pwc-archive/papers-with-abstracts huggingface dataset:
Novelty Accommodating Multi-Agent Planning in High Fidelity Simulated Open World
Authors: James Chao, Wiktor Piotrowski, Roni Stern, Héctor Ortiz-Peña, Mitch Manzanares, Shiwali Mohan, Douglas S. Lange
Date: 2023-06-22 00:00:00
Tasks: AI Agent, Scheduling

Autonomous agents operating within real-world environments often rely on automated planners to ascertain optimal actions towards desired goals or the optimization of a specified objective function. Integral to these agents are common architectural components such as schedulers, tasked with determining the timing for executing planned actions, and execution engines, responsible for carrying out these scheduled actions while monitoring their outcomes. We address the significant challenge that arises when unexpected phenomena, termed \textit{novelties}, emerge within the environment, altering its fundamental characteristics, composition, and dynamics. This challenge is inherent in all deployed real-world applications and may manifest suddenly and without prior notice or explanation. The introduction of novelties into the environment can lead to inaccuracies within the planner's internal model, rendering previously generated plans obsolete. Recent research introduced agent design aimed at detecting and adapting to such novelties. However, these designs lack consideration for action scheduling in continuous time-space, coordination of concurrent actions by multiple agents, or memory-based novelty accommodation. Additionally, the application has been primarily demonstrated in lower fidelity environments. In our study, we propose a general purpose AI agent framework designed to detect, characterize, and adapt to novelties in highly noisy, complex, and stochastic environments that support concurrent actions and external scheduling. We showcase the efficacy of our agent through experimentation within a high-fidelity simulator for realistic military scenarios.
Don't Treat the Symptom, Find the Cause! Efficient Artificial-Intelligence Methods for (Interactive) Debugging
Authors: Patrick Rodler
Date: 2023-06-22 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Recommendation Systems

In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in e-commerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, the power grid to ensure our energy supply, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play. Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above, and many more. It exploits and orchestrates i.a. techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, optimization, stochastics, statistics, decision making under uncertainty, machine learning, as well as calculus, combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems. In this thesis, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these issues.
SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems
Authors: Ehsan Latif, Ramviyas Parasuraman
Date: 2023-06-22 00:00:00
Tasks: Gaussian Processes

The availability of accurate localization is critical for multi-robot exploration strategies; noisy or inconsistent localization causes failure in meeting exploration objectives. We aim to achieve high localization accuracy with contemporary exploration map belief and vice versa without needing global localization information. This paper proposes a novel simultaneous exploration and localization (SEAL) approach, which uses Gaussian Processes (GP)-based information fusion for maximum exploration while performing communication graph optimization for relative localization. Both these cross-dependent objectives were integrated through the Rao-Blackwellization technique. Distributed linearized convex hull optimization is used to select the next-best unexplored region for distributed exploration. SEAL outperformed cutting-edge methods on exploration and localization performance in extensive ROS-Gazebo simulations, illustrating the practicality of the approach in real-world applications.
FLAG: Finding Line Anomalies (in code) with Generative AI
Authors: Baleegh Ahmad, Benjamin Tan, Ramesh Karri, Hammond Pearce
Date: 2023-06-22 00:00:00

Code contains security and functional bugs. The process of identifying and localizing them is difficult and relies on human labor. In this work, we present a novel approach (FLAG) to assist human debuggers. FLAG is based on the lexical capabilities of generative AI, specifically, Large Language Models (LLMs). Here, we input a code file then extract and regenerate each line within that file for self-comparison. By comparing the original code with an LLM-generated alternative, we can flag notable differences as anomalies for further inspection, with features such as distance from comments and LLM confidence also aiding this classification. This reduces the inspection search space for the designer. Unlike other automated approaches in this area, FLAG is language-agnostic, can work on incomplete (and even non-compiling) code and requires no creation of security properties, functional tests or definition of rules. In this work, we explore the features that help LLMs in this classification and evaluate the performance of FLAG on known bugs. We use 121 benchmarks across C, Python and Verilog; with each benchmark containing a known security or functional weakness. We conduct the experiments using two state of the art LLMs in OpenAI's code-davinci-002 and gpt-3.5-turbo, but our approach may be used by other models. FLAG can identify 101 of the defects and helps reduce the search space to 12-17% of source code.
Memristive Reservoirs Learn to Learn
Authors: Ruomin Zhu, Jason K. Eshraghian, Zdenka Kuncic
Date: 2023-06-22 00:00:00

Memristive reservoirs draw inspiration from a novel class of neuromorphic hardware known as nanowire networks. These systems display emergent brain-like dynamics, with optimal performance demonstrated at dynamical phase transitions. In these networks, a limited number of electrodes are available to modulate system dynamics, in contrast to the global controllability offered by neuromorphic hardware through random access memories. We demonstrate that the learn-to-learn framework can effectively address this challenge in the context of optimization. Using the framework, we successfully identify the optimal hyperparameters for the reservoir. This finding aligns with previous research, which suggests that the optimal performance of a memristive reservoir occurs at the `edge of formation' of a conductive pathway. Furthermore, our results show that these systems can mimic membrane potential behavior observed in spiking neurons, and may serve as an interface between spike-based and continuous processes.
SoftGPT: Learn Goal-oriented Soft Object Manipulation Skills by Generative Pre-trained Heterogeneous Graph Transformer
Authors: Junjia Liu, Zhihao LI, WanYu Lin, Sylvain Calinon, Kay Chen Tan, Fei Chen
Date: 2023-06-22 00:00:00
Tasks: Object

Soft object manipulation tasks in domestic scenes pose a significant challenge for existing robotic skill learning techniques due to their complex dynamics and variable shape characteristics. Since learning new manipulation skills from human demonstration is an effective way for robot applications, developing prior knowledge of the representation and dynamics of soft objects is necessary. In this regard, we propose a pre-trained soft object manipulation skill learning model, namely SoftGPT, that is trained using large amounts of exploration data, consisting of a three-dimensional heterogeneous graph representation and a GPT-based dynamics model. For each downstream task, a goal-oriented policy agent is trained to predict the subsequent actions, and SoftGPT generates the consequences of these actions. Integrating these two approaches establishes a thinking process in the robot's mind that provides rollout for facilitating policy learning. Our results demonstrate that leveraging prior knowledge through this thinking process can efficiently learn various soft object manipulation skills, with the potential for direct learning from human demonstrations.
To Spike or Not to Spike? A Quantitative Comparison of SNN and CNN FPGA Implementations
Authors: Patrick Plagwitz, Frank Hannig, Jürgen Teich, Oliver Keszocze
Date: 2023-06-22 00:00:00
Tasks: image-classification, Image Classification

Convolutional Neural Networks (CNNs) are widely employed to solve various problems, e.g., image classification. Due to their compute- and data-intensive nature, CNN accelerators have been developed as ASICs or on FPGAs. Increasing complexity of applications has caused resource costs and energy requirements of these accelerators to grow. Spiking Neural Networks (SNNs) are an emerging alternative to CNN implementations, promising higher resource and energy efficiency. The main research question addressed in this paper is whether SNN accelerators truly meet these expectations of reduced energy requirements compared to their CNN equivalents. For this purpose, we analyze multiple SNN hardware accelerators for FPGAs regarding performance and energy efficiency. We present a novel encoding scheme of spike event queues and a novel memory organization technique to improve SNN energy efficiency further. Both techniques have been integrated into a state-of-the-art SNN architecture and evaluated for MNIST, SVHN, and CIFAR-10 datasets and corresponding network architectures on two differently sized modern FPGA platforms. For small-scale benchmarks such as MNIST, SNN designs provide rather no or little latency and energy efficiency advantages over corresponding CNN implementations. For more complex benchmarks such as SVHN and CIFAR-10, the trend reverses.
MFCCGAN: A Novel MFCC-Based Speech Synthesizer Using Adversarial Learning
Authors: Mohammad Reza Hasanabadi Majid Behdad Davood Gharavian
Date: 2023-06-22 00:00:00

In this paper, we introduce MFCCGAN as a novel speech synthesizer based on adversarial learning that adopts MFCCs as input and generates raw speech waveforms. Benefiting the GAN model capabilities, it produces speech with higher intelligibility than a rule-based MFCC-based speech synthesizer WORLD. We evaluated the model based on a popular intrusive objective speech intelligibility measure (STOI) and quality (NISQA score). Experimental results show that our proposed system outperforms Librosa MFCC- inversion (by an increase of about 26% up to 53% in STOI and 16% up to 78% in NISQA score) and a rise of about 10% in intelligibility and about 4% in naturalness in comparison with conventional rule-based vocoder WORLD that used in the CycleGAN-VC family. However, WORLD needs additional data like F0. Finally, using perceptual loss in discriminators based on STOI could improve the quality more. WebMUSHRA-based subjective tests also show the quality of the proposed approach.
Impacts and Risk of Generative AI Technology on Cyber Defense
Authors: Subash Neupane, Ivan A. Fernandez, Sudip Mittal, Shahram Rahimi
Date: 2023-06-22 00:00:00
Tasks: Face Swapping, Misinformation

Generative Artificial Intelligence (GenAI) has emerged as a powerful technology capable of autonomously producing highly realistic content in various domains, such as text, images, audio, and videos. With its potential for positive applications in creative arts, content generation, virtual assistants, and data synthesis, GenAI has garnered significant attention and adoption. However, the increasing adoption of GenAI raises concerns about its potential misuse for crafting convincing phishing emails, generating disinformation through deepfake videos, and spreading misinformation via authentic-looking social media posts, posing a new set of challenges and risks in the realm of cybersecurity. To combat the threats posed by GenAI, we propose leveraging the Cyber Kill Chain (CKC) to understand the lifecycle of cyberattacks, as a foundational model for cyber defense. This paper aims to provide a comprehensive analysis of the risk areas introduced by the offensive use of GenAI techniques in each phase of the CKC framework. We also analyze the strategies employed by threat actors and examine their utilization throughout different phases of the CKC, highlighting the implications for cyber defense. Additionally, we propose GenAI-enabled defense strategies that are both attack-aware and adaptive. These strategies encompass various techniques such as detection, deception, and adversarial training, among others, aiming to effectively mitigate the risks posed by GenAI-induced cyber threats.
On-orbit model training for satellite imagery with label proportions
Authors: Raúl Ramos-Pollán, Fabio A. González
Date: 2023-06-21 00:00:00
Tasks: Benchmarking, Earth Observation

This work addresses the challenge of training supervised machine or deep learning models on orbiting platforms where we are generally constrained by limited on-board hardware capabilities and restricted uplink bandwidths to upload. We aim at enabling orbiting spacecrafts to (1) continuously train a lightweight model as it acquires imagery; and (2) receive new labels while on orbit to refine or even change the predictive task being trained. For this, we consider chip level regression tasks (i.e. predicting the vegetation percentage of a 20 km$^2$ patch) when we only have coarser label proportions, such as municipality level vegetation statistics (a municipality containing several patches). Such labels proportions have the additional advantage that usually come in tabular data and are widely available in many regions of the world and application areas. This can be framed as a Learning from Label Proportions (LLP) problem setup. LLP applied to Earth Observation (EO) data is still an emerging field and performing comparative studies in applied scenarios remains a challenge due to the lack of standardized datasets. In this work, first, we show how very simple deep learning and probabilistic methods (with {\raise.17ex\hbox{$\scriptstyle\sim$}}5K parameters) generally perform better than standard more complex ones, providing a surprising level of finer grained spatial detail when trained with much coarser label proportions. Second, we publish a set of benchmarking datasets enabling comparative LLP applied to EO, providing both fine grained labels and aggregated data according to existing administrative divisions. Finally, we show how this approach fits an on-orbit training scenario by reducing vastly both the amount of computing and the size of the labels sets. Source code is available at https://github.com/rramosp/llpeo
LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network
Authors: Dongqiangzi Ye, Yufei Xie, Weijia Chen, Zixiang Zhou, Lingting Ge, Hassan Foroosh
Date: 2023-06-21 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation

Due to the difficulty of acquiring large-scale 3D human keypoint annotation, previous methods for 3D human pose estimation (HPE) have often relied on 2D image features and sequential 2D annotations. Furthermore, the training of these networks typically assumes the prediction of a human bounding box and the accurate alignment of 3D point clouds with 2D images, making direct application in real-world scenarios challenging. In this paper, we present the 1st framework for end-to-end 3D human pose estimation, named LPFormer, which uses only LiDAR as its input along with its corresponding 3D annotations. LPFormer consists of two stages: firstly, it identifies the human bounding box and extracts multi-level feature representations, and secondly, it utilizes a transformer-based network to predict human keypoints based on these features. Our method demonstrates that 3D HPE can be seamlessly integrated into a strong LiDAR perception network and benefit from the features extracted by the network. Experimental results on the Waymo Open Dataset demonstrate the state-of-the-art performance, and improvements even compared to previous multi-modal solutions.
DGC-GNN: Leveraging Geometry and Color Cues for Visual Descriptor-Free 2D-3D Matching
Authors: Shuzhe Wang, Juho Kannala, Daniel Barath
Date: 2023-06-21 00:00:00
Proceeding: CVPR 2024 1
Tasks: Graph Neural Network

Matching 2D keypoints in an image to a sparse 3D point cloud of the scene without requiring visual descriptors has garnered increased interest due to its low memory requirements, inherent privacy preservation, and reduced need for expensive 3D model maintenance compared to visual descriptor-based methods. However, existing algorithms often compromise on performance, resulting in a significant deterioration compared to their descriptor-based counterparts. In this paper, we introduce DGC-GNN, a novel algorithm that employs a global-to-local Graph Neural Network (GNN) that progressively exploits geometric and color cues to represent keypoints, thereby improving matching accuracy. Our procedure encodes both Euclidean and angular relations at a coarse level, forming the geometric embedding to guide the point matching. We evaluate DGC-GNN on both indoor and outdoor datasets, demonstrating that it not only doubles the accuracy of the state-of-the-art visual descriptor-free algorithm but also substantially narrows the performance gap between descriptor-based and descriptor-free methods.
Exploring the Role of Audio in Video Captioning
Authors: YuHan Shen, Linjie Yang, Longyin Wen, Haichao Yu, Ehsan Elhamifar, Heng Wang
Date: 2023-06-21 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Video Captioning

Recent focus in video captioning has been on designing architectures that can consume both video and text modalities, and using large-scale video datasets with text transcripts for pre-training, such as HowTo100M. Though these approaches have achieved significant improvement, the audio modality is often ignored in video captioning. In this work, we present an audio-visual framework, which aims to fully exploit the potential of the audio modality for captioning. Instead of relying on text transcripts extracted via automatic speech recognition (ASR), we argue that learning with raw audio signals can be more beneficial, as audio has additional information including acoustic events, speaker identity, etc. Our contributions are twofold. First, we observed that the model overspecializes to the audio modality when pre-training with both video and audio modality, since the ground truth (i.e., text transcripts) can be solely predicted using audio. We proposed a Modality Balanced Pre-training (MBP) loss to mitigate this issue and significantly improve the performance on downstream tasks. Second, we slice and dice different design choices of the cross-modal module, which may become an information bottleneck and generate inferior results. We proposed new local-global fusion mechanisms to improve information exchange across audio and video. We demonstrate significant improvements by leveraging the audio modality on four datasets, and even outperform the state of the art on some metrics without relying on the text modality as the input.
Neural Spectro-polarimetric Fields
Authors: Youngchan Kim, Wonjoon Jin, Sunghyun Cho, Seung-Hwan Baek
Date: 2023-06-21 00:00:00
Tasks: valid

Modeling the spatial radiance distribution of light rays in a scene has been extensively explored for applications, including view synthesis. Spectrum and polarization, the wave properties of light, are often neglected due to their integration into three RGB spectral bands and their non-perceptibility to human vision. However, these properties are known to encompass substantial material and geometric information about a scene. Here, we propose to model spectro-polarimetric fields, the spatial Stokes-vector distribution of any light ray at an arbitrary wavelength. We present Neural Spectro-polarimetric Fields (NeSpoF), a neural representation that models the physically-valid Stokes vector at given continuous variables of position, direction, and wavelength. NeSpoF manages inherently noisy raw measurements, showcases memory efficiency, and preserves physically vital signals - factors that are crucial for representing the high-dimensional signal of a spectro-polarimetric field. To validate NeSpoF, we introduce the first multi-view hyperspectral-polarimetric image dataset, comprised of both synthetic and real-world scenes. These were captured using our compact hyperspectral-polarimetric imaging system, which has been calibrated for robustness against system imperfections. We demonstrate the capabilities of NeSpoF on diverse scenes.
Local 3D Editing via 3D Distillation of CLIP Knowledge
Authors: Junha Hyung, Sungwon Hwang, Daejin Kim, Hyunji Lee, Jaegul Choo
Date: 2023-06-21 00:00:00
Proceeding: CVPR 2023 1
Tasks: NeRF

3D content manipulation is an important computer vision task with many real-world applications (e.g., product design, cartoon generation, and 3D Avatar editing). Recently proposed 3D GANs can generate diverse photorealistic 3D-aware contents using Neural Radiance fields (NeRF). However, manipulation of NeRF still remains a challenging problem since the visual quality tends to degrade after manipulation and suboptimal control handles such as 2D semantic maps are used for manipulations. While text-guided manipulations have shown potential in 3D editing, such approaches often lack locality. To overcome these problems, we propose Local Editing NeRF (LENeRF), which only requires text inputs for fine-grained and localized manipulation. Specifically, we present three add-on modules of LENeRF, the Latent Residual Mapper, the Attention Field Network, and the Deformation Network, which are jointly used for local manipulations of 3D features by estimating a 3D attention field. The 3D attention field is learned in an unsupervised way, by distilling the zero-shot mask generation capability of CLIP to the 3D space with multi-view guidance. We conduct diverse experiments and thorough evaluations both quantitatively and qualitatively.