GenAI4ELab/papercli-papers huggingface dataset:
Neural Language Model Based Attentive Term Dependence Model for Verbose Query (Student Abstract)
Authors: Dipannita Podder; Jiaul H. Paik; Pabitra Mitra
Venue: AAAI 2023 (main)

Abstract: The query-document term matching plays an important role in information retrieval. However, the retrieval performance degrades when the documents get matched with the extraneous terms of the query which frequently arises in verbose queries. To address this problem, we generate the dense vector of
the entire query and individual query terms using the pre-trained BERT (Bidirectional Encoder Representations from Transformers) model and subsequently analyze their relation to focus on the central terms. We then propose a context-aware attentive extension of unsupervised Markov Random Field-based sequential term dependence model that explicitly pays more attention to those contextually central terms. The proposed model utilizes the strengths of the pre-trained large language model for estimating the attention weight of terms and rank the documents in a single pass without any supervision.
Evaluating Factors Influencing COVID-19 Outcomes across Countries Using Decision Trees (Student Abstract)
Authors: Aniruddha Pokhrel; Nikesh Subedi; Saurav Keshari Aryal
Venue: AAAI 2023 (main)

Abstract: While humanity prepares for a post-pandemic world and a return to normality through worldwide vaccination campaigns, each country experienced different levels of impact based on natural, political, regulatory, and socio-economic factors. To prepare for a possible future with COVID-19 and similar outbreaks, it is imperative to understand how each of these factors impacted spread and mortality. We train and tune two decision tree regression models to predict COVID-related cases and deaths using a multitude of features. Our findings suggest that, at the country-level, GDP per capita and comorbidity mortality rate are best predictors for both outcomes. Furthermore, latitude and smoking prevalence are also significantly related to COVID-related spread and mortality.
Ordinal Programmatic Weak Supervision and Crowdsourcing for Estimating Cognitive States (Student Abstract)
Authors: Prakruthi Pradeep; Benedikt Boecking; Nicholas Gisolfi; Jacob R. Kintz; Torin K. Clark; Artur Dubrawski
Venue: AAAI 2023 (main)

Abstract: Crowdsourcing and weak supervision offer methods to efficiently label large datasets. Our work builds on existing weak supervision models to accommodate ordinal target classes, in an effort to recover ground truth from weak, external labels.
We define a parameterized factor function and show that our approach improves over other baselines.
A Probabilistic Graph Diffusion Model for Source Localization (Student Abstract)
Authors: Tangjiang Qian; Xovee Xu; Zhe Xiao; Ting Zhong; Fan Zhou
Venue: AAAI 2023 (main)

Abstract: Source localization, as a reverse problem of graph diffusion, is important for many applications such as rumor tracking, detecting computer viruses, and finding epidemic spreaders. However, it is still under-explored due to the inherent uncertainty of the diffusion process: after a long period of propagation, the same diffusion process may start with diverse sources. Most existing solutions utilize deterministic models and therefore cannot describe the diffusion uncertainty of sources. Moreover, current probabilistic approaches are hard to conduct smooth transformations with variational inference. To overcome the limitations, we propose a probabilistic framework using continuous normalizing flows with invertible transformations and graph neural networks to explicitly model the uncertainty of the diffusion source. Experimental results on two real-world datasets demonstrate the effectiveness of our model over strong baselines.
Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)
Authors: Daking Rai; Yilun Zhou; Bailin Wang; Ziyu Yao
Venue: AAAI 2023 (main)

Abstract: While large language models (LLMs) have demonstrated strong capability in structured prediction tasks such as semantic parsing, few amounts of research have explored the underlying mechanisms of their success. Our work studies different methods for explaining an LLM-based semantic parser and qualitatively discusses the explained model behaviors, hoping to inspire future research toward better understanding them.
Fuzzy C-means: Differences on Clustering Behavior between High Dimensional and Functional Data (Student Abstract)
Authors: Carlos Ramos-Carreño
Venue: AAAI 2023 (main)

Abstract: Fuzzy c-means (FCM) is a generalization of the classical k-means clustering algorithm to the case where an observation can belong to several clusters at the same time.
The algorithm was previously observed to have initialization problems when the number of desired clusters or the number of dimensions of the data are high.
We have tested FCM against clustering problems with functional data, generated from stationary Gaussian processes, and thus in principle infinite-dimensional.
We observed that when the data is more functional in nature, which can be obtained by tuning the length-scale parameter of the Gaussian process, the aforementioned problems do not appear.
This not only indicates that FCM is suitable as a clustering method for functional data, but also illustrates how functional data differs from traditional multivariate data.
In addition this seems to suggest a qualitative way to measure the latent dimensionality of the functional distribution itself.
Photogrammetry and VR for Comparing 2D and Immersive Linguistic Data Collection (Student Abstract)
Authors: Jacob Rubinstein; Cynthia Matuszek; Don Engel
Venue: AAAI 2023 (main)

Abstract: The overarching goal of this work is to enable the collection of language describing a wide variety of objects viewed in virtual reality. We aim to create full 3D models from a small number of ‘keyframe’ images of objects found in the publicly available Grounded Language Dataset (GoLD) using photogrammetry. We will then collect linguistic descriptions by placing our models in virtual reality and having volunteers describe them. To evaluate the impact of virtual reality immersion on linguistic descriptions of the objects, we intend to apply contrastive learning to perform grounded language learning, then compare the descriptions collected from images (in GoLD) versus our models.
RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget (Student Abstract)
Authors: Sourajit Saha; Shaswati Saha; Md Osman Gani; Tim Oates; David Chapman
Venue: AAAI 2023 (main)

Abstract: Learning High-Resolution representations is essential for semantic segmentation. Convolutional neural network (CNN) architectures with downstream and upstream propagation flow are popular for segmentation in medical diagnosis. However, due to performing spatial downsampling and upsampling in multiple stages, information loss is inexorable. On the contrary, connecting layers densely on high spatial resolution is computationally expensive. In this work, we devise a Loose Dense Connection Strategy to connect neurons in subsequent layers with reduced parameters. On top of that, using a m-way Tree structure for feature propagation we propose Receptive Field Chain Network (RFC-Net) that learns high-resolution global features on a compressed computational space. Our experiments demonstrates that RFC Net achieves state-of-the-art performance on Kvasir and CVC-ClinicDB benchmarks for Polyp segmentation. Our code is publicly available at github.com/sourajitcs/RFC-NetAAAI23.
Maximizing Influence Spread through a Dynamic Social Network (Student Abstract)
Authors: Šimon Schierreich
Venue: AAAI 2023 (main)

Abstract: Modern social networks are dynamic in their nature; a new connections are appearing and old connections are disappearing all the time. However, in our algorithmic and complexity studies, we usually model social networks as static graphs.

In this paper, we propose a new paradigm for the study of the well-known Target Set Selection problem, which is a fundamental problem in viral marketing and the spread of opinion through social networks. In particular, we use temporal graphs to capture the dynamic nature of social networks.

We show that the temporal interpretation is, unsurprisingly, NP-complete in general. Then, we study computational complexity of this problem for multiple restrictions of both the threshold function and the underlying graph structure and provide multiple hardness lower-bounds.
Can You Answer This? – Exploring Zero-Shot QA Generalization Capabilities in Large Language Models (Student Abstract)
Authors: Saptarshi Sengupta; Shreya Ghosh; Preslav Nakov; Prasenjit Mitra
Venue: AAAI 2023 (main)

Abstract: The buzz around Transformer-based language models (TLM) such as BERT, RoBERTa, etc. is well-founded owing to their impressive results on an array of tasks. However, when applied to areas needing specialized knowledge (closed-domain), such as medical, finance, etc. their performance takes drastic hits, sometimes more than their older recurrent/convolutional counterparts. In this paper, we explore zero-shot capabilities of large LMs for extractive QA. Our objective is to examine performance change in the face of domain drift i.e. when the target domain data is vastly different in semantic and statistical properties from the source domain and attempt to explain the subsequent behavior. To this end, we present two studies in this paper while planning further experiments later down the road. Our findings indicate flaws in the current generation of TLM limiting their performance on closed-domain tasks.
FakeKG: A Knowledge Graph of Fake Claims for Improving Automated Fact-Checking (Student Abstract)
Authors: Gautam Kishore Shahi
Venue: AAAI 2023 (main)

Abstract: False information could be dangerous if the claim is not debunked timely. Fact-checking organisations get a high volume of claims on different topics with immense velocity. The efficiency of the fact-checkers decreases due to 3V problems volume, velocity and variety. Especially during crises or elections, fact-checkers cannot handle user requests to verify the claim. Until now, no real-time curable centralised corpus of fact-checked articles is available. Also, the same claim is fact-checked by multiple fact-checking organisations with or without judgement. To fill this gap, we introduce FakeKG: A Knowledge Graph-Based approach for improving Automated Fact-checking. FakeKG is a centralised knowledge graph containing fact-checked articles from different sources that can be queried using the SPARQL endpoint. The proposed FakeKG can prescreen claim requests and filter them if the claim is already fact-checked and provide a judgement to the claim. It will also categorise the claim's domain so that the fact-checker can prioritise checking the incoming claims into different groups like health and election. This study proposes an approach for creating FakeKG and its future application for mitigating misinformation.
Can Adversarial Networks Make Uninformative Colonoscopy Video Frames Clinically Informative? (Student Abstract)
Authors: Vanshali Sharma; M.K. Bhuyan; Pradip K. Das
Venue: AAAI 2023 (main)

Abstract: Various artifacts, such as ghost colors, interlacing, and motion blur, hinder diagnosing colorectal cancer (CRC) from videos acquired during colonoscopy. The frames containing these artifacts are called uninformative frames and are present in large proportions in colonoscopy videos. To alleviate the impact of artifacts,  we propose an adversarial network based framework to convert uninformative frames to clinically relevant frames. We examine the effectiveness of the proposed approach by evaluating the translated frames for polyp detection using YOLOv5. Preliminary results present improved detection performance along with elegant qualitative outcomes. We also examine the failure cases to determine the directions for future work.
Bayesian Models for Targeted Cyber Deception Strategies (Student Abstract)
Authors: Nazia Sharmin
Venue: AAAI 2023 (main)

Abstract: We propose a model-driven decision support system (DSS) based on a Bayesian belief network (BBN) to support cyber deception based on a detailed model of attacker beliefs. We discuss this approach using a case study based on passively observed operating system (OS) fingerprinting data. In passive reconnaissance attackers can remain undetected while collecting information to identify systems and plan attacks. Our DSS is intended to support preventative measures to protect the network from successful reconnaissance, such as by modifying features using deception. We validate the prediction accuracy of the model in comparison with a sequential artificial neural network (ANN). We then introduce a deceptive algorithm to select a minimal set of features for OS obfuscation. We show the effectiveness of feature-modification strategies based on our methods using passively collected data to decide what features from a real operating system (OS) to modify to appear as a fake [different] OS.
Scalable Negotiating Agent Strategy via Multi-Issue Policy Network (Student Abstract)
Authors: Takumu Shimizu; Ryota Higa; Toki Takahashi; Katsuhide Fujita; Shinji Nakadai
Venue: AAAI 2023 (main)

Abstract: Previous research on the comprehensive negotiation strategy using deep reinforcement learning (RL) has scalability issues of not performing effectively in the large-sized domains.
We improve negotiation strategy via deep RL by considering an issue-based represented deep policy network to deal with multi-issue negotiation.
The architecture of the proposed learning agent considers the characteristics of multi-issue negotiation domains and policy-based learning.
We demonstrate that proposed method achieve equivalent or higher utility than existing negotiation agents in the large-sized domains.
Efficient Dynamic Batch Adaptation (Student Abstract)
Authors: Cristian Simionescu; George Stoica
Venue: AAAI 2023 (main)

Abstract: In this paper we introduce Efficient Dynamic Batch Adaptation (EDBA), which improves on a previous method that works by adjusting the composition and the size of the current batch. Our improvements allow for Dynamic Batch Adaptation to feasibly scale up for bigger models and datasets, drastically improving model convergence and generalization. We show how the method is still able to perform especially well in data-scarce scenarios, managing to obtain a test accuracy on 100 samples of CIFAR-10 of 90.68%, while the baseline only reaches 23.79%. On the full CIFAR-10 dataset, EDBA reaches convergence in ∼120 epochs while the baseline requires ∼300 epochs.
Development of a Human-Agent Interaction System including Norm and Emotion in an Evacuation Situation (Student Abstract)
Authors: Ephraim Sinyabe Pagou; Vivient Corneille Kamla; Igor Tchappi; Amro Najjar
Venue: AAAI 2023 (main)

Abstract: Agent-based modeling and simulation can provide a powerful test environment for crisis management scenarios. Human agent interaction has limitations in representing norms issued by an agent to a human agent that has emotions. In this study, we present an approach to the interaction between a virtual normative agent and a human agent in an evacuation scenario. Through simulation comparisons, it is shown that the method used in this study can more fully simulate the real-life out come of an emergency situation and also improves the au thenticity of the agent interaction.
Persistent Homology through Image Segmentation (Student Abstract)
Authors: Joshua Slater; Thomas Weighill
Venue: AAAI 2023 (main)

Abstract: The efficacy of topological data analysis (TDA) has been demonstrated in many different machine learning pipelines, particularly those in which structural characteristics of data are highly relevant. However, TDA's usability in large scale machine learning applications is hindered by the significant computational cost of generating persistence diagrams. In this work, a method that allows this computationally expensive process to be approximated by deep neural networks is proposed. Moreover, the method's practicality in estimating 0-dimensional persistence diagrams across a diverse range of images is shown.
TA-DA: Topic-Aware Domain Adaptation for Scientific Keyphrase Identification and Classification (Student Abstract)
Authors: Răzvan-Alexandru Smădu; George-Eduard Zaharia; Andrei-Marius Avram; Dumitru-Clementin Cercel; Mihai Dascalu; Florin Pop
Venue: AAAI 2023 (main)

Abstract: Keyphrase identification and classification is a Natural Language Processing and Information Retrieval task that involves extracting relevant groups of words from a given text related to the main topic. In this work, we focus on extracting keyphrases from scientific documents. We introduce TA-DA, a Topic-Aware Domain Adaptation framework for keyphrase extraction that integrates Multi-Task Learning with Adversarial Training and Domain Adaptation. Our approach improves performance over baseline models by up to 5% in the exact match of the F1-score.
Exploring the Relative Value of Collaborative Optimisation Pathways (Student Abstract)
Authors: Sudarshan Sreeram
Venue: AAAI 2023 (main)

Abstract: Compression techniques in machine learning (ML) independently improve a model’s inference efficiency by reducing its memory footprint while aiming to maintain its quality. This paper lays groundwork in questioning the merit of a compression pipeline involving all techniques as opposed to skipping a few by considering a case study on a keyword spotting model: DS-CNN-S. In addition, it documents improvements to the model’s training and dataset infrastructure. For this model, preliminary findings suggest that a full-scale pipeline isn’t required to achieve a competent memory footprint and accuracy, but a more comprehensive study is required.
Backforward Propagation (Student Abstract)
Authors: George Stoica; Cristian Simionescu
Venue: AAAI 2023 (main)

Abstract: In this paper we introduce Backforward Propagation, a method of completely eliminating Internal Covariate Shift (ICS). Unlike previous methods, which only indirectly reduce the impact of ICS while introducing other biases, we are able to have a surgical view at the effects ICS has on training neural networks. Our experiments show that ICS has a weight regularizing effect on models, and completely removing it enables for faster convergence of the neural network.
Two-Streams: Dark and Light Networks with Graph Convolution for Action Recognition from Dark Videos (Student Abstract)
Authors: Saurabh Suman; Nilay Naharas; Badri Narayan Subudhi; Vinit Jakhetiya
Venue: AAAI 2023 (main)

Abstract: In this article, we propose a two-stream action recognition technique for recognizing human actions from dark videos. The proposed action recognition network consists of an image enhancement network with Self-Calibrated Illumination (SCI) module, followed by a two-stream action recognition network. We have used R(2+1)D as a feature extractor for both streams with shared weights. Graph Convolutional Network (GCN), a temporal graph encoder is utilized to enhance the obtained features which are then further fed to a classification head to recognize the actions in a video. The experimental results are presented on the recent benchmark ``ARID" dark-video database.
ES-Mask: Evolutionary Strip Mask for Explaining Time Series Prediction (Student Abstract)
Authors: Yifei Sun; Cheng Song; Feng Lu; Wei Li; Hai Jin; Albert Y. Zomaya
Venue: AAAI 2023 (main)

Abstract: Machine learning models are increasingly used in time series prediction with promising results. The model explanation of time series prediction falls behind the model development and makes less sense to users in understanding model decisions. This paper proposes ES-Mask, a post-hoc and model-agnostic evolutionary strip mask-based saliency approach for time series applications. ES-Mask designs the mask consisting of strips with the same salient value in consecutive time steps to produce binary and sustained feature importance scores over time for easy understanding and interpretation of time series. ES-Mask uses an evolutionary algorithm to search for the optimal mask by manipulating strips in rounds, thus is agnostic to models by involving no internal model states in the search. The initial experiments on MIMIC-III data set show that ES-Mask outperforms state-of-the-art methods.
Exploration on Physics-Informed Neural Networks on Partial Differential Equations (Student Abstract)
Authors: Hoa Ta; Shi Wen Wong; Nathan McClanahan; Jung-Han Kimn; Kaiqun Fu
Venue: AAAI 2023 (main)

Abstract: Data-driven related solutions are dominating various scientific fields with the assistance of machine learning and data analytics. Finding effective solutions has long been discussed in the area of machine learning. The recent decade has witnessed the promising performance of the Physics-Informed Neural Networks (PINN) in bridging the gap between real-world scientific problems and machine learning models. In this paper, we explore the behavior of PINN in a particular range of different diffusion coefficients under specific boundary conditions. In addition, different initial conditions of partial differential equations are solved by applying the proposed PINN. Our paper illustrates how the effectiveness of the PINN can change under various scenarios. As a result, we demonstrate a better insight into the behaviors of the PINN and how to make the proposed method more robust while encountering different scientific and engineering problems.
Parallel Index-Based Search Algorithm for Coalition Structure Generation (Student Abstract)
Authors: Redha Taguelmimt; Samir Aknine; Djamila Boukredera; Narayan Changder
Venue: AAAI 2023 (main)

Abstract: In this paper, we propose a novel algorithm to address the Coalition Structure Generation (CSG) problem. Specifically, we use a novel representation of the search space that enables it to be explored in a new way. We introduce an index-based exact algorithm. Our algorithm is anytime, produces optimal solutions, and can be run on large-scale problems with hundreds of agents. Our experimental evaluation on a benchmark with several value distributions shows that our representation of the search space that we combined with the proposed algorithm provides high-quality results for the CSG problem and outperforms existing state-of-the-art algorithms.
The Naughtyformer: A Transformer Understands and Moderates Adult Humor (Student Abstract)
Authors: Leonard Tang; Alexander Cai; Jason Wang
Venue: AAAI 2023 (main)

Abstract: Jokes are intentionally written to be funny, but not all jokes are created the same. While recent work has shown impressive results on humor detection in text, we instead investigate the more nuanced task of detecting humor subtypes, especially of the more adult variety. To that end, we introduce a novel jokes dataset filtered from Reddit and solve the subtype
classification task using a finetuned Transformer dubbed the Naughtyformer. Moreover, we show that our model is significantly better at detecting offensiveness in jokes compared to state-of-the-art methods.