pwc-archive/papers-with-abstracts huggingface dataset:
Diversity and bias in audio captioning datasets
Authors: Irene Martin-Morato, Annamaria Mesaros
Date: 2022-11-15 00:00:00
Proceeding: DCASE workshop 2022 11
Tasks: Audio captioning, Diversity

Describing soundscapes in sentences allows better understand- ing of the acoustic scene than a single label indicating the acoustic scene class or a set of audio tags indicating the sound events active in the audio clip. In addition, the richness of natural language allows a range of possible descriptions for the same acoustic scene. In this work, we address the diversity obtained when collecting descrip- tions of soundscapes using crowdsourcing. We study how much the collection of audio captions can be guided by the instructions given in the annotation task, by analysing the possible bias introduced by auxiliary information provided in the annotation process. Our study shows that even when hints are given with the audio content, different annotators describe the same soundscape using different vocabulary. In automatic captioning, hints provided as audio tags represent grounding textual information that facilitates guiding the captioning output towards specific concepts. We also release a new dataset of audio captions and audio tags produced by multiple anno- tators for a subset of the TAU Urban Acoustic Scenes 2019 dataset, suitable for studying guided captioning.
Hypergraph Contrastive Learning for Drug Trafficking Community Detection
Authors: Tianyi Ma, Yiyue Qian, Chuxu Zhang, Yanfang Ye
Date: 2023-12-01 00:00:00
Proceeding: 2023 IEEE International Conference on Data Mining (ICDM) 2023 12
Tasks: Attribute, Community Detection, Contrastive Learning, Hypergraph Contrastive Learning, Hypergraph representations, Representation Learning

In recent decades, due to the lucrative profits, the crime of drug trafficking has evolved with modern technologies. Social media, as one of the popular online platforms, have become direct-to-consumer intermediaries for illicit drug trafficking communities to promote and trade drugs. These group-wise drug trafficking activities pose significant challenges to public health and safety, requiring urgent measures to address this issue. However, existing works against the imminent problem still face limitations, such as primarily analyzing individual roles from a single perspective, ignoring the group-wise relationships, and requiring sufficient labeled samples for model training. To this end, we propose a novel HyperGraph Contrastive Learning framework called HyGCL-DC that employs hypergraph to model the higher-order relationships among users to detect Drug trafficking Communities. Firstly, we build a hypergraph called Twitter-HyDrug including online user nodes and four types of hyperedges to depict the rich group-wise relationships among these users. Then, we leverage hypergraph neural networks to model the rich relationships among nodes and hyperedges in the drug trafficking hypergraph. Furthermore, we design a hypergraph self-supervised contrast module, which integrates the augmentation from the structure view and the attribute view to enhance hypergraph representation learning over unlabeled data. Finally, we design an end-to-end framework that combines the self-supervised contrastive module and the supervised module to classify online drug trafficking communities. To comprehensively study the online drug trafficking problem and evaluate our model, we conduct extensive experiments over Twitter-HyDrug and three citation benchmark hypergraph datasets to demonstrate the effectiveness of our model. Our new data and source code are available at https://github.com/GraphResearcher/HyGCL-DC.
Sorting with Predictions
Authors: Xingjian Bai, Christian Coester
Date: 2023-11-01 00:00:00
Proceeding: NeurIPS 2023 11

We explore the fundamental problem of sorting through the lens of learning-augmented algorithms, where algorithms can leverage possibly erroneous predictions to improve their efficiency. We consider two different settings: In the first setting, each item is provided a prediction of its position in the sorted list. In the second setting, we assume there is a "quick-and-dirty" way of comparing items, in addition to slow-and-exact comparisons. For both settings, we design new and simple algorithms using only $O(\sum_i \log \eta_i)$ exact comparisons, where $\eta_i$ is a suitably defined prediction error for the $i$th element. In particular, as the quality of predictions deteriorates, the number of comparisons degrades smoothly from $O(n)$ to $O(n\log n)$. We prove that the comparison complexity is theoretically optimal with respect to the examined error measures. An experimental evaluation against existing adaptive and non-adaptive sorting algorithms demonstrates the potential of applying learning-augmented algorithms in sorting tasks.
Planning the path with Reinforcement Learning: Optimal Robot Motion Planning in RoboCup Small Size League Environments
Authors: Mateus G. Machado, João G. Melo, Cleber Zanchettin, Pedro H. M. Braga, Pedro V. Cunha, Edna N. S. Barros, Hansenclever F. Bassani
Date: 2024-04-23 00:00:00
Tasks: Motion Planning, Reinforcement Learning (RL)

This work investigates the potential of Reinforcement Learning (RL) to tackle robot motion planning challenges in the dynamic RoboCup Small Size League (SSL). Using a heuristic control approach, we evaluate RL's effectiveness in obstacle-free and single-obstacle path-planning environments. Ablation studies reveal significant performance improvements. Our method achieved a 60% time gain in obstacle-free environments compared to baseline algorithms. Additionally, our findings demonstrated dynamic obstacle avoidance capabilities, adeptly navigating around moving blocks. These findings highlight the potential of RL to enhance robot motion planning in the challenging and unpredictable SSL environment.
Removing Reflections from RAW Photos
Authors: Eric Kee, Adam Pikielny, Kevin Blackburn-Matzen, Marc Levoy
Date: 2024-02-28 00:00:00
Proceeding: CVPR 2025 1

We describe a system to remove real-world reflections from images for consumer photography. Our system operates on linear (RAW) photos, with the (optional) addition of a contextual photo looking in the opposite direction, e.g., using the selfie camera on a mobile device, which helps disambiguate what should be considered the reflection. The system is trained using synthetic mixtures of real-world RAW images, which are combined using a reflection simulation that is photometrically and geometrically accurate. Our system consists of a base model that accepts the captured photo and optional contextual photo as input, and runs at 256p, followed by an up-sampling model that transforms output 256p images to full resolution. The system can produce images for review at 1K in 4.5 to 6.5 seconds on a MacBook or iPhone 14 Pro. We test on RAW photos that were captured in the field and embody typical consumer photographs.
Wiener Guided DIP for Unsupervised Blind Image Deconvolution
Authors: Gustav Bredell, Ertunc Erdil, Bruno Weber, Ender Konukoglu
Date: 2021-12-19 00:00:00
Tasks: Astronomy, Image Deconvolution, Image Generation

Blind deconvolution is an ill-posed problem arising in various fields ranging from microscopy to astronomy. The ill-posed nature of the problem requires adequate priors to arrive to a desirable solution. Recently, it has been shown that deep learning architectures can serve as an image generation prior during unsupervised blind deconvolution optimization, however often exhibiting a performance fluctuation even on a single image. We propose to use Wiener-deconvolution to guide the image generator during optimization by providing it a sharpened version of the blurry image using an auxiliary kernel estimate starting from a Gaussian. We observe that the high-frequency artifacts of deconvolution are reproduced with a delay compared to low-frequency features. In addition, the image generator reproduces low-frequency features of the deconvolved image faster than that of a blurry image. We embed the computational process in a constrained optimization framework and show that the proposed method yields higher stability and performance across multiple datasets. In addition, we provide the code.
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction
Authors: Henry Peng Zou, Vinay Samuel, Yue Zhou, Weizhi Zhang, Liancheng Fang, Zihe Song, Philip S. Yu, Cornelia Caragea
Date: 2024-04-24 00:00:00
Tasks: Attribute, Attribute Value Extraction, Benchmarking, Multimodal Attribute Value Extraction

Existing datasets for attribute value extraction (AVE) predominantly focus on explicit attribute values while neglecting the implicit ones, lack product images, are often not publicly available, and lack an in-depth human inspection across diverse domains. To address these limitations, we present ImplicitAVE, the first, publicly available multimodal dataset for implicit attribute value extraction. ImplicitAVE, sourced from the MAVE dataset, is carefully curated and expanded to include implicit AVE and multimodality, resulting in a refined dataset of 68k training and 1.6k testing data across five domains. We also explore the application of multimodal large language models (MLLMs) to implicit AVE, establishing a comprehensive benchmark for MLLMs on the ImplicitAVE dataset. Six recent MLLMs with eleven variants are evaluated across diverse settings, revealing that implicit value extraction remains a challenging task for MLLMs. The contributions of this work include the development and release of ImplicitAVE, and the exploration and benchmarking of various MLLMs for implicit AVE, providing valuable insights and potential future research directions. Dataset and code are available at https://github.com/HenryPengZou/ImplicitAVE
A Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognition
Authors: Xiao-Yin Liu, Guotao Li, Xiao-Hu Zhou, Xu Liang, Zeng-Guang Hou
Date: 2024-04-19 00:00:00
Tasks: Domain Adaptation, Intent Detection, Multi-Source Unsupervised Domain Adaptation, Unsupervised Domain Adaptation

Accurate recognition of human motion intention (HMI) is beneficial for exoskeleton robots to improve the wearing comfort level and achieve natural human-robot interaction. A classifier trained on labeled source subjects (domains) performs poorly on unlabeled target subject since the difference in individual motor characteristics. The unsupervised domain adaptation (UDA) method has become an effective way to this problem. However, the labeled data are collected from multiple source subjects that might be different not only from the target subject but also from each other. The current UDA methods for HMI recognition ignore the difference between each source subject, which reduces the classification accuracy. Therefore, this paper considers the differences between source subjects and develops a novel theory and algorithm for UDA to recognize HMI, where the margin disparity discrepancy (MDD) is extended to multi-source UDA theory and a novel weight-aware-based multi-source UDA algorithm (WMDD) is proposed. The source domain weight, which can be adjusted adaptively by the MDD between each source subject and target subject, is incorporated into UDA to measure the differences between source subjects. The developed multi-source UDA theory is theoretical and the generalization error on target subject is guaranteed. The theory can be transformed into an optimization problem for UDA, successfully bridging the gap between theory and algorithm. Moreover, a lightweight network is employed to guarantee the real-time of classification and the adversarial learning between feature generator and ensemble classifiers is utilized to further improve the generalization ability. The extensive experiments verify theoretical analysis and show that WMDD outperforms previous UDA methods on HMI recognition tasks.
ADGym: Design Choices for Deep Anomaly Detection
Authors: Minqi Jiang, Chaochuan Hou, Ao Zheng, Songqiao Han, Hailiang Huang, Qingsong Wen, Xiyang Hu, Yue Zhao
Date: 2023-09-27 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Anomaly Detection, Cloud Computing

Deep learning (DL) techniques have recently found success in anomaly detection (AD) across various fields such as finance, medical services, and cloud computing. However, most of the current research tends to view deep AD algorithms as a whole, without dissecting the contributions of individual design choices like loss functions and network architectures. This view tends to diminish the value of preliminary steps like data preprocessing, as more attention is given to newly designed loss functions, network architectures, and learning paradigms. In this paper, we aim to bridge this gap by asking two key questions: (i) Which design choices in deep AD methods are crucial for detecting anomalies? (ii) How can we automatically select the optimal design choices for a given AD dataset, instead of relying on generic, pre-existing solutions? To address these questions, we introduce ADGym, a platform specifically crafted for comprehensive evaluation and automatic selection of AD design elements in deep methods. Our extensive experiments reveal that relying solely on existing leading methods is not sufficient. In contrast, models developed using ADGym significantly surpass current state-of-the-art techniques.
DPO: A Differential and Pointwise Control Approach to Reinforcement Learning
Authors: Minh Nguyen, Chandrajit Bajaj
Date: 2024-04-24 00:00:00
Tasks: Benchmarking, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) in continuous state-action spaces remains challenging in scientific computing due to poor sample efficiency and lack of pathwise physical consistency. We introduce Differential Reinforcement Learning (Differential RL), a novel framework that reformulates RL from a continuous-time control perspective via a differential dual formulation. This induces a Hamiltonian structure that embeds physics priors and ensures consistent trajectories without requiring explicit constraints. To implement Differential RL, we develop Differential Policy Optimization (DPO), a pointwise, stage-wise algorithm that refines local movement operators along the trajectory for improved sample efficiency and dynamic alignment. We establish pointwise convergence guarantees, a property not available in standard RL, and derive a competitive theoretical regret bound of $O(K^{5/6})$. Empirically, DPO outperforms standard RL baselines on representative scientific computing tasks, including surface modeling, grid control, and molecular dynamics, under low-data and physics-constrained conditions.
A Bi-directional Quantum Search Algorithm
Authors: Debanjan Konar, Zain Hafeez, Vaneet Aggarwal
Date: 2024-04-24 00:00:00

Grover's search algorithms, including various partial Grover searches, experience scaling problems as the number of iterations rises with increased qubits, making implementation more computationally expensive. This paper combines Partial Grover's search algorithm and Bi-directional Search to create a fast Grover's quantum search algorithm, referred to as Bi-Directional Grover Search (BDGS). We incorporated a bi-directional search tactic with a partial Grover search, starting from an initial state and a single marked state in parallel. We have shown in this article that our novel approach requires $\frac{\pi}{4\sqrt{2}}\sqrt{N}(1-\sqrt{\frac{1}{b^{r/2k}}})$ iterations over regular Grover Search and Partial Grover Search (PGS), which takes $\frac{\pi}{4}\sqrt{N}\sqrt{1-\frac{1}{b}}$ (here, $N=2^r$ elements, $b$ is the branching factor of partial search, and $k= \lceil\log_2b \rceil$). The proposed BDGS algorithm is benchmarked against the state-of-the-art Depth-First Grover's Search (DFGS) and generic Grover's Search (GS) implementations for $2$ to $20$ qubits and provides promising results. The Qiskit Python implementation of the proposed BDGS algorithm is available on Github (https://github.com/hafeezzwiz21/DFGS-BDGS).
Hybrid LLM/Rule-based Approaches to Business Insights Generation from Structured Data
Authors: Aliaksei Vertsel, Mikhail Rumiantsau
Date: 2024-04-24 00:00:00
Tasks: Decision Making

In the field of business data analysis, the ability to extract actionable insights from vast and varied datasets is essential for informed decision-making and maintaining a competitive edge. Traditional rule-based systems, while reliable, often fall short when faced with the complexity and dynamism of modern business data. Conversely, Artificial Intelligence (AI) models, particularly Large Language Models (LLMs), offer significant potential in pattern recognition and predictive analytics but can lack the precision necessary for specific business applications. This paper explores the efficacy of hybrid approaches that integrate the robustness of rule-based systems with the adaptive power of LLMs in generating actionable business insights.
Deepfakes and Higher Education: A Research Agenda and Scoping Review of Synthetic Media
Authors: Jasper Roe, Mike Perkins
Date: 2024-04-24 00:00:00
Tasks: Face Swapping

The availability of software which can produce convincing yet synthetic media poses both threats and benefits to tertiary education globally. While other forms of synthetic media exist, this study focuses on deepfakes, which are advanced Generative AI (GenAI) fakes of real people. This conceptual paper assesses the current literature on deepfakes across multiple disciplines by conducting an initial scoping review of 182 peer-reviewed publications. The review reveals three major trends: detection methods, malicious applications, and potential benefits, although no specific studies on deepfakes in the tertiary educational context were found. Following a discussion of these trends, this study applies the findings to postulate the major risks and potential mitigation strategies of deepfake technologies in higher education, as well as potential beneficial uses to aid the teaching and learning of both deepfakes and synthetic media. This culminates in the proposal of a research agenda to build a comprehensive, cross-cultural approach to investigate deepfakes in higher education.
Human-in-the-loop Learning for Dynamic Congestion Games
Authors: Hongbo Li, Lingjie Duan
Date: 2024-04-24 00:00:00

Today mobile users learn and share their traffic observations via crowdsourcing platforms (e.g., Waze). Yet such platforms simply cater to selfish users' myopic interests to recommend the shortest path, and do not encourage enough users to travel and learn other paths for future others. Prior studies focus on one-shot congestion games without considering users' information learning, while our work studies how users learn and alter traffic conditions on stochastic paths in a human-in-the-loop manner. Our analysis shows that the myopic routing policy leads to severe under-exploration of stochastic paths. This results in a price of anarchy (PoA) greater than $2$, as compared to the socially optimal policy in minimizing the long-term social cost. Besides, the myopic policy fails to ensure the correct learning convergence about users' traffic hazard beliefs. To address this, we focus on informational (non-monetary) mechanisms as they are easier to implement than pricing. We first show that existing information-hiding mechanisms and deterministic path-recommendation mechanisms in Bayesian persuasion literature do not work with even (\text{PoA}=\infty). Accordingly, we propose a new combined hiding and probabilistic recommendation (CHAR) mechanism to hide all information from a selected user group and provide state-dependent probabilistic recommendations to the other user group. Our CHAR successfully ensures PoA less than (\frac{5}{4}), which cannot be further reduced by any other informational (non-monetary) mechanism. Besides the parallel network, we further extend our analysis and CHAR to more general linear path graphs with multiple intermediate nodes, and we prove that the PoA results remain unchanged. Additionally, we carry out experiments with real-world datasets to further extend our routing graphs and verify the close-to-optimal performance of our CHAR.
GRSN: Gated Recurrent Spiking Neurons for POMDPs and MARL
Authors: Lang Qin, ZiMing Wang, Runhao Jiang, Rui Yan, Huajin Tang
Date: 2024-04-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Spiking neural networks (SNNs) are widely applied in various fields due to their energy-efficient and fast-inference capabilities. Applying SNNs to reinforcement learning (RL) can significantly reduce the computational resource requirements for agents and improve the algorithm's performance under resource-constrained conditions. However, in current spiking reinforcement learning (SRL) algorithms, the simulation results of multiple time steps can only correspond to a single-step decision in RL. This is quite different from the real temporal dynamics in the brain and also fails to fully exploit the capacity of SNNs to process temporal data. In order to address this temporal mismatch issue and further take advantage of the inherent temporal dynamics of spiking neurons, we propose a novel temporal alignment paradigm (TAP) that leverages the single-step update of spiking neurons to accumulate historical state information in RL and introduces gated units to enhance the memory capacity of spiking neurons. Experimental results show that our method can solve partially observable Markov decision processes (POMDPs) and multi-agent cooperation problems with similar performance as recurrent neural networks (RNNs) but with about 50% power consumption.
Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems
Authors: Sarah Keren, Chaimaa Essayeh, Stefano V. Albrecht, Thomas Morstyn
Date: 2024-04-24 00:00:00
Tasks: Multi-agent Reinforcement Learning

The rapidly changing architecture and functionality of electrical networks and the increasing penetration of renewable and distributed energy resources have resulted in various technological and managerial challenges. These have rendered traditional centralized energy-market paradigms insufficient due to their inability to support the dynamic and evolving nature of the network. This survey explores how multi-agent reinforcement learning (MARL) can support the decentralization and decarbonization of energy networks and mitigate the associated challenges. This is achieved by specifying key computational challenges in managing energy networks, reviewing recent research progress on addressing them, and highlighting open challenges that may be addressed using MARL.
Guided AbsoluteGrad: Magnitude of Gradients Matters to Explanation's Localization and Saliency
Authors: Jun Huang, Yan Liu
Date: 2024-04-23 00:00:00

This paper proposes a new gradient-based XAI method called Guided AbsoluteGrad for saliency map explanations. We utilize both positive and negative gradient magnitudes and employ gradient variance to distinguish the important areas for noise deduction. We also introduce a novel evaluation metric named ReCover And Predict (RCAP), which considers the Localization and Visual Noise Level objectives of the explanations. We propose two propositions for these two objectives and prove the necessity of evaluating them. We evaluate Guided AbsoluteGrad with seven gradient-based XAI methods using the RCAP metric and other SOTA metrics in three case studies: (1) ImageNet dataset with ResNet50 model; (2) International Skin Imaging Collaboration (ISIC) dataset with EfficientNet model; (3) the Places365 dataset with DenseNet161 model. Our method surpasses other gradient-based approaches, showcasing the quality of enhanced saliency map explanations through gradient magnitude.