates a novel method for LiDAR-based 3D object detection, addressing major field challenges: sparsity and occlusion. Our approach leverages temporal point cloud sequences to generate frames that provide comprehensive views of objects from multiple angles. To address the challenge of generating these frames in real-time, we employ Knowledge Distillation within a Teacher-Student framework, allowing the Student model to emulate the Teacher’s advanced perception. We pioneered the application of weak-to-strong generalization in computer vision by training our Teacher model on enriched, object-complete data. In this demo, we showcase the exceptional quality of labels produced by the X-Ray Teacher on object-complete frames, showing our method distilling its knowledge to enhance object 3D detection models.
LLM-powered GraphQL Generator for Data Retrieval
Authors: Balaji Ganesan; Sambit Ghosh; Nitin Gupta; Manish Kesarwani; Sameep Mehta; Renuka Sindhgatta
Venue: IJCAI 2024 (main)

Abstract: GraphQL offers an efficient, powerful, and flexible alternative to REST APIs. However, application developers writing GraphQL clients need both technical and domain-specific expertise to reap its benefits, and avoid over-fetching or under-fetching data. Automated GraphQL generation has so far proven to be a hard problem because of complex GraphQL schema and lack of benchmark datasets. To address these issues, our work focuses on building an LLM-powered pipeline that can accept user requirements in natural language along with the complex GraphQL schema and automatically produce the GraphQL query needed to retrieve the necessary data. Automated GraphQL generation helps reduce entry barriers to application developers, broadening GraphQL adoption.
From 2D to 3D: AISG-SLA Visual Localization Challenge
Authors: Jialin Gao; Bill Ong; Darld Lwi; Zhen Hao Ng; Xun Wei Yee; Mun-Thye Mak; Wee Siong Ng; See-Kiong Ng; Hui Ying Teo; Victor Khoo; Georg Bökman; Johan Edstedt; Kirill Brodt; Clémentin Boittiaux; Maxime Ferrera; Stepan Konev
Venue: IJCAI 2024 (main)

Abstract: Research in 3D mapping is crucial for smart city applications, yet the cost of acquiring 3D data often hinders progress. Visual localization, particularly monocular camera position estimation, offers a solution by determining the camera's pose solely through visual cues. However, this task is challenging due to limited data from a single camera. To tackle these challenges, we organized the AISG–SLA Visual Localization Challenge (VLC) at IJCAI 2023 to explore how AI can accurately extract camera pose data from 2D images in 3D space. The challenge attracted over 300 participants worldwide, forming 50+ teams. Winning teams achieved high accuracy in pose estimation using images from a car-mounted camera with low frame rates. The VLC dataset is available for research purposes upon request via vlc-dataset@aisingapore.org.
Enhancing Manufacturing with AI-powered Process Design
Authors: Gianmarco Genalti; Gabriele Corbo; Tommaso Bianchi; Marco Missaglia; Luca Negri; Andrea Sala; Luca Magri; Giacomo Boracchi; Giovanni Miragliotta; Nicola Gatti
Venue: IJCAI 2024 (main)

Abstract: Manufacturing companies are experiencing a transformative journey, moving from labor-intensive processes to integrating cutting-edge technologies such as digitalization and AI. In this demo paper, we present a novel AI tool to enhance manufacturing processes. Remarkably, our work has been developed in collaboration with Agrati S.p.A., a worldwide leading company in the bolts manufacturing sector. In particular, we propose an AI-powered tool to address the problem of automatically generating the production cycle of a bolt. Currently, this decision-making task is performed by process engineers who spend several days to study, draw, and test multiple alternatives before finding the desired production cycle. We cast this task as a model-based planning problem, mapping bolt technical drawings and metal deformations to, potentially continuous, states and actions, respectively. Furthermore, we resort to computer vision tools and visual transformers to design efficient heuristics that make the search affordable in concrete applications. Agrati S.p.A.'s process engineers extensively validated our tool, and they are currently using it to support their work. To the best of our knowledge, ours is the first AI tool dealing with production cycle design in bolt manufacturing.
Plug-and-Play Unsupervised Fault Detection and Diagnosis for Complex Industrial Monitoring
Authors: Maksim Golyadkin; Maria Shtark; Petr Ivanov; Alexander Kozhevnikov; Leonid Zhukov; Ilya Makarov
Venue: IJCAI 2024 (main)

Abstract: Today industrial facilities are equipped with lots of sensors throughout all the production line for monitoring means. Gathered data can be used to detect and predict failures; however, manual labeling of large amounts of data for supervised learning is complicated. This paper introduces an innovative approach to unsupervised fault detection and diagnosis tailored for monitoring industrial chemical processes. We showcase the efficacy of our model using two publicly accessible datasets from the Tennessee Eastman Process, each containing various faults. Furthermore, we illustrate that by fine-tuning the model on a limited amount of labeled data, it achieves performance close to that of a state-of-the-art model trained on the entire dataset.