pwc-archive/papers-with-abstracts huggingface dataset:
Detecting beats in the photoplethysmogram: benchmarking open-source algorithms
Authors: Peter H Charlton, Kevin Kotzen, Elisa Mejía-Mejía, Philip J Aston, Karthik Budidha, Jonathan Mant, Callum Pettit, Joachim A Behar and Panayiotis A Kyriacou
Date: 2022-07-19 00:00:00
Proceeding: Physiological Measurement 2022 7
Tasks: Benchmarking, Photoplethysmography (PPG) beat detection, Photoplethysmography (PPG) heart rate estimation, Rhythm

The photoplethysmogram (PPG) signal is widely used in pulse oximeters and smartwatches. A fundamental step in analysing the PPG is the detection of heartbeats. Several PPG beat detection algorithms have been proposed, although it is not clear which performs best. Objective: This study aimed to: (i) develop a framework with which to design and test PPG beat detectors; (ii) assess the performance of PPG beat detectors in different use cases; and (iii) investigate how their performance is affected by patient demographics and physiology. Approach: Fifteen beat detectors were assessed against electrocardiogram-derived heartbeats using data from eight datasets. Performance was assessed using the F1 score, which combines sensitivity and positive predictive value. Main results: Eight beat detectors performed well in the absence of movement, with F1 scores of ≥90% on hospital data and wearable data collected at rest. Their performance was poorer during exercise, with F1 scores of 55-91%; poorer in neonates than adults with F1 scores of 84-96% in neonates compared to 98-99% in adults; and poorer in atrial fibrillation (AF), with F1 scores of 92-97% in AF, compared to 99-100% in normal sinus rhythm. Significance: Two PPG beat detectors denoted 'MSPTD' and 'qppg' performed best, with complementary performance characteristics. This evidence can be used to inform the choice of PPG beat detector algorithm. The algorithms, datasets, and assessment framework are freely available.
Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images
Authors: Marawan Elbatel, Christina Bornberg, Manasi Kattel, Enrique Almar, Claudio Marrocco, Alessandro Bria
Date: 2022-08-05 00:00:00
Tasks: Image Generation, Object Detection, Semantic Segmentation

In-vitro tests are an alternative to animal testing for the toxicity of medical devices. Detecting cells as a first step, a cell expert evaluates the growth of cells according to cytotoxicity grade under the microscope. Thus, human fatigue plays a role in error making, making the use of deep learning appealing. Due to the high cost of training data annotation, an approach without manual annotation is needed. We propose Seamless Iterative Semi-Supervised correction of Imperfect labels (SISSI), a new method for training object detection models with noisy and missing annotations in a semi-supervised fashion. Our network learns from noisy labels generated with simple image processing algorithms, which are iteratively corrected during self-training. Due to the nature of missing bounding boxes in the pseudo labels, which would negatively affect the training, we propose to train on dynamically generated synthetic-like images using seamless cloning. Our method successfully provides an adaptive early learning correction technique for object detection. The combination of early learning correction that has been applied in classification and semantic segmentation before and synthetic-like image generation proves to be more effective than the usual semi-supervised approach by > 15% AP and > 20% AR across three different readers. Our code is available at https://github.com/marwankefah/SISSI.
AI-based Optimal scheduling of Renewable AC Microgrids with bidirectional LSTM-Based Wind Power Forecasting
Authors: Hossein Mohammadi, Shiva Jokar, Mojtaba Mohammadi, Abdollah KavousiFard, Morteza Dabbaghjamanesh
Date: 2022-07-08 00:00:00
Tasks: Scheduling

In terms of the operation of microgrids, optimal scheduling is a vital issue that must be taken into account. In this regard, this paper proposes an effective framework for optimal scheduling of renewable microgrids considering energy storage devices, wind turbines, micro turbines. Due to the nonlinearity and complexity of operation problems in microgrids, it is vital to use an accurate and robust optimization technique to efficiently solve this problem. To this end, in the proposed framework, the teacher learning-based optimization is utilized to efficiently solve the scheduling problem in the system. Moreover, a deep learning model based on bidirectional long short-term memory is proposed to address the short-term wind power forecasting problem. The feasibility and performance of the proposed framework as well as the effect of wind power forecasting on the operation efficiency are examined using IEEE 33-bus test system. Also, the Australian Wool north wind site data is utilized as a real-world dataset to evaluate the performance of the forecasting model. Results show the effective and efficient performance of the proposed framework in the optimal scheduling of microgrids.
Segmentation of Clustered Nuclei With Shape Markers and Marking Function
Authors: Jierong Cheng, Jagath C. Rajapakse
Date: 2009-04-15 00:00:00
Proceeding: J. Cheng and J. C. Rajapakse $^{\ast}$, "Segmentation of Clustered Nuclei With Shape Markers and Marking Function," in IEEE Transactions on Biomedical Engineering, vol. 56, no. 3, pp. 741-748 2009 4
Tasks: Cell Segmentation, Segmentation

We present a method to separate clustered nuclei from
fluorescence microscopy cellular images, using shape markers and
marking function in a watershed-like algorithm. Shape markers
are extracted using an adaptive H-minima transform. A marking
function based on the outer distance transform is introduced to
accurately separate clustered nuclei. With synthetic images, we
quantitatively demonstrate the performance of our method and
provide comparisons with existing approaches. On mouse neuronal
and Drosophila cellular images, we achieved 6%–7% improvement
of segmentation accuracies over earlier methods.
Index Terms—Active contours, cell segmentation, cellular imaging, fluorescence microscopy, watershed segmentation.
Beer2Vec : Extracting Flavors from Reviews for Thirst-Quenching Recommandations
Authors: Jean-Thomas Baillargeon, Nicolas Garneau
Date: 2022-08-04 00:00:00

This paper introduces the Beer2Vec model that allows the most popular alcoholic beverage in the world to be encoded into vectors enabling flavorful recommendations. We present our algorithm using a unique dataset focused on the analysis of craft beers. We thoroughly explain how we encode the flavors and how useful, from an empirical point of view, the beer vectors are to generate meaningful recommendations. We also present three different ways to use Beer2Vec in a real-world environment to enlighten the pool of craft beer consumers. Finally, we make our model and functionalities available to everybody through a web application.
A Multiple Market Trading Mechanism for Electricity, Renewable Energy Certificate and Carbon Emission Right of Virtual Power Plants
Authors: Zhihong Huang, Ye Guo, Qiuwei Wu, Li Xiao, Hongbin Sun
Date: 2022-08-08 00:00:00

A multiple market trading mechanism for the VPP to participate in electricity, renewable energy certificate (REC) and carbon emission right (CER) markets is proposed. With the introduction of the inventory mechanism of REC and CER, the profit of the VPP increases and better trading decisions with multiple markets are made under the requirements of renewable portfolio standard (RPS) and carbon emission (CE) quota requirements. According to the Karush-Kuhn-Tucker (KKT) conditions of the proposed model, properties of the multiple market trading mechanism are discussed. Results from case studies verify the effectiveness of the proposed model.
Gaze Estimation Approach Using Deep Differential Residual Network
Authors: Longzhao Huang, Yujie Li, Xu Wang, Haoyu Wang, Ahmed Bouridane, Ahmad Chaddad
Date: 2022-08-08 00:00:00
Tasks: Gaze Estimation

Gaze estimation, which is a method to determine where a person is looking at given the person's full face, is a valuable clue for understanding human intention. Similarly to other domains of computer vision, deep learning (DL) methods have gained recognition in the gaze estimation domain. However, there are still gaze calibration problems in the gaze estimation domain, thus preventing existing methods from further improving the performances. An effective solution is to directly predict the difference information of two human eyes, such as the differential network (Diff-Nn). However, this solution results in a loss of accuracy when using only one inference image. We propose a differential residual model (DRNet) combined with a new loss function to make use of the difference information of two eye images. We treat the difference information as auxiliary information. We assess the proposed model (DRNet) mainly using two public datasets (1) MpiiGaze and (2) Eyediap. Considering only the eye features, DRNet outperforms the state-of-the-art gaze estimation methods with $angular-error$ of 4.57 and 6.14 using MpiiGaze and Eyediap datasets, respectively. Furthermore, the experimental results also demonstrate that DRNet is extremely robust to noise images.
Exploiting Shape Cues for Weakly Supervised Semantic Segmentation
Authors: Sungpil Kho, Pilhyeon Lee, Wonyoung Lee, Minsong Ki, Hyeran Byun
Date: 2022-08-08 00:00:00
Tasks: Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS) aims to produce pixel-wise class predictions with only image-level labels for training. To this end, previous methods adopt the common pipeline: they generate pseudo masks from class activation maps (CAMs) and use such masks to supervise segmentation networks. However, it is challenging to derive comprehensive pseudo masks that cover the whole extent of objects due to the local property of CAMs, i.e., they tend to focus solely on small discriminative object parts. In this paper, we associate the locality of CAMs with the texture-biased property of convolutional neural networks (CNNs). Accordingly, we propose to exploit shape information to supplement the texture-biased CNN features, thereby encouraging mask predictions to be not only comprehensive but also well-aligned with object boundaries. We further refine the predictions in an online fashion with a novel refinement method that takes into account both the class and the color affinities, in order to generate reliable pseudo masks to supervise the model. Importantly, our model is end-to-end trained within a single-stage framework and therefore efficient in terms of the training cost. Through extensive experiments on PASCAL VOC 2012, we validate the effectiveness of our method in producing precise and shape-aligned segmentation results. Specifically, our model surpasses the existing state-of-the-art single-stage approaches by large margins. What is more, it also achieves a new state-of-the-art performance over multi-stage approaches, when adopted in a simple two-stage pipeline without bells and whistles.
Improving performance in multi-objective decision-making in Bottles environments with soft maximin approaches
Authors: Benjamin J Smith, Robert Klassert, Roland Pihlakas
Date: 2022-08-08 00:00:00
Tasks: Decision Making

Balancing multiple competing and conflicting objectives is an essential task for any artificial intelligence tasked with satisfying human values or preferences. Conflict arises both from misalignment between individuals with competing values, but also between conflicting value systems held by a single human. Starting with principle of loss-aversion, we designed a set of soft maximin function approaches to multi-objective decision-making. Bench-marking these functions in a set of previously-developed environments, we found that one new approach in particular, 'split-function exp-log loss aversion' (SFELLA), learns faster than the state of the art thresholded alignment objective method (Vamplew et al, 2021) on three of four tasks it was tested on, and achieved the same optimal performance after learning. SFELLA also showed relative robustness improvements against changes in objective scale, which may highlight an advantage dealing with distribution shifts in the environment dynamics. Due to publishing rules, further work could not be presented in the preprint, but in the final published version, we will further compare SFELLA to the multi-objective reward exponentials (MORE) approach (Rolf, 2020), demonstrating that SFELLA performs similarly to MORE in a simple previously-described foraging task, but in a modified foraging environment with a new resource that was not depleted as the agent worked, SFELLA collected more of the new resource with very little cost incurred in terms of the old resource. Overall, we found SFELLA useful for avoiding problems that sometimes occur with a thresholded approach, and more reward-responsive than MORE while retaining its conservative, loss-averse incentive structure.
A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring
Authors: Davide Dalle Pezze, Denis Deronjic, Chiara Masiero, Diego Tosato, Alessandro Beghi, Gian Antonio Susto
Date: 2022-08-08 00:00:00
Tasks: Classification, class-incremental learning, Class Incremental Learning, Continual Learning, Incremental Learning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Continual Learning aims to learn from a stream of tasks, being able to remember at the same time both new and old tasks. While many approaches were proposed for single-class classification, multi-label classification in the continual scenario remains a challenging problem. For the first time, we study multi-label classification in the Domain Incremental Learning scenario. Moreover, we propose an efficient approach that has a logarithmic complexity with regard to the number of tasks, and can be applied also in the Class Incremental Learning scenario. We validate our approach on a real-world multi-label Alarm Forecasting problem from the packaging industry. For the sake of reproducibility, the dataset and the code used for the experiments are publicly available.
Uncertain Bayesian Networks: Learning from Incomplete Data
Authors: Conrad D. Hougen, Lance M. Kaplan, Federico Cerutti, Alfred O. Hero III
Date: 2022-08-08 00:00:00

When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated. Second order estimation methods provide a framework for both estimating the probabilities and quantifying the uncertainty in these estimates. We refer to these cases as uncer tain or second-order Bayesian networks. When such data are complete, i.e., all variable values are observed for each instantiation, the conditional probabilities are known to be Dirichlet-distributed. This paper improves the current state-of-the-art approaches for handling uncertain Bayesian networks by enabling them to learn distributions for their parameters, i.e., conditional probabilities, with incomplete data. We extensively evaluate various methods to learn the posterior of the parameters through the desired and empirically derived strength of confidence bounds for various queries.
A Linear Programming Approach for Resource-Aware Information-Theoretic Tree Abstractions
Authors: Daniel T. Larsson, Dipankar Maity, Panagiotis Tsiotras
Date: 2022-08-08 00:00:00

In this chapter, an integer linear programming formulation for the problem of obtaining task-relevant, multi-resolution, environment abstractions for resource-constrained autonomous agents is presented. The formulation leverages concepts from information-theoretic signal compression, specifically, the information bottleneck (IB) method, to pose an abstraction problem as an optimal encoder search over the space of multi-resolution trees. The abstractions emerge in a task-relevant manner as a function of agent information-processing constraints. We detail our formulation, and show how hierarchical tree structures, signal encoders, and information-theoretic methods for signal compression can be unified under a common theme. A discussion delineating the benefits and drawbacks of our formulation is presented, as well as a detailed explanation how our approach can be interpreted within the context of generating abstractions for resource-constrained autonomous systems. It is shown that the resulting information-theoretic abstraction problem over the space of multi-resolution trees can be formulated as a integer linear programming (ILP) problem. We demonstrate the approach on a number of examples, and provide a discussion detailing the differences of the proposed framework compared to existing methods. Lastly, we consider a linear program relaxation of the ILP problem, thereby demonstrating that multi-resolution information-theoretic tree abstractions can be obtained by solving a convex program.
Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning
Authors: Ting Chen, Ruixiang Zhang, Geoffrey Hinton
Date: 2022-08-08 00:00:00
Tasks: Image Captioning, Image Generation

We present Bit Diffusion: a simple and generic approach for generating discrete data with continuous state and continuous time diffusion models. The main idea behind our approach is to first represent the discrete data as binary bits, and then train a continuous diffusion model to model these bits as real numbers which we call analog bits. To generate samples, the model first generates the analog bits, which are then thresholded to obtain the bits that represent the discrete variables. We further propose two simple techniques, namely Self-Conditioning and Asymmetric Time Intervals, which lead to a significant improvement in sample quality. Despite its simplicity, the proposed approach can achieve strong performance in both discrete image generation and image captioning tasks. For discrete image generation, we significantly improve previous state-of-the-art on both CIFAR-10 (which has 3K discrete 8-bit tokens) and ImageNet-64x64 (which has 12K discrete 8-bit tokens), outperforming the best autoregressive model in both sample quality (measured by FID) and efficiency. For image captioning on MS-COCO dataset, our approach achieves competitive results compared to autoregressive models.
SIAD: Self-supervised Image Anomaly Detection System
Authors: Jiawei Li, Chenxi Lan, Xinyi Zhang, Bolin Jiang, Yuqiu Xie, Naiqi Li, Yan Liu, Yaowei Li, Enze Huo, Bin Chen
Date: 2022-08-08 00:00:00
Tasks: Anomaly Detection, Cloud Computing, Self-Supervised Learning

Recent trends in AIGC effectively boosted the application of visual inspection. However, most of the available systems work in a human-in-the-loop manner and can not provide long-term support to the online application. To make a step forward, this paper outlines an automatic annotation system called SsaA, working in a self-supervised learning manner, for continuously making the online visual inspection in the manufacturing automation scenarios. Benefit from the self-supervised learning, SsaA is effective to establish a visual inspection application for the whole life-cycle of manufacturing. In the early stage, with only the anomaly-free data, the unsupervised algorithms are adopted to process the pretext task and generate coarse labels for the following data. Then supervised algorithms are trained for the downstream task. With user-friendly web-based interfaces, SsaA is very convenient to integrate and deploy both of the unsupervised and supervised algorithms. So far, the SsaA system has been adopted for some real-life industrial applications.
Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
Authors: Haoye Tian, Xunzhu Tang, Andrew Habib, Shangwen Wang, Kui Liu, Xin Xia, Jacques Klein, Tegawendé F. Bissyandé
Date: 2022-08-08 00:00:00
Tasks: Question Answering

In this work, we propose a novel perspective to the problem of patch correctness assessment: a correct patch implements changes that "answer" to a problem posed by buggy behaviour. Concretely, we turn the patch correctness assessment into a Question Answering problem. To tackle this problem, our intuition is that natural language processing can provide the necessary representations and models for assessing the semantic correlation between a bug (question) and a patch (answer). Specifically, we consider as inputs the bug reports as well as the natural language description of the generated patches. Our approach, Quatrain, first considers state of the art commit message generation models to produce the relevant inputs associated to each generated patch. Then we leverage a neural network architecture to learn the semantic correlation between bug reports and commit messages. Experiments on a large dataset of 9135 patches generated for three bug datasets (Defects4j, Bugs.jar and Bears) show that Quatrain can achieve an AUC of 0.886 on predicting patch correctness, and recalling 93% correct patches while filtering out 62% incorrect patches. Our experimental results further demonstrate the influence of inputs quality on prediction performance. We further perform experiments to highlight that the model indeed learns the relationship between bug reports and code change descriptions for the prediction. Finally, we compare against prior work and discuss the benefits of our approach.
INSPIRED2: An Improved Dataset for Sociable Conversational Recommendation
Authors: Ahtsham Manzoor, Dietmar Jannach
Date: 2022-08-08 00:00:00
Tasks: Conversational Recommendation, Recommendation Systems, Retrieval

Conversational recommender systems (CRS) that are able to interact with users in natural language often utilize recommendation dialogs which were previously collected with the help of paired humans, where one plays the role of a seeker and the other as a recommender. These recommendation dialogs include items and entities that indicate the users' preferences. In order to precisely model the seekers' preferences and respond consistently, CRS typically rely on item and entity annotations. A recent example of such a dataset is INSPIRED, which consists of recommendation dialogs for sociable conversational recommendation, where items and entities were annotated using automatic keyword or pattern matching techniques. An analysis of this dataset unfortunately revealed that there is a substantial number of cases where items and entities were either wrongly annotated or annotations were missing at all. This leads to the question to what extent automatic techniques for annotations are effective. Moreover, it is important to study impact of annotation quality on the overall effectiveness of a CRS in terms of the quality of the system's responses. To study these aspects, we manually fixed the annotations in INSPIRED. We then evaluated the performance of several benchmark CRS using both versions of the dataset. Our analyses suggest that the improved version of the dataset, i.e., INSPIRED2, helped increase the performance of several benchmark CRS, emphasizing the importance of data quality both for end-to-end learning and retrieval-based approaches to conversational recommendation. We release our improved dataset (INSPIRED2) publicly at https://github.com/ahtsham58/INSPIRED2.