Reviewerly/exHarmony huggingface dataset:
Improving Multi-atlas Segmentation by Convolutional Neural Network Based Patch Error Estimation

Multi-atlas segmentation (MAS) is widely used in automatically labeling medical images. The performance of patch-based MAS approaches relies on accurate estimation of local patch similarity, a proxy of the probability that an atlas patch provides the same label as the target patch. Learning-based image patch embedding techniques were recently proposed to transform raw intensity to feature maps and yield promising improvements compared to traditional raw intensity or hand-crafted features. In this study, we present a different approach in which the probability of atlas patch generating an erroneous vote, i.e. having a different label from the target patch, is directly estimated from the patches using a convolutional neural network (CNN). Experiments demonstrate that CNN-based estimates improve the segmentation accuracy of popular patch-based MAS techniques, i.e. spatially varying weighted voting and joint label fusion, in the context of segmenting medial temporal lobe subregions in T1-weighted MRI.
Blockchain offers applications well beyond Bitcoin but faces its own limitations

In 2008, Satoshi Nakamoto introduced the world to Bitcoin, a volatile digital currency that’s untethered to any specific institution or country (1). (Satoshi Nakamoto was quickly revealed to be a pseudonym; the true identity of the inventor or inventors remains unknown.) In the years that followed, the value of Bitcoin has soared, plummeted, and soared again while giving rise to a raft of new cryptocurrencies of varying stability and legitimacy. Bitcoin and the others have their boosters and their detractors (see Opinion: Valuation, liquidity price, and stability of cryptocurrencies, https://www.pnas.org/content/115/6/1131).Blockchain will forever be linked to cryptocurrencies, but researchers have begun to explore untapped applications for blockchain’s secure ledger approach. Image credit: Shutterstock/Iaremenko Sergii.But Bitcoin wasn’t just revolutionary as a virtual currency innovation. It also introduced a novel way for investors and others to obtain, monitor, and trade that currency. Users record and secure financial transactions along with a timestamp using blockchain, a ledger system designed by Nakamoto that’s unlike any traditional accounting system used before. A blockchain isn’t stored on a central computer or controlled by a single boss; instead, it’s distributed, which means every user on a blockchain network has a copy of the entire ledger. Information is also protected by a consensus algorithm—a computer program that enables the network to validate transactions.Blockchain will forever be linked to cryptocurrencies, but many researchers have begun to explore untapped applications for blockchain’s secure ledger approach. Its built-in mechanisms of trust and attribution make it appealing as a way to organize networks where people want to share information—a potentially big asset for tracking information in all sorts of science-related systems. Applications include forging new ways of managing distributed electrical grids, tracking regulated food and drugs, monitoring devices in the ever-growing “Internet of Things” (IoT), and perhaps even quickly …
Performance of Classification Analysis: A Comparative Study between PLS-DA and Integrating PCA+LDA

Classification methods are fundamental techniques designed to find mathematical models that are able to recognize the membership of each object to its proper class on the basis of a set of measurements.The issue of classifying objects into groups when variables in an experiment are large will cause the misclassification problems.This study explores the approaches for tackling the classification problem of a large number of independent variables using parametric method namely PLS-DA and PCA+LDA.Data are generated using data simulator; Azure Machine Learning (AML) studio through custom R module.The performance analysis of the PLS-DA was conducted and compared with PCA+LDA model using different number of variables (p) and different sample sizes (n).The performance of PLS-DA and PCA+LDA has been evaluated based on minimum misclassification rate.The results demonstrated that PLS-DA performed better than the PCA+LDA for large sample size.PLS-DA can be considered to have a good and reliable technique to be used when dealing with large datasets for classification task.
Unsupervised feature selection via graph matrix learning and the low-dimensional space learning for classification

Unsupervised feature selection is a powerful tool to select a subset of features for effective representation of high-dimensional data. In this paper, we proposes a novel unsupervised feature selection method via the graph matrix learning and the low-dimensional space learning to obtain their individually optimized result. Furthermore, the global and local correlation of features have been taken into consideration through the low-rank constraint and the feature-level representation property on the graph matrix. Experimental analysis on 15 benchmark datasets verified that our proposed method outperformed the state-of-the-art feature selection methods in terms of classification performance.
3D flow field estimation and assessment for live cell fluorescence microscopy

Abstract Motivation The revolution in light sheet microscopy enables the concurrent observation of thousands of dynamic processes, from single molecules to cellular organelles, with high spatiotemporal resolution. However, challenges in the interpretation of multidimensional data requires the fully automatic measurement of those motions to link local processes to cellular functions. This includes the design and the implementation of image processing pipelines able to deal with diverse motion types, and 3D visualization tools adapted to the human visual system. Results Here, we describe a new method for 3D motion estimation that addresses the aforementioned issues. We integrate 3D matching and variational approach to handle a diverse range of motion without any prior on the shape of moving objects. We compare different similarity measures to cope with intensity ambiguities and demonstrate the effectiveness of the Census signature for both stages. Additionally, we present two intuitive visualization approaches to adapt complex 3D measures into an interpretable 2D view, and a novel way to assess the quality of flow estimates in absence of ground truth. Availability and implementation https://team.inria.fr/serpico/data/3d-optical-flow-data/ Supplementary information Supplementary data are available at Bioinformatics online.
Self-adapting self-organizing migrating algorithm

The self-organizing migrating algorithm is a population-based algorithm belonging to swarm intelligence, which has been successfully applied in several areas for solving non-trivial optimization problems. However, based on our experiments, the original formulation of this algorithm suffers with some shortcomings as loss of population diversity, premature convergence, and the necessity of the control parameters hand-tuning. The main contribution of this paper is the development of the novel algorithm mitigating the mentioned issues of the original self-organizing migrating algorithm. We have applied the ideas of the self-adaptation of the control parameters, the different principle of the leader creation, and the external archive of the successful particles. For some special cases, we are able to utilize the differential grouping to detect the interacting variables effectively removing the need for the perturbation parameter. To prove the efficiency of the novel algorithm, we have performed experiments on fifteen unconstrained problems from the CEC 2015 benchmark. The algorithm is compared with seven well-known evolutionary and swarm algorithms. The results of the experiments indicate that the mechanisms used in the novel algorithm had significantly improved the performance of the original self-organizing migrating algorithm, and the new algorithm can now compete with the selected algorithms.
A Novel Measurement System Based on Binocular Fisheye Vision and its Application in Dynamic Environment

This paper describes the development of a novel measurement system of calculating the three-dimensional (3D) coordinates of a target with image information taken from stereo fisheye cameras. The system has the characteristics of high precision, wide of view, long-distance control, dynamic angles compensation and high frame rate measurement. In order to improve the robustness and precision of the system, a detailed uncertainty analysis of the spatial positioning was made. Moreover, a novel method named Dynamic Angles Compensation Method (DACM) is proposed for first time to complete spatial position measurement in the dynamic environment by angles compensation. This method uses a tilt angle sensor that is installed in the birocular measurement system horizontally to calculate the pitch and roll angles of the measurement system, which is the foundation for calculating the conversion equation to compensate the measurement coordinates error caused by the dynamic environment. Using real data acquired by equipment, we performed experiments covering all the necessary stages to obtain a high-performance stereo fisheye vision system. The validity and accuracy of the proposed method are demonstrated with the statistical analyses of the experimental results.
Malaysian Sign Language Mobile Learning Application : A recommendation app to communicate with hearing-impaired communities

Malaysian Sign Language (MSL) is an important language that is used as the primary communication method for the deaf communities with the others. Currently, the MSL is poorly known by the Malaysians and the existing platform of learning the sign language is inefficient, not to mention the incomplete functionality of the existing mobile learning application in the market. Hence, the purpose of developing this application is aimed to increase the knowledge and recognition of the public towards the MSL and allows them to learn the sign language more effectively. One of the features in this application is sign detection, which could analyze the image captured by phone camera into sign meaning. The application also comprised various categories of the sign for efficient learning and quiz to test user knowledge against their learned sign language. Besides, there is a feedback module for the user to express their opinions and suggestions towards the application to the developer. This application is aimed to help the public to learn MSL efficiently by selecting the category of sign they wish to learn and test themselves by using the quiz module. Besides, the application could also detect the unknown sign by capturing the image and analyze it. The application helped to raise the recognition of MSL among the public and expose the public to the sign language knowledge. It had also become a small help in breaking the barrier of communication between the deaf communities and the public.
Machine learning applications in detecting sand boils from images

Levees provide protection for vast amounts of commercial and residential properties. However, these structures require constant maintenance and monitoring, due to the threat of severe weather, sand boils, subsidence of land, seepage, etc. In this research, we focus on detecting sand boils. Sand boils occur when water under pressure wells up to the surface through a bed of sand. These make levees especially vulnerable. Object detection is a good approach to confirm the presence of sand boils from satellite or drone imagery, which can be utilized to assist in the automated levee monitoring methodology. Since sand boils have distinct features, applying object detection algorithms to it can result in accurate detection. To the best of our knowledge, this research work is the first approach to detect sand boils from images. In this research, we compare some of the latest deep learning methods, Viola-Jones algorithm, and other non-deep learning methods to determine the best performing one. We also train a Stacking-based machine learning method for the accurate prediction of sand boils. The accuracy of our robust model is 95.4%.
Summarizing Legal Rulings: Comparative Experiments

In the context of text summarization, texts in the legal domain have peculiarities related to their length and to their specialized vocabulary.Recent neural networkbased approaches can achieve high-quality scores for text summarization.However, these approaches have been used mostly for generating very short abstracts for news articles.Thus, their applicability to the legal domain remains an open issue.In this work, we experimented with ten extractive and four abstractive models in a real dataset of legal rulings.These models were compared with an extractive baseline based on heuristics to select the most relevant parts of the text.Our results show that abstractive approaches significantly outperform extractive methods in terms of ROUGE scores.
ConCET

Identifying the topic (domain) of each user's utterance in open-domain conversational systems is a crucial step for all subsequent language understanding and response tasks. In particular, for complex domains, an utterance is often routed to a single component responsible for that domain. Thus, correctly mapping a user utterance to the right domain is critical. This is a challenging task: users could mention entities like actors, singers or locations to implicitly indicate the domain, which requires extensive domain knowledge to interpret. To address this problem, we introduce ConCET: a Concurrent Entity-aware conversational Topic classifier, which incorporates entity type information together with the utterance content features. Specifically, ConCET utilizes entity information to enrich the utterance representation, combining character, word, and entity type embeddings into a single representation. However, for rich domains with millions of available entities, unrealistic amounts of labeled training data would be required. To complement our model, we propose a simple and effective method for generating synthetic training data, to augment the typically limited amounts of labeled training data, using commonly available knowledge bases as to generate additional labeled utterances. We extensively evaluate ConCET and our proposed training method first on an openly available human-human conversational dataset called Self-Dialogue, to calibrate our approach against previous state-of-the-art methods; second, we evaluate ConCET on a large dataset of human-machine conversations with real users, collected as part of the Amazon Alexa Prize. Our results show that ConCET significantly improves topic classification performance on both datasets, reaching 8-10% improvements compared to state-of-the-art deep learning methods. We complement our quantitative results with detailed analysis of system performance, which could be used for further improvements of conversational agents.
An automated method for segmentation and quantification of blood vessels in histology images

Alzheimer's disease (AD) is a chronic neuro-degenerative disease that adversely affect many people on a global scale. Despite different diagnostic and therapeutic treatment, there is no cure for AD. The brain is one of the most complex organ and researchers are still trying to understand so as to find a cure. Objective: To complement the efforts of clinical researchers engaged in research in alzheimer's disease, accurate segmentation and quantification of blood vessels in brain images is required. Method: For robust segmentation of blood vessels even in the presence of colour variation, we introduce a fully automated morphological tool that can extract and quantify vessels from haematoxylin and diaminobenzidine stained histology brain image. The method, exploits saturation channel of stained image slides, ISODATA threshold method is applied to obtain a binary image. This helps in eliminating background and remaining with only blood vessels. A one-stage procedure that includes eliminating small artefacts is performed on the binary mask. The intensity of the image is transformed. Joining is performed to deal with fragmentation of intact blood vessels on the images, and artefactual appearance of the blood vessel structures. The artefactual fragments based on measured incoherence with neighbouring tissue are removed. The vessels are then labelled to facilitate quantification. Morphometric measurements are used during the vessel quantification assess both vessels with lumen and vessels without lumen. We have quantified the diameter of blood vessels. Results: The image processing technique is developed in close collaboration with neuroscientist experts to help clinician. We have evaluated our proposed approach qualitatively. The method was validated against their manual quantification results. Qualitative results show that the method can indeed segment the blood vessels in the presence of colour variations and artefacts. The quantitative method produces fairly better results.
Review on secured data capabilities of cryptography, steganography, and watermarking domain

&lt;span&gt;Due to the increment of using Internet to transfer the critical and secret data, many studies interested in secured data and investigated many ways to secure the transferred information. This paper presents a review study on the field that used in a secured data domain. The main objective of this study is explore the capabilities of secured data that used widely by researchers. Furthermore, the benefits and the drawbacks for each of secured data domain are also studied. This paper concludes that cryptography techniques could be utilized with steganography and watermarking in secured data domain to enhance the security mechanisms.&lt;/span&gt;
Helping the Consumers and Producers of Standards, Repositories and Policies to Enable FAIR Data

Thousands of community-developed (meta)data guidelines, models, ontologies, schemas and formats have been created and implemented by several thousand data repositories and knowledge-bases, across all disciplines. These resources are necessary to meet government, funder and publisher expectations of greater transparency and access to and preservation of data related to research publications. This obligates researchers to ensure their data is FAIR, share their data using the appropriate standards, store their data in sustainable and community-adopted repositories, and to conform to funder and publisher data policies. FAIR data sharing also plays a key role in enabling researchers to evaluate, re-analyse and reproduce each other's work. We can map the landscape of relationships between community-adopted standards and repositories, and the journal publisher and funder data policies that recommend their use. In this paper, we show how the work of the GO-FAIR FAIR Standards, Repositories and Policies (StRePo) Implementation Network serves as a central integration and cross-fertilisation point for the reuse of FAIR standards, repositories and data policies in general. Pivotal to this effort, the FAIRsharing, an endorsed flagship resource of the Research Data Alliance that maps the landscape of relationships between community-adopted standards and repositories, and the journal publisher and funder data policies that recommend their use. Lastly, we highlight a number of activities around FAIR tools, services and educational efforts to raise awareness and encourage participation.
Generating Classical Chinese Poems from Vernacular Chinese

Classical Chinese poetry is a jewel in the treasure house of Chinese culture.Previous poem generation models only allow users to employ keywords to interfere the meaning of generated poems, leaving the dominion of generation to the model.In this paper, we propose a novel task of generating classical Chinese poems from vernacular, which allows users to have more control over the semantic of generated poems.We adapt the approach of unsupervised machine translation (UMT) to our task.We use segmentation-based padding and reinforcement learning to address undertranslation and over-translation respectively.According to experiments, our approach significantly improve the perplexity and BLEU compared with typical UMT models.Furthermore, we explored guidelines on how to write the input vernacular to generate better poems.Human evaluation showed our approach can generate high-quality poems which are comparable to amateur poems.
Towards a large-scale twitter observatory for political events

Explosion in usage of social media has made its analysis a relevant topic of interest, and particularly so in the political science area. Within Data Science, no other techniques are more widely accepted and appealing than visualisation. However, with datasets growing in size, visualisation tools also require a paradigm shift to remain useful in big data contexts. This work presents our proposal for a Large-Scale Twitter Observatory that enables researchers to efficiently retrieve, analyse and visualise data from this social network to gain actionable insights and knowledge related with political events. In addition to describing the supporting technologies, we put forward a working pipeline and validate the setup with different examples.
A Novel Joint Character Categorization and Localization Approach for Character-Level Scene Text Recognition

Scene text recognition has become an active research area in pattern recognition in recent years. Currently, the mainstream approach is image-based sequence model. However, such a model usually cannot yield accurate character-level category and location information. To address this deficiency, in this paper, we propose a novel character-level scene text recognition framework for simultaneously categorizing and localizing characters. Moreover, we present an effective joint learning strategy to help the approach to learn from both character-level annotation and word-level annotation. Extensive experiments on five benchmark data sets, including IIIT-5K, SVT, ICDAR03, ICDAR13, and ICDAR15, show promising results. Especially, we confirm that our proposal is more robust to the text length variation and non-language text.
Unified Monogamy Relations of Multipartite Entanglement

Abstract Unified-( q , s ) entanglement $$({{\mathscr{U}}}_{q,s})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>(</mml:mo> <mml:msub> <mml:mrow> <mml:mi>U</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>q</mml:mi> <mml:mo>,</mml:mo> <mml:mi>s</mml:mi> </mml:mrow> </mml:msub> <mml:mo>)</mml:mo> </mml:math> is a generalized bipartite entanglement measure, which encompasses Tsallis- q entanglement, Rényi- q entanglement, and entanglement of formation as its special cases. We first provide the extended ( q ; s ) region of the generalized analytic formula of $${{\mathscr{U}}}_{q,s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>U</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>q</mml:mi> <mml:mo>,</mml:mo> <mml:mi>s</mml:mi> </mml:mrow> </mml:msub> </mml:math> . Then, the monogamy relation based on the squared $${{\mathscr{U}}}_{q,s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>U</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>q</mml:mi> <mml:mo>,</mml:mo> <mml:mi>s</mml:mi> </mml:mrow> </mml:msub> </mml:math> for arbitrary multiqubit mixed states is proved. The monogamy relation proved in this paper enables us to construct an entanglement indicator that can be utilized to identify all genuine multiqubit entangled states even the cases where three tangle of concurrence loses its efficiency. It is shown that this monogamy relation also holds true for the generalized W-class state. The αth power $${{\mathscr{U}}}_{q,s}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>U</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>q</mml:mi> <mml:mo>,</mml:mo> <mml:mi>s</mml:mi> </mml:mrow> </mml:msub> </mml:math> based general monogamy and polygamy inequalities are established for tripartite qubit states.