pwc-archive/papers-with-abstracts huggingface dataset:
Analyzing Generalization in Policy Networks: A Case Study with the Double-Integrator System
Authors: Ruining Zhang, Haoran Han, Maolong Lv, Qisong Yang, Jian Cheng
Date: 2023-12-16 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning

Extensive utilization of deep reinforcement learning (DRL) policy networks in diverse continuous control tasks has raised questions regarding performance degradation in expansive state spaces where the input state norm is larger than that in the training environment. This paper aims to uncover the underlying factors contributing to such performance deterioration when dealing with expanded state spaces, using a novel analysis technique known as state division. In contrast to prior approaches that employ state division merely as a post-hoc explanatory tool, our methodology delves into the intrinsic characteristics of DRL policy networks. Specifically, we demonstrate that the expansion of state space induces the activation function $\tanh$ to exhibit saturability, resulting in the transformation of the state division boundary from nonlinear to linear. Our analysis centers on the paradigm of the double-integrator system, revealing that this gradual shift towards linearity imparts a control behavior reminiscent of bang-bang control. However, the inherent linearity of the division boundary prevents the attainment of an ideal bang-bang control, thereby introducing unavoidable overshooting. Our experimental investigations, employing diverse RL algorithms, establish that this performance phenomenon stems from inherent attributes of the DRL policy network, remaining consistent across various optimization algorithms.
Enhancing Person Re-Identification through Tensor Feature Fusion
Authors: Akram Abderraouf Gharbi, Ammar Chouchane, Mohcene Bessaoudi, Abdelmalik Ouamane, El Ouanas Belabbaci
Date: 2023-12-16 00:00:00
Tasks: Person Re-Identification

In this paper, we present a novel person reidentification (PRe-ID) system that based on tensor feature representation and multilinear subspace learning. Our approach utilizes pretrained CNNs for high-level feature extraction, along with Local Maximal Occurrence (LOMO) and Gaussian Of Gaussian (GOG ) descriptors. Additionally, Cross-View Quadratic Discriminant Analysis (TXQDA) algorithm is used for multilinear subspace learning, which models the data in a tensor framework to enhance discriminative capabilities. Similarity measure based on Mahalanobis distance is used for matching between training and test pedestrian images. Experimental evaluations on VIPeR and PRID450s datasets demonstrate the effectiveness of our method.
RIGHT: Retrieval-augmented Generation for Mainstream Hashtag Recommendation
Authors: Run-Ze Fan, Yixing Fan, Jiangui Chen, Jiafeng Guo, Ruqing Zhang, Xueqi Cheng
Date: 2023-12-16 00:00:00
Tasks: Retrieval, Retrieval-augmented Generation

Automatic mainstream hashtag recommendation aims to accurately provide users with concise and popular topical hashtags before publication. Generally, mainstream hashtag recommendation faces challenges in the comprehensive difficulty of newly posted tweets in response to new topics, and the accurate identification of mainstream hashtags beyond semantic correctness. However, previous retrieval-based methods based on a fixed predefined mainstream hashtag list excel in producing mainstream hashtags, but fail to understand the constant flow of up-to-date information. Conversely, generation-based methods demonstrate a superior ability to comprehend newly posted tweets, but their capacity is constrained to identifying mainstream hashtags without additional features. Inspired by the recent success of the retrieval-augmented technique, in this work, we attempt to adopt this framework to combine the advantages of both approaches. Meantime, with the help of the generator component, we could rethink how to further improve the quality of the retriever component at a low cost. Therefore, we propose RetrIeval-augmented Generative Mainstream HashTag Recommender (RIGHT), which consists of three components: 1) a retriever seeks relevant hashtags from the entire tweet-hashtags set; 2) a selector enhances mainstream identification by introducing global signals; and 3) a generator incorporates input tweets and selected hashtags to directly generate the desired hashtags. The experimental results show that our method achieves significant improvements over state-of-the-art baselines. Moreover, RIGHT can be easily integrated into large language models, improving the performance of ChatGPT by more than 10%.
Identity Curvature Laplace Approximation for Improved Out-of-Distribution Detection
Authors: Maksim Zhdanov, Stanislav Dereka, Sergey Kolesnikov
Date: 2023-12-16 00:00:00
Tasks: Attribute, Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Uncertainty estimation is crucial in safety-critical applications, where robust out-of-distribution (OOD) detection is essential. Traditional Bayesian methods, though effective, are often hindered by high computational demands. As an alternative, Laplace approximation offers a more practical and efficient approach to uncertainty estimation. In this paper, we introduce the Identity Curvature Laplace Approximation (ICLA), a novel method that challenges the conventional posterior covariance formulation by using identity curvature and optimizing prior precision. This innovative design significantly enhances OOD detection performance on well-known datasets such as CIFAR-10, CIFAR-100, and ImageNet, while maintaining calibration scores. We attribute this improvement to the alignment issues between typical feature embeddings and curvature as measured by the Fisher information matrix. Our findings are further supported by demonstrating that incorporating Fisher penalty or sharpness-aware minimization techniques can greatly enhance the uncertainty estimation capabilities of standard Laplace approximation.
Fusion of Deep and Shallow Features for Face Kinship Verification
Authors: Belabbaci El Ouanas, Khammari Mohammed, Chouchane Ammar, Mohcene Bessaoudi, Abdelmalik Ouamane, Akram Abderraouf Gharbi
Date: 2023-12-16 00:00:00
Tasks: Kinship Verification, regression

Kinship verification from face images is a novel and formidable challenge in the realms of pattern recognition and computer vision. This work makes notable contributions by incorporating a preprocessing technique known as Multiscale Retinex (MSR), which enhances image quality. Our approach harnesses the strength of complementary deep (VGG16) and shallow texture descriptors (BSIF) by combining them at the score level using Logistic Regression (LR) technique. We assess the effectiveness of our approach by conducting comprehensive experiments on three challenging kinship datasets: Cornell Kin Face, UB Kin Face and TS Kin Face
Resolving Crash Bugs via Large Language Models: An Empirical Study
Authors: Xueying Du, Mingwei Liu, Juntao Li, Hanlin Wang, Xin Peng, Yiling Lou
Date: 2023-12-16 00:00:00
Tasks: Language Modelling, Large Language Model

Crash bugs cause unexpected program behaviors or even termination, requiring high-priority resolution. However, manually resolving crash bugs is challenging and labor-intensive, and researchers have proposed various techniques for their automated localization and repair. ChatGPT, a recent large language model (LLM), has garnered significant attention due to its exceptional performance across various domains. This work performs the first investigation into ChatGPT's capability in resolve real-world crash bugs, focusing on its effectiveness in both localizing and repairing code-related and environment-related crash bugs. Specifically, we initially assess ChatGPT's fundamental ability to resolve crash bugs with basic prompts in a single iteration. We observe that ChatGPT performs better at resolving code-related crash bugs compared to environment-related ones, and its primary challenge in resolution lies in inaccurate localization. Additionally, we explore ChatGPT's potential with various advanced prompts. Furthermore, by stimulating ChatGPT's self-planning, it methodically investigates each potential crash-causing environmental factor through proactive inquiry, ultimately identifying the root cause of the crash. Based on our findings, we propose IntDiagSolver, an interaction methodology designed to facilitate precise crash bug resolution through continuous interaction with LLMs. Evaluating IntDiagSolver on multiple LLMs reveals consistent enhancement in the accuracy of crash bug resolution, including ChatGPT, Claude, and CodeLlama.
Weight-Entanglement Meets Gradient-Based Neural Architecture Search
Authors: Rhea Sanjay Sukthanker, Arjun Krishnakumar, Mahmoud Safari, Frank Hutter
Date: 2023-12-16 00:00:00
Tasks: Neural Architecture Search

Weight sharing is a fundamental concept in neural architecture search (NAS), enabling gradient-based methods to explore cell-based architecture spaces significantly faster than traditional blackbox approaches. In parallel, weight \emph{entanglement} has emerged as a technique for intricate parameter sharing among architectures within macro-level search spaces. %However, the macro structure of such spaces poses compatibility challenges for gradient-based NAS methods. %As a result, blackbox optimization methods have been commonly employed, particularly in conjunction with supernet training, to maintain search efficiency. %Due to the inherent differences in the structure of these search spaces, these Since weight-entanglement poses compatibility challenges for gradient-based NAS methods, these two paradigms have largely developed independently in parallel sub-communities. This paper aims to bridge the gap between these sub-communities by proposing a novel scheme to adapt gradient-based methods for weight-entangled spaces. This enables us to conduct an in-depth comparative assessment and analysis of the performance of gradient-based NAS in weight-entangled search spaces. Our findings reveal that this integration of weight-entanglement and gradient-based NAS brings forth the various benefits of gradient-based methods (enhanced performance, improved supernet training properties and superior any-time performance), while preserving the memory efficiency of weight-entangled spaces. The code for our work is openly accessible \href{https://anonymous.4open.science/r/TangleNAS-527C}{here}
Decomposing Hard SAT Instances with Metaheuristic Optimization
Authors: Daniil Chivilikhin, Artem Pavlenko, Alexander Semenov
Date: 2023-12-16 00:00:00
Tasks: Evolutionary Algorithms, Metaheuristic Optimization

In the article, within the framework of the Boolean Satisfiability problem (SAT), the problem of estimating the hardness of specific Boolean formulas w.r.t. a specific complete SAT solving algorithm is considered. Based on the well-known Strong Backdoor Set (SBS) concept, we introduce the notion of decomposition hardness (d-hardness). If $B$ is an arbitrary subset of the set of variables occurring in a SAT formula $C$, and $A$ is an arbitrary complete SAT solver , then the d-hardness expresses an estimate of the hardness of $C$ w.r.t. $A$ and $B$. We show that the d-hardness of $C$ w.r.t. a particular $B$ can be expressed in terms of the expected value of a special random variable associated with $A$, $B$, and $C$. For its computational evaluation, algorithms based on the Monte Carlo method can be used. The problem of finding $B$ with the minimum value of d-hardness is formulated as an optimization problem for a pseudo-Boolean function whose values are calculated as a result of a probabilistic experiment. To minimize this function, we use evolutionary algorithms. In the experimental part, we demonstrate the applicability of the concept of d-hardness and the methods of its estimation to solving hard unsatisfiable SAT instances.
Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model
Authors: Shunsuke Horii, Yoichi Chikahara
Date: 2023-12-16 00:00:00
Tasks: Bayesian Inference, Decision Making, Heterogeneous Treatment Effect Estimation, Uncertainty Quantification

Estimating heterogeneous treatment effects across individuals has attracted growing attention as a statistical tool for performing critical decision-making. We propose a Bayesian inference framework that quantifies the uncertainty in treatment effect estimation to support decision-making in a relatively small sample size setting. Our proposed model places Gaussian process priors on the nonparametric components of a semiparametric model called a partially linear model. This model formulation has three advantages. First, we can analytically compute the posterior distribution of a treatment effect without relying on the computationally demanding posterior approximation. Second, we can guarantee that the posterior distribution concentrates around the true one as the sample size goes to infinity. Third, we can incorporate prior knowledge about a treatment effect into the prior distribution, improving the estimation efficiency. Our experimental results show that even in the small sample size setting, our method can accurately estimate the heterogeneous treatment effects and effectively quantify its estimation uncertainty.
From Dialogue to Diagram: Task and Relationship Extraction from Natural Language for Accelerated Business Process Prototyping
Authors: Sara Qayyum, Muhammad Moiz Asghar, Muhammad Fouzan Yaseen
Date: 2023-12-16 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Dependency Parsing, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

The automatic transformation of verbose, natural language descriptions into structured process models remains a challenge of significant complexity - This paper introduces a contemporary solution, where central to our approach, is the use of dependency parsing and Named Entity Recognition (NER) for extracting key elements from textual descriptions. Additionally, we utilize Subject-Verb-Object (SVO) constructs for identifying action relationships and integrate semantic analysis tools, including WordNet, for enriched contextual understanding. A novel aspect of our system is the application of neural coreference resolution, integrated with the SpaCy framework, enhancing the precision of entity linkage and anaphoric references. Furthermore, the system adeptly handles data transformation and visualization, converting extracted information into BPMN (Business Process Model and Notation) diagrams. This methodology not only streamlines the process of capturing and representing business workflows but also significantly reduces the manual effort and potential for error inherent in traditional modeling approaches.
ResoNet: Robust and Explainable ENSO Forecasts with Hybrid Convolution and Transformer Networks
Authors: Pumeng Lyu, Tao Tang, Fenghua Ling, Jing-Jia Luo, Niklas Boers, Wanli Ouyang, Lei Bai
Date: 2023-12-16 00:00:00

Recent studies have shown that deep learning (DL) models can skillfully predict the El Ni\~no-Southern Oscillation (ENSO) forecasts over 1.5 years ahead. However, concerns regarding the reliability of predictions made by DL methods persist, including potential overfitting issues and lack of interpretability. Here, we propose ResoNet, a DL model that combines convolutional neural network (CNN) and Transformer architectures. This hybrid architecture design enables our model to adequately capture local SSTA as well as long-range inter-basin interactions across oceans. We show that ResoNet can robustly predict ESNO at lead times between 19 and 26 months, thus outperforming existing approaches in terms of the forecast horizon. According to an explainability method applied to ResoNet predictions of El Ni\~no and La Ni\~na events from 1- to 18-month lead, we find that it predicts the Ni\~no3.4 index based on multiple physically reasonable mechanisms, such as the Recharge Oscillator concept, Seasonal Footprint Mechanism, and Indian Ocean capacitor effect. Moreover, we demonstrate that for the first time, the asymmetry between El Ni\~no and La Ni\~na development can be captured by ResoNet. Our results could help alleviate skepticism about applying DL models for ENSO prediction and encourage more attempts to discover and predict climate phenomena using AI methods.
Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation
Authors: Xiaobin Huang, Lei Song, Ke Xue, Chao Qian
Date: 2023-12-16 00:00:00
Tasks: Bayesian Optimization, Density Estimation

Bayesian optimization (BO) is a sample-efficient method and has been widely used for optimizing expensive black-box functions. Recently, there has been a considerable interest in BO literature in optimizing functions that are affected by context variable in the environment, which is uncontrollable by decision makers. In this paper, we focus on the optimization of functions' expectations over continuous context variable, subject to an unknown distribution. To address this problem, we propose two algorithms that employ kernel density estimation to learn the probability density function (PDF) of continuous context variable online. The first algorithm is simpler, which directly optimizes the expectation under the estimated PDF. Considering that the estimated PDF may have high estimation error when the true distribution is complicated, we further propose the second algorithm that optimizes the distributionally robust objective. Theoretical results demonstrate that both algorithms have sub-linear Bayesian cumulative regret on the expectation objective. Furthermore, we conduct numerical experiments to empirically demonstrate the effectiveness of our algorithms.
M^2ConceptBase: A Fine-Grained Aligned Concept-Centric Multimodal Knowledge Base
Authors: Zhiwei Zha, Jiaan Wang, Zhixu Li, Xiangru Zhu, Wei Song, Yanghua Xiao
Date: 2023-12-16 00:00:00
Tasks: cross-modal alignment, Knowledge Graphs, Language Modelling, Large Language Model, Visual Question Answering (VQA)

Multimodal knowledge bases (MMKBs) provide cross-modal aligned knowledge crucial for multimodal tasks. However, the images in existing MMKBs are generally collected for entities in encyclopedia knowledge graphs. Therefore, detailed groundings of visual semantics with linguistic concepts are lacking, which are essential for the visual concept cognition ability of multimodal models. Addressing this gap, we introduce M^2ConceptBase, the first concept-centric MMKB. M^2ConceptBase models concepts as nodes with associated images and detailed textual descriptions. We propose a context-aware multimodal symbol grounding approach to align concept-image and concept-description pairs using context information from image-text datasets. Comprising 951K images and 152K concepts, M^2ConceptBase links each concept to an average of 6.27 images and a single description, ensuring comprehensive visual and textual semantics. Human studies confirm more than 95% alignment accuracy, underscoring its quality. Additionally, our experiments demonstrate that M^2ConceptBase significantly enhances VQA model performance on the OK-VQA task. M^2ConceptBase also substantially improves the fine-grained concept understanding capabilities of multimodal large language models through retrieval augmentation in two concept-related tasks, highlighting its value.
Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data
Authors: Youssra Cheriguene, Wael Jaafar, Halim Yanikomeroglu, Chaker Abdelaziz Kerrache
Date: 2023-12-16 00:00:00
Tasks: Federated Learning

Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent and identically distributed -IID) collected data. In addition, UAVs may demonstrate unintentional misbehavior in which the latter may fail to send updates to the FL server due, for instance, to UAVs' disconnectivity from the FL system caused by high mobility, unavailability, or battery depletion. Such challenges may significantly affect the convergence of the FL model. A recent way to tackle these challenges is client selection, based on customized criteria that consider UAV computing power and energy consumption. However, most existing client selection schemes neglected the participants' reliability. Indeed, FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server, by either providing targeted false predictions for specifically chosen inputs or by compromising the global model's accuracy through tampering with the local model. Hence, we propose in this paper a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores, while eliminating malicious UAVs from training. Through experiments, we assess the effectiveness of our scheme in resisting different attack scenarios, in terms of convergence and achieved model accuracy. Finally, we demonstrate the performance superiority of the proposed approach compared to baseline methods.
DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated Content
Authors: Wentao Wang, Xuanyao Huang, Tianyang Wang, Swalpa Kumar Roy
Date: 2023-12-16 00:00:00
Tasks: Image Generation, Language Modeling, Language Modelling, Large Language Model

This paper explores the image synthesis capabilities of GPT-4, a leading multi-modal large language model. We establish a benchmark for evaluating the fidelity of texture features in images generated by GPT-4, comprising manually painted pictures and their AI-generated counterparts. The contributions of this study are threefold: First, we provide an in-depth analysis of the fidelity of image synthesis features based on GPT-4, marking the first such study on this state-of-the-art model. Second, the quantitative and qualitative experiments fully reveals the limitations of the GPT-4 model in image synthesis. Third, we have compiled a unique benchmark of manual drawings and corresponding GPT-4-generated images, introducing a new task to advance fidelity research in AI-generated content (AIGC). The dataset is available at: \url{https://github.com/rickwang28574/DeepArt}.
Annotation-free Automatic Music Transcription with Scalable Synthetic Data and Adversarial Domain Confusion
Authors: Gakusei Sato, Taketo Akama
Date: 2023-12-16 00:00:00
Tasks: Music Transcription

Automatic Music Transcription (AMT) is a vital technology in the field of music information processing. Despite recent enhancements in performance due to machine learning techniques, current methods typically attain high accuracy in domains where abundant annotated data is available. Addressing domains with low or no resources continues to be an unresolved challenge. To tackle this issue, we propose a transcription model that does not require any MIDI-audio paired data through the utilization of scalable synthetic audio for pre-training and adversarial domain confusion using unannotated real audio. In experiments, we evaluate methods under the real-world application scenario where training datasets do not include the MIDI annotation of audio in the target data domain. Our proposed method achieved competitive performance relative to established baseline methods, despite not utilizing any real datasets of paired MIDI-audio. Additionally, ablation studies have provided insights into the scalability of this approach and the forthcoming challenges in the field of AMT research.