Reviewerly/exHarmony huggingface dataset:
EXTraction of EMR numerical data: an efficient and generalizable tool to EXTEND clinical research

Abstract Background Electronic medical records (EMR) contain numerical data important for clinical outcomes research, such as vital signs and cardiac ejection fractions (EF), which tend to be embedded in narrative clinical notes. In current practice, this data is often manually extracted for use in research studies. However, due to the large volume of notes in datasets, manually extracting numerical data often becomes infeasible. The objective of this study is to develop and validate a natural language processing (NLP) tool that can efficiently extract numerical clinical data from narrative notes. Results To validate the accuracy of the tool EXTraction of EMR Numerical Data (EXTEND), we developed a reference standard by manually extracting vital signs from 285 notes, EF values from 300 notes, glycated hemoglobin (HbA1C), and serum creatinine from 890 notes. For each parameter of interest, we calculated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F 1 score of EXTEND using two metrics. (1) completion of data extraction, and (2) accuracy of data extraction compared to the actual values in the note verified by chart review. At the note level, extraction by EXTEND was considered correct only if it accurately detected and extracted all values of interest in a note. Using manually-annotated labels as the gold standard, the note-level accuracy of EXTEND in capturing the numerical vital sign values, EF, HbA1C and creatinine ranged from 0.88 to 0.95 for sensitivity, 0.95 to 1.0 for specificity, 0.95 to 1.0 for PPV, 0.89 to 0.99 for NPV, and 0.92 to 0.96 in F 1 scores. Compared to the actual value level, the sensitivity, PPV, and F 1 score of EXTEND ranged from 0.91 to 0.95, 0.95 to 1.0 and 0.95 to 0.96. Conclusions EXTEND is an efficient, flexible tool that uses knowledge-based rules to extract clinical numerical parameters with high accuracy. By increasing dictionary terms and developing new rules, the usage of EXTEND can easily be expanded to extract additional numerical data important in clinical outcomes research.
A Graph Autoencoder Approach to Causal Structure Learning

Causal structure learning has been a challenging task in the past decades and several mainstream approaches such as constraint- and score-based methods have been studied with theoretical guarantees. Recently, a new approach has transformed the combinatorial structure learning problem into a continuous one and then solved it using gradient-based optimization methods. Following the recent state-of-the-arts, we propose a new gradient-based method to learn causal structures from observational data. The proposed method generalizes the recent gradient-based methods to a graph autoencoder framework that allows nonlinear structural equation models and is easily applicable to vector-valued variables. We demonstrate that on synthetic datasets, our proposed method outperforms other gradient-based methods significantly, especially on large causal graphs. We further investigate the scalability and efficiency of our method, and observe a near linear training time when scaling up the graph size.
A Feature Based Approach to Detect Fake Profiles in Twitter

Social networking platforms, particularly sites like Twitter and Facebook have grown tremendously in the past decade and has solicited the interest of millions of users. They have become a preferred means of communication, due to which it has also attracted the interest of various malicious entities such as spammers. The growing number of users on social media has also created the problem of fake accounts. These false and fake identities are intensively involved in malicious activities such as spreading abuse, misinformation, spamming and artificially inflating the number of users in an application to promote and sway public opinion. Detecting these fake identities, thus becomes important to protect genuine users from malicious intents. To address this issue, we aim to use a feature-based approach to identify these fake profiles on social media platforms. We have used twenty-four features to identify fake accounts efficiently. To verify the classification results three classification algorithms are used. Experimental results show that our model was able to reach 97.9% accuracy using the Random Forest algorithm. Hence, the proposed approach is efficient in detecting fake profiles.
Structure-functional analysis and synthesis of deep convolutional neural networks

A general approach to a structure-functional analysis and synthesis (SFAS) of deep neural networks (CNN). The new approach allows to define regularly: from which structure-functional elements (SFE) CNNs can be constructed; what are required mathematical properties of an SFE; which combinations of SFEs are valid; what are the possible ways of development and training of deep networks for analysis and recognition of an irregular, heterogeneous data or a data with a complex structure (such as irregular arrays, data of various shapes of various origin, trees, skeletons, graph structures, 2D, 3D, and ND point clouds, triangulated surfaces, analytical data descriptions, etc.) The required set of SFE was defined. Techniques were proposed that solve the problem of structure-functional analysis and synthesis of a CNN using SFEs and rules for their combination.
El discurso del odio hacia migrantes y refugiados a través del tono y los marcos de los mensajes en Twitter

This study has the objective of identifying the latent tone in Spanish tweets about migrants and refugees and the frames with which each of the groups is represented, understanding the negative frames as possible indicators of rejection and hatred. For this purpose, a content analysis of 1469 tweets in Spanish was carried out. The work resolves that the tone of the tweets in Spanish about displaced people is predominantly negative (72%), although there is a greater frequency of negative tweets about migrants (80.8%) than about refugees (53.8%). As for the frames, at a general level, in the analyzed tweets predominates the threat frame (31.78%). Nevertheless, when comparing the groups, the frame that stands out in the tweets about migrants ins the threat frame (37.4%), while the two frames that predominate in the tweets about refugees are victimization (42.4%) and normalization frame (25.8%). The findings show that the treatment of the two groups of displaced people on Twitter differs significantly, which could mean the existence of more negative attitudes, such as greater rejection and hatred towards immigrants than towards refugees.
New-day statistical thinking: A bold proposal for a radical change in practices

Abstract In this commentary, I argue why we should stop engaging in null hypothesis statistical significance testing altogether. Artificial and misleading it may be, but we know how to play the p value threshold and null hypothesis-testing game. We feel secure; we love the certainty. The fly in the ointment is that the conventions have led to questionable research practices. Wasserstein, Schirm, &amp; Lazar (Am Stat 73(sup1):1–19, 2019. 10.1080/00031305.2019.1583913 ) explain why, in their thought-provoking editorial introducing a special issue of The American Statistician : “As ‘statistical significance’ is used less, statistical thinking will be used more.” Perhaps we empirical researchers can together find a way to work ourselves out of the straitjacket that binds us.
On Using SpecAugment for End-to-End Speech Translation

This work investigates a simple data augmentation technique, SpecAugment, for end-to-end speech translation. SpecAugment is a low-cost implementation method applied directly to the audio input features and it consists of masking blocks of frequency channels, and/or time steps. We apply SpecAugment on end-to-end speech translation tasks and achieve up to +2.2\% \BLEU on LibriSpeech Audiobooks En->Fr and +1.2% on IWSLT TED-talks En->De by alleviating overfitting to some extent. We also examine the effectiveness of the method in a variety of data scenarios and show that the method also leads to significant improvements in various data conditions irrespective of the amount of training data.
Composite constructions of self-dual codes from group rings and new extremal self-dual binary codes of length 68

<p style='text-indent:20px;'>We describe eight composite constructions from group rings where the orders of the groups are 4 and 8, which are then applied to find self-dual codes of length 16 over <inline-formula><tex-math id="M1">\begin{document}$ \mathbb{F}_4 $\end{document}</tex-math></inline-formula>. These codes have binary images with parameters <inline-formula><tex-math id="M2">\begin{document}$ [32,16,8] $\end{document}</tex-math></inline-formula> or <inline-formula><tex-math id="M3">\begin{document}$ [32,16,6] $\end{document}</tex-math></inline-formula>. These are lifted to codes over <inline-formula><tex-math id="M4">\begin{document}$ \mathbb{F}_4+u\mathbb{F}_4 $\end{document}</tex-math></inline-formula>, to obtain codes with Gray images of extremal self-dual binary codes of length 64. Finally, we use a building-up method over <inline-formula><tex-math id="M5">\begin{document}$ \mathbb{F}_2+u\mathbb{F}_2 $\end{document}</tex-math></inline-formula> to obtain new extremal binary self-dual codes of length 68. We construct 11 new codes via the building-up method and 2 new codes by considering possible neighbors.
A dataset of 1050-tampered color and grayscale images (CG-1050)

This paper presents the CG-1050 dataset consisting of 100 original images, 1050 tampered images and their corresponding masks. The dataset is organized into four directories: original images, tampered images, mask images, and a description file. The directory of original images includes 15 color and 85 grayscale images. The directory of tampered images has 1050 images obtained through one of the following type of tampering: copy-move, cut-paste, retouching, and colorizing. The true mask between every pair of original and its tampered image is included in the mask directory (1380 masks). The description file shows the names of the images (i.e., original, tampered and mask), the image description, the photo location, the type of tampering, and the manipulated object in the image. With this dataset, the researchers can train and validate fake image classification methods, either for labelling the tampered image or for forgery pixel-detection.
Semantic Mask for Transformer based End-to-End Speech Recognition

Attention-based encoder-decoder model has achieved impressive results for both automatic speech recognition (ASR) and text-to-speech (TTS) tasks. This approach takes advantage of the memorization capacity of neural networks to learn the mapping from the input sequence to the output sequence from scratch, without the assumption of prior knowledge such as the alignments. However, this model is prone to overfitting, especially when the amount of training data is limited. Inspired by SpecAugment and BERT, in this paper, we propose a semantic mask based regularization for training such kind of end-to-end (E2E) model. The idea is to mask the input features corresponding to a particular output token, e.g., a word or a word-piece, in order to encourage the model to fill the token based on the contextual information. While this approach is applicable to the encoder-decoder framework with any type of neural network architecture, we study the transformer-based model for ASR in this work. We perform experiments on Librispeech 960h and TedLium2 data sets, and achieve the state-of-the-art performance on the test set in the scope of E2E models.
Learning to Represent Programs with Property Signatures

We introduce the notion of property signatures, a representation for programs and program specifications meant for consumption by machine learning algorithms. Given a function with input type τ_in and output type τ_out, a property is a function of type: (τ_in, τ_out) → Bool that (informally) describes some simple property of the function under consideration. For instance, if τ_in and τ_out are both lists of the same type, one property might ask ‘is the input list the same length as the output list?’. If we have a list of such properties, we can evaluate them all for our function to get a list of outputs that we will call the property signature. Crucially, we can ‘guess’ the property signature for a function given only a set of input/output pairs meant to specify that function. We discuss several potential applications of property signatures and show experimentally that they can be used to improve over a baseline synthesizer so that it emits twice as many programs in less than one-tenth of the time.
Tracking the polarisation state of light via Hong-Ou-Mandel interferometry

We provide a statistically robust and accurate framework to measure and track the polarisation state of light employing Hong-Ou-Mandel interference. This is achieved by combining the concepts of maximum likelihood estimation and Fisher information applied to photon detection events. Such an approach ensures that the Cramér-Rao bound is saturated and changes to the polarisation state are established in an optimal manner. Using this method, we show that changes in the linear polarisation state can be measured with 0.6 arcminute precision (0.01 degrees).
Statistical Analysis of Dual-task Gait Characteristics for Cognitive Score Estimation

Traditional approaches for the screening of cognitive function are often based on paper tests, such as Mini-Mental State Examination (MMSE), that evaluate the degree of cognitive impairment and provide a score of patient's mental ability. Procedures for conducting paper tests require time investment involving a questioner and not suitable to be carried out frequently. Previous studies showed that dementia impaired patients are not capable of multi-tasking efficiently. Based on this observation an automated system utilizing Kinect device for collecting primarily patient's gait data who carry out locomotion and calculus tasks individually (i.e., single-tasks) and then simultaneously (i.e., dual-task) was introduced. We installed this system in three elderly facilities and collected 10,833 behavior data from 90 subjects. We conducted analyses of the acquired information extracting 12 features of single- and dual-task performance developed a method for automatic dementia score estimation to investigate determined which characteristics are the most important. In result, a machine learning algorithm using single and dual-task performance classified subjects with an MMSE score of 23 or lower with a recall 0.753 and a specificity 0.799. We found the gait characteristics were important features in the score estimation, and referring to both single and dual-task features was effective.
Sequential Recommendation with Relation-Aware Kernelized Self-Attention

Recent studies identified that sequential Recommendation is improved by the attention mechanism. By following this development, we propose Relation-Aware Kernelized Self-Attention (RKSA) adopting a self-attention mechanism of the Transformer with augmentation of a probabilistic model. The original self-attention of Transformer is a deterministic measure without relation-awareness. Therefore, we introduce a latent space to the self-attention, and the latent space models the recommendation context from relation as a multivariate skew-normal distribution with a kernelized covariance matrix from co-occurrences, item characteristics, and user information. This work merges the self-attention of the Transformer and the sequential recommendation by adding a probabilistic model of the recommendation task specifics. We experimented RKSA over the benchmark datasets, and RKSA shows significant improvements compared to the recent baseline models. Also, RKSA were able to produce a latent space model that answers the reasons for recommendation.
Compact Implementations of HIGHT Block Cipher on IoT Platforms

Recent lightweight block cipher competition (FELICS Triathlon) evaluates efficient implementations of block ciphers for Internet of things (IoT) environment. In the competition, the implementation of HIGHT block cipher achieved the most efficient lightweight block cipher, in terms of code size (ROM), memory (RAM), and execution time. In this paper, we further investigate lightweight features of HIGHT block cipher and present the optimized implementations of both software and hardware for low-end IoT platforms, including resource-constrained devices (8-bit AVR and 32-bit ARM Cortex-M3) and application-specific integrated circuit (ASIC). By using proposed optimization methods, the implemented HIGHT block cipher shows better performance compared to previous state-of-the-art implementations.
A hidden Markov model for analyzing eye-tracking of moving objects

Eye-tracking provides an opportunity to generate and analyze high-density data relevant to understanding cognition. However, while events in the real world are often dynamic, eye-tracking paradigms are typically limited to assessing gaze toward static objects. In this study, we propose a generative framework, based on a hidden Markov model (HMM), for using eye-tracking data to analyze behavior in the context of multiple moving objects of interest. We apply this framework to analyze data from a recent visual object tracking task paradigm, TrackIt, for studying selective sustained attention in children. Within this paradigm, we present two validation experiments to show that the HMM provides a viable approach to studying eye-tracking data with moving stimuli, and to illustrate the benefits of the HMM approach over some more naive possible approaches. The first experiment utilizes a novel 'supervised' variant of TrackIt, while the second compares directly with judgments made by human coders using data from the original TrackIt task. Our results suggest that the HMM-based method provides a robust analysis of eye-tracking data with moving stimuli, both for adults and for children as young as 3.5–6 years old.
Using gravitational search algorithm enhanced by fuzzy for resource allocation in cloud computing environments

The aim of this paper is to allocate resources to tasks and scheduling tasks on existing virtual machines (VMs) in cloud environments, so that the time to finish the last work and average of all tasks execution time are minimized, and loads are distributed balanced on virtual machines. Since task scheduling in the cloud environment is a continuous process, so scheduling improvements, although slight, play an important role in cloud efficiency. On the other hand the resource allocation problem in cloud computing and user tasks scheduling on existing virtual machines is a NP-hard problem, and traditional algorithms requires exponential time to examine search space of this problem in sequence and finding the best answer, therefore we used Gravitational Search Algorithm (GSA) that has a high efficiency in solving nonlinear problems, for solving this problem. To do this, we create masses by combining sequences of tasks assigned to all machines. Each mass position is a solution of the problem. Then we find the best possible assignment using the gravitational search algorithm. We used fuzzy logic to determine the number of masses that affect one another during the implementation of the GSA. To calculate the cost, we use a combination of Make_ span (Time to finish the last task) and Mean_ Flow_Time (Average of all tasks execution time) and Load_ imbalance. The results show that the proposed method achieves more optimal response than genetic algorithm and GSA without fuzzy for resource allocation. It means that proposed algorithm allocated resources to tasks with less make span and mean_ flow time and more load balancing than other two algorithms.
Implementation of a general single-qubit positive operator-valued measure on a circuit-based quantum computer

We derive a deterministic protocol to implement a general single-qubit positive operator-valued measure (POVM) on near-term circuit-based quantum computers. The protocol has a modular structure such that an $n$-element POVM is implemented as a sequence of $(n\ensuremath{-}1)$ circuit modules. Each module performs a two-element POVM. Two variations of the protocol are suggested---one optimal in terms of number of ancilla qubits, the other optimal in terms of number of qubit gate operations and quantum circuit depth. We use the protocol to implement two- and three-element POVMs on two publicly available quantum computing devices. The results we obtain suggest that implementing nontrivial POVMs could be within the reach of the current noisy quantum computing devices.
Towards the standardization of quantum state verification using optimal strategies

Quantum devices for generating entangled states have been extensively studied and widely used. As so, it becomes necessary to verify that these devices truly work reliably and efficiently as they are specified. Here, we experimentally realize the recently proposed two-qubit entangled state verification strategies using both local measurements (nonadaptive) and active feed-forward operations (adaptive) with a photonic platform. About 3283/536 number of copies ($N$) are required to achieve a 99% confidence to verify the target quantum state for nonadaptive/adaptive strategies. These optimal strategies provide the Heisenberg scaling of the infidelity $\epsilon$ as a function of $N$ ($\epsilon$ $\sim$ $N^r$) with the parameter $r=-1$, exceeding the standard quantum limit with $r=-0.5$. We experimentally obtain the scaling parameter of $r=-0.88\pm$0.03 and $-0.78\pm$0.07 for nonadaptive and adaptive strategies, respectively. Our experimental work could serve as a standardized procedure for the verification of quantum states.
Solving X+1 + X + a = 0 over finite fields

Solving the equation Pa(X):=Xq+1+X+a=0 over the finite field FQ, where Q=pn,q=pk and p is a prime, arises in many different contexts including finite geometry, the inverse Galois problem [2], the construction of difference sets with Singer parameters [8], determining cross-correlation between m-sequences [9], [15] and the construction of error-correcting codes [5], as well as speeding up the index calculus method for computing discrete logarithms on finite fields [11], [12] and on algebraic curves [18]. Subsequently, in [3], [13], [14], [6], [4], [16], [7], [19], the FQ-zeros of Pa(X) have been studied. It was shown in [3] that their number is 0, 1, 2 or pgcd⁡(n,k)+1. Some criteria for the number of the FQ-zeros of Pa(x) were found in [13], [14], [6], [16], [19]. However, while the ultimate goal is to identify all the FQ-zeros, even in the case p=2, it was solved only under the condition gcd⁡(n,k)=1 [16]. We discuss this equation without any restriction on p and gcd⁡(n,k). Criteria for the number of the FQ-zeros of Pa(x) are proved by a new methodology. For the cases of one or two FQ-zeros, we provide explicit expressions for these rational zeros in terms of a. For the case of pgcd⁡(n,k)+1 rational zeros, we provide a parametrization of such a's and express the pgcd⁡(n,k)+1 rational zeros by using that parametrization.