Reviewerly/exHarmony huggingface dataset:
Hierarchical Multi-class Segmentation of Glioma Images Using Networks with Multi-level Activation Function

For many segmentation tasks, especially for the biomedical image, the topological prior is vital information which is useful to exploit. The containment/nesting is a typical inter-class geometric relationship. In the MICCAI Brain tumor segmentation challenge, with its three hierarchically nested classes ‘whole tumor’, ‘tumor core’, ‘active tumor’, the nested classes relationship is introduced into the 3D-residual-Unet architecture. The network comprises a context aggregation pathway and a localization pathway, which encodes increasingly abstract representation of the input as going deeper into the network, and then recombines these representations with shallower features to precisely localize the interest domain via a localization path. The nested-class-prior is combined by proposing the multi-class activation function and its corresponding loss function. The model is trained on the training dataset of Brats2018, and 20% of the dataset is regarded as the validation dataset to determine parameters. When the parameters are fixed, we retrain the model on the whole training dataset. The performance achieved on the validation leaderboard is 86%, 77% and 72% Dice scores for the whole tumor, enhancing tumor and tumor core classes without relying on ensembles or complicated post-processing steps. Based on the same start-of-the-art network architecture, the accuracy of nested-class (enhancing tumor) is reasonably improved from 69% to 72% compared with the traditional Softmax-based method which blind to topological prior.
Coherent States in Quantum Optics: An Oriented Overview

In this survey, various generalizations of Glauber–Sudarshan coherent states are described in a unified way, with their statistical properties and their possible role in non-standard quantizations of the classical electromagnetic field. Some statistical photon-counting aspects of Perelomov SU(2) and SU(1, 1) coherent states are emphasized.
Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems

The increasing role of recommender systems in many aspects of society makes it essential to consider how such systems may impact social good. Various modifications to recommendation algorithms have been proposed to improve their performance for specific socially relevant measures. However, previous proposals are often not easily adapted to different measures, and they generally require the ability to modify either existing system inputs, the system's algorithm, or the system's outputs. As an alternative, in this paper we introduce the idea of improving the social desirability of recommender system outputs by adding more data to the input, an approach we view as as providing 'antidote' data to the system. We formalize the antidote data problem, and develop optimization-based solutions. We take as our model system the matrix factorization approach to recommendation, and we propose a set of measures to capture the polarization or fairness of recommendations. We then show how to generate antidote data for each measure, pointing out a number of computational efficiencies, and discuss the impact on overall system accuracy. Our experiments show that a modest budget for antidote data can lead to significant improvements in the polarization or fairness of recommendations.
A Large Range of Motion 3D MEMS Scanner With Five Degrees of Freedom

Here, we discuss a novel, mixed mode 3D XYZ scanner built within a single foundry process.The device has a large range of motion in X, Y, and Z (14.1 µm in X and Y and 97.9 µm in Z) and can also rotate about two axes (7.4°), making it a 5 degree of freedom scanner.Vertical actuation can be accomplished with both thermal actuators, which have a larger range of motion, and capacitive actuators, which are faster, responding fully up to 3.2 kHz.Although useful for many applications, including scanning probe microscopy, micrometer scale optical microscopy, and manipulation of biological objects, the device was designed to be a 3D scanner for spray-painting atoms upon a surface with nanoscale precision and resolution for nanofabrication.Demonstrating the ability to combine the device with other complicated MEMS systems, it is integrated with an XY scanner designed to serve as a shutter of atomic flux.The full system has 7 degrees of freedom and 12 actuation motors, and because it is built in a low cost commercial foundry with a robust and stable process, it is easy and inexpensive to fabricate multiple copies or integrate into other complicated systems, making a system of systems.
Stable and Fair Classification

Fair classification has been a topic of intense study in machine learning, and several algorithms have been proposed towards this important task. However, in a recent study, Friedler et al. observed that fair classification algorithms may not be stable with respect to variations in the training dataset -- a crucial consideration in several real-world applications. Motivated by their work, we study the problem of designing classification algorithms that are both fair and stable. We propose an extended framework based on fair classification algorithms that are formulated as optimization problems, by introducing a stability-focused regularization term. Theoretically, we prove a stability guarantee, that was lacking in fair classification algorithms, and also provide an accuracy guarantee for our extended framework. Our accuracy guarantee can be used to inform the selection of the regularization parameter in our framework. To the best of our knowledge, this is the first work that combines stability and fairness in automated decision-making tasks. We assess the benefits of our approach empirically by extending several fair classification algorithms that are shown to achieve the best balance between fairness and accuracy over the Adult dataset. Our empirical results show that our framework indeed improves the stability at only a slight sacrifice in accuracy.
Use Bots to Improve GitHub Pull-Request Feedback

Rising enrollments make it difficult for instructors and teaching assistants to give adequate feedback on each student's work. In our software engineering course, we have 50-120 students each semester. Our course projects require students to submit GitHub pull requests as deliverables for their open-source software (OSS) projects. We have set up a static code analyzer and a continuous integration service on GitHub to help students check code style and functionality. However, these tools cannot enforce system-specific customized guidelines and do not explicitly display detailed information. In this study, we discuss how we bypass the limitations of existing tools by implementing three Internet bots. The Expertiza Bot can help detect violations of more than 35 system-specific guidelines. The Travis CI Bot can explicitly display instant test execution results on the GitHub pull-request page. The Code Climate Bot can insert pull-request comments to remind students to fix issues detected by the static code analyzer. These bots are either open source or free for OSS projects, and can be easily integrated with GitHub repositories. Our survey results show that more than 70% of students think the advice given by the bots is useful. We tallied the amount of feedback given by the bots and the teaching staff for each GitHub pull request. Results show that bots can provide significantly more feedback (six times more on average) than teaching staff. Bots can also offer more timely feedback than teaching staff and help student contributions avoid more than 33% system-specific guideline violations.
Hunting and Gathering – Verifiable Random Functions from Standard Assumptions with Short Proofs

A verifiable random function (VRF) is a pseudorandom function, where outputs can be publicly verified. That is, given an output value together with a proof, one can check that the function was indeed correctly evaluated on the corresponding input. At the same time, the output of the function is computationally indistinguishable from random for all non-queried inputs. We present the first construction of a VRF which meets the following properties at once: It supports an exponential-sized input space, it achieves full adaptive security based on a non-interactive constant-size assumption and its proofs consist of only a logarithmic number of group elements for inputs of arbitrary polynomial length. Our construction can be instantiated in symmetric bilinear groups with security based on the decision linear assumption. We build on the work of Hofheinz and Jager (TCC 2016), who were the first to construct a verifiable random function with security based on a non-interactive constant-size assumption. Basically, their VRF is a matrix product in the exponent, where each matrix is chosen according to one bit of the input. In order to allow verification given a symmetric bilinear map, a proof consists of all intermediary results. This entails a proof size of $$\varOmega (L)$$ group elements, where L is the bit-length of the input. Our key technique, which we call hunting and gathering, allows us to break this barrier by rearranging the function, which – combined with the partitioning techniques of Bitansky (TCC 2017) – results in a proof size of $$\ell $$ group elements for arbitrary $$\ell \in \omega (1)$$ .
Blockchain and the Economics of Customer Satisfaction

Companies can satisfy their customers’ by delivering high value. They can do so by reducing product costs or by improving customers perception of benefits of the products. Blockchain can help achieve these goals.
What Web Template Extractor Should I Use? A Benchmarking and Comparison for Five Template Extractors

A Web template is a resource that implements the structure and format of a website, making it ready for plugging content into already formatted and prepared pages. For this reason, templates are one of the main development resources for website engineers, because they increase productivity. Templates are also useful for the final user, because they provide uniformity and a common look and feel for all webpages. However, from the point of view of crawlers and indexers, templates are an important problem, because templates usually contain irrelevant information, such as advertisements, menus, and banners. Processing and storing this information leads to a waste of resources (storage space, bandwidth, etc.). It has been measured that templates represent between 40% and 50% of data on the Web. Therefore, identifying templates is essential for indexing tasks. There exist many techniques and tools for template extraction, but, unfortunately, it is not clear at all which template extractor should a user/system use, because they have never been compared, and because they present different (complementary) features such as precision, recall, and efficiency. In this work, we compare the most advanced template extractors. We implemented and evaluated five of the most advanced template extractors in the literature. To compare all of them, we implemented a workbench, where they have been integrated and evaluated. Thanks to this workbench, we can provide a fair empirical comparison of all methods using the same benchmarks, technology, implementation language, and evaluation criteria.
Aspectos emocionais e autocuidado de pacientes com Diabetes Mellitus Tipo 2 em Terapia Renal Substitutiva

Introduction: Diabetes mellitus (DM) is considered to be an important public health problem, both due to the number of people affected, the disabilities and mortality, and the costs involved in controlling and treating its complications.Objective: To evaluate the emotional aspects and self-care of patients with T2DM undergoing hemodialysis (HD) and to correlate these variables with the time of dialysis and the time of diagnosis of DM.Method: Individuals over 40 years of age, with Type 2 DM in HD for at least 3 months at the State Hospital of Bauru.Sociodemographic information was collected, and the participants answered the B-PAID questionnaires (perspective of the impact of DM on quality of life and emotional aspects) and the QAD (self-care).The data were presented descriptively and used Spearman's Correlation and the Chi-square Association Test (p <0.05).Results: Fifty-four individuals, aged 62 ± 9 years, predominance of white skin color and incomplete elementary school were studied.In the B-PAID the mean score was 23 (7-43) points, demonstrating low emotional distress, and in the QAD the lowest adherence was in the "Physical Activity" domain and greater "Foot care and medication use".There was a positive correlation between DM time and HD time (p = 0.04, r = 0.27) and association between heart disease and B-PAID (p = 0.02).Conclusion: The individuals studied presented low emotional distress, good foot care and those with a lower incidence of heart disease suffer less from DM.
Qualitative Metrics on Breast Cancer Diagnosis with Neuro Fuzzy Inference Systems

The diagnosis of the Healthcare systems are playing prominent role with the advancement of latest technologies.Decision support systems may generate fruitful outcomes for better diagnosis for Breast cancer.The present context of the paper describes about BCD-NFIS that merely reduces usage of featured datasets using Fuzzy networks and produces enhanced accuracy of 98.24% much better results than the older approaches.The BCD-NFIS uses the methodology of Inference systems, Neural Fuzzy logic and BCD to overcome the problems.This would be very much helpful for rising physicians to simplify the diagnosis patterns through exploiting Information technology for Breast cancer.
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization

Two types of zeroth-order stochastic algorithms have recently been designed for nonconvex optimization respectively based on the first-order techniques SVRG and SARAH/SPIDER. This paper addresses several important issues that are still open in these methods. First, all existing SVRG-type zeroth-order algorithms suffer from worse function query complexities than either zeroth-order gradient descent (ZO-GD) or stochastic gradient descent (ZO-SGD). In this paper, we propose a new algorithm ZO-SVRG-Coord-Rand and develop a new analysis for an existing ZO-SVRG-Coord algorithm proposed in Liu et al. 2018b, and show that both ZO-SVRG-Coord-Rand and ZO-SVRG-Coord (under our new analysis) outperform other exiting SVRG-type zeroth-order methods as well as ZO-GD and ZO-SGD. Second, the existing SPIDER-type algorithm SPIDER-SZO (Fang et al. 2018) has superior theoretical performance, but suffers from the generation of a large number of Gaussian random variables as well as a $\sqrt{\epsilon}$-level stepsize in practice. In this paper, we develop a new algorithm ZO-SPIDER-Coord, which is free from Gaussian variable generation and allows a large constant stepsize while maintaining the same convergence rate and query complexity, and we further show that ZO-SPIDER-Coord automatically achieves a linear convergence rate as the iterate enters into a local PL region without restart and algorithmic modification.
Extracting and prioritizing knowledge risk components by considering the knowledge map

Purpose This paper aims to identify knowledge risk components. The present research also tends to determine the relationship between knowledge risk components, the importance of each component and the final ranking of components. Design/methodology/approach This is applied research regarding type, as well as being a descriptive, analytical study regarding performance; it is done according to the opinion of experts. Library studies are applied to extract knowledge risk components. After extracting components, components were screened using the interview technique. In addition, network analytical process method has been used for considering the interrelationship of components and determining their values. Findings After making the required analysis and studies, a total of 17 knowledge risk components were identified in four clusters. The four clusters include knowledge cluster, knowledge map cluster, organization cluster and expert cluster. It is to be noted that the extracted components are prioritized in each cluster. In the regarded case study, different parts of the organization have been evaluated in terms of exposure to knowledge risk. Originality/value Identifying the knowledge risk components enables the organization for moving toward the implementation of the knowledge management system and informing the organization in connection with risk aversion. In fact, such components provide the chance for the organization to identify risks inherent in each department of any organization and develop the necessary measures to reduce the risk in risky areas.
Exploring Knowledge Graphs in an Interpretable Composite Approach for Text Entailment

Recognizing textual entailment is a key task for many semantic applications, such as Question Answering, Text Summarization, and Information Extraction, among others. Entailment scenarios can range from a simple syntactic variation to more complex semantic relationships between pieces of text, but most approaches try a one-size-fits-all solution that usually favors some scenario to the detriment of another. We propose a composite approach for recognizing text entailment which analyzes the entailment pair to decide whether it must be resolved syntactically or semantically. We also make the answer interpretable: whenever an entailment is solved semantically, we explore a knowledge base composed of structured lexical definitions to generate natural language humanlike justifications, explaining the semantic relationship holding between the pieces of text. Besides outperforming wellestablished entailment algorithms, our composite approach gives an important step towards Explainable AI, using world knowledge to make the semantic reasoning process explicit and understandable.
From Genesis to Creole Language

Singlish can be interesting to the computational linguistics community both linguistically, as a major low-resource creole based on English, and computationally, for information extraction and sentiment analysis of regional social media. In our conference paper, Wang et al. (2017), we investigated part-of-speech (POS) tagging and dependency parsing for Singlish by constructing a treebank under the Universal Dependencies scheme and successfully used neural stacking models to integrate English syntactic knowledge for boosting Singlish POS tagging and dependency parsing, achieving the state-of-the-art accuracies of 89.50% and 84.47% for Singlish POS tagging and dependency, respectively. In this work, we substantially extend Wang et al. (2017) by enlarging the Singlish treebank to more than triple the size and with much more diversity in topics, as well as further exploring neural multi-task models for integrating English syntactic knowledge. Results show that the enlarged treebank has achieved significant relative error reduction of 45.8% and 15.5% on the base model, 27% and 10% on the neural multi-task model, and 21% and 15% on the neural stacking model for POS tagging and dependency parsing, respectively. Moreover, the state-of-the-art Singlish POS tagging and dependency parsing accuracies have been improved to 91.16% and 85.57%, respectively. We make our treebanks and models available for further research.
Rapidly-Exploring Quotient-Space Trees: Motion Planning Using Sequential Simplifications

Motion planning problems can be simplified by admissible projections of the configuration space to sequences of lower-dimensional quotient-spaces, called sequential simplifications. To exploit sequential simplifications, we present the Quotient-space Rapidly-exploring Random Trees (QRRT) algorithm. QRRT takes as input a start and a goal configuration, and a sequence of quotient-spaces. The algorithm grows trees on the quotient-spaces both sequentially and simultaneously to guarantee a dense coverage. QRRT is shown to be (1) probabilistically complete, and (2) can reduce the runtime by at least one order of magnitude. However, we show in experiments that the runtime varies substantially between different quotient-space sequences. To find out why, we perform an additional experiment, showing that the more narrow an environment, the more a quotient-space sequence can reduce runtime.
Axiomatic Characterization of Data-Driven Influence Measures for Classification

We study the following problem: given a labeled dataset and a specific datapoint ∼x, how did the i-th feature influence the classification for ∼x? We identify a family of numerical influence measures — functions that, given a datapoint ∼x, assign a numeric value φi(∼x) to every feature i, corresponding to how altering i’s value would influence the outcome for ∼x. This family, which we term monotone influence measures (MIM), is uniquely derived from a set of desirable properties, or axioms. The MIM family constitutes a provably sound methodology for measuring feature influence in classification domains; the values generated by MIM are based on the dataset alone, and do not make any queries to the classifier. While this requirement naturally limits the scope of our framework, we demonstrate its effectiveness on data.
Deep Learning for Natural Language Inference

This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting-edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning models for language understanding and reasoning.
A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data

Summary There are many data sources that produce large volumes of data. The Big Data nature requires new distributed processing approaches to extract the valuable information. Real‐time sentiment analysis is one of the most demanding research areas that requires powerful Big Data analytics tools such as Spark. Prior literature survey work has shown that, though there are many conventional sentiment analysis researches, there are only few works realizing sentiment analysis in real time. One major point that affects the quality of real‐time sentiment analysis is the confidence of the generated data. In more clear terms, it is a valuable research question to determine whether the owner that generates sentiment is genuine or not. Since data generated by fake personalities may decrease accuracy of the outcome, a smart/intelligent service that can identify the source of data is one of the key points in the analysis. In this context, we include a fake account detection service to the proposed framework. Both sentiment analysis and fake account detection systems are trained and tested using Naïve Bayes model from Apache Spark's machine learning library. The developed system consists of four integrated software components, ie, (i) machine learning and streaming service for sentiment prediction, (ii) a Twitter streaming service to retrieve tweets, (iii) a Twitter fake account detection service to assess the owner of the retrieved tweet, and (iv) a real‐time reporting and dashboard component to visualize the results of sentiment analysis. The sentiment classification performances of the system for offline and real‐time modes are 86.77% and 80.93%, respectively.