bluuebunny/crossref_metadata_2025 huggingface dataset:
Learning Generalizable and Composable Abstractions for Transfer in Reinforcement Learning
Authors: Nayyar Rashmeet Kaur
Published: March 2024

Reinforcement Learning (RL) in complex environments presents many challenges: agents require learning concise representations of both environments and behaviors for efficient reasoning and generalizing experiences to new, unseen situations. However, RL approaches can be sample-inefficient and difficult to scale, especially in long-horizon sparse reward settings. To address these issues, the goal of my doctoral research is to develop methods that automatically construct semantically meaningful state and temporal abstractions for efficient transfer and generalization. In my work, I develop hierarchical approaches for learning transferable, generalizable knowledge in the form of symbolically represented options, as well as for integrating search techniques with RL to solve new problems by efficiently composing the learned options. Empirical results show that the resulting approaches effectively learn and transfer knowledge, achieving superior sample efficiency compared to SOTA methods while also enhancing interpretability.
Learning from an Infant’s Visual Experience
Authors: Sanyal Deepayan
Published: March 2024

Infants see a selective view of the world: they see some objects with high frequency and from a wide range of viewpoints (e.g., their toys during playing) while a much larger set of objects are seen much more rarely and from limited viewpoints (e.g., objects they see outdoors). Extensive, repeated visual experiences with a small number of objects during infancy plays a big role in the development of human visual skills. Internet-style datasets that are commonly used in computer vision research do not contain the regularities that result from such repeated, structured experiences with a few objects. This has led to a dearth of models that learn by exploiting these regularities. In my PhD dissertation, I use deep learning models to investigate how regularities in an infant's visual experience can be leveraged for visual representation learning.
Learning Neuro-Symbolic Abstractions for Robot Planning and Learning
Authors: Shah Naman
Published: March 2024

Although state-of-the-art hierarchical robot planning algorithms allow robots to efficiently compute long-horizon motion plans for achieving user desired tasks, these methods typically rely upon environment-dependent state and action abstractions that need to be hand-designed by experts. On the other hand, non-hierarchical robot planning approaches fail to compute solutions for complex tasks that require reasoning over a long horizon. My research addresses these problems by proposing an approach for learning abstractions and developing hierarchical planners that efficiently use learned abstractions to boost robot planning performance and provide strong guarantees of reliability.
Neuro-Symbolic Integration for Reasoning and Learning on Knowledge Graphs
Authors: Werner Luisa
Published: March 2024

The goal of this thesis is to address knowledge graph completion tasks using neuro-symbolic methods. Neuro-symbolic methods allow the joint utilization of symbolic information defined as meta-rules in ontologies and knowledge graph embedding methods that represent entities and relations of the graph in a low-dimensional vector space. This approach has the potential to improve the resolution of knowledge graph completion tasks in terms of reliability, interpretability, data-efficiency and robustness.
Visual Abstract Reasoning in Computational Imagery
Authors: Yang Yuan
Published: March 2024

Despite current AI’s human-like behavior, super efficiency, and unbelievable ability to handle complex games, we still complain that it shows no sign of creativity, originality, or novelty outside its training set, and that it fails to develop new insights into old experience or establish understanding of new experience. In short, it generates content from its training set, but does not invent content. A fundamental reason for this is that current AI is incapable of abstraction and reasoning in an abstract, generalizable, and systematic way. Think, for instance, of what AI systems we can build if we have a base system that can answer this simple question—when two things are the same. Instead of studying these high-level questions, I put my thesis in the context of visual abstract reasoning (VAR), a task widely used in human intelligence tests. A classical example of this task is Raven’s Progressive Matrices (RPM, see Figure 1), a family of intelligence tests that was designed to measure eductive ability, i.e., the ability to make meaning out of confusion and generate high-level, usually nonverbal, schemata which make it easy to handle complexity. A similar concept to eductive ability is fluid intelligence, or the ability to discriminate and perceive complex relationships when no recourse to answers is stored in memory. Whether eductive ability or fluid intelligence, RPM points to the qualities that have been lacking in AI. To explore these qualities in AI, I propose the following research questions.
Multipartite Entity Resolution: Motivating a K-Tuple Perspective (Student Abstract)
Authors: Aberbach Adin , Kejriwal Mayank , Shen Ke
Published: March 2024

Entity Resolution (ER) is the problem of algorithmically matching records, mentions, or entries that refer to the same underlying real-world entity. Traditionally, the problem assumes (at most) two datasets, between which records need to be matched. There is considerably less research in ER when k > 2 datasets are involved. The evaluation of such multipartite ER (M-ER) is especially complex, since the usual ER metrics assume (whether implicitly or explicitly) k < 3. This paper takes the first step towards motivating a k-tuple approach for evaluating M-ER. Using standard algorithms and k-tuple versions of metrics like precision and recall, our preliminary results suggest a significant difference compared to aggregated pairwise evaluation, which would first decompose the M-ER problem into independent bipartite problems and then aggregate their metrics. Hence, M-ER may be more challenging and warrant more novel approaches than current decomposition-based pairwise approaches would suggest.
Interpreting Temporal Knowledge Graph Reasoning (Student Abstract)
Authors: Chen Bin , Yang Kai , Tai Wenxin , Cheng Zhangtao , Liu Leyuan , Zhong Ting , Zhou Fan
Published: March 2024

Temporal knowledge graph reasoning is an essential task that holds immense value in diverse real-world applications. Existing studies mainly focus on leveraging structural and sequential dependencies, excelling in tasks like entity and link prediction. However, they confront a notable interpretability gap in their predictions, a pivotal facet for comprehending model behavior. In this study, we propose an innovative method, LSGAT, which not only exhibits remarkable precision in entity predictions but also enhances interpretability by identifying pivotal historical events influencing event predictions. LSGAT enables concise explanations for prediction outcomes, offering valuable insights into the otherwise enigmatic "black box" reasoning process. Through an exploration of the implications of the most influential events, it facilitates a deeper understanding of the underlying mechanisms governing predictions.
The Language Model Can Have the Personality: Joint Learning for Personality Enhanced Language Model (Student Abstract)
Authors: Chen Tianyi , Cao Feiqi , Ding Yihao , Han Caren
Published: March 2024

With the introduction of large language models, chatbots are becoming more conversational to communicate effectively and capable of handling increasingly complex tasks. To make a chatbot more relatable and engaging, we propose a new language model idea that maps the human-like personality.
In this paper, we propose a systematic Personality-Enhanced Language Model (PELM) approach by using a joint learning mechanism of personality classification and language generation tasks. The proposed PELM leverages a dataset of defined personality typology, Myers-Briggs Type Indicator, and produces a Personality-Enhanced Language Model by using a joint learning and cross-teaching structure consisting of a classification and language modelling to incorporate personalities via both distinctive types and textual information. The results show that PELM can generate better personality-based outputs than baseline models.
MapLE: Matching Molecular Analogues Promptly with Low Computational Resources by Multi-Metrics Evaluation (Student Abstract)
Authors: Chen Xiaojian , Liao Chuyue , Gu Yanhui , Li Yafei , Wang Jinlan , Chen Yi , Kitsuregawa Masaru
Published: March 2024

Matching molecular analogues is a computational chemistry and bioinformatics research issue which is used to identify molecules that are structurally or functionally similar to a target molecule. Recent studies on matching analogous molecules have predominantly concentrated on enhancing effectiveness, often sidelining computational efficiency, particularly in contexts of low computational resources. This oversight poses challenges in many real applications (e.g., drug discovery, catalyst generation and so forth). To tackle this issue, we propose a general strategy named MapLE, aiming to promptly match analogous molecules with low computational resources by multi-metrics evaluation. Experimental evaluation conducted on a public biomolecular dataset validates the excellent and efficient performance of the proposed strategy.
Memory-Augmenting Decoder-Only Language Models through Encoders (Student Abstract)
Authors: Galatolo Alessio , Winkle Katie
Published: March 2024

The Transformer architecture has seen a lot of attention in recent years also thanks to its ability to scale well and allow massive parallelism during training. This has made possible the development of Language Models (LMs) of increasing size and the discovery of latent abilities that completely outclass traditional methods e.g. rule-based systems. However, they also introduced new issues, like their inability to retain the history of previous interactions due to their stateless nature or the difficulty in controlling their generation. Different attempts have been made to address these issues, e.g. a `brute force' approach to solving the memory issue is to include the full conversation history in the context window, a solution that is limited by the quadratic scalability of Transformers. In this work, we explore computationally practical solutions to the memory problem. We propose to augment the decoder-only architecture of (most) Large LMs with a (relatively small) memory encoder. Its output is prepended to the decoder's input in a similar fashion to recent works in Adapters and the original Transformer architecture. Initial experiments show promising results, however future work is needed to compare with State-of-the-Art methods.
Multilingual Medical Language Models: A Path to Improving Lay Health Worker Effectiveness (Student Abstract)
Authors: Gangavarapu Agasthya
Published: March 2024

The COVID-19 pandemic has exacerbated the challenges faced by healthcare delivery in developing nations, placing additional strain on already fragile infrastructure and healthcare systems. This has prompted an increased reliance on lay healthcare workers (LHWs) to meet the surging demand for services. Due to limited formal training, many LHWs have resorted to using unreliable sources, such as internet searches, to access medical information.

Large language models (LLMs) offer a promising opportunity to support LHWs by providing accurate, context-sensitive information for improving healthcare delivery, provided they are appropriately fine-tuned on domain-specific multilingual data. This paper delves into critical issues and presents potential solutions for developing LLM-powered virtual assistants tailored to LHWs serving Telugu and Hindi-speaking populations. Key focal points include the customization of language and content to suit local contexts, the integration of feedback mechanisms to continuously enhance assistance quality, and the delicate balance between automation and human oversight.
Enhancing Transcription Factor Prediction through Multi-Task Learning (Student Abstract)
Authors: Gao Liyuan , Zhang Matthew , Sheng Victor S.
Published: March 2024

Transcription factors (TFs) play a fundamental role in gene regulation by selectively binding to specific DNA sequences. Understanding the nature and behavior of these TFs is essential for insights into gene regulation dynamics. In this study, we introduce a robust multi-task learning framework specifically tailored to harness both TF-specific annotations and TF-related domain annotations, thereby enhancing the accuracy of TF predictions. Notably, we incorporate cutting-edge language models that have recently garnered attention for their outstanding performance across various fields, particularly in biological computations like protein sequence modeling. Comparative experimental analysis with existing models, DeepTFactor and TFpredict, reveals that our multi-task learning framework achieves an accuracy exceeding 92% across four evaluation metrics on the TF prediction task, surpassing both competitors. Our work marks a significant leap in the domain of TF prediction, enriching our comprehension of gene regulatory mechanisms and paving the way for the discovery of novel regulatory motifs.
Towards a Transformer-Based Reverse Dictionary Model for Quality Estimation of Definitions (Student Abstract)
Authors: Guité-Vinet Julien , Blondin Massé Alexandre , Sadat Fatiha
Published: March 2024

In the last years, several variants of transformers have emerged. In this paper, we compare different transformer-based models for solving the reverse dictionary task and explore their use in the context of a serious game called The Dictionary Game.
Disentanglement-Guided Spatial-Temporal Graph Neural Network for Metro Flow Forecasting (Student Abstract)
Authors: Hong Jinyu , Kuang Ping , Gao Qiang , Zhou Fan
Published: March 2024

In recent intelligent transportation applications, metro flow forecasting has received much attention from researchers. Most prior arts endeavor to explore spatial or temporal dependencies while ignoring the key characteristic patterns underlying historical flows, e.g., trend and periodicity. Although the multiple granularity distillations or spatial dependency correlation can promote the flow estimation. However, the potential noise and spatial dynamics are under-explored. To this end, we propose a novel Disentanglement-Guided Spatial-Temporal Graph Neural Network or DGST to address the above concerns. It contains a Disentanglement Pre-training procedure for characteristic pattern disentanglement learning, a Characteristic Pattern Prediction for different future characteristic explorations, and a Spatial-Temporal Correlation for spatial-temporal dynamic learning. Experiments on a real-world dataset demonstrate the superiority of our DGST.
Power Grid Anomaly Detection via Hybrid LSTM-GIN Model (Student Abstract)
Authors: Jobe Amelia , Ky Richard , Luo Sandra , Dhamsania Akshay , Purohit Sumit , Serra Edoardo
Published: March 2024

Cyberattacks on power grids pose significant risks to national security. Power grid attacks typically lead to abnormal readings in power output, frequency, current, and voltage. Due to the interconnected structure of power grids, abnormalities can spread throughout the system and cause widespread power outages if not detected and dealt with promptly. Our research proposes a novel anomaly detection system for power grids that prevents overfitting. We created a network graph to represent the structure of the power grid, where nodes represent power grid components like generators and edges represent connections between nodes such as overhead power lines. We combine the capabilities of Long Short-Term Memory (LSTM) models with a Graph Isomorphism Network (GIN) in a hybrid model to pinpoint anomalies in the grid. We train our model on each category of nodes that serves a similar structural purpose to prevent overfitting of the model. We then assign each node in the graph a unique signature using a GIN. Our model achieved a 99.92% accuracy rate, which is significantly higher than a version of our model without structural encoding, which had an accuracy level of 97.30%. Our model allows us to capture structural and temporal components of power grids and develop an attack detection system with high accuracy without overfitting.
Multivariate Time-Series Imagification with Time Embedding in Constrained Environments (Student Abstract)
Authors: Kang Seung Woo , Jo Ohyun
Published: March 2024

We present an imagification approach for multivariate time-series data tailored to constrained NN-based forecasting model training environments. Our imagification process consists of two key steps: Re-stacking and time embedding. In the Re-stacking stage, time-series data are arranged based on high correlation, forming the first image channel using a sliding window technique. The time embedding stage adds two additional image channels by incorporating real-time information. We evaluate our method by comparing it with three benchmark imagification techniques using a simple CNN-based model. Additionally, we conduct a comparison with LSTM, a conventional time-series forecasting model. Experimental results demonstrate that our proposed approach achieves three times faster model training termination while maintaining forecasting accuracy.
LaMAR: Laplacian Pyramid for Multimodal Adaptive Super Resolution (Student Abstract)
Authors: Kasliwal Aditya , Kamani Aryan , Gakhar Ishaan , Seth Pratinav , Rallabandi Sriya
Published: March 2024

Recent advances in image-to-image translation involve the integration of non-visual imagery in deep models. Non-visual sensors, although more costly, often produce low-resolution images. To combat this, methods using RGB images to enhance the resolution of these modalities have been introduced. Fusing these modalities to achieve high-resolution results demands models with millions of parameters and extended inference times. We present LaMAR, a lightweight model. It employs Laplacian image pyramids combined with a low-resolution thermal image for Guided Thermal Super Resolution. By decomposing the RGB image into a Laplacian pyramid, LaMAR preserves image details and avoids high-resolution feature map computations, ensuring efficiency. With faster inference times and fewer parameters, our model demonstrates state-of-the-art results.
IncepSeqNet: Advancing Signal Classification with Multi-Shape Augmentation (Student Abstract)
Authors: Kim Jongseok , Jo Ohyun
Published: March 2024

This work proposes and analyzes IncepSeqNet which is a new model combining the Inception Module with the innovative Multi-Shape Augmentation technique. IncepSeqNet excels in feature extraction from sequence signal data consisting of a number of complex numbers to achieve superior classification accuracy across various SNR(Signal-to-Noise Ratio) environments. Experimental results demonstrate IncepSeqNet’s outperformance of existing models, particularly at low SNR levels. Furthermore, we have confirmed its applicability in practical 5G systems by using real-world signal data.
Meta-Crafting: Improved Detection of Out-of-Distributed Texts via Crafting Metadata Space (Student Abstract)
Authors: Koo Ryan , Kim Yekyung , Kang Dongyeop , Kim Jaehyung
Published: March 2024

Detecting out-of-distribution (OOD) samples is crucial for robust NLP models. Recent works observe two OOD types: background shifts (style change) and semantic shifts (content change), but existing detection methods vary in effectiveness for each type. To this end, we propose Meta-Crafting, a unified OOD detection method by constructing a new discriminative feature space utilizing 7 model-driven metadata chosen empirically that well detects both types of shifts. Our experimental results demonstrate state-of-the-art robustness to both shifts and significantly improved detection on stress datasets.
Attacking CNNs in Histopathology with SNAP: Sporadic and Naturalistic Adversarial Patches (Student Abstract)
Authors: Kumar Daya , Sharma Abhijith , Narayan Apurva
Published: March 2024

Convolutional neural networks (CNNs) are being increasingly
adopted in medical imaging. However, in the race for
developing accurate models, their robustness is often overlooked.
This elicits a significant concern given the safety-critical
nature of the healthcare system. Here, we highlight
the vulnerability of CNNs against a sporadic and naturalistic
adversarial patch attack (SNAP). We train SNAP to mislead
the ResNet50 model predicting metastasis in histopathological
scans of lymph node sections, lowering the accuracy by
27%. This work emphasizes the need for defense strategies
before deploying CNNs in critical healthcare settings.
A SAT Solver and Computer Algebra Attack on the Minimum Kochen-Specker Problem (Student Abstract)
Authors: Li Zhengyu , Bright Curtis , Ganesh Vijay
Published: March 2024

The problem of finding the minimum three-dimensional Kochen–Specker (KS) vector system, an important problem in quantum foundations, has remained open for over 55 years. We present a new method to address this problem based on a combination of a Boolean satisfiability (SAT) solver and a computer algebra system (CAS). Our approach improved the lower bound on the size of a KS system from 22 to 24. More importantly, we provide the first computer-verifiable proof certificate of a lower bound to the KS problem with a proof size of 41.6 TiB for order 23. The efficiency is due to the powerful combination of SAT solvers and CAS-based orderly generation.
Graph Clustering Methods Derived from Column Subset Selection (Student Abstract)
Authors: Mao Wei , Wan Guihong , Schweitzer Haim
Published: March 2024

Spectral clustering is a powerful clustering technique. It leverages the spectral properties of graphs to partition data points into meaningful clusters. The most common criterion for evaluating multi-way spectral clustering is NCut. Column Subset Selection is an important optimization technique in the domain of feature selection and dimension reduction which aims to identify a subset of columns of a given data matrix that can be used to approximate the entire matrix. We show that column subset selection can be used to compute spectral clustering and use this to obtain new graph clustering algorithms.
Fast and Knowledge-Free Deep Learning for General Game Playing (Student Abstract)
Authors: Maras Michał , Kępa Michał , Kowalski Jakub , Szykuła Marek
Published: March 2024

We develop a method of adapting the AlphaZero model to General Game Playing (GGP) that focuses on faster model generation and requires less knowledge to be extracted from the game rules. The dataset generation uses MCTS playing instead of self-play; only the value network is used, and attention layers replace the convolutional ones. This allows us to abandon any assumptions about the action space and board topology. We implement the method within the Regular Boardgames GGP system and show that we can build models outperforming the UCT baseline for most games efficiently.
Towards Robustness to Natural Variations and Distribution Shift (Student Abstract)
Authors: Martínez-Martínez Josué , Brown Olivia , Caceres Rajmonda
Published: March 2024

This research focuses on improving the robustness of machine learning systems to natural variations and distribution shifts. A design trade space is presented, and various methods are compared, including adversarial training, data augmentation techniques, and novel approaches inspired by model-based robust optimization formulations.
Topological and Node Noise Filtering on 3D Meshes Using Graph Neural Networks (Student Abstract)
Authors: Mashurov Vladimir , Semenova Natalia
Published: March 2024

Topological and node noise filtration are typically considered separately. Graph Neural Networks (GNN) are commonly used for node noise filtration, as they offer high efficiency and low exploitation costs. This paper explores the solution of joint node and topological noise filtration through the use of graph neural networks. Since treating a 3D mesh as a graph is challenging, an indicator function grid representation is employed as input for GNNs to perform the joint filtering. The resulting machine learning model is inspired by point cloud to mesh reconstruction algorithms and demonstrates low computational requirements during inference, producing successful results for smooth, watertight 3D models.
Welfare Maximization in Perpetual Voting (Student Abstract)
Authors: Neoh Tzeh Yuan , Teh Nicholas
Published: March 2024

We study the computational problems associated with maximizing various welfare objectives—namely utilitarian welfare, egalitarian welfare, and Nash welfare—in perpetual voting, a sequential collective decision-making framework. Prior work look into notions of fairness over time and study extensions of single-round voting rules to the multi-round setting.
We show that while a utilitarian-welfare maximizing outcome can be computed efficiently, an outcome that maximizes egalitarian or Nash welfare is computationally intractable, even in the case of two candidates. We complement this by showing that maximizing egalitarian welfare is fixed-parameter tractable in the number of agents, and maximizing egalitarian or Nash welfare is W[2]-hard and slicewise polynomial in the number of timesteps. We also provide an approximation algorithm for maximizing egalitarian welfare and study strategyproofness with respect to these welfare objectives. Finally, we show that a simple greedy algorithm can achieve approximate proportionality in this setting.
Large Language Models as Planning Domain Generators (Student Abstract)
Authors: Oswald James , Srinivas Kavitha , Kokel Harsha , Lee Junkyu , Katz Michael , Sohrabi Shirin
Published: March 2024

The creation of planning models, and in particular domain
models, is among the last bastions of tasks that require exten-
sive manual labor in AI planning; it is desirable to simplify
this process for the sake of making planning more accessi-
ble. To this end, we investigate whether large language mod-
els (LLMs) can be used to generate planning domain models
from textual descriptions. We propose a novel task for this
as well as a means of automated evaluation for generated do-
mains by comparing the sets of plans for domain instances.
Finally, we perform an empirical analysis of 7 large language
models, including coding and chat models across 9 different
planning domains. Our results show that LLMs, particularly
larger ones, exhibit some level of proficiency in generating
correct planning domains from natural language descriptions
Graph Anomaly Detection with Diffusion Model-Based Graph Enhancement (Student Abstract)
Authors: Pang Shikang , Xiao Chunjing , Tai Wenxin , Cheng Zhangtao , Zhou Fan
Published: March 2024

Graph anomaly detection has gained significant research interest across various domains. Due to the lack of labeled data, contrastive learning has been applied in detecting anomalies and various scales of contrastive strategies have been initiated. However, these methods might force two instances (e.g., node-level and subgraph-level representations) with different category labels to be consistent during model training, which can adversely impact the model robustness. To tackle this problem, we present a novel contrastive learning framework with the Diffusion model-based graph Enhancement module for Graph Anomaly Detection, DEGAD. In this framework, we design a diffusion model-based graph enhancement module to manipulate neighbors to generate enhanced graphs, which can efficiently alleviate the inconsistent problem. Further, based on the enhanced graphs, we present a multi-scale contrastive module to discriminate anomalies. Experimental results demonstrate the superiority of our model.
Skip-GANomaly++: Skip Connections and Residual Blocks for Anomaly Detection (Student Abstract)
Authors: Park June-Young , Hong Jae-Ryung , Kim Min-Hye , Kim Tae-Joon
Published: March 2024

Anomaly detection is a critical task across various domains. Fundamentally, anomaly detection models offer methods to identify unusual patterns that do not align with expected behaviors. Notably, in the medical field, detecting anomalies in medical imagery or biometrics can facilitate early diagnosis of diseases. Consequently, we propose the Skip-GANomaly++ model, an enhanced and more efficient version of the conventional anomaly detection models. The proposed model's performance was evaluated through comparative experiments. Experimental results demonstrated superior performance across most classes compared to the previous models.
Learning Random Noise Salient Feature Fusion Siamese Network for Low-Resolution Object Tracking (Student Abstract)
Authors: Rahman Md Maklachur , Hammond Tracy
Published: March 2024

Despite Siamese trackers’ substantial potential, they offer sub-optimal tracking performance in low-resolution (LR) contexts. We introduce a Random Noise Salient Feature Fusion Learning Network to address this issue. This method integrates random noise-infused feature maps into a similaritylearning matching model. This integration acts as an effective regularization technique, enhancing the network’s generalization capabilities in LR environments. Additionally, by integrating attention mechanisms, we enhance the discriminative ability of the network, assigning more weights to important features. This directs the network’s focus toward the most salient regions of the feature map, ensuring improved accuracy without a significant increase in parameter overhead, and maintaining a high operating speed. To validate the effectiveness of our method, we performed qualitative and quantitative comparisons with state-of-the-art (SOTA) trackers.
Instance-Wise Laplace Mechanism via Deep Reinforcement Learning (Student Abstract)
Authors: Ryu Sehyun , Joo Hosung , Jang Jonggyu , Yang Hyun Jong
Published: March 2024

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: Sances Richard , Kotevska Olivera , Laiu Paul
Published: March 2024

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.
DDViT: Double-Level Fusion Domain Adapter Vision Transformer (Student Abstract)
Authors: Sun Linpeng , Sheng Victor S.
Published: March 2024

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: Swindle Adrian , McNealy Derrick , Krishnan Giri , Ramyaa Ramyaa
Published: March 2024

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.
Gaze-Based Interaction Adaptation for People with Involuntary Head Movements (Student Abstract)
Authors: Tong Cindy , Chan Rosanna
Published: March 2024

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: Tyagi Aryan , Rao Aryaman , Rao Shubhanshu , Singh Raj Kumar
Published: March 2024

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.
Intersection of Artificial Intelligence and Medical Education (Student Abstract)
Authors: Wu Keefer P. , Tsang Patricia C.
Published: March 2024

Can advanced AI-driven technologies transform the traditionally arduous educational process in medicine? This study takes a deep dive into how the publicly available OpenAI ChatGPT-3.5 performs in answering board-style questions designed for physicians training to become pathologists. Correctly answering 75% of 543 questions using an engaging and fast-paced format was an impressive performance. It underscores the potential as well as improvement opportunities of using interactive AI in future medical training.
Amplifying Diversity and Quality in Commonsense Knowledge Graph Completion (Student Abstract)
Authors: Yu Liu , Tian Fenghui , Kuang Ping , Zhou Fan
Published: March 2024

Conventional commonsense knowledge graph completion (CKGC) methods provide inadequate sequence when fine-tuning or generating stages and incorporate full fine-tuning, which fail to align with the autoregressive model's pre-training patterns and have insufficient parameter efficiency. Moreover, decoding through beam or greedy search produces low diversity and high similarity in generated tail entities. Hence, we resort to prefix-tuning and propose a lightweight, effective pipeline to enhance the quality and diversity of extracted commonsense knowledge. Precisely, we measure head entity similarity to yield and then concatenate top-k tuples before each target tuple for prefix-tuning the source LM, thereby improving the efficiency and speed for pretrained models; then, we design a penalty-tailored diverse beam search (p-DBS) for decoding tail entities, producing a greater quantity and diversity of generated commonsense tuples; besides, a filter strategy is utilized to filter out invalid commonsense knowledge. Through extensive automatic evaluations, including ChatGPT scoring, our method can extract diverse, novel, and accurate commonsense knowledge (CK).
Biases Mitigation and Expressiveness Preservation in Language Models: A Comprehensive Pipeline (Student Abstract)
Authors: Yu Liu , Guo Ludie , Kuang Ping , Zhou Fan
Published: March 2024

Pre-trained language models (PLMs) have greatly transformed various downstream tasks, yet frequently display social biases from training data, raising fairness concerns. Recent efforts to debias PLMs come with limitations: they either fine-tune the entire parameters in PLMs, which is time-consuming and disregards the expressiveness of PLMs, or ignore the reintroducing biases from downstream tasks when applying debiased models to them. Hence, we propose a two-stage pipeline to mitigate biases from both internal and downstream contexts while preserving expressiveness in language models. Specifically, for the debiasing procedure, we resort to continuous prefix-tuning, not fully fine-tuning the PLM, in which we design a debiasing term for optimization and an alignment term to keep words’ relative distances and ensure the model's expressiveness. For downstream tasks, we perform causal intervention across different demographic groups for invariant predictions. Results on three GLUE tasks show our method alleviates biases from internal and downstream contexts, while keeping PLM expressiveness intact.
Leverage the Explainability of Transformer Models to Improve the DNA 5-Methylcytosine Identification (Student Abstract)
Authors: Zeng Wenhuan , Huson Daniel H.
Published: March 2024

DNA methylation is an epigenetic mechanism for regulating gene expression, and it plays an important role in many biological processes. While methylation sites can be identified using laboratory techniques, much work is being done on developing computational approaches using machine learning. Here, we present a deep-learning algorithm for determining the 5-methylcytosine status of a DNA sequence. We propose an ensemble framework that treats the self-attention score as an explicit feature that is added to the encoder layer generated by fine-tuned language models. We evaluate the performance of the model under different data distribution scenarios.
THGFormer: Time-Aware Hypergraph Learning for Multimodal Social Media Popularity Prediction (Student Abstract)
Authors: Zhang Jienan , Liu Jie , Cheng Zhangtao , Xu Xovee , Liu Fang , Zhong Ting , Zhang Kunpeng
Published: March 2024

Social media popularity prediction of multimodal user-generated content (UGC) is a crucial task for many real-world applications. However, existing efforts are often limited by missing inter-instance correlations and UGC temporal patterns. To address these issues, we propose a novel time-aware hypergraph Transformer framework, THGFormer. It fully represents inter-instance and intra-instance relations by hypergraphs, captures the temporal dependencies with a time encoder, and enhances UGC's representations via a neighborhood knowledge aggregation. Extensive experiments conducted on two real-world datasets demonstrate that THGFormer outperforms state-of-the-art popularity prediction models across several settings.
Biomedical Knowledge Graph Embedding with Householder Projection (Student Abstract)
Authors: Zhang Sensen , Liang Xun , Niu Simin , Zhang Xuan , Feng Chen , Ma Yuefeng
Published: March 2024

Researchers have applied knowledge graph embedding (KGE) techniques with advanced neural network techniques, such as capsule networks, for predicting drug-drug interactions (DDIs) and achieved remarkable results. However, most ignore molecular structure and position features between drug pairs. They cannot model the biomedical field's significant relational mapping properties (RMPs,1-N, N-1, N-N) relation. To solve these problems, we innovatively propose CDHse that consists of two crucial modules: 1) Entity embedding module, we obtain position feature obtained by PubMedBERT and Convolutional Neural Network (CNN), obtain molecular structure feature with Graphic Nuaral Network (GNN), obtain entity embedding feature of drug pairs, and then incorporate these features into one synthetic feature. 2) Knowledge graph embedding module, the synthetic feature is Householder projections and then embedded in the complex vector space for training. In this paper, we have selected several advanced models for the DDIs task and performed experiments on three standard BioKG to validate the effectiveness of CDHse.
Evaluating AI Red Teaming’s Readiness to Address Environmental Harms: A Thematic Analysis of LLM Discourse
Authors: Au Amy
Published: March 2024

This research explores the discourse surrounding red teaming and aims to identify any themes in the online discussion of potential environmental harms stemming from Large Language Models (LLMs). Focusing on the AI Red Teaming event at DEFCON 31, this study employs reflexive thematic analysis on diverse social networking site sources to extract insights into public discussion of LLM red teaming and its environmental implications. The findings intend to inform future research, highlighting the need for responsible AI development that addresses environmental concerns.
Flow-Event Autoencoder: Event Stream Object Recognition Dataset Generation with Arbitrary High Temporal Resolution
Authors: Chen Minghai
Published: March 2024

Event camera has unique advantages in high temporal resolution and dynamic range and has shown potentials in several computer vision tasks. However, due to the novelty of this hardware, there’s a lack of large benchmark DVS event-stream datasets, including datasets for object recognition. In this work, we proposed an encoder-decoder method to augment event stream dataset from image and optical flow with arbitrary temporal resolution for object recognition task. We believe this proposed method can be generalized well in augmenting event stream vision data for object recognition and will help advance the development of event vision paradigm.
Statistically Principled Deep Learning for SAR Image Segmentation
Authors: Goldberg Cassandra
Published: March 2024

This paper proposes a novel approach for Synthetic Aperture Radar (SAR) image segmentation by incorporating known statistical properties of SAR into deep learning models. We generate synthetic data using the Generalized Gamma distribution, modify the U-Net architecture to encompass statistical moments, and employ stochastic distance losses for improved segmentation performance. Evaluation against traditional methods will reveal the potential of this approach to advance SAR image analysis, with broader applications in environmental monitoring and general image segmentation tasks.
Defog Artificial Intelligence Glasses: Neural Networks for the Imperfect Real World
Authors: Rojas Nilton
Published: March 2024

This research investigates the generalization capabilities of neural networks in deep learning when applied to real-world scenarios where data often contains imperfections, focusing on their adaptability to both noisy and non-noisy scenarios for image retrieval tasks. Our study explores approaches to preserve all available data, regardless of quality, for diverse tasks. The evaluation of results varies per task, due to the ultimate goal of developing a technique to extract relevant information while disregarding noise in the final network design for each specific task. The aim is to enhance accessibility and efficiency of AI across diverse tasks, particularly for individuals or countries with limited resources, lacking access to high-quality data. The dedication is directed towards fostering inclusivity and unlocking the potential of AI for wide-spread societal benefit.
Validation, Robustness, and Accuracy of Perturbation-Based Sensitivity Analysis Methods for Time-Series Deep Learning Models
Authors: Wang Zhengguang
Published: March 2024

This work undertakes studies to evaluate Interpretability Methods for Time Series Deep Learning. Sensitivity analysis assesses how input changes affect the output, constituting a key component of interpretation. Among the post-hoc interpretation methods such as back-propagation, perturbation, and approximation, my work will investigate perturbation-based sensitivity Analysis methods on modern Transformer models to benchmark their performances. Specifically, my work intends to answer three research questions: 1) Do different sensitivity analysis methods yield comparable outputs and attribute importance rankings? 2) Using the same sensitivity analysis method, do different Deep Learning models impact the output of the sensitivity analysis? 3) How well do the results from sensitivity analysis methods align with the ground truth?
Enhancing Machine Translation Experiences with Multilingual Knowledge Graphs
Authors: Conia Simone , Lee Daniel , Li Min , Minhas Umar Farooq , Li Yunyao
Published: March 2024

Translating entity names, especially when a literal translation is not correct, poses a significant challenge. Although Machine Translation (MT) systems have achieved impressive results, they still struggle to translate cultural nuances and language-specific context. In this work, we show that the integration of multilingual knowledge graphs into MT systems can address this problem and bring two significant benefits: i) improving the translation of utterances that contain entities by leveraging their human-curated aliases from a multilingual knowledge graph, and, ii) increasing the interpretability of the translation process by providing the user with information from the knowledge graph.