pwc-archive/papers-with-abstracts huggingface dataset:
FLIM-based Salient Object Detection Networks with Adaptive Decoders
Authors: Gilson Junior Soares, Matheus Abrantes Cerqueira, Jancarlo F. Gomes, Laurent Najman, Silvio Jamil F. Guimarães, Alexandre Xavier Falcão
Date: 2025-04-29 00:00:00
Tasks: object-detection, Object Detection, Salient Object Detection

Salient Object Detection (SOD) methods can locate objects that stand out in an image, assign higher values to their pixels in a saliency map, and binarize the map outputting a predicted segmentation mask. A recent tendency is to investigate pre-trained lightweight models rather than deep neural networks in SOD tasks, coping with applications under limited computational resources. In this context, we have investigated lightweight networks using a methodology named Feature Learning from Image Markers (FLIM), which assumes that the encoder's kernels can be estimated from marker pixels on discriminative regions of a few representative images. This work proposes flyweight networks, hundreds of times lighter than lightweight models, for SOD by combining a FLIM encoder with an adaptive decoder, whose weights are estimated for each input image by a given heuristic function. Such FLIM networks are trained from three to four representative images only and without backpropagation, making the models suitable for applications under labeled data constraints as well. We study five adaptive decoders; two of them are introduced here. Differently from the previous ones that rely on one neuron per pixel with shared weights, the heuristic functions of the new adaptive decoders estimate the weights of each neuron per pixel. We compare FLIM models with adaptive decoders for two challenging SOD tasks with three lightweight networks from the state-of-the-art, two FLIM networks with decoders trained by backpropagation, and one FLIM network whose labeled markers define the decoder's weights. The experiments demonstrate the advantages of the proposed networks over the baselines, revealing the importance of further investigating such methods in new applications.
Optimizing Deep Neural Networks using Safety-Guided Self Compression
Authors: Mohammad Zbeeb, Mariam Salman, Mohammad Bazzi, Ammar Mohanna
Date: 2025-05-01 00:00:00
Tasks: Language Modeling, Language Modelling, Quantization

The deployment of deep neural networks on resource-constrained devices necessitates effective model com- pression strategies that judiciously balance the reduction of model size with the preservation of performance. This study introduces a novel safety-driven quantization framework that leverages preservation sets to systematically prune and quantize neural network weights, thereby optimizing model complexity without compromising accuracy. The proposed methodology is rigorously evaluated on both a convolutional neural network (CNN) and an attention-based language model, demonstrating its applicability across diverse architectural paradigms. Experimental results reveal that our framework achieves up to a 2.5% enhancement in test accuracy relative to the original unquantized models while maintaining 60% of the initial model size. In comparison to conventional quantization techniques, our approach not only augments generalization by eliminating parameter noise and retaining essential weights but also reduces variance, thereby ensuring the retention of critical model features. These findings underscore the efficacy of safety-driven quantization as a robust and reliable strategy for the efficient optimization of deep learn- ing models. The implementation and comprehensive experimental evaluations of our framework are publicly accessible at GitHub.
Analysis of the vulnerability of machine learning regression models to adversarial attacks using data from 5G wireless networks
Authors: Leonid Legashev, Artur Zhigalov, Denis Parfenov
Date: 2025-05-01 00:00:00
Tasks: Adversarial Attack, regression

This article describes the process of creating a script and conducting an analytical study of a dataset using the DeepMIMO emulator. An advertorial attack was carried out using the FGSM method to maximize the gradient. A comparison is made of the effectiveness of binary classifiers in the task of detecting distorted data. The dynamics of changes in the quality indicators of the regression model were analyzed in conditions without adversarial attacks, during an adversarial attack and when the distorted data was isolated. It is shown that an adversarial FGSM attack with gradient maximization leads to an increase in the value of the MSE metric by 33% and a decrease in the R2 indicator by 10% on average. The LightGBM binary classifier effectively identifies data with adversarial anomalies with 98% accuracy. Regression machine learning models are susceptible to adversarial attacks, but rapid analysis of network traffic and data transmitted over the network makes it possible to identify malicious activity
Towards Lightweight Hyperspectral Image Super-Resolution with Depthwise Separable Dilated Convolutional Network
Authors: Usman Muhammad, Jorma Laaksonen, Lyudmila Mihaylova
Date: 2025-05-01 00:00:00
Tasks: Hyperspectral Image Super-Resolution, Image Super-Resolution, Super-Resolution

Deep neural networks have demonstrated highly competitive performance in super-resolution (SR) for natural images by learning mappings from low-resolution (LR) to high-resolution (HR) images. However, hyperspectral super-resolution remains an ill-posed problem due to the high spectral dimensionality of the data and the scarcity of available training samples. Moreover, existing methods often rely on large models with a high number of parameters or require the fusion with panchromatic or RGB images, both of which are often impractical in real-world scenarios. Inspired by the MobileNet architecture, we introduce a lightweight depthwise separable dilated convolutional network (DSDCN) to address the aforementioned challenges. Specifically, our model leverages multiple depthwise separable convolutions, similar to the MobileNet architecture, and further incorporates a dilated convolution fusion block to make the model more flexible for the extraction of both spatial and spectral features. In addition, we propose a custom loss function that combines mean squared error (MSE), an L2 norm regularization-based constraint, and a spectral angle-based loss, ensuring the preservation of both spectral and spatial details. The proposed model achieves very competitive performance on two publicly available hyperspectral datasets, making it well-suited for hyperspectral image super-resolution tasks. The source codes are publicly available at: \href{https://github.com/Usman1021/lightweight}{https://github.com/Usman1021/lightweight}.
Nonlinear multi-objective optimization model for a biomass direct fired power generation supply chain using a case study in China
Authors: Qinliang Tan, Tingran Wang, Yimei Zhang, Xinyan Miao, Jun Zhu
Date: 2017-08-13 00:00:00
Proceeding: 2017 2017 8

Using renewable resources to generate electricity is becoming increasingly common all over the world.
 As the largest energy consumer in the world, China has established many biomass direct-fired power
 plants in recent years. However, most of these power plants would not survive without government
 subsidies because of the high cost of fuel. This study focuses on the fuel supply chain of biomass direct
f
 ired power generation, including the collection, transportation, processing, and storage of fuel; the study
 presents a nonlinear multi-objective optimization model. The purpose of this work is to determine the
 optimal quantity of electricity generation, as well as the ideal blending ratio, acquisition quantity and
 price of each kind of fuel so as to maximize the profit margins of biomass power plants and provide the
 most protection possible of social welfare considering environmental factors. The model we present was
 applied in a biomass direct-fired power plant in Heilongjiang Province in Northeast China; the subject
 plant was chosen from 13 biomass power plants that were investigated. Based on the case study, we
 obtained values for the optimal results and compared them with the actual situation. The optimal results
 confirmed that under the existing technical conditions, the overall benefit of biomass power generation
 leaves significant room for improvement, mainly by means of designing a more reasonable fuel supply
 mode. Finally, a sensitivity analysis showed that the best and most integrated efficiency in biomass
 power generation was achieved when the biomass electricity on-grid price was approximately 0.65
 Yuan/kWh.
Frustration, dynamics and catalysis
Authors: R. Gonzalo Parra, Diego U. Ferreiro
Date: 2025-05-01 00:00:00

The controlled dissipation of chemical potentials is the fundamental way cells make a living. Enzyme-mediated catalysis allows the various transformations to proceed at biologically relevant rates with remarkable precision and efficiency. Theory, experiments and computational studies coincide to show that local frustration is a useful concept to relate protein dynamics with catalytic power. Local frustration gives rise to the asperities of the energy landscapes that can harness the thermal fluctuations to guide the functional protein motions. We review here recent advances into these relationships from various fields of protein science. The biologically relevant dynamics is tuned by the evolution of protein sequences that modulate the local frustration patterns to near optimal values.
DynClean: Training Dynamics-based Label Cleaning for Distantly-Supervised Named Entity Recognition
Authors: Qi Zhang, Huitong Pan, Zhijia Chen, Longin Jan Latecki, Cornelia Caragea, Eduard Dragut
Date: 2025-04-06 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, NER

Distantly Supervised Named Entity Recognition (DS-NER) has attracted attention due to its scalability and ability to automatically generate labeled data. However, distant annotation introduces many mislabeled instances, limiting its performance. Most of the existing work attempt to solve this problem by developing intricate models to learn from the noisy labels. An alternative approach is to attempt to clean the labeled data, thus increasing the quality of distant labels. This approach has received little attention for NER. In this paper, we propose a training dynamics-based label cleaning approach, which leverages the behavior of a model as training progresses to characterize the distantly annotated samples. We also introduce an automatic threshold estimation strategy to locate the errors in distant labels. Extensive experimental results demonstrate that: (1) models trained on our cleaned DS-NER datasets, which were refined by directly removing identified erroneous annotations, achieve significant improvements in F1-score, ranging from 3.18% to 8.95%; and (2) our method outperforms numerous advanced DS-NER approaches across four datasets.
Grokking in the Wild: Data Augmentation for Real-World Multi-Hop Reasoning with Transformers
Authors: Roman Abramov, Felix Steinbauer, Gjergji Kasneci
Date: 2025-04-29 00:00:00
Tasks: Data Augmentation, Knowledge Graphs, Memorization, World Knowledge

Transformers have achieved great success in numerous NLP tasks but continue to exhibit notable gaps in multi-step factual reasoning, especially when real-world knowledge is sparse. Recent advances in grokking have demonstrated that neural networks can transition from memorizing to perfectly generalizing once they detect underlying logical patterns - yet these studies have primarily used small, synthetic tasks. In this paper, for the first time, we extend grokking to real-world factual data and address the challenge of dataset sparsity by augmenting existing knowledge graphs with carefully designed synthetic data to raise the ratio $\phi_r$ of inferred facts to atomic facts above the threshold required for grokking. Surprisingly, we find that even factually incorrect synthetic data can strengthen emergent reasoning circuits rather than degrade accuracy, as it forces the model to rely on relational structure rather than memorization. When evaluated on multi-hop reasoning benchmarks, our approach achieves up to 95-100% accuracy on 2WikiMultiHopQA - substantially improving over strong baselines and matching or exceeding current state-of-the-art results. We further provide an in-depth analysis of how increasing $\phi_r$ drives the formation of generalizing circuits inside Transformers. Our findings suggest that grokking-based data augmentation can unlock implicit multi-hop reasoning capabilities, opening the door to more robust and interpretable factual reasoning in large-scale language models.
Towardseffective teaching assistants: From intent-based chatbots to LLM-poweredteachingassistants
Authors: Bashaer Alsafaria, C, ∗, Eric Atwella, Aisha Walkerb, Martin Callaghana
Date: 2024-08-20 00:00:00
Proceeding: Natural Language Processing Journal 8 (2024) 100101 2024 8
Tasks: Answer Generation, Chatbot, Large Language Model, Question-Answer-Generation, RAG

As chatbot technology undergoes a transformative phase in the era of artificial intelligence (AI), the integration of advanced AI models emerges as a focal point for reshaping conversational agents within the education sector. This paper explores the evolution of educational chatbot development, specifically focusing on building a teaching assistant for Data Mining and Text Analytics courses at the University of Leeds. The primary objective is to investigate and compare traditional intent-based chatbot approaches with the advanced retrievalaugmented generation (RAG) method, aiming to improve the efficiency and adaptability of teaching assistants in higher education. The study begins with the development of an Amazon Alexa teaching skill, assessing the efficacy of traditional chatbot development in higher education. To enrich the chatbot knowledge base, the research then employs an automated question–answer generation (QAG) approach using the QG Lumos Learning tool to extract contextually grounded question–answer datasets from course materials. Subsequently, the RAG-based system is proposed, leveraging LangChain with the OpenAI GPT-3.5 Turbo model. Findings highlight limitations in intent-based approaches, emphasising the need for more adaptive solutions. The proposed RAG-based teaching assistant demonstrates significant improvements in efficiently handling diverse queries, representing a paradigm shift in educational chatbot capabilities. These findings provide an indepth understanding of the development phase, specifically illustrating the impact on chatbot performance by contrasting traditional methods with large language model-based approaches. The study contributes valuable perspectives on enhancing adaptability and effectiveness in AI-powered educational tools, providing essential considerations for future developments in the field.
Solving Copyright Infringement on Short Video Platforms: Novel Datasets and an Audio Restoration Deep Learning Pipeline
Authors: Minwoo Oh, Minsu Park, Eunil Park
Date: 2025-04-30 00:00:00
Tasks: Music Source Separation, Video Restoration

Short video platforms like YouTube Shorts and TikTok face significant copyright compliance challenges, as infringers frequently embed arbitrary background music (BGM) to obscure original soundtracks (OST) and evade content originality detection. To tackle this issue, we propose a novel pipeline that integrates Music Source Separation (MSS) and cross-modal video-music retrieval (CMVMR). Our approach effectively separates arbitrary BGM from the original OST, enabling the restoration of authentic video audio tracks. To support this work, we introduce two domain-specific datasets: OASD-20K for audio separation and OSVAR-160 for pipeline evaluation. OASD-20K contains 20,000 audio clips featuring mixed BGM and OST pairs, while OSVAR160 is a unique benchmark dataset comprising 1,121 video and mixed-audio pairs, specifically designed for short video restoration tasks. Experimental results demonstrate that our pipeline not only removes arbitrary BGM with high accuracy but also restores OSTs, ensuring content integrity. This approach provides an ethical and scalable solution to copyright challenges in user-generated content on short video platforms.
Learning to Rank Aspects and Opinions for Comparative Explanations
Authors: Trung-Hoang Le, Hady W. Lauw
Date: 2025-01-16 00:00:00
Proceeding: Machine Learning 2025 1
Tasks: Explainable Recommendation, Learning-To-Rank

Comparative recommendation explanations help to make sense of recommendations by comparing a recommended item along some aspects of interest with one or many items being considered. This work extends the notion of comparative explanations, by going beyond merely better/worse statements, to further incorporate aspect-level opinions for more informative comparisons. To enhance the quality of both the personalized recommendation and the explanation, we incorporate optimization objectives that preserve relative rankings of aspects and opinions, in addition to the classical rankings of overall preferences for items. We integrate the multiple ranking objectives and multi-tensor factorization together. Experiments on datasets of different domains validate the efficacy of our proposed framework in both recommendation and comparative explanation against comparable explainable recommendation baselines.
Question-Attentive Review-Level for Neural Rating Regression
Authors: Trung-Hoang Le, Hady W. Lauw
Date: 2024-12-13 00:00:00
Proceeding: ACM Transactions on Intelligent Systems and Technology 2024 12
Tasks: regression

Recommendation explanations help to improve their acceptance by end users. Explanations come in many different forms. One that is of interest here is presenting an existing review of the recommended item as the explanation. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance or “attention” of each review to the recommendation objective. Our focus is improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution jointly to the recommendation objective. The benefits are two-fold. For one, QA aids in selecting more useful reviews. For another, QA itself could accompany a well-aligned review in an expanded form of explanation. Experiments on datasets of ten product categories showcase the efficacies of our method as compared to comparable baselines in identifying useful reviews and QAs, while maintaining parity in recommendation performance.
Hypergraphs with Attention on Reviews for Explainable Recommendation
Authors: Theis E. Jendal, Trung-Hoang Le, Hady W. Lauw, Matteo Lissandrini, Peter Dolog and Katja Hose
Date: 2024-03-20 00:00:00
Proceeding: European Conference on Information Retrieval 2024 3
Tasks: Explainable Recommendation, Recommendation Systems

Given a recommender system based on reviews, the chal-lenges are how to effectively represent the review data and how to explainthe produced recommendations. We propose a novel review-specific Hy-pergraph (HG) model, and further introduce a model-agnostic explaina-bility module. The HG model captures high-order connections betweenusers, items, aspects, and opinions while maintaining information aboutthe review. The explainability module can use the HG model to ex-plain a prediction generated by any model. We propose a path-restrictedreview-selection method biased by the user preference for item reviewsand propose a novel explanation method based on a review graph. Ex-periments on real-world datasets confirm the ability of the HG model tocapture appropriate explanations.
Selecting Comparative Sets of Reviews Across Multiple Items
Authors: Trung-Hoang Le, Hady W. Lauw
Date: 2025-03-28 00:00:00
Proceeding: International Conference on Extending Database Technology (EDBT) 2025 3

While choosing among several products, users may look up reviews from each product they are considering. Due to the large number of reviews of products, selecting representative reviews from one product alone is already a challenging problem. In this work, we further aim to conduct review selection for multiple products simultaneously for comparative purposes. We formulate objective functions that synchronize the review selection and design efficient algorithms to optimize for the objective functions. To narrow down the potentially long list of comparison items into a shorter list of more similar items, we construct a graph representing items’ similarity and design efficient algorithms to find the heaviest 𝑘-subgraph including the target item. The results are validated on real world datasets on various product categories.
Towards AI-Driven Policing: Interdisciplinary Knowledge Discovery from Police Body-Worn Camera Footage
Authors: Anita Srbinovska, Angela Srbinovska, Vivek Senthil, Adrian Martin, John McCluskey, Jonathan Bateman, Ernest Fokoué
Date: 2025-04-28 00:00:00

This paper proposes a novel interdisciplinary framework for analyzing police body-worn camera (BWC) footage from the Rochester Police Department (RPD) using advanced artificial intelligence (AI) and statistical machine learning (ML) techniques. Our goal is to detect, classify, and analyze patterns of interaction between police officers and civilians to identify key behavioral dynamics, such as respect, disrespect, escalation, and de-escalation. We apply multimodal data analysis by integrating video, audio, and natural language processing (NLP) techniques to extract meaningful insights from BWC footage. We present our methodology, computational techniques, and findings, outlining a practical approach for law enforcement while advancing the frontiers of knowledge discovery from police BWC data.
Nuclear Partition Functions at Temperatures Exceeding 10^10 K
Authors: T. Rauscher
Date: 2003-04-02 00:00:00

Nuclear partition functions were calculated for a grid of temperatures from 1.2x10^10 K to 2.75x10^11 K (1<=kT<=24 MeV) within a Fermi-gas approach, including all nuclides from the proton-dripline to the neutron-dripline with proton number 9<=Z<=85. The calculation is based on a nuclear level density description published elsewhere, thus extending the previous tables of partition functions beyond 10^10 K. Additional high temperature corrections had to be applied.
Frankenstein Optimizer: Harnessing the Potential by Revisiting Optimization Tricks
Authors: Chia-Wei Hsu, Nien-Ti Tsou, Yu-cheng Chen, Yang Jeong Park, Ju Li
Date: 2025-03-04 00:00:00
Tasks: Few-Shot Learning

Gradient-based optimization drives the unprecedented performance of modern deep neural network models across diverse applications. Adaptive algorithms have accelerated neural network training due to their rapid convergence rates; however, they struggle to find ``flat minima" reliably, resulting in suboptimal generalization compared to stochastic gradient descent (SGD). By revisiting various adaptive algorithms' mechanisms, we propose the Frankenstein optimizer, which combines their advantages. The proposed Frankenstein dynamically adjusts first- and second-momentum coefficients according to the optimizer's current state to directly maintain consistent learning dynamics and immediately reflect sudden gradient changes. Extensive experiments across several research domains such as computer vision, natural language processing, few-shot learning, and scientific simulations show that Frankenstein surpasses existing adaptive algorithms and SGD empirically regarding convergence speed and generalization performance. Furthermore, this research deepens our understanding of adaptive algorithms through centered kernel alignment analysis and loss landscape visualization during the learning process. Code is available at https://github.com/acctouhou/Frankenstein_optimizer