GenAI4ELab/papercli-papers huggingface dataset:
Hey, Siri! Why Are You Biased against Women? (Student Abstract)
Authors: Surakshya Aryal; Mikel K. Ngueajio; Saurav Keshari Aryal; Gloria Washington
Venue: AAAI 2023 (main)

Abstract: The intersection of pervasive technology and verbal communication has resulted in the creation of Automatic Speech Recognition Systems (ASRs), which automate the conversion of spontaneous speech into texts. ASR enables human-computer interactions through speech and is rapidly integrated into our daily lives. However, the research studies on current ASR technologies have reported unfulfilled social inclusivity and accentuated biases and stereotypes towards minorities. In this work, we provide a review of examples and evidence to demonstrate preexisting sexist behavior in ASR systems through a systematic review of research literature over the past five years. For each article, we also provide the ASR technology used, highlight specific instances of reported bias, discuss the impact of this bias on the female community, and suggest possible methods of mitigation. We believe this paper will provide insights into the harm that unchecked AI-powered technologies can have on a community by contributing to the growing body of research on this topic and underscoring the need for technological inclusivity for all demographics, especially women.
FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features (Student Abstract)
Authors: Hankyul Baek; Won Joon Yun; Joongheon Kim
Venue: AAAI 2023 (main)

Abstract: Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to prevent barren plateaus, and it introduces the lack of the feature information. In this paper, we propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits, which is called fidelity-variation training (FV-Training).
PanTop: Pandemic Topic Detection and Monitoring System (Student Abstract)
Authors: Yangxiao Bai; Kaiqun Fu
Venue: AAAI 2023 (main)

Abstract: Diverse efforts to combat the COVID-19 pandemic have continued throughout the past two years. Governments have announced plans for unprecedentedly rapid vaccine development, quarantine measures, and economic revitalization. They contribute to a more effective pandemic response by determining the precise opinions of individuals regarding these mitigation measures. In this paper, we propose a deep learning-based topic monitoring and storyline extraction system for COVID-19 that is capable of analyzing public sentiment and pandemic trends. The proposed method is able to retrieve Twitter data related to COVID-19 and conduct spatiotemporal analysis. Furthermore, a deep learning component of the system provides monitoring and modeling capabilities for topics based on advanced natural language processing models. A variety of visualization methods are applied to the project to show the distribution of each topic. Our proposed system accurately reflects how public reactions change over time along with pandemic topics.
Social Intelligence towards Human-AI Teambuilding (Student Abstract)
Authors: Morgan E Bailey; Frank E Pollick
Venue: AAAI 2023 (main)

Abstract: As Artificial Intelligence (AI) continues to develop, it becomes vital to understand more of the nuances of Human-AI interactions. This study aims to uncover how developers can design AI to feel more human in a work environment where only written feedback is possible. Participants will identify a location from Google Maps. To do this successfully, participants must rely on the answers provided by their teammates, one AI and one human. The experiment will run a 2x4 de-sign where AI's responses will either be designed in a human style (high humanness) or state a one-word answer (low humanness), the latter of which is more typical in machines and AI. The reliability of the AI will either be 60% or 90%, and the human will be 30%. Participants will be given a series of questionnaires to rate their opinions of the AI and rate feelings of trust, confidence and performance throughout the study. Following this study, the aim is to identify specific design elements that allow AI to feel human and successfully appear to have social intelligence in more interactive settings.
Robust Training for AC-OPF (Student Abstract)
Authors: Fuat Can Beylunioglu; Mehrdad Pirnia; P. Robert Duimering; Vijay Ganesh
Venue: AAAI 2023 (main)

Abstract: Electricity network operators use computationally demanding mathematical models to optimize AC power flow (AC-OPF). Recent work applies neural networks (NN) rather than optimization methods to estimate locally optimal solutions. However, NN training data is costly and current models cannot guarantee optimal or feasible solutions. This study proposes a robust NN training approach, which starts with a small amount of seed training data and uses iterative feedback to generate additional data in regions where the model makes poor predictions. The method is applied to non-linear univariate and multivariate test functions, and an IEEE 6-bus AC-OPF system. Results suggest robust training can achieve NN prediction performance similar to, or better than, regular NN training, while using significantly less data.
IdProv: Identity-Based Provenance for Synthetic Image Generation (Student Abstract)
Authors: Harshil Bhatia; Jaisidh Singh; Gaurav Sangwan; Aparna Bharati; Richa Singh; Mayank Vatsa
Venue: AAAI 2023 (main)

Abstract: Recent advancements in Generative Adversarial Networks (GANs) have made it possible to obtain high-quality face images of synthetic identities. These networks see large amounts of real faces in order to learn to generate realistic looking synthetic images. However, the concept of a synthetic identity for these images is not very well-defined. In this work, we verify identity leakage from the training set containing real images into the latent space and propose a novel method, IdProv, that uses image composition to trace the source of identity signals in the generated image.
Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract)
Authors: Charles Bourbeau; Audrey Durand
Venue: AAAI 2023 (main)

Abstract: This work investigates the evolution of latent space when deep learning models are trained incrementally in non-stationary environments that stem from concept drift. We propose a methodology for visualizing the incurred change in latent representations. We further show that classes not targeted by concept drift can be negatively affected, suggesting that the observation of all classes during learning may regularize the latent space.
Model Selection of Graph Signage Models Using Maximum Likelihood (Student Abstract)
Authors: Angelina Brilliantova; Ivona Bezáková
Venue: AAAI 2023 (main)

Abstract: Complex systems across various domains can be naturally modeled as signed networks with positive and negative edges. In this work, we design a new class of signage models and show how to select the model parameters that best fit real-world datasets using maximum likelihood.
Optimal Execution via Multi-Objective Multi-Armed Bandits (Student Abstract)
Authors: Francois Buet-Golfouse; Peter Hill
Venue: AAAI 2023 (main)

Abstract: When trying to liquidate a large quantity of a particular stock, the price of that stock is likely to be affected by trades, thus leading to a reduced expected return if we were to sell the entire quantity at once. This leads to the problem of optimal execution, where the aim is to split the sell order into several smaller sell orders over the course of a period of time, to optimally balance stock price with market risk. This problem can be defined in terms of difference equations. Here, we show how we can reformulate this as a multi-objective problem, which we solve with a novel multi-armed bandit algorithm.
Lightweight Transformer for Multi-Modal Object Detection (Student Abstract)
Authors: Yue Cao; Yanshuo Fan; Junchi Bin; Zheng Liu
Venue: AAAI 2023 (main)

Abstract: It has become a common practice for many perceptual systems to integrate information from multiple sensors to improve the accuracy of object detection. For example, autonomous vehicles use visible light, and infrared (IR) information to ensure that the car can cope with complex weather conditions. However, the accuracy of the algorithm is usually a trade-off between the computational complexity and memory consumption. In this study, we evaluate the performance and complexity of different fusion operators in multi-modal object detection tasks. On top of that, a Poolformer-based fusion operator (PoolFuser) is proposed to enhance the accuracy of detecting targets without compromising the efficiency of the detection framework.
Reconsidering Deception in Social Robotics: The Role of Human Vulnerability (Student Abstract)
Authors: Rachele Carli; Amro Najjar
Venue: AAAI 2023 (main)

Abstract: The literature on deception in human-robot interaction (henceforth HRI) could be divided between: (i) those who consider it essential to maximise users' end utility and robotic performance; (ii) those who consider it unethical, because it is potentially dangerous for individuals' psychological integrity. 
However, it has now been proven that humans are naturally prone to anthropomorphism and emotional attachment to inanimate objects. 
Consequently, despite ethical concerns, the argument for the total elimination of deception could reveal to be a pointless exercise.
Rather, it is suggested here to conceive deception in HRI as a dynamic to be modulated and graded, in order to both promote innovation and protect fundamental human rights. To this end, the concept of vulnerability could serve as an objective balancing criterion.
Know Your Enemy: Identifying Adversarial Behaviours in Deep Reinforcement Learning Agents (Student Abstract)
Authors: Seán Caulfield Curley; Karl Mason; Patrick Mannion
Venue: AAAI 2023 (main)

Abstract: It has been shown that an agent can be trained with an adversarial policy which achieves high degrees of success against a state-of-the-art DRL victim despite taking unintuitive actions. This prompts the question: is this adversarial behaviour detectable through the observations of the victim alone? We find that widely used classification methods such as random forests are only able to achieve a maximum of ≈71% test set accuracy when classifying an agent for a single timestep. However, when the classifier inputs are treated as time-series data, test set classification accuracy is increased significantly to ≈98%. This is true for both classification of episodes as a whole, and for “live” classification at each timestep in an episode. These classifications can then be used to “react” to incoming attacks and increase the overall win rate against Adversarial opponents by approximately 17%. Classification of the victim’s own internal activations in response to the adversary is shown to achieve similarly impressive accuracy while also offering advantages like increased transferability to other domains.
An Emotion-Guided Approach to Domain Adaptive Fake News Detection Using Adversarial Learning (Student Abstract)
Authors: Arkajyoti Chakraborty; Inder Khatri; Arjun Choudhry; Pankaj Gupta; Dinesh Kumar Vishwakarma; Mukesh Prasad
Venue: AAAI 2023 (main)

Abstract: Recent works on fake news detection have shown the efficacy of using emotions as a feature for improved performance. However, the cross-domain impact of emotion-guided features for fake news detection still remains an open problem. In this work, we propose an emotion-guided, domain-adaptive, multi-task approach for cross-domain fake news detection, proving the efficacy of emotion-guided models in cross-domain settings for various datasets.
Deep Anomaly Detection and Search via Reinforcement Learning (Student Abstract)
Authors: Chao Chen; Dawei Wang; Feng Mao; Zongzhang Zhang; Yang Yu
Venue: AAAI 2023 (main)

Abstract: Semi-supervised anomaly detection is a data mining task which aims at learning features from partially-labeled datasets. We propose Deep Anomaly Detection and Search (DADS) with reinforcement learning. During the training process, the agent searches for possible anomalies in unlabeled dataset to enhance performance. Empirically, we compare DADS with several methods in the settings of leveraging known anomalies to detect both other known and unknown anomalies. Results show that DADS achieves good performance.
Towards Deployment-Efficient and Collision-Free Multi-Agent Path Finding (Student Abstract)
Authors: Feng Chen; Chenghe Wang; Fuxiang Zhang; Hao Ding; Qiaoyong Zhong; Shiliang Pu; Zongzhang Zhang
Venue: AAAI 2023 (main)

Abstract: Multi-agent pathfinding (MAPF) is essential to large-scale robotic coordination tasks. Planning-based algorithms show their advantages in collision avoidance while avoiding exponential growth in the number of agents. Reinforcement-learning (RL)-based algorithms can be deployed efficiently but cannot prevent collisions entirely due to the lack of hard constraints. This paper combines the merits of planning-based and RL-based MAPF methods to propose a deployment-efficient and collision-free MAPF algorithm. The experiments show the effectiveness of our approach.
SkateboardAI: The Coolest Video Action Recognition for Skateboarding (Student Abstract)
Authors: Hanxiao Chen
Venue: AAAI 2023 (main)

Abstract: Impressed by the coolest skateboarding sports program from 2021 Tokyo Olympic Games, we are the first to curate the original real-world video datasets "SkateboardAI" in the wild, even self-design and implement diverse uni-modal and multi-modal video action recognition approaches to recognize different tricks accurately. For uni-modal methods, we separately apply (1)CNN and LSTM; (2)CNN and BiLSTM; (3)CNN and BiLSTM with effective attention mechanisms; (4)Transformer-based action recognition pipeline. Transferred to the multi-modal conditions, we investigated the two-stream Inflated-3D architecture on "SkateboardAI" datasets to compare its performance with uni-modal cases. In sum, our objective is developing an excellent AI sport referee for the coolest skateboarding competitions.
AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection (Student Abstract)
Authors: Haoyang Chen; Shuai Liu; Feng Lu; Wei Li; Bin Sheng; Mi Li; Hai Jin; Albert Y. Zomaya
Venue: AAAI 2023 (main)

Abstract: Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly.
Self-Paced Learning Based Graph Convolutional Neural Network for Mixed Integer Programming (Student Abstract)
Authors: Li Chen; Hua Xu; Ziteng Wang; Chengming Wang; Yu Jiang
Venue: AAAI 2023 (main)

Abstract: Graph convolutional neural network (GCN) based methods have achieved noticeable performance in solving mixed integer programming problems (MIPs). However, the generalization of existing work is limited due to the problem structure. This paper proposes a self-paced learning (SPL) based GCN network (SPGCN) with curriculum learning (CL) to make the utmost of samples. SPGCN employs a GCN model to imitate the branching variable selection during the branch and bound process, while the training process is conducted in a self-paced fashion. Specifically, SPGCN contains a loss-based automatic difficulty measurer, where the training loss of the sample represents the difficulty level. In each iteration, a dynamic training dataset is constructed according to the difficulty level for GCN model training. Experiments on four NP-hard datasets verify that CL can lead to generalization improvement and convergence speedup in solving MIPs, where SPL performs better than predefined CL methods.
Multi-Modal Protein Knowledge Graph Construction and Applications (Student Abstract)
Authors: Siyuan Cheng; Xiaozhuan Liang; Zhen Bi; Huajun Chen; Ningyu Zhang
Venue: AAAI 2023 (main)

Abstract: Existing data-centric methods for protein science generally cannot sufficiently capture and leverage biology knowledge, which may be crucial for many protein tasks. To facilitate research in this field, we create ProteinKG65, a knowledge graph for protein science. Using gene ontology and Uniprot knowledge base as a basis, we transform and integrate various kinds of knowledge with aligned descriptions and protein sequences, respectively, to GO terms and protein entities. ProteinKG65 is mainly dedicated to providing a specialized protein knowledge graph, bringing the knowledge of Gene Ontology to protein function and structure prediction. We also illustrate the potential applications of ProteinKG65 with a prototype. Our dataset can be downloaded at  https://w3id.org/proteinkg65.
CasODE: Modeling Irregular Information Cascade via Neural Ordinary Differential Equations (Student Abstract)
Authors: Zhangtao Cheng; Xovee Xu; Ting Zhong; Fan Zhou; Goce Trajcevski
Venue: AAAI 2023 (main)

Abstract: Predicting information cascade popularity is a fundamental problem for understanding the nature of information propagation on social media. However, existing works fail to capture an essential aspect of information propagation: the temporal irregularity of cascade event -- i.e., users' re-tweetings at random and non-periodic time instants. In this work, we present a novel framework CasODE for information cascade prediction with neural ordinary differential equations (ODEs). CasODE generalizes the discrete state transitions in RNNs to continuous-time dynamics for modeling the irregular-sampled events in information cascades. Experimental evaluations on real-world datasets demonstrate the advantages of the CasODE over baseline approaches.
SR-AnoGAN: You Never Detect Alone. Super Resolution in Anomaly Detection (Student Abstract)
Authors: Minjong Cheon
Venue: AAAI 2023 (main)

Abstract: Despite the advance in deep learning algorithms, implementing supervised learning algorithms in medical datasets is difficult owing to the medical data's properties. This paper proposes SR-AnoGAN, which could generate higher resolution images and conduct anomaly detection more efficiently than AnoGAN. The most distinctive part of the proposed model is incorporating CNN and SRGAN into AnoGAN for reconstructing high-resolution images. Experimental results from X-ray datasets(pneumonia, covid-19) verify that the SR-AnoGAN outperforms the previous AnoGAN model through qualitative and quantitative approaches. Therefore, this paper shows the possibility of resolving data imbalance problems prevalent in the medical field, and proposing more precise diagnosis.
Transformer-Based Named Entity Recognition for French Using Adversarial Adaptation to Similar Domain Corpora (Student Abstract)
Authors: Arjun Choudhry; Pankaj Gupta; Inder Khatri; Aaryan Gupta; Maxime Nicol; Marie-Jean Meurs; Dinesh Kumar Vishwakarma
Venue: AAAI 2023 (main)

Abstract: Named Entity Recognition (NER) involves the identification and classification of named entities in unstructured text into predefined classes. NER in languages with limited resources, like French, is still an open problem due to the lack of large, robust, labelled datasets. In this paper, we propose a transformer-based NER approach for French using adversarial adaptation to similar domain or general corpora for improved feature extraction and better generalization. We evaluate our approach on three labelled datasets and show that our adaptation framework outperforms the corresponding non-adaptive models for various combinations of transformer models, source datasets and target corpora.
Disentangling the Benefits of Self-Supervised Learning to Deployment-Driven Downstream Tasks of Satellite Images (Student Abstract)
Authors: Zhuo Deng; Yibing Wei; Mingye Zhu; Xueliang Wang; Junchi Zhou; Zhicheng Yang; Hang Zhou; Zhenjie Cao; Lan Ma; Mei Han; Jui-Hsin Lai
Venue: AAAI 2023 (main)

Abstract: In this paper, we investigate the benefits of self-supervised learning (SSL) to downstream tasks of satellite images. Unlike common student academic projects, this work focuses on the advantages of the SSL for deployment-driven tasks which have specific scenarios with low or high-spatial resolution images. Our preliminary experiments demonstrate the robust benefits of the SSL trained by medium-resolution (10m) images to both low-resolution (100m) scene classification case (4.25%↑) and very high-resolution (5cm) aerial image segmentation case (1.96%↑), respectively.
Performance Disparities between Accents in Automatic Speech Recognition (Student Abstract)
Authors: Alex DiChristofano; Henry Shuster; Shefali Chandra; Neal Patwari
Venue: AAAI 2023 (main)

Abstract: In this work, we expand the discussion of bias in Automatic Speech Recognition (ASR) through a large-scale audit. Using a large and global data set of speech, we perform an audit of some of the most popular English ASR services. We show that, even when controlling for multiple linguistic covariates, ASR service performance has a statistically significant relationship to the political alignment of the speaker's birth country with respect to the United States' geopolitical power.
Demystify the Gravity Well in the Optimization Landscape (Student Abstract)
Authors: Jason Xiaotian Dou; Runxue Bao; Susan Song; Shuran Yang; Yanfu Zhang; Paul Pu Liang; Haiyi Harry Mao
Venue: AAAI 2023 (main)

Abstract: We provide both empirical and theoretical insights to demystify the gravity well phenomenon in the optimization landscape. We start from describe the problem setup and theoretical results (an escape time lower bound) of the Softmax Gravity Well (SGW) in the literature. Then we move toward the understanding of a recent observation called ASR gravity well. We provide an explanation of why normal distribution with high variance can lead to suboptimal plateaus from an energy function point of view. We also contribute to the empirical insights of curriculum learning by comparison of policy initialization by different normal distributions. Furthermore, we provide the ASR escape time lower bound to understand the ASR gravity well theoretically. Future work includes more specific modeling of the reward as a function of time and quantitative evaluation of normal distribution’s influence on policy initialization.