GenAI4ELab/papercli-papers huggingface dataset:
Well-Written Knowledge Graphs: Most Effective RDF Syntaxes for Triple Linearization in End-to-End Extraction of Relations from Texts (Student Abstract)
Authors: Célian Ringwald; Fabien Gandon; Catherine Faron; Franck Michel; Hanna Abi Akl
Venue: AAAI 2024 (main)

Abstract: Seq-to-seq generative models recently gained attention for solving the relation extraction task. By approaching this problem as an end-to-end task, they surpassed encoder-based-only models. Little research investigated the effects of the output syntaxes on the training process of these models. Moreover, a limited number of approaches were proposed for extracting ready-to-load knowledge graphs following the RDF standard. In this paper, we consider that a set of triples can be linearized in many different ways, and we evaluate the combined effect of the size of the language models and different RDF syntaxes on the task of relation extraction from Wikipedia abstracts.
FAIR-FER: A Latent Alignment Approach for Mitigating Bias in Facial Expression Recognition (Student Abstract)
Authors: Syed Sameen Ahmad Rizvi; Aryan Seth; Pratik Narang
Venue: AAAI 2024 (main)

Abstract: Facial Expression Recognition (FER) is an extensively explored research problem in the domain of computer vision and artificial intelligence. FER, a supervised learning problem, requires significant training data representative of multiple socio-cultural demographic attributes. However, most of the FER dataset consists of images annotated by humans, which propagates individual and demographic biases. This work attempts to mitigate this bias using representation learning based on latent spaces, thereby increasing a deep learning model's fairness and overall accuracy.
Partially Observable Hierarchical Reinforcement Learning with AI Planning (Student Abstract)
Authors: Brandon Rozek; Junkyu Lee; Harsha Kokel; Michael Katz; Shirin Sohrabi
Venue: AAAI 2024 (main)

Abstract: Partially observable Markov decision processes (POMDPs) challenge reinforcement learning agents due to incomplete knowledge of the environment. Even assuming monotonicity in uncertainty, it is difficult for an agent to know how and when to stop exploring for a given task. In this abstract, we discuss how to use hierarchical reinforcement learning (HRL) and AI Planning (AIP) to improve exploration when the agent knows possible valuations of unknown predicates and how to discover them. By encoding the uncertainty in an abstract planning model, the agent can derive a high-level plan which is then used to decompose the overall POMDP into a tree of semi-POMDPs for training. We evaluate our agent's performance on the MiniGrid domain and show how guided exploration may improve agent performance.
Finetuning LLMs for Automatic Concept to TTI Prompt Generation (Student Abstract)
Authors: Jeremy Rutter; Maneesh Reddy Chamakura; Justin Delgado; Gene Louis Kim
Venue: AAAI 2024 (main)

Abstract: Our work explores bridging the gap between large language models and text-to-image models to create a tool for quickly and easily generating high quality images from a given concept. In our experiments we successfully improved image quality with only a preliminary utilization of the available resources for finetuning.
Instance-Wise Laplace Mechanism via Deep Reinforcement Learning (Student Abstract)
Authors: Sehyun Ryu; Hosung Joo; Jonggyu Jang; Hyun Jong Yang
Venue: AAAI 2024 (main)

Abstract: Recent research has shown a growing interest in per-instance differential privacy (pDP), highlighting the fact that each data instance within a dataset may incur distinct levels of privacy loss.
However, conventional additive noise mechanisms apply identical noise to all query outputs, thereby deteriorating data statistics.
In this study, we propose an instance-wise Laplace mechanism, which adds non-identical Laplace noises to the query output for each data instance.
A challenge arises from the complex interaction of additive noise, where the noise introduced to individual instances impacts the pDP of other instances, adding complexity and resilience to straightforward solutions.
To tackle this problem, we introduce an instance-wise Laplace mechanism algorithm via deep reinforcement learning and validate its ability to better preserve data statistics on a real dataset, compared to the original Laplace mechanism.
Frequency Oracle for Sensitive Data Monitoring (Student Abstract)
Authors: Richard Sances; Olivera Kotevska; Paul Laiu
Venue: AAAI 2024 (main)

Abstract: As data privacy issues grow, finding the best privacy preservation algorithm for each situation is increasingly essential. This research has focused on understanding the frequency oracles (FO) privacy preservation algorithms. FO conduct the frequency estimation of any value in the domain. The aim is to explore how each can be best used and recommend which one to use with which data type. We experimented with different data scenarios and federated learning settings. Results showed clear guidance on when to use a specific algorithm.
Adapting Animal Models to Assess Sufficiency of Fluid Resuscitation in Humans (Student Abstract)
Authors: Ryan Schuerkamp; Xinyu Li; Brian Kunzer; Leonard S. Weiss; Hernando Gómez; Francis X. Guyette; Michael R. Pinsky; Artur Dubrawski
Venue: AAAI 2024 (main)

Abstract: Fluid resuscitation is an initial treatment frequently employed to treat shock, restore lost blood, protect tissues from injury, and prevent organ dysfunction in critically ill patients. However, it is not without risk (e.g., overly aggressive resuscitation may cause organ damage and even death). We leverage machine learning models trained to assess sufficiency of resuscitation in laboratory animals subjected to induced hemorrhage and transfer them to use with human trauma patients. Our key takeaway is that animal experiments and models can inform human healthcare, especially when human data is limited or when collecting relevant human data via potentially harmful protocols is unfeasible.
Rider Posture-Based Continuous Authentication with Few-Shot Learning for Mobility Scooters (Student Abstract)
Authors: Devan Shah; Ruoqi Huang; Tingting Chen; Murtuza Jadliwala
Venue: AAAI 2024 (main)

Abstract: Current practice of mobility scooter user authentication using physical keys and traditional password-based one-time security mechanisms cannot meet the needs of many mobility scooter riders, especially senior citizens having issues in recalling memory. Now seamless authentication approaches are needed to provide ongoing protection for mobility scooters against takeovers and unauthorized access. Existing continuous authentication techniques do not work well in a mobility scooter setting due to issues such as user comfort, deployment cost and enrollment time, among others. In that direction, our contributions in this research effort are two-fold: (i) we propose a novel system that incorporates advances in few-shot learning, hierarchical processing, and contextual embedding to establish continuous authentication for mobility scooter riders using only posture data. This security system, trained on data collected from real mobility scooter riders, demonstrates quick enrollment and easy deployability, while successfully serving as an unobtrusive first layer of security. (ii) we provide to the research community the largest publicly available repository of mobility scooter riders' body key-points data to enable further research in this direction.
Coordination of Emergent Demand Changes via Value-Based Negotiation for Supply Chain Management (Student Abstract)
Authors: Takumu Shimizu; Ryota Higa; Katsuhide Fujita; Shinji Nakadai
Venue: AAAI 2024 (main)

Abstract: We propose an automated negotiation for a reinforcement learning agent to adapt the agent to unexpected situations such as demand changes in supply chain management (SCM).
Existing studies that consider reinforcement learning and SCM assume a centralized environment where the coordination of chain components is hierarchical rather than through negotiations between agents.
This study focused on a negotiation agent that considered the value function of reinforcement learning for SCM as its utility function in automated negotiation.
We demonstrated that the proposed approach could avoid inventory shortages under increased demand requests from the terminal customer.
Faithful Trip Recommender Using Diffusion Guidance (Student Abstract)
Authors: Wenzheng Shu; Yanlong Huang; Wenxin Tai; Zhangtao Cheng; Bei Hui; Goce Trajcevski
Venue: AAAI 2024 (main)

Abstract: Trip recommendation aims to plan user’s travel based on their specified preferences. Traditional heuristic and statistical approaches often fail to capture the intricate nuances of user intentions, leading to subpar performance. Recent deep-learning methods show attractive accuracy but struggle to generate faithful trajectories that match user intentions. In this work, we propose a DDPM-based incremental knowledge injection module to ensure the faithfulness of the generated trajectories. Experiments on two datasets verify the effectiveness of our approach.
Diverse Yet Biased: Towards Mitigating Biases in Generative AI (Student Abstract)
Authors: Akshit Singh
Venue: AAAI 2024 (main)

Abstract: Generative Artificial Intelligence (AI) has garnered significant attention for its remarkable ability to generate text, images, and other forms of content. However, an inherent and increasingly concerning issue within generative AI systems is bias. These AI models often exhibit an Anglo-centric bias and tend to overlook the importance of diversity. This can be attributed to their training on extensive datasets sourced from the internet, which inevitably inherit the biases present in those data sources. Employing these datasets leads to AI-generated content that mirrors and perpetuates existing biases, encompassing various aspects such as gender, ethnic and cultural stereotypes. Addressing bias in generative AI is a complex challenge that necessitates substantial efforts. In order to tackle this issue, we propose a methodology for constructing moderately sized datasets with a social inclination. These datasets can be employed to rectify existing imbalances in datasets or to train models to generate socially inclusive material. Additionally, we present preliminary findings derived from training our model on these socially inclined datasets.
Confidence Is All You Need for MI Attacks (Student Abstract)
Authors: Abhishek Sinha; Himanshi Tibrewal; Mansi Gupta; Nikhar Waghela; Shivank Garg
Venue: AAAI 2024 (main)

Abstract: In this evolving era of machine learning security, membership inference attacks have emerged as a potent threat to the confidentiality of sensitive data. In this attack, adversaries aim to determine whether a particular point was used during the training of a target model. This paper proposes a new method to gauge a data point’s membership in a model’s training set. Instead of correlating loss with membership, as is traditionally done, we have leveraged the fact that training examples generally exhibit higher confidence values when classified into their actual class. During training, the model is essentially being ’fit’ to the training data and might face particular difficulties in generalization to unseen data. This asymmetry leads to the model achieving higher confidence on the training data as it exploits the specific patterns and noise present in the training data. Our proposed approach leverages the confidence values generated by the machine-learning model. These confidence values provide a probabilistic measure of the model’s certainty in its predictions and can further be used to infer the membership of a given data point. Additionally, we also introduce another variant of our method that allows us to carry out this attack without knowing the ground truth(true class) of a given data point, thus offering an edge over existing label-dependent attack methods.
Investigation into Training Dynamics of Learned Optimizers (Student Abstract)
Authors: Jan Sobotka; Petr Šimánek
Venue: AAAI 2024 (main)

Abstract: Modern machine learning heavily relies on optimization, and as deep learning models grow more complex and data-hungry, the search for efficient learning becomes crucial. Learned optimizers disrupt traditional handcrafted methods such as SGD and Adam by learning the optimization strategy itself, potentially speeding up training. However, the learned optimizers' dynamics are still not well understood. To remedy this, our work explores their optimization trajectories from the perspective of network architecture symmetries and proposed parameter update distributions.
Learning to Build Solutions in Stochastic Matching Problems Using Flows (Student Abstract)
Authors: William St-Arnaud; Margarida Carvalho; Golnoosh Farnadi
Venue: AAAI 2024 (main)

Abstract: Generative Flow Networks, known as GFlowNets, have been introduced in recent times, presenting an exciting possibility for neural networks to model distributions across various data structures. In this paper, we broaden their applicability to encompass scenarios where the data structures are optimal solutions of a combinatorial problem. Concretely, we propose the use of GFlowNets to learn the distribution of optimal solutions for kidney exchange problems (KEPs), a generalized form of matching problems involving cycles.
DDViT: Double-Level Fusion Domain Adapter Vision Transformer (Student Abstract)
Authors: Linpeng Sun; Victor S. Sheng
Venue: AAAI 2024 (main)

Abstract: With the help of Vision transformers (ViTs), medical image segmentation was able to achieve outstanding performance. In particular, they overcome the limitation of convolutional neural networks (CNNs) which rely on local receptive fields. ViTs use self-attention mechanisms to consider relationships between all image pixels or patches simultaneously. However, they require large datasets for training and did not perform well on capturing low-level features. To that end, we propose DDViT, a novel ViT model that unites a CNN to alleviate data-hunger for medical image segmentation with two multi-scale feature representations. Significantly, our approach incorporates a ViT with a plug-in domain adapter (DA) with Double-Level Fusion (DLF) technique, complemented by a mutual knowledge distillation paradigm, facilitating the seamless exchange of knowledge between a universal network and specialized domain-specific network branches. The DLF framework plays a pivotal role in our encoder-decoder architecture, combining the innovation of the TransFuse module with a robust CNN-based encoder. Extensive experimentation across diverse medical image segmentation datasets underscores the remarkable efficacy of DDViT when compared to alternative approaches based on CNNs and Transformer-based models.
Evaluation of Large Language Models on Code Obfuscation (Student Abstract)
Authors: Adrian Swindle; Derrick McNealy; Giri Krishnan; Ramyaa Ramyaa
Venue: AAAI 2024 (main)

Abstract: Obfuscation intends to decrease interpretability of code and identification of code behavior. Large Language Models(LLMs) have been proposed for code synthesis and code analysis. This paper attempts to understand how well LLMs can analyse code and identify code behavior. Specifically, this paper systematically evaluates several LLMs’ capabilities to detect obfuscated code and identify behavior across a variety of obfuscation techniques with varying levels of complexity. LLMs proved to be better at detecting obfuscations that changed identifiers, even to misleading ones, compared to obfuscations involving code insertions (unused variables, as well as variables that replace constants with expressions that evaluate to those constants). Hardest to detect were obfuscations that layered multiple simple transformations. For these, only 20-40% of the LLMs’ responses were correct. Adding misleading documentation was also successful in misleading LLMs. We provide all our code to replicate results at https://github.com/SwindleA/LLMCodeObfuscation. Overall, our results suggest a gap in LLMs’ ability to understand code.
Graph Anomaly Detection via Prototype-Aware Label Propagation (Student Abstract)
Authors: Hui Tang; Xun Liang; Sensen Zhang
Venue: AAAI 2024 (main)

Abstract: Detecting anomalies on attributed graphs is a challenging task since labelled anomalies are highly labour-intensive by taking specialized domain knowledge to make anomalous samples not as available as normal ones. Moreover, graphs contain complex structure information as well as attribute information, leading to anomalies that can be typically hidden in the structure space, attribute space, and the mix of both. In this paper, we propose a novel model for graph anomaly detection named ProGAD. Specifically, ProGAD takes advance of label propagation to infer high-quality pseudo labels by considering the structure and attribute inconsistencies between normal and abnormal samples. Meanwhile, ProGAD introduces the prior knowledge of class distribution to correct and refine pseudo labels with a prototype-aware strategy. Experiments demonstrate that ProGAD achieves strong performance compared with the current state-of-the-art methods.
Gaze-Based Interaction Adaptation for People with Involuntary Head Movements (Student Abstract)
Authors: Cindy Tong; Rosanna Chan
Venue: AAAI 2024 (main)

Abstract: Gaze estimation is an important research area in computer vision and machine learning. Eye-tracking and gaze-based interactions have made assistive technology (AT) more accessible to people with physical limitations. However, a non-negligible proportion of existing AT users, including those having dyskinetic cerebral palsy (CP) or severe intellectual disabilities (ID), have difficulties in using eye trackers due to their involuntary body movements. In this paper, we propose an adaptation method pertaining to head movement prediction and fixation smoothing to stabilize our target users' gaze points on the screen and improve their user experience (UX) in gaze-based interaction. Our empirical experimentation shows that our method significantly shortens the users' selection time and increases their selection accuracy.
COPD-FlowNet: Elevating Non-invasive COPD Diagnosis with CFD Simulations (Student Abstract)
Authors: Aryan Tyagi; Aryaman Rao; Shubhanshu Rao; Raj Kumar Singh
Venue: AAAI 2024 (main)

Abstract: Chronic Obstructive Pulmonary Disorder (COPD) is a prevalent respiratory disease that significantly impacts the quality of life of affected individuals. This paper presents COPD-FlowNet, a novel deep-learning framework that leverages a custom Generative Adversarial Network (GAN) to generate synthetic Computational Fluid Dynamics (CFD) velocity flow field images specific to the trachea of COPD patients. These synthetic images serve as a valuable resource for data augmentation and model training. Additionally, COPD-FlowNet incorporates a custom Convolutional Neural Network (CNN) architecture to predict the location of the obstruction site.
Equivalence between Graph Spectral Clustering and Column Subset Selection (Student Abstract)
Authors: Guihong Wan; Wei Mao; Yevgeniy R. Semenov; Haim Schweitzer
Venue: AAAI 2024 (main)

Abstract: The common criteria for evaluating spectral clustering are NCut and RatioCut. The seemingly unrelated column subset selection (CSS) problem aims to compute a column subset that linearly approximates the entire matrix. A common criterion is the approximation error in the Frobenius norm (ApproxErr). We show that any algorithm for CSS can be viewed as a clustering algorithm that minimizes NCut by applying it to a matrix formed from graph edges. Conversely, any clustering algorithm can be seen as identifying a column subset from that matrix. In both cases, ApproxErr and NCut have the same value. Analogous results hold for RatioCut with a slightly different matrix. Therefore, established results for CSS can be mapped to spectral clustering. We use this to obtain new clustering algorithms, including an optimal one that is similar to A*. This is the first nontrivial clustering algorithm with such an optimality guarantee. A variant of the weighted A* runs much faster and provides bounds on the accuracy. Finally, we use the results from spectral clustering to prove the NP-hardness of CSS from sparse matrices.
Opening the Black Box: Unraveling the Classroom Dialogue Analysis (Student Abstract)
Authors: Deliang Wang
Venue: AAAI 2024 (main)

Abstract: This paper explores proposing interpreting methods from explainable artificial intelligence to address the interpretability issues in deep learning-based models for classroom dialogue. Specifically, we developed a Bert-based model to automatically detect student talk moves within classroom dialogues, utilizing the TalkMoves dataset. Subsequently, we proposed three generic interpreting methods, namely saliency, input*gradient, and integrated gradient, to explain the predictions of classroom dialogue models by computing input relevance (i.e., contribution). The experimental results show that the three interpreting methods can effectively unravel the classroom dialogue analysis, thereby potentially fostering teachers' trust.
The CoachAI Badminton Environment: A Novel Reinforcement Learning Environment with Realistic Opponents (Student Abstract)
Authors: Kuang-Da Wang; Wei-Yao Wang; Yu-Tse Chen; Yu-Heng Lin; Wen-Chih Peng
Venue: AAAI 2024 (main)

Abstract: The growing demand for precise sports analysis has been explored to improve athlete performance in various sports (e.g., basketball, soccer). However, existing methods for different sports face challenges in validating strategies in environments due to simple rule-based opponents leading to performance gaps when deployed in real-world matches. In this paper, we propose the CoachAI Badminton Environment, a novel reinforcement learning (RL) environment with realistic opponents for badminton, which serves as a compelling example of a turn-based game. It supports researchers in exploring various RL algorithms with the badminton context by integrating state-of-the-art tactical-forecasting models and real badminton game records. The Badminton Benchmarks are proposed with multiple widely adopted RL algorithms to benchmark the performance of simulating matches against real players. To advance novel algorithms and developments in badminton analytics, we make our environment open-source, enabling researchers to simulate more complex badminton sports scenarios based on this foundation. Our code is available at https://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI%20Badminton%20Environment.
Counterfactual Graph Learning for Anomaly Detection with Feature Disentanglement and Generation (Student Abstract)
Authors: Yutao Wei; Wenzheng Shu; Zhangtao Cheng; Wenxin Tai; Chunjing Xiao; Ting Zhong
Venue: AAAI 2024 (main)

Abstract: Graph anomaly detection has received remarkable research interests, and various techniques have been employed for enhancing detection performance. However, existing models tend to learn dataset-specific spurious correlations based on statistical associations. A well-trained model might suffer from performance degradation when applied to newly observed nodes with different environments. To handle this situation, we propose CounterFactual Graph Anomaly Detection model, CFGAD. In this model, we design a gradient-based separator to disentangle node features into class features and environment features. Then, we present a weight-varying diffusion model to combine class features and environment features from different nodes to generate counterfactual samples. These counterfactual samples will be adopted to enhance model robustness. Comprehensive experiments demonstrate the effectiveness of our CFGAD.