Reviewerly/exHarmony huggingface dataset:
Federated Partially Supervised Learning With Limited Decentralized Medical Images

Data government has played an instrumental role in securing the privacy-critical infrastructure in the medical domain and has led to an increased need of federated learning (FL). While decentralization can limit the effectiveness of standard supervised learning, the impact of decentralization on partially supervised learning remains unclear. Besides, due to data scarcity, each client may have access to only limited partially labeled data. As a remedy, this work formulates and discusses a new learning problem federated partially supervised learning (FPSL) for limited decentralized medical images with partial labels. We study the impact of decentralized partially labeled data on deep learning-based models via an exemplar of FPSL, namely, federated partially supervised learning multi-label classification. By dissecting FedAVG, a seminal FL framework, we formulate and analyze two major challenges of FPSL and propose a simple yet robust FPSL framework, FedPSL, which addresses these challenges. In particular, FedPSL contains two modules, task-dependent model aggregation and task-agnostic decoupling learning, where the first module addresses the weight assignment and the second module improves the generalization ability of the feature extractor. We provide a comprehensive empirical understanding of FSPL under data scarcity with simulated experiments. The empirical results not only indicate that FPSL is an under-explored problem with practical value but also show that the proposed FedPSL can achieve robust performance against baseline methods on data challenges such as data scarcity and domain shifts. The findings of this study also pose a new research direction towards label-efficient learning on medical images.
Deep Portrait Delighting

We present a deep neural network for removing undesirable shading features from an unconstrained portrait image, recovering the underlying texture. Our training scheme incorporates three regularization strategies: masked loss, to emphasize high-frequency shading features; soft-shadow loss, which improves sensitivity to subtle changes in lighting; and shading-offset estimation, to supervise separation of shading and texture. Our method demonstrates improved delighting quality and generalization when compared with the state-of-the-art. We further demonstrate how our delighting method can enhance the performance of light-sensitive computer vision tasks such as face relighting and semantic parsing, allowing them to handle extreme lighting conditions.
A Literature Review of User Studies in Extended Reality Applications for Archaeology

In the present study we conducted a systematic review on user studies for Archaeology in eXtended Reality of the last 10 years.After a screening and selection process, 52 articles were selected for an in-depth analysis.Their classification follows different axes: devices, location dependency, type of users, interaction and collaboration.We also organised the existing user studies according to tasks, evaluation measurements, number of participants, and how the study was conducted (pre-test and/or post-test, formative and summative evaluation, quantitative and qualitative data).We found an intertwined relation between Archaeology and Cultural Heritage, which is reflected in the vast presence of applications for museum exhibitions and tours on archaeological sites.Similarities between systems developed for archaeologists and for general public were also investigated.Our purpose was to find a common ground between different user studies that could help designers of the next systems have a base on which they can build their system.We also highlighted which would be the preferred and most suitable evaluation techniques, when they are needed, with the type of users to address.The results show a heterogeneity of measurable variables and possible choices, but some guidelines could be derived.
Interactive Image Inpainting Using Semantic Guidance

Image inpainting approaches have achieved significant progress with the help of deep neural networks. How-ever, existing approaches mainly focus on leveraging the priori distribution learned by neural networks to produce a single inpainting result or further yielding multiple solutions, where the controllability is not well studied. This paper develops a novel image inpainting approach that enables users to customize the inpainting result by their own preference or memory. Specifically, our approach is composed of two stages that utilize the prior of neural network and user’s guidance to jointly inpaint corrupted images. In the first stage, an autoencoder based on a novel external spatial attention mechanism is deployed to produce reconstructed features of the corrupted image and a coarse inpainting result that provides semantic mask as the medium for user interaction. In the second stage, a semantic decoder that takes the reconstructed features as prior is adopted to synthesize a fine inpainting result guided by user’s customized semantic mask, so that the final inpainting result will share the same content with user’s guidance while the textures and colors reconstructed in the first stage are preserved. Extensive experiments demonstrate the superiority of our approach in terms of inpainting quality and controllability.
Automated Detection of Typed Links in Issue Trackers

Stakeholders in software projects use issue trackers like JIRA to capture and manage issues, including requirements and bugs. To ease issue navigation and structure project knowledge, stakeholders manually connect issues via links of certain types that reflect different dependencies, such as Epic-, Block-, Duplicate-, or Relate-links. Based on a large dataset of 15 JIRA repositories, we study how well state-of-the-art machine learning models can automatically detect common link types. We found that a pure BERT model trained on titles and descriptions of linked issues significantly outperforms other optimized deep learning models, achieving an encouraging average macro F1-score of 0.64 for detecting 9 popular link types across all repositories (weighted F1-score of 0.73). For the specific Subtask- and Epic-links, the model achieved top F1-scores of 0.89 and 0.97, respectively. Our model does not simply learn the textual similarity of the issues. In general, shorter issue text seems to improve the prediction accuracy with a strong negative correlation of −0.70. We found that Relate-links often get confused with the other links, which suggests that they are likely used as default links in unclear cases. We also observed significant differences across the repositories, depending on how they are used and by whom.
No-reference Image Quality Assessment via Non-local Dependency Modeling

In this paper, we propose a no-reference image quality assessment method based on non-local features learned by a graph neural network (GNN). The proposed quality assessment framework is rooted in the view that the human visual system perceives image quality with long-dependency constructed among different regions, inspiring us to explore the non-local interactions in quality prediction. Instead of relying on convolutional neural network (CNN) based quality assessment methods that primarily focus on local field features, the GNN aiming for non-local quality perception facilitates modeling such long-dependency. In particular, we first adopt superpixel segmentation for the graph nodes construction. Subsequently, a spatial attention module is proposed to integrate the long- and short-range dependencies among the nodes of the whole image. The learned non-local features are finally combined with the local features extracted by the pre-trained CNN, achieving superior performance to the features utilized individually. Experimental results on intra-dataset and cross-dataset settings verify our proposed method's effectiveness and advanced generalization capability. Source codes are publicly accessible at https://github.com/SuperBruceJia/NLNet-IQA for scientific reproducible research.
Classifying Gender Based on Life Partner Choosing Factor using Supervised Machine Learning

Nowadays, gender Prediction has become a popular subject in machine learning and predicting gender by analyzing some text or names is very common while predicting gender by their taste or favourites is not so popular. As a result, for this paper, we established an aim of predicting people’s gender based on their preferences or requirements. There are a lot of choices and desires we want in our life partner, that’s why it was easy to detect gender on the basis of our choices. From ‘data storing’ to ‘selecting a model’ we have followed a modern workflow. We have made a public survey with proper questionnaires and encompass 758 data from different persons and tried to know their choices of choosing a life partner. We have tested our datum with 8 different Machine Learning Algorithms and from these algorithms, five algorithms-Gradient Boosting Classifier (GBC), Stochastic Gradient Classifier, eXtreme Gradient Boost (XGB), Decision Tree Classifier (DT), Random Forest (RF) comprises favorable accuracy from 90%-95.39%. The best correctness we have found is from the RF machine learning algorithm with 95.39% accuracy. The model can be a useful notion to apply in any Life Partner Chosen type applications (e.g., Wedding Service-Shaadi.com), according to the approach that arises from this study.
Fine-Grained Image Classification by Class and Image-Specific Decomposition With Multiple Views

Fine-grained image classification attempts to accurately classify images that are similar to each other. Multiview information is often used to improve the classification accuracy. Although great progress has been made, fine-grained image classification methods still have two drawbacks. On the one hand, they often treat each image independently without considering image correlations within the same class along with the distinctive characters of each image. On the other hand, multiview correlations are often used during classifier training, leaving the correlations of different views unconsidered. To solve these two problems, in this paper, we propose a novel fine-grained image classification method by class and image-specific decomposition with multiviews (CISD-MV). For each view, we treat images of the same class jointly by decomposing the class and image-specific information. Since images of different classes are similar and correlated, we linearly model class correlations of images using decomposed low-rank parts. In addition, for each image, the representations of different views are correlated, and we use linear transformation to model view correlations. We jointly optimise for the class and image-specific components along with the class correlation and view correlation transformation matrixes. A testing image is assigned to the class that has the minimum summed reconstruction error. We conduct fine-grained image classification experiments on several public fine-grained image datasets. Experimental results and analysis show the effectiveness of the proposed method.
RA-CNN

Emotion is a feeling that can be expressed by different mediums. Emotion analysis is a key task in NLP which is responsible for judging the emotional tendency of texts. Currently, in a complex multi-semantic environment, it still suffers from poor performance. Traditional methods usually require human intervention, while deep learning always has a trade-off between local and global features. To solve the problem that deep learning models generalize poorly for emotion analysis, this article proposed a semantic-enhanced method called RA-CNN, a classification model under a multi-semantic environment. It integrates CNN for local feature extraction, RNN for global feature extraction, and attention mechanism for feature scaling. As a result, it can acquire the correct meaning of sentences. After experimenting with the hotel review dataset, it has an improvement in positive feeling classification compared with the baseline model (3%~13%), and it showed a competitive performance compared with ordinary deep learning models (~1%). On negative feeling classification, it also performed well close to other models.
Long-Term Spatial–Temporal Evolution of Seismicity of the 2010 Ms 7.1 Yushu, Qinghai, China Earthquake

On 14 April 2010, a destructive earthquake (Ms 7.1) occurred along the Ganzi–Yushu fault (GYF), which is the boundary between the Songpan–Ganzi and Qiangtang Blocks of the eastern Tibetan Plateau. In this study, we present an effective workflow for microearthquake detection and location that assembles three main methods including a graphics processing unit-based match and locate technology (GPU M&L), a deep-neural-network-based noise-reduction technique (DeepDenoiser), and a U-net-based phase picking method (PhaseNet). Following this workflow, a high-resolution catalog has been constructed based on continuous seismic data from 2008 to 2018 recorded by 13 seismic stations deployed by Qinghai seismic network in Yushu, Qinghai. The GPU M&L is first applied to detect earthquakes which are usually hard to pick by routine methods due to their low signal-to-noise ratios. Then the DeepDenoiser is applied to remove noises from events we detected by GPU M&L, providing a better picking of seismic phases. The PhaseNet and hypoDD are conducted to pick seismic phases and relocate detected events, respectively. The spatial–temporal evolution of the foreshock sequence of the Yushu earthquake suggests that a triggered cascade model is responsible for the nucleation of the mainshock. The mainshock and aftershocks southeast of the mainshock mainly rupture on the primary northwest (NW)-trending GYF while the foreshocks and aftershocks on the NW segment rupture along the northeast (NE)-trending fault normal to the strike of the GYF. The average cumulative slip rate of the GYF estimated from six repeating earthquakes is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.4~\pm ~1.7$ </tex-math></inline-formula> mm/year, which is consistent with the geodetic and geological observations.
MUGAN: Thermal Infrared Image Colorization Using Mixed-Skipping UNet and Generative Adversarial Network

Common cameras cannot capture high quality image in the night or extreme weather conditions that without enough light, while thermal infrared(TIR) cameras are not limited in this situation. Hence, TIR imaging technique is widely used in military, surveillance, nighttime traffic and other scenarios. However, TIR images are monochromatic, and the majority of details of such images are lost, which are difficult for human or computer system to analyze. Translating TIR images into visible images is beneficial to subsequent observation or further processing. Though there are some advances to realize the transformation from TIR images to color visible images, edge distortion and semantic confusion remain to be solved. Therefore, we propose a Mixed-Skipping UNet(MS-UNet) based image colorization model joint Generative Adversarial Network, which is denoted by MUGAN. Firstly, the dense skip connections of UNet++ and full-scale skip connections of UNet 3+ are combined to form the MS-UNet, which is regarded as the generator. In addition, we design a feature extraction module in MS-UNet to effectively capture the multi-scale features in source image. Then, a novel attention mechanism module is designed for decoding stage which can help decoder of MS-UNet to focus on the important information. Moreover, we explore the effect of different loss functions in TIR image colorization task, and the loss function with excellent performance is selected to further optimize the training process of the model. Extensive experiments prove the superiority of our method in the task of TIR image colorization.
XLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-Based Textual Knowledge Source

Question answering (QA) is a natural language understanding task within the fields of information retrieval and information extraction that has attracted much attention from the computational linguistics and artificial intelligence research community in recent years because of the strong development of machine reading comprehension-based models. A reader-based QA system is a high-level search engine that can find correct answers to queries or questions in open-domain or domain-specific texts using machine reading comprehension (MRC) techniques. The majority of advancements in data resources and machine-learning approaches in the MRC and QA systems especially are developed significantly in two resource-rich languages such as English and Chinese. A low-resource language like Vietnamese has witnessed a scarcity of research on QA systems. This paper presents XLMRQA, the first Vietnamese QA system using a supervised transformer-based reader on the Wikipedia-based textual knowledge source (using the UIT-ViQuAD corpus), outperforming the two robust QA systems using deep neural network models: DrQA and BERTserini with 24.46% and 6.28%, respectively. From the results obtained on the three systems, we analyze the influence of question types on the performance of the QA systems.
Influence of Feature Selection Methods on Breast Cancer Early Prediction Phase using Classification and Regression Tree

In recent years, healthcare data has been growing exponentially. The major challenge is to predict and analyze all this data effectively. Feature selection is a solution in which a subset of informative features is selected from a high-dimensional dataset. Feature selection helps to increase accuracy and remove irrelevant features. In the medical domain, selecting important features for healthcare is essential as it directly affects human health. Several filters, wrapper, and embedded feature selection techniques will be examined in this study including generic univariate selects, select percentile, select k best, Pearson correlation coefficient, mutual information, relief-f, recursive feature elimination, recursive feature elimination with cross-validation, sequential forward selection, sequential backward selection, and select-from-model. The aim is to make the healthcare predictions model named classification and regression tree more accurate by employing feature selection methods, to accurately detect breast cancer in its early stages, where the data is collected from Sebha oncology center in the south of Libya. The performance of the classification and regression tree was seen to be noticeably enhanced when eliminated irrelevant features. Later, our model outperforms other classification methods, namely: logistic regression, naive Bayes, and K-nearest neighbors, by using the optimal subset of features identified by recursive feature elimination.
Video Instance Segmentation via Multi-Scale Spatio-Temporal Split Attention Transformer

State-of-the-art transformer-based video instance segmentation (VIS) approaches typically utilize either single-scale spatio-temporal features or per-frame multi-scale features during the attention computations. We argue that such an attention computation ignores the multi-scale spatio-temporal feature relationships that are crucial to tackle target appearance deformations in videos. To address this issue, we propose a transformer-based VIS framework, named MS-STS VIS, that comprises a novel multi-scale spatio-temporal split (MS-STS) attention module in the encoder. The proposed MS-STS module effectively captures spatio-temporal feature relationships at multiple scales across frames in a video. We further introduce an attention block in the decoder to enhance the temporal consistency of the detected instances in different frames of a video. Moreover, an auxiliary discriminator is introduced during training to ensure better foreground-background separability within the multi-scale spatio-temporal feature space. We conduct extensive experiments on two benchmarks: Youtube-VIS (2019 and 2021). Our MS-STS VIS achieves state-of-the-art performance on both benchmarks. When using the ResNet50 backbone, our MS-STS achieves a mask AP of 50.1%, outperforming the best reported results in literature by 2.7% and by 4.8% at higher overlap threshold of $$\text {AP}_{\texttt{75}}$$ , while being comparable in model size and speed on Youtube-VIS 2019 val. set. When using the Swin Transformer backbone, MS-STS VIS achieves mask AP of 61.0% on Youtube-VIS 2019 val. set. Source code is available at https://github.com/OmkarThawakar/MSSTS-VIS .
Accelerated Reinforcement Learning for Temporal Logic Control Objectives

This paper addresses the problem of learning control policies for mobile robots, modeled as unknown Markov Decision Processes (MDPs), that are tasked with temporal logic missions, such as sequencing, coverage, or surveillance. The MDP captures uncertainty in the workspace structure and the outcomes of control decisions. The control objective is to synthesize a control policy that maximizes the probability of accomplishing a high-level task, specified as a Linear Temporal Logic (LTL) formula. To address this problem, we propose a novel accelerated model-based reinforcement learning (RL) algorithm for LTL control objectives that is capable of learning control policies significantly faster than related approaches. Its sample-efficiency relies on biasing exploration towards directions that may contribute to task satisfaction. This is accomplished by leveraging an automaton representation of the LTL task as well as a continuously learned MDP model. Finally, we provide comparative experiments that demonstrate the sample efficiency of the proposed method against recent RL methods for LTL objectives.
Augmented Scale Models: Presenting Multivariate Data Around Physical Scale Models in Augmented Reality

Recent research in immersive visualisations has explored the use of physical 3D models together with virtual data visualisations, with a simple data encoding. However, little is known about how this technique could be extended towards more complex multivariate data with multiple charts. We present augmented scale models, immersive visualisations that place charts of multivariate data via Augmented Reality (AR) registered to physical 3D models. We identified two main factors for presenting AR charts in the limited display space around the models: 1) how charts are laid out (Slides vs Dashboard), and 2) how the chart views are arranged in the 3D space (On Scale Model, On Table, On Virtual Board). In a within-subject user study, we evaluated these two design considerations. We found that chart layout and view arrangement do not affect task error but do vary in response time. Dashboard and Slides perform equally well on simple tasks that require comparison of a single chart across scale models. However, in more complex tasks such as comparing multiple charts within a scale model or across several scale models, Dashboard shows no decrease in time performance, while Slides’s time performance decreases significantly. We also found that On Scale Model has the fastest performance, has good chart-scale model integration, and supports charts comparison well. On Table and On Virtual Board on the other hand, show a trade-off between the ability to support charts comparison across models and the chart-scale model integration.