Reviewerly/exHarmony huggingface dataset:
Localizing Content: The Roles of Technical &amp; Professional Communicators and Machine Learning in Personalized Chatbot Responses

Purpose: This study demonstrates that microcontent, a snippet of personalized content that responds to users' needs, is a form of localization reliant on a content ecology. In contributing to users' localized experiences, technical communicators should recognize their work as part of an assemblage in which users, content, and metrics augment each other to produce personalized content that can be consumed by and delivered through artificial intelligence (AI)-assisted technology.&lt;br/&gt; Method: We use an exploratory case study on an AI-driven chatbot to demonstrate the assemblage of user, content, metrics, and AI. By understanding assemblage roles and function of different units used to build AI systems, technical and professional communicators can contribute to microcontent development. We define microcontent as a localized form of content deployed by AI and quickly consumed by a human user through online interfaces.&lt;br/&gt; Results: We identify five insertion points where technical communicators can participate in localizing content:&lt;br/&gt; • Creating structured content for bots to better meet user needs&lt;br/&gt; • Training corpora for bots with data-informed user personas that can better address specific needs of user groups&lt;br/&gt; • Developing chatbot user interfaces that are more responsive to user needs&lt;br/&gt; • Developing effective human-in-the-loop approaches by moderating content for refining future human-chatbot interactions&lt;br/&gt; • Creating more ethically and user-centered data practices with different stakeholders.&lt;br/&gt; Conclusion: Technical communicators should teach, research, and practice competencies and skills to advocate for localized users in assemblages of user, content, metrics, and AI.
Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis

Identification of clinical phenotypes in critically ill COVID-19 patients could improve understanding of the disease heterogeneity and enable prognostic and predictive enrichment. However, previous attempts did not take into account temporal dynamics with high granularity. By including the dimension of time, we aim to gain further insights into the heterogeneity of COVID-19.We used granular data from 3202 adult COVID patients in the Dutch Data Warehouse that were admitted to one of 25 Dutch ICUs between February 2020 and March 2021. Parameters including demographics, clinical observations, medications, laboratory values, vital signs, and data from life support devices were selected. Twenty-one datasets were created that each covered 24 h of ICU data for each day of ICU treatment. Clinical phenotypes in each dataset were identified by performing cluster analyses. Both evolution of the clinical phenotypes over time and patient allocation to these clusters over time were tracked.The final patient cohort consisted of 2438 COVID-19 patients with a ICU mortality outcome. Forty-one parameters were chosen for cluster analysis. On admission, both a mild and a severe clinical phenotype were found. After day 4, the severe phenotype split into an intermediate and a severe phenotype for 11 consecutive days. Heterogeneity between phenotypes appears to be driven by inflammation and dead space ventilation. During the 21-day period, only 8.2% and 4.6% of patients in the initial mild and severe clusters remained assigned to the same phenotype respectively. The clinical phenotype half-life was between 5 and 6 days for the mild and severe phenotypes, and about 3 days for the medium severe phenotype.Patients typically do not remain in the same cluster throughout intensive care treatment. This may have important implications for prognostic or predictive enrichment. Prominent dissimilarities between clinical phenotypes are predominantly driven by inflammation and dead space ventilation.
Real-time face mask position recognition system based on MobileNet model

COVID-19 is a highly contagious disease that was first identified in 2019, and has since taken more than six million lives world wide till date, while also causing considerable economic, social, cultural and political turmoil. As a way to limit its spread, the World Health Organization and medical experts have advised properly wearing face masks, social distancing and hand sanitization, besides vaccination. However, people wear masks sometimes uncovering their mouths and/or noses consciously or unconsciously, thereby lessening the effectiveness of the protection they provide. A system capable of automatic recognition of face mask position could alert and ensure that an individual is wearing a mask properly before entering a crowded public area and putting themselves and others at risk. We first develop and publicly release a dataset of face mask images, which are collected from 391 individuals of different age groups and gender. Then, we study six different architectures of pre-trained deep learning models, and finally propose a model developed by fine tuning the pre-trained state of the art MobileNet model. We evaluate the performance (accuracy, F1-score, and Cohen's Kappa) of this model on the proposed dataset and MaskedFace-Net, a publicly available synthetic dataset created by image editing. Its performance is also compared to other existing methods. The proposed MobileNet is found as the best model providing an accuracy, F1-score, and Cohen's Kappa of 99.23%, 99.22%, and 99.19%, respectively for face mask position recognition. It outperforms the accuracy of the best existing model by about 2%. Finally, an automatic face mask position recognition system has been developed, which can recognize if an individual is wearing a mask correctly or incorrectly. The proposed model performs very well with no drop in recognition accuracy from real images captured by a camera.
Understanding writing styles of scientific papers in the IS-LS domain: Evidence from abstracts over the past three decades

To some extent, written academic discourse represents the knowledge and practices of the academic community. Studies investigating writing styles in various disciplines have flourished, but fewer studies have leveraged multi-perspective linguistic indices to analyze academic writings, especially in the information science and library science (IS-LS) domain. This study attempts to provide an overview of how writing styles have evolved over the past 30 years across various subfields in IS-LS from multiple perspectives, that is, lexical complexity, cohesion, syntactic complexity, and readability. Based on a large set of abstracts of academic papers published in IS-LS, the empirical findings showed that the readability, cohesion, and lexical sophistication of abstracts in the IS-LS domain have increased over time, indicating that abstracts tend to contain more information but become less accessible. The gradual improvement in cohesion suggests that academic writing logic has increased, and the rigor of knowledge construction of scientific papers has improved. Furthermore, considerable linguistic variations emerged between subfields in the IS-LS domain, particularly at the lexical level. This study suggested that different subfields had various writing styles due to their research topics, methodologies, orientations, etc. The study also found that papers published in top quartile journals and those that gained higher citations typically had larger lexical density, lexical sophistication, cohesion, and readability. This suggests that influential papers tend to carry more information, address more complex scientific issues, and exercise caution in knowledge construction and presentation.
A Standardized Approach for Skin Detection: Analysis of the Literature and Case Studies

Skin detection involves identifying skin and non-skin areas in a digital image and is commonly used in various applications, such as analyzing hand gestures, tracking body parts, and facial recognition. The process of distinguishing between skin and non-skin regions in a digital image is widely used in a variety of applications, ranging from hand-gesture analysis to body-part tracking to facial recognition. Skin detection is a challenging problem that has received a lot of attention from experts and proposals from the research community in the context of intelligent systems, but the lack of common benchmarks and unified testing protocols has hampered fairness among approaches. Comparisons are very difficult. Recently, the success of deep neural networks has had a major impact on the field of image segmentation detection, resulting in various successful models to date. In this work, we survey the most recent research in this field and propose fair comparisons between approaches, using several different datasets. The main contributions of this work are (i) a comprehensive review of the literature on approaches to skin-color detection and a comparison of approaches that may help researchers and practitioners choose the best method for their application; (ii) a comprehensive list of datasets that report ground truth for skin detection; and (iii) a testing protocol for evaluating and comparing different skin-detection approaches. Moreover, we propose an ensemble of convolutional neural networks and transformers that obtains a state-of-the-art performance.
Data sharing and reuse practices: disciplinary differences and improvements needed

Purpose This study investigates differences and commonalities in data production, sharing and reuse across the widest range of disciplines yet and identifies types of improvements needed to promote data sharing and reuse. Design/methodology/approach The first authors of randomly selected publications from 2018 to 2019 in 20 Scopus disciplines were surveyed for their beliefs and experiences about data sharing and reuse. Findings From the 3,257 survey responses, data sharing and reuse are still increasing but not ubiquitous in any subject area and are more common among experienced researchers. Researchers with previous data reuse experience were more likely to share data than others. Types of data produced and systematic online data sharing varied substantially between subject areas. Although the use of institutional and journal-supported repositories for sharing data is increasing, personal websites are still frequently used. Combining multiple existing datasets to answer new research questions was the most common use. Proper documentation, openness and information on the usability of data continue to be important when searching for existing datasets. However, researchers in most disciplines struggled to find datasets to reuse. Researchers' feedback suggested 23 recommendations to promote data sharing and reuse, including improved data access and usability, formal data citations, new search features and cultural and policy-related disciplinary changes to increase awareness and acceptance. Originality/value This study is the first to explore data sharing and reuse practices across the full range of academic discipline types. It expands and updates previous data sharing surveys and suggests new areas of improvement in terms of policy, guidance and training programs. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2021-0423 .
Efficient Skeleton-Based Action Recognition via Joint-Mapping strategies

Graph convolutional networks (GCNs) have brought remarkable progress in skeleton-based action recognition. However, high computational cost and large model size make models difficult to be applied in real-world embedded system. Specifically, GCN that is applied in automated surveillance system pre-require models such as pedestrian detection and human pose estimation. Therefore, each model should be computationally lightweight and whole process should be operated in real-time. In this paper, we propose two different joint-mapping modules to reduce the number of joint representations, alleviating a total computational cost and model size. Our models achieve better accuracy-latency trade-off compared to previous state-ofthe-arts on two datasets, namely NTU RGB+D and NTU RGB+D 120, demonstrating the suitability for practical applications. Furthermore, we measure the latency of the models by using TensorRT framework to compare the models from a practical perspective.
Backprop Induced Feature Weighting for Adversarial Domain Adaptation with Iterative Label Distribution Alignment

The requirement for large labeled datasets is one of the limiting factors for training accurate deep neural networks. Unsupervised domain adaptation tackles this problem of limited training data by transferring knowledge from one domain, which has many labeled data, to a different domain for which little to no labeled data is available. One common approach is to learn domain-invariant features for example with an adversarial approach. Previous methods often train the domain classifier and label classifier network separately, where both classification networks have little interaction with each other. In this paper, we introduce a classifier-based backprop-induced weighting of the feature space. This approach has two main advantages. Firstly, it lets the domain classifier focus on features that are important for the classification, and, secondly, it couples the classification and adversarial branch more closely. Furthermore, we introduce an iterative label distribution alignment method, that employs results of previous runs to approximate a class-balanced dataloader. We conduct experiments and ablation studies on three benchmarks Office-31, Office-Home, and DomainNet to show the effectiveness of our proposed algorithm.
Out-of-distribution Detection via Frequency-regularized Generative Models

Modern deep generative models can assign high likelihood to inputs drawn from outside the training distribution, posing threats to models in open-world deployments. While much research attention has been placed on defining new test-time measures of OOD uncertainty, these methods do not fundamentally change how deep generative models are regularized and optimized in training. In particular, generative models are shown to overly rely on the background information to estimate the likelihood. To address the issue, we propose a novel frequency-regularized learning (FRL) framework for OOD detection, which incorporates high-frequency information into training and guides the model to focus on semantically relevant features. FRL effectively improves performance on a wide range of generative architectures, including variational auto-encoder, GLOW, and PixelCNN++. On a new large-scale evaluation task, FRL achieves the state-of-the-art performance, outperforming a strong baseline Likelihood Regret by 10.7% (AUROC) while achieving 147× faster inference speed. Extensive ablations show that FRL improves the OOD detection performance while preserving the image generation quality. Code is available at https://github.com/mu-cai/FRL.
Pik-Fix: Restoring and Colorizing Old Photos

Restoring and inpainting the visual memories that are present, but often impaired, in old photos remains an intriguing but unsolved research topic. Decades-old photos often suffer from severe and commingled degradation such as cracks, defocus, and color-fading, which are difficult to treat individually and harder to repair when they interact. Deep learning presents a plausible avenue, but the lack of large-scale datasets of old photos makes addressing this restoration task very challenging. Here we present a novel reference-based end-to-end learning framework that is able to both repair and colorize old, degraded pictures. Our proposed framework consists of three modules: a restoration sub-network that conducts restoration from degradations, a similarity network that performs color histogram matching and color transfer, and a colorization subnet that learns to predict the chroma elements of images conditioned on chromatic reference signals. The overall system makes uses of color histogram priors from reference images, which greatly reduces the need for large-scale training data. We have also created a first-of-a-kind public dataset of real old photos that are paired with ground truth "pristine" photos that have been manually restored by PhotoShop experts. We conducted extensive experiments on this dataset and synthetic datasets, and found that our method significantly outperforms previous state-of-the-art models using both qualitative comparisons and quantitative measurements. The code is available at https://github.com/DerrickXuNu/Pik-Fix.
Few-shot Object Detection via Improved Classification Features

Few-shot object detection (FSOD) aims to transfer knowledge from base classes to novel classes, which receives widespread attention recently. The performance of current techniques is, however, limited by the poor classification ability and the improper features in the detection head. To circumvent this issue, we propose a Multi-level Feature Enhancement (MFE) model to improve the feature for classification from three different perspectives, including the spatial level, the task level and the regularization level. First, we revise the classifier’s input feature at the spatial level by using information from the regression head. Secondly, we separate the RoI-Align feature into two different feature distributions in order to improve features at the task level. Finally, taking into account the overfitting problem in FSOD, we design a simple but efficient regularization enhancement module to sample features into various distributions and enhance the regularization ability of classification. Extensive experiments show that our method achieves competitive results on PASCAL VOC datasets, and exceeds current state-of-the-art methods in all shot settings on challenging MS-COCO datasets.
Holistic Interaction Transformer Network for Action Detection

Actions are about how we interact with the environment, including other people, objects, and ourselves. In this paper, we propose a novel multi-modal Holistic Interaction Transformer Network (HIT) that leverages the largely ignored, but critical hand and pose information essential to most human actions. The proposed HIT network is a comprehensive bi-modal framework that comprises an RGB stream and a pose stream. Each of them separately models person, object, and hand interactions. Within each sub-network, an Intra-Modality Aggregation module (IMA) is introduced that selectively merges individual interaction units. The resulting features from each modality are then glued using an Attentive Fusion Mechanism (AFM). Finally, we extract cues from the temporal context to better classify the occurring actions using cached memory. Our method significantly outperforms previous approaches on the J-HMDB, UCF101-24, and MultiSports datasets. We also achieve competitive results on AVA. The code will be available at https://github.com/joslefaure/HIT.
Single-Image HDR Reconstruction by Multi-Exposure Generation

High dynamic range (HDR) imaging is an indispensable technique in modern photography. Traditional methods focus on HDR reconstruction from multiple images, solving the core problems of image alignment, fusion, and tone mapping, yet having a perfect solution due to ghosting and other visual artifacts in the reconstruction. Recent attempts at single-image HDR reconstruction show a promising alternative: by learning to map pixel values to their irradiance using a neural network, one can bypass the align-and-merge pipeline completely yet still obtain a high-quality HDR image. In this work, we propose a weakly supervised learning method that inverts the physical image formation process for HDR reconstruction via learning to generate multiple exposures from a single image. Our neural network can invert the camera response to reconstruct pixel irradiance before synthesizing multiple exposures and hallucinating details in under- and over-exposed regions from a single input image. To train the network, we propose a representation loss, a reconstruction loss, and a perceptual loss applied on pairs of under- and over-exposure images and thus do not require HDR images for training. Our experiments show that our proposed model can effectively reconstruct HDR images. Our qualitative and quantitative results show that our method achieves state-of-the-art performance on the DrTMO dataset. Our code is available at https://github.com/VinAIResearch/single_image_hdr.
Towards Online Domain Adaptive Object Detection

Existing object detection models assume both the training and test data are sampled from the same source do-main. This assumption does not hold true when these detectors are deployed in real-world applications, where they en-counter new visual domains. Unsupervised Domain Adaptation (UDA) methods are generally employed to mitigate the adverse effects caused by domain shift. Existing UDA methods operate in an offline manner where the model is first adapted toward the target domain and then deployed in real-world applications. However, this offline adaptation strategy is not suitable for real-world applications as the model frequently encounters new domain shifts. Hence, it is critical to develop a feasible UDA method that generalizes to the new domain shifts encountered during deployment time in a continuous online manner. To this end, we propose a novel unified adaptation framework that adapts and improves generalization on the target domain in both offline and online settings. Specifically, we introduce MemXformer - a cross-attention transformer-based memory module where items in the memory take advantage of domain shifts and record prototypical patterns of the target distribution. Further, MemXformer produces strong positive and negative pairs to guide a novel contrastive loss, which enhances target-specific representation learning. Experiments on diverse detection benchmarks show that the proposed strategy producs state-of-the-art performance in both offline and online settings. To the best of our knowledge, this is the first work to address online and offline adaptation settings for object detection. Source code: https://github.com/Vibashan/memXformer-online-da
Towards Interpretable Video Anomaly Detection

Most video anomaly detection approaches are based on data-intensive end-to-end trained neural networks, which extract spatiotemporal features from videos. The extracted feature representations in such approaches are not interpretable, which prevents the automatic identification of anomaly cause. To this end, we propose a novel framework which can explain the detected anomalous event in a surveillance video. In addition to monitoring objects independently, we also monitor the interactions between them to detect anomalous events and explain their root causes. Specifically, we demonstrate that the scene graphs obtained by monitoring the object interactions provide an interpretation for the context of the anomaly while performing competitively with respect to the recent state-of-the-art approaches. Moreover, the proposed interpretable method enables cross-domain adaptability (i.e., transfer learning in another surveillance scene), which is not feasible for most existing end-to-end methods due to the lack of sufficient labeled training data for every surveillance scene. The quick and reliable detection performance of the proposed method is evaluated both theoretically (through an asymptotic optimality proof) and empirically on the popular benchmark datasets.