GenAI4ELab/papercli-papers huggingface dataset:
Reliable Neuro-Symbolic Abstractions for Planning and Learning
Authors: Naman Shah
Venue: IJCAI 2023 (main)

Abstract: Although state-of-the-art hierarchical robot planning algorithms allow robots to efficiently compute long-horizon motion plans for achieving user desired tasks, these methods typically rely upon environment-dependent state and action abstractions that need to be hand-designed by experts. On the other hand, non-hierarchical robot planning approaches fail to compute solutions for complex tasks that require reasoning over a long horizon. My research addresses these problems by proposing an approach for learning abstractions and developing hierarchical planners that efficiently use learned abstractions to boost robot planning performance and provide strong guarantees of reliability.
On Building a Semi-Automated Framework for Generating Causal Bayesian Networks from Raw Text
Authors: Solat J. Sheikh
Venue: IJCAI 2023 (main)

Abstract: The availability of a large amount of unstructured text has generated interest in utilizing it for future decision-making and developing strategies in various critical domains. Despite some progress, automatically generating accurate reasoning models from the raw text is still an active area of research. Furthermore, most proposed approaches focus on a specific do-main. As such, their suggested transformation methods are usually unreliable when applied to other domains. This research aims to develop a framework, SCANER (Semi-automated CAusal Network Extraction from Raw text), to convert raw text into Causal Bayesian Networks (CBNs). The framework will then be employed in various domains to demonstrate its utilization as a decision-support tool. The preliminary experiments have focused on three domains: political narratives, food insecurity, and medical sciences. The future focus is on developing BNs from political narratives and modifying them through various methods to reduce the level of aggressiveness or extremity in the narratives without causing conflict among the masses or countries.
Automated Content Moderation Using Transparent Solutions and Linguistic Expertise
Authors: Veronika Solopova
Venue: IJCAI 2023 (main)

Abstract: Since the dawn of Transformer-based models, the trade-off between transparency and accuracy has been a topical issue in the NLP community. Working towards ethical and transparent automated content moderation (ACM), my goal is to find where it is still relevant to implement linguistic expertise. I show that transparent statistical models based on linguistic knowledge can still be competitive, while linguistic features have many other useful applications.
Sample Efficient Paradigms for Personalized Assessment of Taskable AI Systems
Authors: Pulkit Verma
Venue: IJCAI 2023 (main)

Abstract: The vast diversity of internal designs of taskable black-box AI systems and their nuanced zones of safe functionality make it difficult for a layperson to use them without unintended side effects. The focus of my dissertation is to develop paradigms that would enable a user to assess and understand the limits of an AI system's safe operability. We develop a personalized AI assessment module that lets an AI system execute instruction sequences in simulators and answer queries about these executions. Our results show that such a primitive query-response capability is sufficient to efficiently derive a user-interpretable model of the system's capabilities in fully observable settings.
A Framework for Participatory Budgeting with Resource Pooling
Authors: Jeremy Vollen
Venue: IJCAI 2023 (main)

Abstract: Participatory budgeting (PB) is an implementation of direct democracy that allows members of a community to make collective budgeting decisions. However, existing PB processes rely upon a pre-defined central budget. We introduce a framework for pooling resources in addition to selecting projects, which we call PB with Resource Pooling. We motivate the key characteristics of this model and the basic properties we would like a mechanism to satisfy. We summarize results and discuss interesting questions related to our framework.
Object Detection in Real Open Environment
Authors: Xiaowei Zhao
Venue: IJCAI 2023 (main)

Abstract: Although object detection has achieved impressive progress and has a broad impact, it still encounters significant challenges in accurately detecting objects in open-world scenarios. The data in real-world open scenes often exhibit characteristics of limited annotations, such as very few annotated samples or even unknown classes with no annotations. Our studies mainly focus on applying detection to various open scenes and addressing the challenges of sparse samples and unknown classes. The comprehensive research aims to develop more powerful and efficient methods for object detection in the open world, making them more suitable for real-world applications.
SemFORMS: Automatic Generation of Semantic Transforms By Mining Data Science Code
Authors: Ibrahim Abdelaziz; Julian Dolby; Udayan Khurana; Horst Samulowitz; Kavitha Srinivas
Venue: IJCAI 2023 (main)

Abstract: Careful choice of feature transformations in a dataset can help predictive model performance, data understanding and data exploration. However, finding useful features is a challenge, and while recent Automated Machine Learning (AutoML) systems provide some limited automation for feature engineering or data exploration, it is still mostly done by humans. We demonstrate a system called SemFORMS (Semantic Transforms), which attempts to mine useful expressions for a dataset from access to a repository of code that may target the same dataset/similar dataset. In many enterprises, numerous data scientists often work on the same or similar datasets, but are largely unaware of each other's work. SemFORMS finds appropriate code from such a repository, and normalizes the code to be an actionable transform that can prepended into any AutoML pipeline. We demonstrate SemFORMS operating over example datasets from the OpenML benchmarks where it sometimes leads to significant improvements in AutoML performance.
Bias On Demand: Investigating Bias with a Synthetic Data Generator
Authors: Joachim Baumann; Alessandro Castelnovo; Andrea Cosentini; Riccardo Crupi; Nicole Inverardi; Daniele Regoli
Venue: IJCAI 2023 (main)

Abstract: Machine Learning (ML) systems are increasingly being adopted to make decisions that might have a significant impact on people's lives. Because these decision-making systems rely on data-driven learning, the risk is that they will systematically propagate the bias embedded in the data. To prevent harmful consequences, it is essential to comprehend how and where bias is introduced and possibly how to mitigate it. We demonstrate Bias on Demand, a framework to generate synthetic datasets with different types of bias, which is available as an open-source toolkit and as a pip package. We include a demo of our proposed synthetic data generator, in which we illustrate experiments on different scenarios to showcase the interconnection between biases and their effect on performance and fairness evaluations. We encourage readers to explore the full paper for a more detailed analysis.
SiWare: Contextual Understanding of Industrial Data for Situational Awareness
Authors: Anuradha Bhamidipaty; Elham Khabiri; Bhavna Agrawal; Yingjie Li
Venue: IJCAI 2023 (main)

Abstract: SiWare is an AI-powered Knowledge Discovery system, that helps unlock new insights and accelerates data-driven decisions with contextualized Industrial data. SiWare links and fuses heterogeneous data sources with an industry semantic model leveraging multiple AI capabilities to provide system-wide visibility into operational characteristics. As part of this demo paper, we describe the requirements for such a system, and deployment aspects, and demonstrate the benefits in two industrial scenarios.
Automated Planning for Generating and Simulating Traffic Signal Strategies
Authors: Saumya Bhatnagar; Rongge Guo; Keith McCabe; Thomas McCluskey; Francesco Percassi; Mauro Vallati
Venue: IJCAI 2023 (main)

Abstract: There is a growing interest in the use of AI techniques for urban traffic control, with a particular focus on traffic signal optimisation. Model-based approaches such as planning demonstrated to be capable of dealing in real-time with unexpected or unusual traffic conditions, as well as with the usual traffic patterns. Further, the knowledge models on which such techniques rely to generate traffic signal strategies are in fact simulation models of traffic, hence can be used by traffic authorities to test and compare different approaches. In this work, we present a framework that relies on automated planning to generate and simulate traffic signal strategies in a urban region. To demonstrate the capabilities of the framework, we consider real-world data collected from sensors deployed in a major corridor of the Kirklees region of the United Kingdom.
NeoMaPy: A Framework for Computing MAP Inference on Temporal Knowledge Graphs
Authors: Victor David; Raphael Fournier-S'niehotta; Nicolas Travers
Venue: IJCAI 2023 (main)

Abstract: Markov Logic Networks (MLN) are used for reasoning on uncertain and inconsistent temporal data. We proposed the TMLN (Temporal Markov Logic Network) which extends them with sorts/types, weights on rules and facts, and various temporal consistencies. The NeoMaPy framework integrates it as a knowledge graph based on conflict graphs which offers flexibility for reasoning with parametric Maximum A Posteriori (MAP) inferences, efficiency with an optimistic heuristic and interactive graph visualization for results explanation.
Latent Inspector: An Interactive Tool for Probing Neural Network Behaviors Through Arbitrary Latent Activation
Authors: Daniel Geißler; Bo Zhou; Paul Lukowicz
Venue: IJCAI 2023 (main)

Abstract: This work presents an active software instrument allowing deep learning architects to interactively inspect neural network models' output behavior from user-manipulated values in any latent layer. Latent Inspector offers multiple dimension reduction techniques to visualize the model's high dimensional latent layer output in human-perceptible, two-dimensional plots. The system is implemented with Node.js front end for interactive user input and Python back end for interacting with the model. By utilizing a general and modular architecture, our proposed solution dynamically adapts to a versatile range of models and data structures. Compared to already existing tools, our asynchronous approach of separating the training process from the inspection offers additional possibilities, such as interactive data generation, by actively working with the model instead of visualizing training logs. Overall, Latent Inspector demonstrates the possibilities as well as the appearing limits for providing a generalized, tool-based concept for enhancing model insight in terms of explainable and transparent AI.
Understanding the Night-Sky? Developing AI-Enabled System for Exploring Night-Light Usage Patterns
Authors: Jakob Hederich; Shreya Ghosh; Zeyu He; Prasenjit Mitra
Venue: IJCAI 2023 (main)

Abstract: We present a demonstration of nighttime light pattern (NTL) analysis system. Our tool named NightVIEW is powered by an efficient system architecture to easily export and analyse a huge volume of spatial data (NTL), image segmentation and clustering algorithms to find unusual NTL patterns and identify hotspots of excess night light usage as well as finding semantics of cities.
Practical Model Reductions for Verification of Multi-Agent Systems
Authors: Wojciech Jamroga; Yan Kim
Venue: IJCAI 2023 (main)

Abstract: Formal verification of intelligent agents is often computationally infeasible due to state-space explosion. We present a tool for reducing the impact of the explosion by means of state abstraction that is (a) easy to use and understand by non-experts, and (b) agent-based in the sense that it operates on a modular representation of the system, rather than on its huge explicit state model.
A Human-in-the-Loop Tool for Annotating Passive Acoustic Monitoring Datasets
Authors: Hannes Kath; Thiago S. Gouvêa; Daniel Sonntag
Venue: IJCAI 2023 (main)

Abstract: Deep learning methods are well suited for data analysis in several domains, but application is often limited by technical entry barriers and the availability of large annotated datasets. We present an interactive machine learning tool for annotating passive acoustic monitoring datasets created for wildlife monitoring, which are time-consuming and costly to annotate manually. The tool, designed as a web application, consists of an interactive user interface implementing a human-in-the-loop workflow. Class label annotations provided manually as bounding boxes drawn over a spectrogram are consumed by a deep generative model (DGM) that learns a low-dimensional representation of the input data, as well as the available class labels. The learned low-dimensional representation is displayed as an interactive interface element, where new bounding boxes can be efficiently generated by the user with lasso-selection; alternatively, the DGM can propose new, automatically generated bounding boxes on demand. The user can accept, edit, or reject annotations suggested by the model, thus owning final judgement. Generated annotations can be used to fine-tune the underlying model, thus closing the loop. Investigations of the prediction accuracy and first empirical experiments show promising results on an artificial data set, laying the ground for application to a real life scenario.
Optimized Crystallographic Graph Generation for Material Science
Authors: Astrid Klipfel; Yaël Frégier; Adlane Sayede; Zied Bouraoui
Venue: IJCAI 2023 (main)

Abstract: Graph neural networks are widely used in machine learning applied to chemistry, and in particular for material science discovery. For crystalline materials, however, generating graph-based representation from geometrical information for neural networks is not a trivial task. The periodicity of crystalline needs efficient implementations to be processed in real-time under a massively parallel environment. With the aim of training graph-based generative models of new material discovery, we propose an efficient tool to generate cutoff graphs and k-nearest-neighbours graphs of periodic structures within GPU optimization. We provide pyMatGraph a Pytorch-compatible framework to generate graphs in real-time during the training of neural network architecture. Our tool can update a graph of a structure, making generative models able to update the geometry and process the updated graph during the forward propagation on the GPU side. Our code is publicly available at https://github.com/aklipf/mat-graph.
SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning
Authors: Baihan Lin; Guillermo Cecchi; Djallel Bouneffouf
Venue: IJCAI 2023 (main)

Abstract: We present a novel recommendation system designed to provide real-time treatment strategies to therapists during psychotherapy sessions. Our system utilizes a turn-level rating mechanism that forecasts the therapeutic outcome by calculating a similarity score between the profound representation of a scoring inventory and the patient's current spoken sentence. By transcribing and segmenting the continuous audio stream into patient and therapist turns, our system conducts immediate evaluation of their therapeutic working alliance. The resulting dialogue pairs, along with their computed working alliance ratings, are then utilized in a deep reinforcement learning recommendation system. In this system, the sessions are treated as users, while the topics are treated as items. To showcase the system's effectiveness, we not only evaluate its performance using an existing dataset of psychotherapy sessions but also demonstrate its practicality through a web app. Through this demo, we aim to provide a tangible and engaging experience of our recommendation system in action.
Fedstellar: A Platform for Training Models in a Privacy-preserving and Decentralized Fashion
Authors: Enrique Tomás Martínez Beltrán; Pedro Miguel Sánchez Sánchez; Sergio López Bernal; Gérôme Bovet; Manuel Gil Pérez; Gregorio Martínez Pérez; Alberto Huertas Celdrán
Venue: IJCAI 2023 (main)

Abstract: This paper presents Fedstellar, a platform for training decentralized Federated Learning (FL) models in heterogeneous topologies in terms of the number of federation participants and their connections. Fedstellar allows users to build custom topologies, enabling them to control the aggregation of model parameters in a decentralized manner. The platform offers a Web application for creating, managing, and connecting nodes to ensure data privacy and provides tools to measure, monitor, and analyze the performance of the nodes. The paper describes the functionalities of Fedstellar and its potential applications. To demonstrate the applicability of the platform, different use cases are presented in which decentralized, semi-decentralized, and centralized architectures are compared in terms of model performance, convergence time, and network overhead when collaboratively classifying hand-written digits using the MNIST dataset.
Plansformer Tool: Demonstrating Generation of Symbolic Plans Using Transformers
Authors: Vishal Pallagani; Bharath Muppasani; Biplav Srivastava; Francesca Rossi; Lior Horesh; Keerthiram Murugesan; Andrea Loreggia; Francesco Fabiano; Rony Joseph; Yathin Kethepalli
Venue: IJCAI 2023 (main)

Abstract: Plansformer is a novel tool that utilizes a fine-tuned language model based on transformer architecture to generate symbolic plans. Transformers are a type of neural network architecture that have been shown to be highly effective in a range of natural language processing tasks. Unlike traditional planning systems that use heuristic-based search strategies, Plansformer is fine-tuned on specific classical planning domains to generate high-quality plans that are both fluent and feasible. Plansformer takes the domain and problem files as input (in PDDL) and outputs a sequence of actions that can be executed to solve the problem. We demonstrate the effectiveness of Plansformer on a variety of benchmark problems and provide both qualitative and quantitative results obtained during our evaluation, including its limitations. Plansformer has the potential to significantly improve the efficiency and effectiveness of planning in various domains, from logistics and scheduling to natural language processing and human-computer interaction. In addition, we provide public access to Plansformer via a website as well as an API endpoint; this enables other researchers to utilize our tool for planning and execution. The demo video is available at https://youtu.be/_1rlctCGsrk
Humming2Music: Being A Composer As Long As You Can Humming
Authors: Yao Qiu; Jinchao Zhang; Huiying Ren; Yong Shan; Jie Zhou
Venue: IJCAI 2023 (main)

Abstract: Creating a piece of music is difficult for people who have never been trained to compose. We present an automatic music generation system to lower the threshold of creating music. The system takes the user's humming as input and creates full music based on the humming melody. The system consists of five modules: 1) humming transcription, 2) melody generation, 3) broken chord generation, 4) accompaniment generation, and 5) audio synthesis. The first module transcribes the user's humming audio to a score, and then the melody generation module composes a complete melody based on the user's humming melody. After that, the third module will generate a broken chord track to accompany the full melody, and the fourth module will create more accompanying tracks. Finally, the audio synthesis module mixes all the tracks to generate the music. Through the user experiment, our system can generate high-quality music with natural expression based on the user's humming input.
Modeling the Impact of Policy Interventions for Sustainable Development
Authors: Sowmith Nandan Rachuri; Arpitha Malavalli; Niharika Sri Parasa; Pooja Bassin; Srinath Srinivasa
Venue: IJCAI 2023 (main)

Abstract: There is an increasing demand to design policy interventions to achieve various targets specified by the UN Sustainable Development Goals by 2030. Designing interventions is a complex task given that the system may often respond in unexpected ways to a given intervention. This could be due to interventions towards a given target, affecting other unrelated variables, and/or interventions leading to acute disparities in nearby geographic areas. In order to address such issues, we propose a novel concept called Stress Modeling that analyzes the holistic impact of a policy intervention by taking into account the interactions within a system, after the intervention. The simulation is based on the postulate that complex systems of interacting entities tend to settle down into "low energy'' configurations by minimizing differentials in capabilities of neighbouring entities. The simulation shows how policy impact percolates through geospatial boundaries over time and can be applied at any granularity. The theory and the corresponding package have been explained along with a case study analyzing a fertilizer policy in the Agro-climatic Zones of the state of Karnataka, India.
LingGe: An Automatic Ancient Chinese Poem-to-Song Generation System
Authors: Yong Shan; Jinchao Zhang; Huiying Ren; Yao Qiu; Jie Zhou
Venue: IJCAI 2023 (main)

Abstract: This paper presents a novel system, named LingGe ("伶歌" in Chinese), to generate songs for ancient Chinese poems automatically. LingGe takes the poem as the lyric, composes music conditioned on the lyric, and finally outputs a full song including the singing and the accompaniment. It consists of four modules: rhythm recognition, melody generation, accompaniment generation, and audio synthesis. Firstly, the rhythm recognition module analyzes the song structure and rhythm according to the poem. Secondly, the melody generation module assembles the rhythm into the template and then generates the melody. Thirdly, the accompaniment generation module predicts the accompaniment in harmony with the melody. Finally, the audio synthesis module generates singing and accompaniment audio and then mixes them to obtain songs. The results show that LingGe can generate high-quality and expressive songs for ancient Chinese poems, both in harmony and rhythm.
AutoML for Outlier Detection with Optimal Transport Distances
Authors: Prabhant Singh; Joaquin Vanschoren
Venue: IJCAI 2023 (main)

Abstract: Automated machine learning (AutoML) has been widely researched and adopted for supervised problems, but progress in unsupervised settings has been limited. We propose `"LOTUS", a novel framework to automate outlier detection based on meta-learning. Our premise is that the selection of the optimal outlier detection technique depends on the inherent properties of the data distribution. We leverage optimal transport to find the dataset with the most similar underlying distribution, and then apply the outlier detection techniques that proved to work best for that data distribution. We evaluate the robustness of our framework and find that it outperforms all state-of-the-art automated outlier detection tools. This approach can also be easily generalized to automate other unsupervised settings.