Reviewerly/exHarmony huggingface dataset:
Training Data Augmentation for Detecting Adverse Drug Reactions in User-Generated Content

Social media provides a timely yet challenging data source for adverse drug reaction (ADR) detection.Existing dictionary-based, semisupervised learning approaches are intrinsically limited by the coverage and maintainability of laymen health vocabularies.In this paper, we introduce a data augmentation approach that leverages variational autoencoders to learn high-quality data distributions from a large unlabeled dataset, and subsequently, to automatically generate a large labeled training set from a small set of labeled samples.This allows for efficient social-media ADR detection with low training and re-training costs to adapt to the changes and emergence of informal medical laymen terms.An extensive evaluation performed on Twitter and Reddit data shows that our approach matches the performance of fully-supervised approaches while requiring only 25% of training data.
What Gets Echoed? Understanding the “Pointers” in Explanations of Persuasive Arguments

Explanations are central to everyday life, and are a topic of growing interest in the AI community.To investigate the process of providing natural language explanations, we leverage the dynamics of the /r/ChangeMyView subreddit to build a dataset with 36K naturally occurring explanations of why an argument is persuasive.We propose a novel word-level prediction task to investigate how explanations selectively reuse, or echo, information from what is being explained (henceforth, explanandum).We develop features to capture the properties of a word in the explanandum, and show that our proposed features not only have relatively strong predictive power on the echoing of a word in an explanation, but also enhance neural methods of generating explanations.In particular, while the non-contextual properties of a word itself are more valuable for stopwords, the interaction between the constituent parts of an explanandum is crucial in predicting the echoing of content words.We also find intriguing patterns of a word being echoed.For example, although nouns are generally less likely to be echoed, subjects and objects can, depending on their source, be more likely to be echoed in the explanations.
Neural Similarity Learning

Inner product-based convolution has been the founding stone of convolutional neural networks (CNNs), enabling end-to-end learning of visual representation. By generalizing inner product with a bilinear matrix, we propose the neural similarity which serves as a learnable parametric similarity measure for CNNs. Neural similarity naturally generalizes the convolution and enhances flexibility. Further, we consider the neural similarity learning (NSL) in order to learn the neural similarity adaptively from training data. Specifically, we propose two different ways of learning the neural similarity: static NSL and dynamic NSL. Interestingly, dynamic neural similarity makes the CNN become a dynamic inference network. By regularizing the bilinear matrix, NSL can be viewed as learning the shape of kernel and the similarity measure simultaneously. We further justify the effectiveness of NSL with a theoretical viewpoint. Most importantly, NSL shows promising performance in visual recognition and few-shot learning, validating the superiority of NSL over the inner product-based convolution counterparts.
Towards Making a Dependency Parser See

We explore whether it is possible to leverage eye-tracking data in an RNN dependency parser (for English) when such information is only available during training -i.e.no aggregated or token-level gaze features are used at inference time.To do so, we train a multitask learning model that parses sentences as sequence labeling and leverages gaze features as auxiliary tasks.Our method also learns to train from disjoint datasets, i.e. it can be used to test whether already collected gaze features are useful to improve the performance on new non-gazed annotated treebanks.Accuracy gains are modest but positive, showing the feasibility of the approach.It can serve as a first step towards architectures that can better leverage eye-tracking data or other complementary information available only for training sentences, possibly leading to improvements in syntactic parsing.
Assessing Data Postprocessing for Quantum Estimation

Quantum sensors are among the most promising quantum technologies, allowing to attain the ultimate precision limit for parameter estimation.In order to achieve this, it is required to fully control and optimize what constitutes the hardware part of the sensors, i.e. the preparation of the probe states and the correct choice of the measurements to be performed.However careful considerations must be drawn also for the software components: a strategy must be employed to find a so-called optimal estimator.Here we review the most common approaches used to find the optimal estimator both with unlimited and limited resources.Furthermore, we present an attempt at a more complete characterization of the estimator by means of higher-order moments of the probability distribution, showing that most information is already conveyed by the standard bounds.
Visual Attention Guided Pixel-Wise Just Noticeable Difference Model

The just noticeable difference (JND) models in pixel domain are generally composed of luminance adaptation (LA) and contrast masking (CM), which takes edge masking (EM) and texture masking (TM) into consideration. However, in existing pixel-wise JND models, CM is not evaluated appropriately since they overestimate the masking effect of regular oriented texture regions and neglect the visual attention characteristic of human eyes for the real image. In this work, a novel JND model in pixel domain is proposed, where orderly texture masking (OTM) for regular texture areas (also called orderly texture regions) and disorderly texture masking (DTM) for complex texture areas (also called disorderly texture regions) are presented based on the orientation complexity. Meanwhile, the visual saliency is set as the weighting factor and is incorporated into CM evaluation to enhance JND thresholds. Experimental results indicate that compared with existing relevant JND profiles, the proposed JND model tolerates more distortion in the same perceptual quality, and brings better visual perception in the same level of the injected JND-noise energy.
Parallelizing a multi-objective optimization approach for extractive multi-document text summarization

Currently, automatic multi-document text summarization is an important task in many fields of knowledge, due to the continuous exponential growth of information on the Internet. Nevertheless, this task is computationally demanding. In the last years, automatic text summarization has been addressed by using multi-objective optimization approaches. In particular, recently, the Multi-Objective Artificial Bee Colony (MOABC) algorithm has obtained very good results. This work focuses on the parallelization of this approach. Several steps have been carried out for this goal. After a time profiling of the algorithm, a runtime comparison has been performed between the use of different random number generators within the algorithm. Then, a parallel implementation of the MOABC algorithm has been designed following its original scheme, in which the main steps are parallelized, and different parallel schedules have been studied and compared. Finally, a second design based on the asynchronous behavior of the bee colony in nature has been implemented and compared. Experiments have been carried out with datasets from Document Understanding Conference (DUC). The results show that the asynchronous design improves greatly the parallel design, being more than 55 times faster with 64 threads than the standard design. An efficiency of 86.72% has been reported for 64 threads.
Context-Aware Multi-View Attention Networks for Emotion Cause Extraction

Emotion cause extraction aims at automatically identifying cause clauses for a certain emotion expressed in a document. It is an important task in emotion analysis since it helps form a deeper understanding of emotion text. Detecting potential causes of user emotion in online contents is beneficial to public opinion monitoring, government decision-making, and other security-related applications. Existing studies treat this task as a binary clause-level classification problem, which considers each clause separately and omits the context information of clauses. Moreover, previous work only models emotion-dependent linguistic representations of clauses but ignores emotion-independent features in clauses including cause indicators. To address the above two issues, we formalize this task as a sequence labeling problem and propose the COntext-aware Multi-View attention networks (COMV) for emotion cause extraction. Our proposed model integrates context information and learns multi-view clause representations. Experimental results show that our model outperforms existing state-of-the-art methods.
Repeated-root constacyclic codes of length $ 3\ell^mp^s $

<p style='text-indent:20px;'>Let <inline-formula><tex-math id="M3">\begin{document}$ p $\end{document}</tex-math></inline-formula> be a prime different from 3, and <inline-formula><tex-math id="M4">\begin{document}$ \ell $\end{document}</tex-math></inline-formula> be an odd prime different from 3 and <inline-formula><tex-math id="M5">\begin{document}$ p $\end{document}</tex-math></inline-formula>. In terms of generator polynomials, structures of constacyclic codes and their duals of length <inline-formula><tex-math id="M6">\begin{document}$ 3\ell^mp^s $\end{document}</tex-math></inline-formula> over <inline-formula><tex-math id="M7">\begin{document}$ \mathbb{F}_q $\end{document}</tex-math></inline-formula> are established, where <inline-formula><tex-math id="M8">\begin{document}$ q $\end{document}</tex-math></inline-formula> is a power of <inline-formula><tex-math id="M9">\begin{document}$ p $\end{document}</tex-math></inline-formula>. We discuss the enumeration of all cyclic codes of length <inline-formula><tex-math id="M10">\begin{document}$ 3\cdot2^s\ell^m $\end{document}</tex-math></inline-formula>, that generalizes the construction of [<xref ref-type="bibr" rid="b15">15</xref>] (2016), which is the special case of <inline-formula><tex-math id="M11">\begin{document}$ m = 1 $\end{document}</tex-math></inline-formula>. In addition, as an application, the characterization and enumeration of all linear complementary dual cyclic codes of length <inline-formula><tex-math id="M12">\begin{document}$ 6\ell^mp^s $\end{document}</tex-math></inline-formula> over <inline-formula><tex-math id="M13">\begin{document}$ \mathbb{F}_q $\end{document}</tex-math></inline-formula> are obtained.
Keyword Spotting for Hearing Assistive Devices Robust to External Speakers

Keyword spotting (KWS) is experiencing an upswing due to the pervasiveness of small electronic devices that allow interaction with them via speech.Often, KWS systems are speakerindependent, which means that any person -user or notmight trigger them.For applications like KWS for hearing assistive devices this is unacceptable, as only the user must be allowed to handle them.In this paper we propose KWS for hearing assistive devices that is robust to external speakers.A stateof-the-art deep residual network for small-footprint KWS is regarded as a basis to build upon.By following a multi-task learning scheme, this system is extended to jointly perform KWS and users' own-voice/external speaker detection with a negligible increase in the number of parameters.For experiments, we generate from the Google Speech Commands Dataset a speech corpus emulating hearing aids as a capturing device.Our results show that this multi-task deep residual network is able to achieve a KWS accuracy relative improvement of around 32% with respect to a system that does not deal with external speakers.
Indexing weighted sequences: Neat and efficient

A weighted sequence is a sequence of probability mass functions over a finite alphabet. A weighted index is a data structure constructed for a weighted sequence and a threshold 1z that, given a string pattern, reports all positions where it occurs in the weighted sequence with probability at least 1z. We present an O(nz)-time construction of an O(nz)-sized weighted index for a weighted sequence of length n that answers queries in optimal time. The previous solution by Amir et al. (2008) required O(nz2log⁡z) time and space. Our main tools are a construction of a family of ⌊z⌋ strings that carries the information about all the strings that occur in a weighted sequence and a more straightforward solution to so-called property indexing. We present applications of our weighted index, in particular in approximate and general scenarios that were introduced by Biswas et al. (2016), and provide its implementation.
Radial Hilbert transform for phase retrieval using two-wavelength three-frame phase-shifting interferometry

Dual-wavelength interferometry (DWI) without phase unwrapping is widely applied in optical measurement and imaging. For improving measurement efficiency and increasing the range of applications, a method of retrieving a phase using fewer interferograms and shorter calculating time is proposed in this study. The proposed method requires only three multiplexed and phase-shifted interferograms. By subtracting the other two interferograms from the first frame image and using the Hilbert transform (HT), we first obtained the phase at each wavelength and then calculated the continuous phase at the synthetic wavelength. The method is applicable to open-loop fringes obtained in an off-axis interferometric system and closed-loop fringes obtained in an on-axis system. We used a combination of the radial HT and coordinate conversion method to solve the problem of having to use the conventional HT for closed-loop fringes. The feasibility and applicability of the method are verified using simulation and experimental results.
Making Deep Neural Networks Robust to Label Noise: Cross-Training With a Novel Loss Function

Deep neural networks (DNNs) have achieved astonishing results on a variety of supervised learning tasks owing to a large scale of well-labeled training data. However, as recent researches have pointed out, the generalization performance of DNNs is likely to sharply deteriorate when training data contains label noise. In order to address this problem, a novel loss function is proposed to guide DNNs to pay more attention to clean samples via adaptively weighing the traditional cross-entropy loss. Under the guidance of this loss function, a cross-training strategy is designed by leveraging two synergic DNN models, each of which plays the roles of both updating its own parameters and generating curriculums for the other one. In addition, this paper further proposes an online data filtration mechanism and integrates it into the final cross-training framework, which simultaneously optimizes DNN models and filters out noisy samples. The proposed approach is evaluated through a great deal of experiments on several benchmark datasets with man-made or real-world label noise, and the results have demonstrated its robustness to different noise types and noise scales.
Convolutional Neural Networks-Based Locating Relevant Buggy Code Files for Bug Reports Affected by Data Imbalance

Software bug localization is very important in software engineering, but it is also complicated and time consuming.To improve the efficiency of developers, researchers have developed various traditional bug localization and machine learning bug localization methods.In this paper, we propose a novel method that improves bug localization performance.First, surface lexical correlation matching between bug reports and source code files is used to obtain features by deep neural network.Second, to solve the lexical gap between bug reports and source code files, semantic correlation matching between them is used to obtain features based on word embedding and sentence embedding by deep neural network.Then, the joint features obtained by the surface lexical and semantic correlation matching are fused into a unified feature representation for bug reports and source code files.In addition, since our experimental datasets are imbalanced data, we use a focal loss function to solve the impact of data imbalance.Finally, our method obtains the relatively high bug localization performance compared to other classic methods.
A New Artificial Bee Colony Based on Multiple Search Strategies and Dimension Selection

Artificial bee colony (ABC) algorithm is a popular optimization technique with strong search ability. Although ABC has the ability to handle complex optimization problems, it suffers from weak exploitation and slow convergence. In order to tackle this issue, a new ABC variant based on multiple search strategies and dimension selection (ABC-MSDS) is proposed in this paper. Firstly, multiple search strategies based on dual strategy pool are designed. Compared to other existing ABC with multiple search strategies, our approach constructs two strategy pools for employed and onlooker bees, respectively. Secondly, a new dimension selection method is used to replace the random dimension selection in the standard ABC. In the search process, each dimension is chosen one by one in terms of the quality of offspring. Finally, a modified scout bee phase is employed to accelerate the search. Experimental study is conducted on classical benchmark problems and CEC 2013 shifted and rotated problems. The performance of ABC-MSDS is compared with several recently published ABC variants. Computational results demonstrate the effectiveness of our approach.
An efficient approach to near-optimal 3D trajectory design in cluttered environments for multirotor UAVs

This paper presents a framework for 3D trajectory design in cluttered environments for multirotor unmanned aerial vehicles. The proposed method employs C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> continuous piecewise Bézier curves to generate smooth and obstacle-free trajectories starting from the path waypoints. The construction of the curves requires the definition of two parameters difficult to estimate, in particular when looking for optimal design. Differential evolution optimization is used to estimate their values on a variety of randomly generated paths. Since the optimization process is too demanding for real-time implementation, an approach to find efficiently near-optimal parameters values is proposed and validated on a simulated dataset.
ADVERSARIAL DOMAIN ADAPTATION FOR THE CLASSIFICATION OF AERIAL IMAGES AND HEIGHT DATA USING CONVOLUTIONAL NEURAL NETWORKS

Abstract. Domain adaptation (DA) can drastically decrease the amount of training data needed to obtain good classification models by leveraging available data from a source domain for the classification of a new (target) domains. In this paper, we address deep DA, i.e. DA with deep convolutional neural networks (CNN), a problem that has not been addressed frequently in remote sensing. We present a new method for semi-supervised DA for the task of pixel-based classification by a CNN. After proposing an encoder-decoder-based fully convolutional neural network (FCN), we adapt a method for adversarial discriminative DA to be applicable to the pixel-based classification of remotely sensed data based on this network. It tries to learn a feature representation that is domain invariant; domain-invariance is measured by a classifier’s incapability of predicting from which domain a sample was generated. We evaluate our FCN on the ISPRS labelling challenge, showing that it is close to the best-performing models. DA is evaluated on the basis of three domains. We compare different network configurations and perform the representation transfer at different layers of the network. We show that when using a proper layer for adaptation, our method achieves a positive transfer and thus an improved classification accuracy in the target domain for all evaluated combinations of source and target domains.
Incremental Graph Pattern Based Node Matching with Multiple Updates

Graph Pattern based Node Matching (GPNM) has been proposed to find all the matches of the nodes in a data graph GD based on a given pattern graph GP. GPNM has been increasingly adopted in many applications such as group finding and expert recommendation, in which data graphs are frequently updated overtime. Moreover, many typical pattern graphs frequently and repeatedly appear in users' queries in a short period of time, e.g., social graph searches on Facebook. To deliver a GPNM result in such applications, the existing GPNM methods have to perform an incremental GPNM procedure for each of the updates in the data graph, which is computationally expensive. To address this problem, in this paper, we first analyze the elimination relationships between multiple updates in GD and the hierarchical structure between these elimination relationships. Then, we generate an Elimination Hierarchy Tree (EH-Tree) to index the elimination relationships and propose an EH-Tree based GPNM method, called EHGPNM, considering the elimination relationships between multiple updates in GD. EH-GPNM first delivers the GPNM result of an initial query, and then delivers the GPNM result of a subsequent query, based on the initial GPNM result and the multiple updates of GD that occur between those two queries. The experimental results on five real-world social graphs demonstrate that our proposed EH-GPNM is much more efficient than the state-of-the-art GPNM methods.
Breast Cancer Prediction Using a Hybrid Data Mining Model

Today, with the emergence of data mining technology and access to useful data, valuable information in different areas can be explored. Data mining uses machine learning algorithms to extract useful relationships and knowledge from a large amount of data and offers an automatic tool for various predictions and classifications. One of the most common applications of data mining in medicine and health-care is to predict different types of breast cancer which has attracted the attention of many scientists. In this paper, a hybrid model employing three algorithms of Naive Bayes Network, RBF Network, and K-means clustering is presented to predict breast cancer type. In the proposed model, the voting approach is used to combine the results obtained from the above three algorithms. Dataset used in this study is called Breast Cancer Wisconsin taken from data sources of UCI. The proposed model is implemented in MATLAB and its efficiency in predicting breast cancer type is evaluated on Breast Cancer Wisconsin dataset. Results show that the proposed hybrid model achieves an accuracy of 99% and mean absolute error of 0.019 which is superior over other models.
Error suppression in adiabatic quantum computing with qubit ensembles

Abstract Incorporating protection against quantum errors into adiabatic quantum computing (AQC) is an important task due to the inevitable presence of decoherence. Here, we investigate an error-protected encoding of the AQC Hamiltonian, where qubit ensembles are used in place of qubits. Our Hamiltonian only involves total spin operators of the ensembles, offering a simpler route towards error-corrected quantum computing. Our scheme is particularly suited to neutral atomic gases where it is possible to realize large ensemble sizes and produce ensemble-ensemble entanglement. We identify a critical ensemble size N c where the nature of the first excited state becomes a single particle perturbation of the ground state, and the gap energy is predictable by mean-field theory. For ensemble sizes larger than N c , the ground state becomes protected due to the presence of logically equivalent states and the AQC performance improves with N , as long as the decoherence rate is sufficiently low.
Formation of Soft skills in the conditions of social and public practices of students in the Implementation of the educational program in the ideology of the CDIO International initiative

С. И. Осипова, Н. В