pwc-archive/papers-with-abstracts huggingface dataset:
UWAT-GAN: Fundus Fluorescein Angiography Synthesis via Ultra-wide-angle Transformation Multi-scale GAN
Authors: Zhaojie Fang, Zhanghao Chen, Pengxue Wei, Wangting Li, Shaochong Zhang, Ahmed Elazab, Gangyong Jia, Ruiquan Ge, Changmiao Wang
Date: 2023-07-21 00:00:00
Tasks: Decoder, Generative Adversarial Network, Image Generation, Medical Image Generation

Fundus photography is an essential examination for clinical and differential diagnosis of fundus diseases. Recently, Ultra-Wide-angle Fundus (UWF) techniques, UWF Fluorescein Angiography (UWF-FA) and UWF Scanning Laser Ophthalmoscopy (UWF-SLO) have been gradually put into use. However, Fluorescein Angiography (FA) and UWF-FA require injecting sodium fluorescein which may have detrimental influences. To avoid negative impacts, cross-modality medical image generation algorithms have been proposed. Nevertheless, current methods in fundus imaging could not produce high-resolution images and are unable to capture tiny vascular lesion areas. This paper proposes a novel conditional generative adversarial network (UWAT-GAN) to synthesize UWF-FA from UWF-SLO. Using multi-scale generators and a fusion module patch to better extract global and local information, our model can generate high-resolution images. Moreover, an attention transmit module is proposed to help the decoder learn effectively. Besides, a supervised approach is used to train the network using multiple new weighted losses on different scales of data. Experiments on an in-house UWF image dataset demonstrate the superiority of the UWAT-GAN over the state-of-the-art methods. The source code is available at: https://github.com/Tinysqua/UWAT-GAN.
Cascaded multitask U-Net using topological loss for vessel segmentation and centerline extraction
Authors: Pierre Rougé, Nicolas Passat, Odyssée Merveille
Date: 2023-07-21 00:00:00
Tasks: Segmentation

Vessel segmentation and centerline extraction are two crucial preliminary tasks for many computer-aided diagnosis tools dealing with vascular diseases. Recently, deep-learning based methods have been widely applied to these tasks. However, classic deep-learning approaches struggle to capture the complex geometry and specific topology of vascular networks, which is of the utmost importance in most applications. To overcome these limitations, the clDice loss, a topological loss that focuses on the vessel centerlines, has been recently proposed. This loss requires computing, with a proposed soft-skeleton algorithm, the skeletons of both the ground truth and the predicted segmentation. However, the soft-skeleton algorithm provides suboptimal results on 3D images, which makes the clDice hardly suitable on 3D images. In this paper, we propose to replace the soft-skeleton algorithm by a U-Net which computes the vascular skeleton directly from the segmentation. We show that our method provides more accurate skeletons than the soft-skeleton algorithm. We then build upon this network a cascaded U-Net trained with the clDice loss to embed topological constraints during the segmentation. The resulting model is able to predict both the vessel segmentation and centerlines with a more accurate topology.
3D Skeletonization of Complex Grapevines for Robotic Pruning
Authors: Eric Schneider, Sushanth Jayanth, Abhisesh Silwal, George Kantor
Date: 2023-07-21 00:00:00

Robotic pruning of dormant grapevines is an area of active research in order to promote vine balance and grape quality, but so far robotic efforts have largely focused on planar, simplified vines not representative of commercial vineyards. This paper aims to advance the robotic perception capabilities necessary for pruning in denser and more complex vine structures by extending plant skeletonization techniques. The proposed pipeline generates skeletal grapevine models that have lower reprojection error and higher connectivity than baseline algorithms. We also show how 3D and skeletal information enables prediction accuracy of pruning weight for dense vines surpassing prior work, where pruning weight is an important vine metric influencing pruning site selection.
Identifying document similarity using a fast estimation of the Levenshtein Distance based on compression and signatures
Authors: Peter Coates, Frank Breitinger
Date: 2023-07-21 00:00:00

Identifying document similarity has many applications, e.g., source code analysis or plagiarism detection. However, identifying similarities is not trivial and can be time complex. For instance, the Levenshtein Distance is a common metric to define the similarity between two documents but has quadratic runtime which makes it impractical for large documents where large starts with a few hundred kilobytes. In this paper, we present a novel concept that allows estimating the Levenshtein Distance: the algorithm first compresses documents to signatures (similar to hash values) using a user-defined compression ratio. Signatures can then be compared against each other (some constrains apply) where the outcome is the estimated Levenshtein Distance. Our evaluation shows promising results in terms of runtime efficiency and accuracy. In addition, we introduce a significance score allowing examiners to set a threshold and identify related documents.
RCVaR: an Economic Approach to Estimate Cyberattacks Costs using Data from Industry Reports
Authors: Muriel Figueredo Franco, Fabian Künzler, Jan von der Assen, Chao Feng, Burkhard Stiller
Date: 2023-07-20 00:00:00
Tasks: Management

Digitization increases business opportunities and the risk of companies being victims of devastating cyberattacks. Therefore, managing risk exposure and cybersecurity strategies is essential for digitized companies that want to survive in competitive markets. However, understanding company-specific risks and quantifying their associated costs is not trivial. Current approaches fail to provide individualized and quantitative monetary estimations of cybersecurity impacts. Due to limited resources and technical expertise, SMEs and even large companies are affected and struggle to quantify their cyberattack exposure. Therefore, novel approaches must be placed to support the understanding of the financial loss due to cyberattacks. This article introduces the Real Cyber Value at Risk (RCVaR), an economical approach for estimating cybersecurity costs using real-world information from public cybersecurity reports. RCVaR identifies the most significant cyber risk factors from various sources and combines their quantitative results to estimate specific cyberattacks costs for companies. Furthermore, RCVaR extends current methods to achieve cost and risk estimations based on historical real-world data instead of only probability-based simulations. The evaluation of the approach on unseen data shows the accuracy and efficiency of the RCVaR in predicting and managing cyber risks. Thus, it shows that the RCVaR is a valuable addition to cybersecurity planning and risk management processes.
UMLS-KGI-BERT: Data-Centric Knowledge Integration in Transformers for Biomedical Entity Recognition
Authors: Aidan Mannion, Thierry Chevalier, Didier Schwab, Lorraine Geouriot
Date: 2023-07-20 00:00:00
Tasks: Document Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Question Answering, Sentiment Analysis

Pre-trained transformer language models (LMs) have in recent years become the dominant paradigm in applied NLP. These models have achieved state-of-the-art performance on tasks such as information extraction, question answering, sentiment analysis, document classification and many others. In the biomedical domain, significant progress has been made in adapting this paradigm to NLP tasks that require the integration of domain-specific knowledge as well as statistical modelling of language. In particular, research in this area has focused on the question of how best to construct LMs that take into account not only the patterns of token distribution in medical text, but also the wealth of structured information contained in terminology resources such as the UMLS. This work contributes a data-centric paradigm for enriching the language representations of biomedical transformer-encoder LMs by extracting text sequences from the UMLS. This allows for graph-based learning objectives to be combined with masked-language pre-training. Preliminary results from experiments in the extension of pre-trained LMs as well as training from scratch show that this framework improves downstream performance on multiple biomedical and clinical Named Entity Recognition (NER) tasks.
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing
Authors: Le Peng, Gaoxiang Luo, Sicheng Zhou, jiandong chen, Rui Zhang, Ziyue Xu, Ju Sun
Date: 2023-07-20 00:00:00
Tasks: Federated Learning, One-Shot Learning

Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP). However, the medical field faces challenges in training LMs due to limited data access and privacy constraints imposed by regulations like the Health Insurance Portability and Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR). Federated learning (FL) offers a decentralized solution that enables collaborative learning while ensuring data privacy. In this study, we evaluated FL on 2 biomedical NLP tasks encompassing 8 corpora using 6 LMs. Our results show that: 1) FL models consistently outperformed models trained on individual clients' data and sometimes performed comparably with models trained with polled data; 2) with the fixed number of total data, FL models training with more clients produced inferior performance but pre-trained transformer-based models exhibited great resilience. 3) FL models significantly outperformed large language models using zero-/one-shot learning and offered lightning inference speed.
Generator-Retriever-Generator Approach for Open-Domain Question Answering
Authors: Abdelrahman Abdallah, Adam Jatowt
Date: 2023-07-21 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Open-Domain Question Answering, Question Answering, Retrieval, TriviaQA

Open-domain question answering (QA) tasks usually require the retrieval of relevant information from a large corpus to generate accurate answers. We propose a novel approach called Generator-Retriever-Generator (GRG) that combines document retrieval techniques with a large language model (LLM), by first prompting the model to generate contextual documents based on a given question. In parallel, a dual-encoder network retrieves documents that are relevant to the question from an external corpus. The generated and retrieved documents are then passed to the second LLM, which generates the final answer. By combining document retrieval and LLM generation, our approach addresses the challenges of open-domain QA, such as generating informative and contextually relevant answers. GRG outperforms the state-of-the-art generate-then-read and retrieve-then-read pipelines (GENREAD and RFiD) improving their performance by at least by +5.2, +4.2, and +1.6 on TriviaQA, NQ, and WebQ datasets, respectively. We provide code, datasets, and checkpoints at https://github.com/abdoelsayed2016/GRG.
Is ChatGPT Involved in Texts? Measure the Polish Ratio to Detect ChatGPT-Generated Text
Authors: Lingyi Yang, Feng Jiang, Haizhou Li
Date: 2023-07-21 00:00:00
Tasks: Misinformation, Text Generation

The remarkable capabilities of large-scale language models, such as ChatGPT, in text generation have impressed readers and spurred researchers to devise detectors to mitigate potential risks, including misinformation, phishing, and academic dishonesty. Despite this, most previous studies have been predominantly geared towards creating detectors that differentiate between purely ChatGPT-generated texts and human-authored texts. This approach, however, fails to work on discerning texts generated through human-machine collaboration, such as ChatGPT-polished texts. Addressing this gap, we introduce a novel dataset termed HPPT (ChatGPT-polished academic abstracts), facilitating the construction of more robust detectors. It diverges from extant corpora by comprising pairs of human-written and ChatGPT-polished abstracts instead of purely ChatGPT-generated texts. Additionally, we propose the "Polish Ratio" method, an innovative measure of the degree of modification made by ChatGPT compared to the original human-written text. It provides a mechanism to measure the degree of ChatGPT influence in the resulting text. Our experimental results show our proposed model has better robustness on the HPPT dataset and two existing datasets (HC3 and CDB). Furthermore, the "Polish Ratio" we proposed offers a more comprehensive explanation by quantifying the degree of ChatGPT involvement.
MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems
Authors: Thilo von Neumann, Christoph Boeddeker, Marc Delcroix, Reinhold Haeb-Umbach
Date: 2023-07-21 00:00:00
Tasks: Sentence

MeetEval is an open-source toolkit to evaluate all kinds of meeting transcription systems. It provides a unified interface for the computation of commonly used Word Error Rates (WERs), specifically cpWER, ORC-WER and MIMO-WER along other WER definitions. We extend the cpWER computation by a temporal constraint to ensure that only words are identified as correct when the temporal alignment is plausible. This leads to a better quality of the matching of the hypothesis string to the reference string that more closely resembles the actual transcription quality, and a system is penalized if it provides poor time annotations. Since word-level timing information is often not available, we present a way to approximate exact word-level timings from segment-level timings (e.g., a sentence) and show that the approximation leads to a similar WER as a matching with exact word-level annotations. At the same time, the time constraint leads to a speedup of the matching algorithm, which outweighs the additional overhead caused by processing the time stamps.
OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples
Authors: Ryuto Koike, Masahiro Kaneko, Naoaki Okazaki
Date: 2023-07-21 00:00:00
Tasks: Adversarial Attack, Adversarial Attack Detection, DeepFake Detection, In-Context Learning, Language Modelling, Large Language Model, LLM-generated Text Detection, Text Generation

Large Language Models (LLMs) have achieved human-level fluency in text generation, making it difficult to distinguish between human-written and LLM-generated texts. This poses a growing risk of misuse of LLMs and demands the development of detectors to identify LLM-generated texts. However, existing detectors lack robustness against attacks: they degrade detection accuracy by simply paraphrasing LLM-generated texts. Furthermore, a malicious user might attempt to deliberately evade the detectors based on detection results, but this has not been assumed in previous studies. In this paper, we propose OUTFOX, a framework that improves the robustness of LLM-generated-text detectors by allowing both the detector and the attacker to consider each other's output. In this framework, the attacker uses the detector's prediction labels as examples for in-context learning and adversarially generates essays that are harder to detect, while the detector uses the adversarially generated essays as examples for in-context learning to learn to detect essays from a strong attacker. Experiments in the domain of student essays show that the proposed detector improves the detection performance on the attacker-generated texts by up to +41.3 points F1-score. Furthermore, the proposed detector shows a state-of-the-art detection performance: up to 96.9 points F1-score, beating existing detectors on non-attacked texts. Finally, the proposed attacker drastically degrades the performance of detectors by up to -57.0 points F1-score, massively outperforming the baseline paraphrasing method for evading detection.
DEFTri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce
Authors: Ipsita Mohanty
Date: 2023-07-21 00:00:00
Proceeding: ECNLP (ACL) 2022 5
Tasks: Multi Label Text Classification, Multi-Label Text Classification, Representation Learning, text-classification, Text Classification

Defect Triage is a time-sensitive and critical process in a large-scale agile software development lifecycle for e-commerce. Inefficiencies arising from human and process dependencies in this domain have motivated research in automated approaches using machine learning to accurately assign defects to qualified teams. This work proposes a novel framework for automated defect triage (DEFTri) using fine-tuned state-of-the-art pre-trained BERT on labels fused text embeddings to improve contextual representations from human-generated product defects. For our multi-label text classification defect triage task, we also introduce a Walmart proprietary dataset of product defects using weak supervision and adversarial learning, in a few-shot setting.
Topic Identification For Spontaneous Speech: Enriching Audio Features With Embedded Linguistic Information
Authors: Dejan Porjazovski, Tamás Grósz, Mikko Kurimo
Date: 2023-07-21 00:00:00
Tasks: Automatic Speech Recognition, speech-recognition, Speech Recognition

Traditional topic identification solutions from audio rely on an automatic speech recognition system (ASR) to produce transcripts used as input to a text-based model. These approaches work well in high-resource scenarios, where there are sufficient data to train both components of the pipeline. However, in low-resource situations, the ASR system, even if available, produces low-quality transcripts, leading to a bad text-based classifier. Moreover, spontaneous speech containing hesitations can further degrade the performance of the ASR model. In this paper, we investigate alternatives to the standard text-only solutions by comparing audio-only and hybrid techniques of jointly utilising text and audio features. The models evaluated on spontaneous Finnish speech demonstrate that purely audio-based solutions are a viable option when ASR components are not available, while the hybrid multi-modal solutions achieve the best results.
Is Weakly-supervised Action Segmentation Ready For Human-Robot Interaction? No, Let's Improve It With Action-union Learning
Authors: Fan YangShigeyuki OdashimaShoichi MasuiShan Jiang
Date: 2023-10-22 00:00:00
Proceeding: IROS 2023 2023 10
Tasks: Action Recognition, Action Segmentation, Segmentation, Weakly Supervised Action Localization, Weakly Supervised Action Segmentation (Transcript)

Action segmentation plays an important role in enabling robots to automatically understand human activities. To train the action recognition model, while obtaining action labels for all frames is costly, annotating timestamp labels for weak supervision is cost-effective. However, existing methods may not fully utilize timestamp labels, which leads to insufficient performance. To alleviate this issue, we proposed a novel learning pattern in our training stage, which maximizes the probability of action union of surrounding timestamps for unlabeled frames. In our inference stage, we provided a new refinement solution to generate better hard-assigned action classes from soft-assigned predictions. Importantly, our methods are model-agnostic and can be applied to existing frameworks. On three commonly used action-segmentation data, our method outperforms previous timestamp-supervision methods and achieves new state-of-the-art performance. Moreover , our method uses less than 1% of fully-supervised labels to obtain comparable or even better results.
Pose2Sim: An open-source Python package for multiview markerless kinematics
Authors: David Pagnon, Mathieu Domalain, Lionel Reveret
Date: 2022-09-14 00:00:00
Proceeding: Journal of OpenSource Software JOSS 2022 9

Pose2Sim provides a workflow for 3D markerless kinematics, as an alternative to the more usual marker-based motion capture methods. Pose2Sim stands for “OpenPose to OpenSim”, as it uses OpenPose inputs (2D coordinates obtained from multiple videos) and leads to an OpenSim result (full-body 3D joint angles).
The repository presents a framework for:
• Detecting 2D joint coordinates from videos, e.g. via OpenPose (Cao et al., 2019)
• Calibrating cameras
• Detecting the person of interest
• Triangulating 2D joint coordinates and storing them as 3D positions in a .trc file
• Filtering these calculated 3D positions
• Scaling and running inverse kinematics via OpenSim (Delp et al., 2007; Seth, 2018), in
order to obtain full-body 3D joint angles
Each task is easily customizable, and requires only moderate Python skills. Pose2Sim is accessible at https://github.com/perfanalytics/pose2sim.
A Conditional Multinomial Mixture Model for Superset Label Learning
Authors: LiPing Liu, Thomas Dietterich
Date: 2012-12-03 00:00:00
Proceeding: 12 2012 12

In the superset label learning problem (SLL), each training instance provides a set of candidate labels of which one is the true label of the instance. As in ordinary regression, the candidate label set is a noisy version of the true label. In this work, we solve the problem by maximizing the likelihood of the candidate label sets of training instances. We propose a probabilistic model, the Logistic StickBreaking Conditional Multinomial Model (LSB-CMM), to do the job. The LSBCMM is derived from the logistic stick-breaking process. It first maps data points to mixture components and then assigns to each mixture component a label drawn from a component-specific multinomial distribution. The mixture components can capture underlying structure in the data, which is very useful when the model is weakly supervised. This advantage comes at little cost, since the model introduces few additional parameters. Experimental tests on several real-world problems with superset labels show results that are competitive or superior to the state of the art. The discovered underlying structures also provide improved explanations of the classification predictions.
Exploring Efficiency of Vision Transformers for Self-Supervised Monocular Depth Estimation
Authors: Aleksei Karpov, Ilya Makarov
Date: 2022-12-27 00:00:00
Proceeding: ISMAR 2022 12
Tasks: Decoder, Monocular Depth Estimation, Self-Supervised Learning

Depth estimation is a crucial task for the creation of depth maps, one of the most important components for augmented reality (AR) and other applications. However, the most widely used hardware for AR and smartphones has only sparse depth sensors with different ground truth depth acquisition methods. Thus, depth estimation models that are robust for downstream AR tasks performance can only be trained reliably using self-supervised learning based on camera information. Previous works in the field mostly focus on self-supervised models with pure convolutional architectures, without taking global spatial context into account.In this paper, we utilize vision transformer architectures for self-supervised monocular depth estimation and propose VTDepth, a vision transformer-based model, which provides a solution to the problem of the global spatial context. We compare various combinations of convolutional and transformer architectures for self-supervised depth estimation and show that the best combination of models is an encoder with a transformer basis and convolutional decoder. Our experiments demonstrate the efficiency of VTDepth for self-supervised depth estimation. Our set of models achieves state-of-the-art performance for self-supervised learning on NYUv2 and KITTI datasets. Our code is available at https://github.com/ahbpp/VTDepth.