GenAI4ELab/papercli-papers huggingface dataset:
SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification
Authors: Tianci Liu; Haoyu Wang; Yaqing Wang; Xiaoqian Wang; Lu Su; Jing Gao
Venue: AAAI 2023 (main)

Abstract: Recent years have witnessed increasing concerns towards unfair decisions made by machine learning algorithms. To improve fairness in model decisions, various fairness notions have been proposed and many fairness-aware methods are developed. However, most of existing definitions and methods focus only on single-label classification. Fairness for multi-label classification, where each instance is associated with more than one labels, is still yet to establish. To fill this gap, we study fairness-aware multi-label classification in this paper. We start by extending Demographic Parity (DP) and Equalized Opportunity (EOp), two popular fairness notions, to multi-label classification scenarios. Through a systematic study, we show that on multi-label data, because of unevenly distributed labels, EOp usually fails to construct a reliable estimate on labels with few instances. We then propose a new framework named Similarity s-induced Fairness (sγ -SimFair). This new framework utilizes data that have similar labels when estimating fairness on a particular label group for better stability, and can unify DP and EOp. Theoretical analysis and experimental results on real-world datasets together demonstrate the advantage of sγ -SimFair over existing methods on multi-label classification tasks.
Human Mobility Modeling during the COVID-19 Pandemic via Deep Graph Diffusion Infomax
Authors: Yang Liu; Yu Rong; Zhuoning Guo; Nuo Chen; Tingyang Xu; Fugee Tsung; Jia Li
Venue: AAAI 2023 (main)

Abstract: Non-Pharmaceutical Interventions (NPIs), such as social gathering restrictions, have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of people. To support policy-makers, multiple studies have first modelled human mobility via macro indicators (e.g., average daily travel distance) and then study the effectiveness of NPIs. In this work, we focus on mobility modelling and, from a micro perspective, aim to predict locations that will be visited by COVID-19 cases. Since NPIs generally cause economic and societal loss, such a prediction benefits governments when they design and evaluate them. However, in real-world situations, strict privacy data protection regulations result in severe data sparsity problems (i.e., limited case and location information).
To address these challenges and jointly model variables including a geometric graph, a set of diffusions and a set of locations, we propose a model named Deep Graph Diffusion Infomax (DGDI). We show the maximization of DGDI can be bounded by two tractable components: a univariate Mutual Information (MI) between geometric graph and diffusion representation, and a univariate MI between diffusion representation and location representation. To facilitate the research of COVID-19 prediction, we present two benchmarks that contain geometric graphs and location histories of COVID-19 cases. Extensive experiments on the two benchmarks show that DGDI significantly outperforms other competing methods.
Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery
Authors: Yunchao (Lance) Liu; Yu Wang; Oanh Vu; Rocco Moretti; Bobby Bodenheimer; Jens Meiler; Tyler Derr
Venue: AAAI 2023 (main)

Abstract: In computer-aided drug discovery, quantitative structure activity relation models are trained to predict biological activity from chemical structure. Despite the recent success of applying graph neural network to this task, important chemical information such as molecular chirality is ignored. To fill this crucial gap, we propose Molecular-Kernel Graph NeuralNetwork (MolKGNN) for molecular representation learning, which features SE(3)-/conformation invariance, chirality-awareness, and interpretability. For our MolKGNN, we first design a molecular graph convolution to capture the chemical pattern by comparing the atom's similarity with the learnable molecular kernels. Furthermore, we propagate the similarity score to capture the higher-order chemical pattern. To assess the method, we conduct a comprehensive evaluation with nine well-curated datasets spanning numerous important drug targets that feature realistic high class imbalance and it demonstrates the superiority of MolKGNN over other graph neural networks in computer-aided drug discovery. Meanwhile, the learned kernels identify patterns that agree with domain knowledge, confirming the pragmatic interpretability of this approach.  Our code and supplementary material are publicly available at https://github.com/meilerlab/MolKGNN.
Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability
Authors: Zhexiong Liu; Licheng Liu; Yiqun Xie; Zhenong Jin; Xiaowei Jia
Venue: AAAI 2023 (main)

Abstract: Spatio-temporal machine learning is critically needed for a variety of societal applications, such as agricultural monitoring, hydrological forecast, and traffic management. These applications greatly rely on regional features that characterize spatial and temporal differences. However, spatio-temporal data often exhibit complex patterns and significant data variability across different locations. The labels in many real-world applications can also be limited, which makes it difficult to separately train independent models for different locations. Although meta learning has shown promise in model adaptation with small samples, existing meta learning methods remain limited in handling a large number of heterogeneous tasks, e.g., a large number of locations with varying data patterns. To bridge the gap, we propose task-adaptive formulations and a model-agnostic meta-learning framework that transforms regionally heterogeneous data into location-sensitive meta tasks. We conduct task adaptation following an easy-to-hard task hierarchy in which different meta models are adapted to tasks of different difficulty levels. One major advantage of our proposed method is that it improves the model adaptation to a large number of heterogeneous tasks. It also enhances the model generalization  by automatically adapting the meta model of the corresponding difficulty level to any new tasks. We demonstrate the superiority of our proposed framework over a diverse set of baselines and state-of-the-art meta-learning frameworks. Our extensive experiments on real crop yield data show the effectiveness of the proposed method in handling spatial-related heterogeneous tasks in real societal applications.
A Composite Multi-Attention Framework for Intraoperative Hypotension Early Warning
Authors: Feng Lu; Wei Li; Zhiqiang Zhou; Cheng Song; Yifei Sun; Yuwei Zhang; Yufei Ren; Xiaofei Liao; Hai Jin; Ailin Luo; Albert Y. Zomaya
Venue: AAAI 2023 (main)

Abstract: Intraoperative hypotension (IOH) events warning plays a crucial role in preventing postoperative complications, such as postoperative delirium and mortality. Despite significant efforts, two fundamental problems limit its wide clinical use. The well-established IOH event warning systems are often built on proprietary medical devices that may not be available in all hospitals. The warnings are also triggered mainly through a predefined IOH event that might not be suitable for all patients. This work proposes a composite multi-attention (CMA) framework to tackle these problems by conducting short-term predictions on user-definable IOH events using vital signals in a low sampling rate with demographic characteristics. Our framework leverages a multi-modal fusion network to make four vital signals and three demographic characteristics as input modalities. For each modality, a multi-attention mechanism is used for feature extraction for better model training. Experiments on two large-scale real-world data sets show that our method can achieve up to 94.1% accuracy on IOH events early warning while the signals sampling rate is reduced by 3000 times. Our proposal CMA can achieve a mean absolute error of 4.50 mm Hg in the most challenging 15-minute mean arterial pressure prediction task and the error reduction by 42.9% compared to existing solutions.
Bugs in the Data: How ImageNet Misrepresents Biodiversity
Authors: Alexandra Sasha Luccioni; David Rolnick
Venue: AAAI 2023 (main)

Abstract: ImageNet-1k is a dataset often used for benchmarking machine learning (ML) models and evaluating tasks such as image recognition and object detection. Wild animals make up 27% of ImageNet-1k but, unlike classes representing people and objects, these data have not been closely scrutinized. In the current paper, we analyze the 13,450 images from 269 classes that represent wild animals in the ImageNet-1k validation set, with the participation of expert ecologists. We find that many of the classes are ill-defined or overlapping, and that 12% of the images are incorrectly labeled, with some classes having >90% of images incorrect. We also find that both the wildlife-related labels and images included in ImageNet-1k present significant geographical and cultural biases, as well as ambiguities such as artificial animals, multiple species in the same image, or the presence of humans. Our findings highlight serious issues with the extensive use of this dataset for evaluating ML systems, the use of such algorithms in wildlife-related tasks, and more broadly the ways in which ML datasets are commonly created and curated.
LUCID: Exposing Algorithmic Bias through Inverse Design
Authors: Carmen Mazijn; Carina Prunkl; Andres Algaba; Jan Danckaert; Vincent Ginis
Venue: AAAI 2023 (main)

Abstract: AI systems can create, propagate, support, and automate bias in decision-making processes. To mitigate biased decisions, we both need to understand the origin of the bias and define what it means for an algorithm to make fair decisions. Most group fairness notions assess a model's equality of outcome by computing statistical metrics on the outputs. We argue that these output metrics encounter intrinsic obstacles and present a complementary approach that aligns with the increasing focus on equality of treatment. By Locating Unfairness through Canonical Inverse Design (LUCID), we generate a canonical set that shows the desired inputs for a model given a preferred output. The canonical set reveals the model's internal logic and exposes potential unethical biases by repeatedly interrogating the decision-making process. We evaluate LUCID on the UCI Adult and COMPAS data sets and find that some biases detected by a canonical set differ from those of output metrics. The results show that by shifting the focus towards equality of treatment and looking into the algorithm's internal workings, the canonical sets are a valuable addition to the toolbox of algorithmic fairness evaluation.
Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network
Authors: Mehrtash Mehrabi; Walid Masoudimansour; Yingxue Zhang; Jie Chuai; Zhitang Chen; Mark Coates; Jianye Hao; Yanhui Geng
Venue: AAAI 2023 (main)

Abstract: The mobile communication enabled by cellular networks is the one of the main foundations of our modern society. Optimizing the performance of cellular networks and providing massive connectivity with improved coverage and user experience has a considerable social and economic impact on our daily life. This performance relies heavily on the configuration of the network parameters. However, with the massive increase in both the size and complexity of cellular networks, network management, especially parameter configuration, is becoming complicated. The current practice, which relies largely on experts' prior knowledge, is not adequate and will require lots of domain experts and high maintenance costs. In this work, we propose a learning-based framework for handover parameter configuration. The key challenge, in this case, is to tackle the complicated dependencies between neighboring cells and jointly optimize the whole network. Our framework addresses this challenge in two ways. First, we introduce a novel approach to imitate how the network responds to different network states and parameter values, called auto-grouping graph convolutional network (AG-GCN). During the parameter configuration stage, instead of solving the global optimization problem, we design a local multi-objective optimization strategy where each cell considers several local performance metrics to balance its own performance and its neighbors. We evaluate our proposed algorithm via a simulator constructed using real network data. We demonstrate that the handover parameters our model can find, achieve better average network throughput compared to those recommended by experts as well as alternative baselines, which can bring better network quality and stability. It has the potential to massively reduce costs arising from human expert intervention and maintenance.
Help Me Heal: A Reinforced Polite and Empathetic Mental Health and Legal Counseling Dialogue System for Crime Victims
Authors: Kshitij Mishra; Priyanshu Priya; Asif Ekbal
Venue: AAAI 2023 (main)

Abstract: The potential for conversational agents offering mental health and legal counseling in an autonomous, interactive, and vitally accessible environment is getting highlighted due to the increased access to information through the internet and mobile devices. A counseling conversational agent should be able to offer higher engagement mimicking the real-time counseling sessions. The ability to empathize or comprehend and feel another person’s emotions and experiences is a crucial quality that promotes effective therapeutic bonding and rapport-building. Further, the use of polite encoded language in the counseling reflects the nobility and creates a familiar, warm, and comfortable atmosphere to resolve human issues. Therefore, focusing on these two aspects, we propose a Polite and Empathetic Mental Health and Legal Counseling Dialogue System (Po-Em-MHLCDS) for the victims of crimes. To build Po-Em-MHLCDS, we first create a Mental Health and Legal Counseling Dataset (MHLCD) by recruiting six employees who are asked to converse with each other, acting as a victim and the agent interchangeably following a fixed stated guidelines. Second, the MHLCD dataset is annotated with three informative labels, viz. counseling strategies, politeness, and empathy. Lastly, we train the Po-Em-MHLCDS in a reinforcement learning framework by designing an efficient and effective reward function to reinforce correct counseling strategy, politeness and empathy while maintaining contextual-coherence and non-repetitiveness in the generated responses. Our extensive automatic and human evaluation demonstrate the strength of the proposed system. Codes and Data can be accessed at https://www.iitp.ac.in/ ai-nlp-ml/resources.html#MHLCD or https://github.com/Mishrakshitij/Po-Em-MHLCDS
Carburacy: Summarization Models Tuning and Comparison in Eco-Sustainable Regimes with a Novel Carbon-Aware Accuracy
Authors: Gianluca Moro; Luca Ragazzi; Lorenzo Valgimigli
Venue: AAAI 2023 (main)

Abstract: Generative transformer-based models have reached cutting-edge performance in long document summarization. Nevertheless, this task is witnessing a paradigm shift in developing ever-increasingly computationally-hungry solutions, focusing on effectiveness while ignoring the economic, environmental, and social costs of yielding such results. Accordingly, such extensive resources impact climate change and raise barriers to small and medium organizations distinguished by low-resource regimes of hardware and data. As a result, this unsustainable trend has lifted many concerns in the community, which directs the primary efforts on the proposal of tools to monitor models' energy costs. Despite their importance, no evaluation measure considering models' eco-sustainability exists yet. In this work, we propose Carburacy, the first carbon-aware accuracy measure that captures both model effectiveness and eco-sustainability. We perform a comprehensive benchmark for long document summarization, comparing multiple state-of-the-art quadratic and linear transformers on several datasets under eco-sustainable regimes. Finally, thanks to Carburacy, we found optimal combinations of hyperparameters that let models be competitive in effectiveness with significantly lower costs.
Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering
Authors: Duy M. H. Nguyen; Hoang Nguyen; Truong T. N. Mai; Tri Cao; Binh T. Nguyen; Nhat Ho; Paul Swoboda; Shadi Albarqouni; Pengtao Xie; Daniel Sonntag
Venue: AAAI 2023 (main)

Abstract: Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions.
In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches.
For the Underrepresented in Gender Bias Research: Chinese Name Gender Prediction with Heterogeneous Graph Attention Network
Authors: Zihao Pan; Kai Peng; Shuai Ling; Haipeng Zhang
Venue: AAAI 2023 (main)

Abstract: Achieving gender equality is an important pillar for humankind’s sustainable future. Pioneering data-driven gender bias research is based on large-scale public records such as scientific papers, patents, and company registrations, covering female researchers, inventors and entrepreneurs, and so on. Since gender information is often missing in relevant datasets, studies rely on tools to infer genders from names. However, available open-sourced Chinese gender-guessing tools are not yet suitable for scientific purposes, which may be partially responsible for female Chinese being underrepresented in mainstream gender bias research and affect their universality. Specifically, these tools focus on character-level information while overlooking the fact that the combinations of Chinese characters in multi-character names, as well as the components and pronunciations of characters, convey important messages. As a first effort, we design a Chinese Heterogeneous Graph Attention (CHGAT) model to capture the heterogeneity in component relationships and incorporate the pronunciations of characters. Our model largely surpasses current tools and also outperforms the state-of-the-art algorithm. Last but not least, the most popular Chinese name-gender dataset is single-character based with far less female coverage from an unreliable source, naturally hindering relevant studies. We open-source a more balanced multi-character dataset from an official source together with our code, hoping to help future research promoting gender equality.
FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms
Authors: Peng Qi; Yuyan Bu; Juan Cao; Wei Ji; Ruihao Shui; Junbin Xiao; Danding Wang; Tat-Seng Chua
Venue: AAAI 2023 (main)

Abstract: Short video platforms have become an important channel for news sharing, but also a new breeding ground for fake news. To mitigate this problem, research of fake news video detection has recently received a lot of attention. Existing works face two roadblocks: the scarcity of comprehensive and largescale datasets and insufficient utilization of multimodal information. Therefore, in this paper, we construct the largest Chinese short video dataset about fake news named FakeSV, which includes news content, user comments, and publisher profiles simultaneously. To understand the characteristics of fake news videos, we conduct exploratory analysis of FakeSV from different perspectives. Moreover, we provide a new multimodal detection model named SV-FEND, which exploits the cross-modal correlations to select the most informative features and utilizes the social context information for detection. Extensive experiments evaluate the superiority of the proposed method and provide detailed comparisons of different methods and modalities for future works. Our dataset and codes are available in https://github.com/ICTMCG/FakeSV.
EINNs: Epidemiologically-Informed Neural Networks
Authors: Alexander Rodríguez; Jiaming Cui; Naren Ramakrishnan; Bijaya Adhikari; B. Aditya Prakash
Venue: AAAI 2023 (main)

Abstract: We introduce EINNs, a framework crafted for epidemic forecasting that builds upon the theoretical grounds provided by mechanistic models as well as the data-driven expressibility afforded by AI models, and their capabilities to ingest heterogeneous information. Although neural forecasting models have been successful in multiple tasks, predictions well-correlated with epidemic trends and long-term predictions remain open challenges. Epidemiological ODE models contain mechanisms that can guide us in these two tasks; however, they have limited capability of ingesting data sources and modeling composite signals. Thus, we propose to leverage work in physics-informed neural networks to learn latent epidemic dynamics and transfer relevant knowledge to another neural network which ingests multiple data sources and has more appropriate inductive bias. In contrast with previous work, we do not assume the observability of complete dynamics and do not need to numerically solve the ODE equations during training. Our thorough experiments on all US states and HHS regions for COVID-19 and influenza forecasting showcase the clear benefits of our approach in both short-term and long-term forecasting as well as in learning the mechanistic dynamics over other non-trivial alternatives.