Reviewerly/exHarmony huggingface dataset:
Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives

Synthetic aperture radar (SAR) images are affected by a spatially correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Despeckling is an important task that aims to remove such noise so as to improve the accuracy of all downstream image processing tasks. The first despeckling methods date back to the 1970s, and several model-based algorithms have been developed in the years since. The field has received growing attention, sparked by the availability of powerful deep learning models that have yielded excellent performance for inverse problems in image processing. This article surveys the literature on deep learning methods applied to SAR despeckling, covering both supervised and the more recent self-supervised approaches. We provide a critical analysis of existing methods, with the objective of recognizing the most promising research lines; identify the factors that have limited the success of deep models; and propose ways forward in an attempt to fully exploit the potential of deep learning for SAR despeckling.
Challenges and Opportunities of Immersive Technologies for Mindfulness Meditation: A Systematic Review

Mindfulness is considered an important factor of an individual's subjective well-being. Consequently, Human-Computer Interaction (HCI) has investigated approaches that strengthen mindfulness, i.e., by inventing multimedia technologies to support mindfulness meditation. These approaches often use smartphones, tablets, or consumer-grade desktop systems to allow everyday usage in users' private lives or in the scope of organized therapies. Virtual, Augmented, and Mixed Reality (VR, AR, MR; in short: XR) significantly extend the design space for such approaches. XR covers a wide range of potential sensory stimulation, perceptive and cognitive manipulations, content presentation, interaction, and agency. These facilities are linked to typical XR-specific perceptions that are conceptually closely related to mindfulness research, such as (virtual) presence and (virtual) embodiment. However, a successful exploitation of XR that strengthens mindfulness requires a systematic analysis of the potential interrelation and influencing mechanisms between XR technology, its properties, factors, and phenomena and existing models and theories of the construct of mindfulness. This article reports such a systematic analysis of XR-related research from HCI and life sciences to determine the extent to which existing research frameworks on HCI and mindfulness can be applied to XR technologies, the potential of XR technologies to support mindfulness, and open research gaps. Fifty papers of ACM Digital Library and National Institutes of Health's National Library of Medicine (PubMed) with and without empirical efficacy evaluation were included in our analysis. The results reveal that at the current time, empirical research on XR-based mindfulness support mainly focuses on therapy and therapeutic outcomes. Furthermore, most of the currently investigated XR-supported mindfulness interactions are limited to vocally guided meditations within nature-inspired virtual environments. While an analysis of empirical research on those systems did not reveal differences in mindfulness compared to non-mediated mindfulness practices, various design proposals illustrate that XR has the potential to provide interactive and body-based innovations for mindfulness practice. We propose a structured approach for future work to specify and further explore the potential of XR as mindfulness-support. The resulting framework provides design guidelines for XR-based mindfulness support based on the elements and psychological mechanisms of XR interactions.
Decrypting distributed ledger design—taxonomy, classification and blockchain community evaluation

More than 1000 distributed ledger technology (DLT) systems raising $600 billion in investment in 2016 feature the unprecedented and disruptive potential of blockchain technology. A systematic and data-driven analysis, comparison and rigorous evaluation of the different design choices of distributed ledgers and their implications is a challenge. The rapidly evolving nature of the blockchain landscape hinders reaching a common understanding of the techno-socio-economic design space of distributed ledgers and the cryptoeconomies they support. To fill this gap, this paper makes the following contributions: (i) A conceptual architecture of DLT systems with which (ii) a taxonomy is designed and (iii) a rigorous classification of DLT systems is made using real-world data and wisdom of the crowd. (iv) A DLT design guideline is the end result of applying machine learning methodologies on the classification data. Compared to related work and as defined in earlier taxonomy theory, the proposed taxonomy is highly comprehensive, robust, explanatory and extensible. The findings of this paper can provide new insights and better understanding of the key design choices evolving the modeling complexity of DLT systems, while identifying opportunities for new research contributions and business innovation.The online version contains supplementary material available at 10.1007/s10586-021-03256-w.
Unsupervised brain tumor segmentation using a symmetric-driven adversarial network

The aim of this study was to computationally model, in an unsupervised manner, a manifold of symmetry variations in normal brains, such that the learned manifold can be used to segment brain tumors from magnetic resonance (MR) images that fail to exhibit symmetry. An unsupervised brain tumor segmentation method, named as symmetric driven generative adversarial network (SD-GAN), was proposed. SD-GAN model was trained to learn a non-linear mapping between the left and right brain images, and thus being able to present the variability of the (symmetry) normal brains. The trained SD-GAN was then used to reconstruct normal brains and to segment brain tumors based on higher reconstruction errors arising from their being unsymmetrical. SD-GAN was evaluated on two public benchmark datasets (Multi-modal Brain Tumor Image Segmentation (BRATS) 2012 and 2018). SD-GAN provided best performance with tumor segmentation accuracy superior to the state-of-the-art unsupervised segmentation methods and performed comparably (<3% lower in Dice score) to the supervised U-Net (the most widely used supervised method for medical images). This study demonstrated that symmetric features presenting variations (i.e., inherent anatomical variations) can be modelled using unannotated normal MR images and thus be used in segmenting tumors.
A Content and Sentiment Analysis of Greek Tweets during the Pandemic

During the time of the coronavirus, strict prevention policies, social distancing, and limited contact with others were enforced in Greece. As a result, Twitter and other social media became an important place of interaction, and conversation became online. The aim of this study is to examine Twitter discussions around COVID-19 in Greece. Twitter was chosen because of the critical role it played during the global health crisis. Tweets were recorded over four time periods. NodeXL Pro was used to identify word pairs, create semantic networks, and analyze them. A lexicon-based sentiment analysis was also performed. The main topics of conversation were extracted. “New cases” are heavily discussed throughout, showing fear of transmission of the virus in the community. Mood analysis showed fluctuations in mood over time. Positive emotions weakened and negative emotions increased. Fear is the dominant sentiment. Timely knowledge of people’s sentiment can be valuable for government agencies to develop efficient strategies to better manage the situation and use efficient communication guidelines in Twitter to disseminate accurate, reliable information and control panic.
Towards a New Learning Experience through a Mobile Application with Augmented Reality in Engineering Education

With the rise of information technology and digitization, education has been faced with the need to adopt new learning models using technology to create innovative educational methodologies. In addition, due to pandemic restrictions and in order to help contain the spread of the virus (COVID-19), all educational institutions have been forced to switch immediately to online education. The application of augmented reality (AR) in education provides important benefits, such as increased engagement and interactivity, and can help to minimize the negative effects of the disruption of face-to-face education. Therefore, this paper focuses on describing the effect of an augmented reality mobile application (NetAR) that was developed for engineering students as a complement to traditional education. To achieve this objective, an experimental group and a control group were established to work with the application for three weeks for three hours a day. Moreover, there are a number of usability issues with AR that may impact learning effectiveness and motivation. Therefore, the usability of the application was evaluated with the IBM Computer System Usability Questionnaire (CSUQ) tool. The usability results show that users are satisfied with NetAR, and the statistical data from the control group indicate that the application positively affects learning.
Spatial Dependency Parsing for Semi-Structured Document Information Extraction

Information Extraction (IE) for semistructured document images is often approached as a sequence tagging problem by classifying each recognized input token into one of the IOB (Inside, Outside, and Beginning) categories.However, such problem setup has two inherent limitations that (1) it cannot easily handle complex spatial relationships and (2) it is not suitable for highly structured information, which are nevertheless frequently observed in real-world document images.To tackle these issues, we first formulate the IE task as spatial dependency parsing problem that focuses on the relationship among text tokens in the documents.Under this setup, we then propose SPADE (SPAtial DEpendency parser) that models highly complex spatial relationships and an arbitrary number of information layers in the documents in an end-to-end manner.We evaluate it on various kinds of documents such as receipts, name cards, forms, and invoices, and show that it achieves a similar or better performance compared to strong baselines including BERT-based IOB taggger.
Research and Application of the Interactive English Online Teaching System Based on the Internet of Things

Nowadays, due to the pandemic and other problems, the establishment of physical classes is a big headache for both students and teachers, due to which the education system all over the world is shifted to the online system from the physical system. Advance technologies such as the Internet of Things (IoT) are playing a significant part in various sectors of life such as health, business, and education. In order to effectively improve the effect of online English teaching, this study designed an interactive online English teaching system based on the IoT technology. This study proposes three topological structures for the establishment of the proposed IoT-based online English teaching system. Based on the analysis of the three topological structures of the IoT, this study chooses to design each submodule of the front and back of the system in the network IoT environment to realize the daily operation and various functions of the system and to realize the interactive design of both of the teacher and student side. Based on this approach, an online English teaching system is designed, and the teaching quality based on this system is evaluated with the help of an algorithm known as grey relational analysis algorithm. The experimental results show that, after the application of this system, students have access to the teaching materials and content in a short period of time; and the English test scores were improved and were significantly higher as compared to the traditional teaching system. In addition, at the same time, the internal consistency reliability of the proposed system is very high which fully demonstrates the effectiveness of the proposed system.
Attention-based Multi-Level Fusion Network for Light Field Depth Estimation

Depth estimation from Light Field (LF) images is a crucial basis for LF related applications. Since multiple views with abundant information are available, how to effectively fuse features of these views is a key point for accurate LF depth estimation. In this paper, we propose a novel attention-based multi-level fusion network. Combined with the four-branch structure, we design intra-branch fusion strategy and inter-branch fusion strategy to hierarchically fuse effective features from different views. By introducing the attention mechanism, features of views with less occlusions and richer textures are selected inside and between these branches to provide more effective information for depth estimation. The depth maps are finally estimated after further aggregation. Experimental results shows the proposed method achieves state-of-the-art performance in both quantitative and qualitative evaluation, which also ranks first in the commonly used HCI 4D Light Field Benchmark.
Intrinsic Bias Metrics Do Not Correlate with Application Bias

Natural Language Processing (NLP) systems learn harmful societal biases that cause them to amplify inequality as they are deployed in more and more situations.To guide efforts at debiasing these systems, the NLP community relies on a variety of metrics that quantify bias in models.Some of these metrics are intrinsic, measuring bias in word embedding spaces, and some are extrinsic, measuring bias in downstream tasks that the word embeddings enable.Do these intrinsic and extrinsic metrics correlate with each other?We compare intrinsic and extrinsic metrics across hundreds of trained models covering different tasks and experimental conditions.Our results show no reliable correlation between these metrics that holds in all scenarios across tasks and languages.We urge researchers working on debiasing to focus on extrinsic measures of bias, and to make using these measures more feasible via creation of new challenge sets and annotated test data.To aid this effort, we release code, a new intrinsic metric, and an annotated test set focused on gender bias in hate speech.
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models

Text representation models are prone to exhibit a range of societal biases, reflecting the noncontrolled and biased nature of the underlying pretraining data, which consequently leads to severe ethical issues and even bias amplification.Recent work has predominantly focused on measuring and mitigating bias in pretrained language models.Surprisingly, the landscape of bias measurements and mitigation resources and methods for conversational language models is still very scarce: it is limited to only a few types of bias, artificially constructed resources, and completely ignores the impact that debiasing methods may have on the final performance in dialog tasks, e.g., conversational response generation.In this work, we present REDDITBIAS, the first conversational data set grounded in the actual human conversations from Reddit, allowing for bias measurement and mitigation across four important bias dimensions: gender, race, religion, and queerness.Further, we develop an evaluation framework which simultaneously 1) measures bias on the developed REDDITBIAS resource, and 2) evaluates model capability in dialog tasks after model debiasing.We use the evaluation framework to benchmark the widely used conversational DialoGPT model along with the adaptations of four debiasing methods.Our results indicate that DialoGPT is biased with respect to religious groups and that some debiasing techniques can remove this bias while preserving downstream task performance.
Part-guided graph convolution networks for person re-identification

Recently, part-based deep models have achieved promising performance in person re-identification (Re-ID), yet these models ignore the inter-local relationship of the corresponding parts among pedestrian images and the intra-local relationship between adjacent parts in one pedestrian image. As a result, the feature representations are hard to learn the information from the same parts of other pedestrian images and are lack of the contextual information of pedestrian. In this paper, we propose a novel deep graph model named Part-Guided Graph Convolution Network (PGCN) for person Re-ID, which could simultaneously learn the inter-local relationship and the intra-local relationship for feature representations. Specifically, we construct the inter-local graph using the local features extracted from the same parts of pedestrian images and build the adjacency matrix using the similarity so as to mine the inter-local relationship. Meanwhile, we construct the intra-local graph using the local features extracted from different body parts in one pedestrian image, and propose the fractional dynamic mechanism (FDM) to accurately describe the correlations between adjacent parts in the optimization process. Finally, after the graph convolutional operation, the inter-local relationship and the intra-local relationship are injected into the feature representations of pedestrian images. Extensive experiments are conducted on Market-1501, CUHK03, DukeMTMC-reID and MSMT17, and the experimental results show the proposed PGCN exceeds state-of-the-art methods by an overwhelming margin.
Underwater Target Recognition Based on Improved YOLOv4 Neural Network

The YOLOv4 neural network is employed for underwater target recognition. To improve the accuracy and speed of recognition, the structure of YOLOv4 is modified by replacing the upsampling module with a deconvolution module and by incorporating depthwise separable convolution into the network. Moreover, the training set used in the YOLO network is preprocessed by using a modified mosaic augmentation, in which the gray world algorithm is used to derive two images when performing mosaic augmentation. The recognition results and the comparison with the other target detectors demonstrate the effectiveness of the proposed YOLOv4 structure and the method of data preprocessing. According to both subjective and objective evaluation, the proposed target recognition strategy can effectively improve the accuracy and speed of underwater target recognition and reduce the requirement of hardware performance as well.
Design of English Automatic Translation System Based on Machine Intelligent Translation and Secure Internet of Things

With the rapid development of Internet technology and the development of economic globalization, international exchanges in various fields have become increasingly active, and the need for communication between languages has become increasingly clear. As an effective tool, automatic translation can perform equivalent translation between different languages while preserving the original semantics. This is very important in practice. This paper focuses on the Chinese-English machine translation model based on deep neural networks. In this paper, we use the end-to-end encoder and decoder framework to create a neural machine translation model, the machine automatically learns its function, and the data is converted into word vectors in a distributed method and can be directly through the neural network perform the mapping between the source language and the target language. Research experiments show that, by adding part of the voice information to verify the effectiveness of the model performance improvement, the performance of the translation model can be improved. With the superimposition of the number of network layers from two to four, the improvement ratios of each model are 5.90%, 6.1%, 6.0%, and 7.0%, respectively. Among them, the model with an independent recurrent neural network as the network structure has the largest improvement rate and a higher improvement rate, so the system has high availability.
Assessing the Performance of Clinical Natural Language Processing Systems: Development of an Evaluation Methodology

Background Clinical natural language processing (cNLP) systems are of crucial importance due to their increasing capability in extracting clinically important information from free text contained in electronic health records (EHRs). The conversion of a nonstructured representation of a patient’s clinical history into a structured format enables medical doctors to generate clinical knowledge at a level that was not possible before. Finally, the interpretation of the insights gained provided by cNLP systems has a great potential in driving decisions about clinical practice. However, carrying out robust evaluations of those cNLP systems is a complex task that is hindered by a lack of standard guidance on how to systematically approach them. Objective Our objective was to offer natural language processing (NLP) experts a methodology for the evaluation of cNLP systems to assist them in carrying out this task. By following the proposed phases, the robustness and representativeness of the performance metrics of their own cNLP systems can be assured. Methods The proposed evaluation methodology comprised five phases: (1) the definition of the target population, (2) the statistical document collection, (3) the design of the annotation guidelines and annotation project, (4) the external annotations, and (5) the cNLP system performance evaluation. We presented the application of all phases to evaluate the performance of a cNLP system called “EHRead Technology” (developed by Savana, an international medical company), applied in a study on patients with asthma. As part of the evaluation methodology, we introduced the Sample Size Calculator for Evaluations (SLiCE), a software tool that calculates the number of documents needed to achieve a statistically useful and resourceful gold standard. Results The application of the proposed evaluation methodology on a real use-case study of patients with asthma revealed the benefit of the different phases for cNLP system evaluations. By using SLiCE to adjust the number of documents needed, a meaningful and resourceful gold standard was created. In the presented use-case, using as little as 519 EHRs, it was possible to evaluate the performance of the cNLP system and obtain performance metrics for the primary variable within the expected CIs. Conclusions We showed that our evaluation methodology can offer guidance to NLP experts on how to approach the evaluation of their cNLP systems. By following the five phases, NLP experts can assure the robustness of their evaluation and avoid unnecessary investment of human and financial resources. Besides the theoretical guidance, we offer SLiCE as an easy-to-use, open-source Python library.
Beyond Goldfish Memory: Long-Term Open-Domain Conversation

Despite recent improvements in open-domain dialogue models, state-of-the-art models are trained and evaluated on short conversations with little context.In contrast, the long-term conversation setting has hardly been studied.In this work we collect and release a humanhuman dataset consisting of multiple chat sessions whereby the speaking partners learn about each other's interests and discuss the things they have learnt from past sessions.We show how existing models trained on existing datasets perform poorly in this long-term conversation setting in both automatic and human evaluations, and we study long-context models that can perform much better.In particular, we find retrieval-augmented methods and methods with an ability to summarize and recall previous conversations outperform the standard encoder-decoder architectures currently considered state-of-the-art.* We use this term colloquially, see Agranoff et al. (1965) for evidence of goldfish long-term memory.