Reviewerly/exHarmony huggingface dataset:
Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies

In recent years, Internet users are reporting Adverse Drug Events (ADE) on social media, blogs and health forums. Because of the large volume of reports, pharmacovigilance is seeking to resort to NLP to monitor these outlets. We propose for the first time the use of the SpanBERT architecture for the task of ADE extraction: this new version of the popular BERT transformer showed improved capabilities with multi-token text spans. We validate our hypothesis with experiments on two datasets (SMM4H and CADEC) with different text typologies (tweets and blog posts), finding that SpanBERT combined with a CRF outperforms all the competitors on both of them.
One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition

Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models). For example, in the case of historical ciphered manuscripts, which are usually written with invented alphabets to hide the message contents. Thus, in this paper we address this problem through a data generation technique based on Bayesian Program Learning (BPL). Contrary to traditional generation approaches, which require a huge amount of annotated images, our method is able to generate human-like handwriting using only one sample of each symbol in the alphabet. After generating symbols, we create synthetic lines to train state-of-the-art HTR architectures in a segmentation free fashion. Quantitative and qualitative analyses were carried out and confirm the effectiveness of the proposed method.
Semi-Supervised Batch Active Learning Via Bilevel Optimization

Active learning is an effective technique for reducing the labeling cost by improving data efficiency. In this work, we propose a novel batch acquisition strategy for active learning in the setting where the model training is performed in a semi-supervised manner. We formulate our approach as a data summarization problem via bilevel optimization, where the queried batch consists of the points that best summarize the unlabeled data pool. We show that our method is highly effective in keyword detection tasks in the regime when only few labeled samples are available.
A MATURITY FRAMEWORK FOR CYBERSECURITY GOVERNANCE IN ORGANIZATIONS

Digitalization necessarily leads organizations to rethink their cybersecurity principles in order to counter all the risks inherent in cybercrime. Cybersecurity governance brings together all the essential elements of cyber defense and effective risk management. Without such governance, dangerous gaps persist, and assets are inevitably compromised. Given the critical decisions that need to be made in an ever-changing cyber threat environment, cybersecurity standards are a critical way for companies to ensure that their security strategy and policies are consistently and measurably implemented. The aim of this paper is to propose a capability maturity framework to assess and improve cybersecurity governance in organizations. The finding will help organizations to evaluate their cybersecurity governance capabilities.
Online Unsupervised Learning Using Ensemble Gaussian Processes with Random Features

Gaussian process latent variable models (GPLVMs) are powerful, yet computationally heavy tools for nonlinear dimensionality reduction. Existing scalable variants utilize low- rank kernel matrix approximants that in essence subsample the embedding space. This work develops an efficient online approach based on random features by replacing spatial with spectral subsampling. The novel approach bypasses the need for optimizing over spatial samples, without sacrificing performance. Different from GPLVM, whose performance depends on the choice of the kernel, the proposed algorithm relies on an ensemble of kernels - what allows adaptation to a wide range of operating environments. It further allows for initial exploration of a richer function space, relative to methods adhering to a single fixed kernel, followed by sequential contraction of the search space as more data become available. Tests on benchmark datasets demonstrate the effectiveness of the proposed method.
Fast Quantum Subroutines for the Simplex Method

We propose quantum subroutines for the simplex method that avoid classical computation of the basis inverse. For an \(m \times n\) constraint matrix with at most \(d_c\) nonzero elements per column, at most d nonzero elements per column or row of the basis, basis condition number \(\kappa \), and optimality tolerance \(\epsilon \), we show that pricing can be performed in \(\tilde{O}(\frac{1}{\epsilon }\kappa d \sqrt{n}(d_c n + d m))\) time, where the \(\tilde{O}\) notation hides polylogarithmic factors. If the ratio n/m is larger than a certain threshold, the running time of the quantum subroutine can be reduced to \(\tilde{O}(\frac{1}{\epsilon }\kappa d^{1.5} \sqrt{d_c} n \sqrt{m})\). The steepest edge pivoting rule also admits a quantum implementation, increasing the running time by a factor \(\kappa ^2\). Classically, pricing requires \(O(d_c^{0.7} m^{1.9} + m^{2 + o(1)} + d_c n)\) time in the worst case using the fastest known algorithm for sparse matrix multiplication, and \(O(d_c^{0.7} m^{1.9} + m^{2 + o(1)} + m^2n)\) with steepest edge. Furthermore, we show that the ratio test can be performed in \(\tilde{O}(\frac{t}{\delta } \kappa d^2 m^{1.5})\) time, where \(t, \delta \) determine a feasibility tolerance; classically, this requires \(O(m^2)\) time in the worst case. For well-conditioned sparse problems the quantum subroutines scale better in m and n, and may therefore have a worst-case asymptotic advantage. An important feature of our paper is that this asymptotic speedup does not depend on the data being available in some “quantum form”: the input of our quantum subroutines is the natural classical description of the problem, and the output is the index of the variables that should leave or enter the basis.
Enhancing Just-in-Time Defect Prediction Using Change Request-based Metrics

Identifying defective software components as early as their commit helps to reduce significant software development and maintenance costs. In recent years, several studies propose to use just-in-time (JIT) defect prediction techniques to identify changes that could introduce defects at check-in time. To predict defect introducing changes, JIT defect prediction approaches use change metrics collected from software repositories. These change metrics, however, capture code and code change related information. Information related to the change requests (e.g., clarity of change request and difficulty to implement the change) that could determine the change&#x2019;s proneness to introducing new defects are not studied. In this study, we propose to augment the publicly available change metrics dataset with six change request- based metrics collected from issue tracking systems. To build the prediction model, we used five machine learning algorithms: AdaBoost, XGBoost, Deep Neural Network, Random Forest and Logistic Regression. The proposed approach is evaluated using a dataset collected from four open source software systems, i.e., Eclipse platform, Eclipse JDT, Bugzilla and Mozilla. The results show that the augmented dataset improves the performance of JIT defect prediction in 19 out of 20 cases. F1-score of JIT defect prediction in the four systems is improved by an average of 4.8%, 3.4%, 1.7%, 1.1% and 1.1% while using AdaBoost, XGBoost, Deep Neural Network, Random Forest and Logistic Regression, respectively.
Improving Convolutional Neural Networks with Competitive Activation Function

The activation function is the basic component of the convolutional neural network (CNN), which provides the nonlinear transformation capability required by the network. Many activation functions make the original input compete with different linear or nonlinear mapping terms to obtain different nonlinear transformation capabilities. Until recently, the original input of funnel activation (FReLU) competed with the spatial conditions, so FReLU not only has the ability of nonlinear transformation but also has the ability of pixelwise modeling. We summarize the competition mechanism in the activation function and then propose a novel activation function design template: competitive activation function (CAF), which promotes competition among different elements. CAF generalizes all activation functions that use competition mechanisms. According to CAF, we propose a parametric funnel rectified exponential unit (PFREU). PFREU promotes competition among linear mapping, nonlinear mapping, and spatial conditions. We conduct experiments on four datasets of different sizes, and the experimental results of three classical convolutional neural networks proved the superiority of our method.
Fast and Comprehensive Simulation Methodology for Layout-Based Power-Noise Side-Channel Leakage Analysis

Side-channel attacks can non-invasively extract secret information from hardware devices with a large number of “side-channel” measurements. For example, measuring the dynamic voltage drop of a cryptographic chip can disclose the secret keys by power-noise side-channel emission. To identify the design vulnerabilities and to verify the design countermeasures against side-channel attacks, design time simulation tools are required. However, simulation of these power-noise side-channel emissions are highly complex and computationally intensive due to the scale of the time-domain simulation and the multi-physics models involved. In this paper, we have proposed a fast and comprehensive simulation methodology for the layout based power-noise Side-Channel Leakage Analysis (SCLA). To enable fast dynamic power-noise side-channel emission simulations in time domain, a novel Direct Vector Control (DVC) method is precisely applied to security-sensitive nets to capture all sources of side-channel leakage. Further, we have enabled a location dependent power-noise SCLA to root cause the potential layout design weakness. As a case study, we have validated the proposed SCLA methodology on a test chip which has two 128-bit AES ASIC implementations (CMOS based standard-cell logic and Wave Dynamic Differential Logic (WDDL)) embedded in it. Both Simulation results in terms of measurement-to-disclosure (MTD) have been correlated with silicon measurements to demonstrate the accurate detection of side-channel leakage by our simulation framework.
A Hierarchical Method for Kannada-MNIST Classification Based on Convolutional Neural Networks

Handwritten digit classification considers one of the crucial subjects in machine vision due to its numerous practical usages in many recognition systems. In this regard, Kannada-MNIST was introduced as a challenging dataset. On the other hand, deep neural networks, especially convolutional neural networks, give us an encouraging promise to solve such a problem. In this paper, as a result, we propose a new hierarchically combination method with the help of two CNN models designed from scratch. The results of this novel approach on the Kannada-MNIST dataset indicate its excellent performance because the accuracy on the training, validation, and test sets are 99.86%, 99.66%, and 99.80%, respectively. Fortunately, this proposed method has been able to overcome all the state-of-the-art solutions with the best performance on this dataset.
Visualizing Examples of Deep Neural Networks at Scale

Many programmers want to use deep learning due to its superior accuracy in many challenging domains. Yet our formative study with ten programmers indicated that, when constructing their own deep neural networks (DNNs), they often had a difficult time choosing appropriate model structures and hyperparameter values. This paper presents ExampleNet—a novel interactive visualization system for exploring common and uncommon design choices in a large collection of open-source DNN projects. ExampleNet provides a holistic view of the distribution over model structures and hyperparameter settings in the corpus of DNNs, so users can easily filter the corpus down to projects tackling similar tasks and compare design choices made by others. We evaluated ExampleNet in a within-subjects study with sixteen participants. Compared with the control condition (i.e., online search), participants using ExampleNet were able to inspect more online examples, make more data-driven design decisions, and make fewer design mistakes.
An Effective Semantic Code Clone Detection Framework Using Pairwise Feature Fusion

Code clones. In this work, we propose a novel detection framework using machine learning for automated detection of all four type of clones. The features extracted from a pair of code blocks are combined for possible detection of a clone with respect to a reference block. We use AST and PDG features of both code blocks to prepare labelled training samples after fusing the two feature vectors using three different alternatives. We use six state-of-the-art classification models including Deep Convolutional Neural Network to assess the prediction performance of our scheme. To access the effectiveness of our framework we use seven datasets and compare its performance with five state-of-the-art clone detectors. We also compare a large number of algorithms for code clone detection. Comparing the performance of a large number of machine learning techniques, ANN and non-ANN, using such features, and establishing that fusing of AST and PDG features gives competitive results using deep learning as well as boosted tree algorithms, we find that boosted tree algorithms like XGBoost are quite competitive in clone detection. Experimental results demonstrate that our approach outperforms existing clone detection methods in terms of prediction accuracy.
Analyzing Autoencoder-Based Acoustic Word Embeddings

Recent studies have introduced methods for learning acoustic word embeddings (AWEs)---fixed-size vector representations of words which encode their acoustic features. Despite the widespread use of AWEs in speech processing research, they have only been evaluated quantitatively in their ability to discriminate between whole word tokens. To better understand the applications of AWEs in various downstream tasks and in cognitive modeling, we need to analyze the representation spaces of AWEs. Here we analyze basic properties of AWE spaces learned by a sequence-to-sequence encoder-decoder model in six typologically diverse languages. We first show that these AWEs preserve some information about words' absolute duration and speaker. At the same time, the representation space of these AWEs is organized such that the distance between words' embeddings increases with those words' phonetic dissimilarity. Finally, the AWEs exhibit a word onset bias, similar to patterns reported in various studies on human speech processing and lexical access. We argue this is a promising result and encourage further evaluation of AWEs as a potentially useful tool in cognitive science, which could provide a link between speech processing and lexical memory.
Free-Form Image Inpainting via Contrastive Attention Network

Most deep learning based image inpainting approaches adopt autoencoder or its variants to fill missing regions in images. Encoders are usually utilized to learn powerful representational spaces, which are important for dealing with sophisticated learning tasks. Specifically, in image inpainting tasks, masks with any shapes can appear anywhere in images (i.e., free-form masks) which form complex patterns. It is difficult for encoders to capture such powerful representations under this complex situation. To tackle this problem, we propose a self-supervised Siamese inference network to improve the robustness and generalization. It can encode contextual semantics from full resolution images and obtain more discriminative representations. we further propose a multi-scale decoder with a novel dual attention fusion module (DAF), which can combine both the restored and known regions in a smooth way. This multi-scale architecture is benefit for decoding discriminative representations learned by encoders into images layer by layer. In this way, unknown regions will be filled naturally from outside to inside. Qualitative and quantitative experiments on multiple datasets, including facial and natural datasets (i.e., Celeb-HQ, Pairs Street View, Places2 and ImageNet), demonstrate that our proposed method outperforms state-of-the-art methods in generating high-quality inpainting results.
Audiovisual Highlight Detection in Videos

In this paper, we test the hypothesis that interesting events in unstructured videos are inherently audiovisual. We combine deep image representations for object recognition and scene understanding with representations from an audiovisual affect recognition model. To this set, we include content agnostic audio-visual synchrony representations and mel-frequency cepstral coefficients to capture other intrinsic properties of audio. These features are used in a modular supervised model. We present results from two experiments: efficacy study of single features on the task, and an ablation study where we leave one feature out at a time. For the video summarization task, our results indicate that the visual features carry most information, and including audiovisual features improves over visual-only information. To better study the task of highlight detection, we run a pilot experiment with highlights annotations for a small subset of video clips and fine-tune our best model on it. Results indicate that we can transfer knowledge from the video summarization task to a model trained specifically for the task of highlight detection.
A Multi-task Cascaded Algorithm with Optimized Convolution Neural Network for Face Detection

In this paper, a face detection algorithm based on optimizing Multi-task cascaded convolution neural network (MTCNN) was proposed. In particular, be based on the CNN structure of MTCNN, using its optimized cascaded CNN to form the framework of this. The two stages before are used to predict face, and the third network needs to do face prediction and landmark location. Besides, Mosaic technology had being used to adjust the data distribution of the dataset. Our method achieves accuracy over the original techniques on the challenging FDDB by 2.7%, reaching 97.6%, while our model size is reduced by about 1%.
Progressive Learning Algorithm for Efficient Person Re- Identification

This paper studies the problem of Person Re-Identification (ReID) for large-scale applications. Recent research efforts have been devoted to building complicated part models, which introduce considerably high computational cost and memory consumption, inhibiting its practicability in large-scale applications. This paper aims to develop a novel learning strategy to find efficient feature embeddings while maintaining the balance of accuracy and model complexity. More specifically, we find by enhancing the classical triplet loss together with cross-entropy loss, our method can explore the hard examples and build a discriminant feature embedding yet compact enough for large-scale applications. Our method is carried out progressively using Bayesian optimization, and we call it the Progressive Learning Algorithm (PLA). Extensive experiments on three large-scale datasets show that our PLA is comparable or better than the-state-of-the-arts. Especially, on the challenging Market-ISOI dataset, we achieve Rank-1=94.7%/mAP=89.4% while saving at least 30 % parameters than strong part models.
Periodnet: A Non-Autoregressive Waveform Generation Model with a Structure Separating Periodic and Aperiodic Components

We propose PeriodNet, a non-autoregressive (non-AR) waveform generation model with a new model structure for modeling periodic and aperiodic components in speech waveforms. The non-AR waveform generation models can generate speech waveforms parallelly and can be used as a speech vocoder by conditioning an acoustic feature. Since a speech waveform contains periodic and aperiodic components, both components should be appropriately modeled to generate a high-quality speech waveform. However, it is difficult to decompose the components from a natural speech waveform in advance. To address this issue, we propose a parallel model and a series model structure separating periodic and aperiodic components. The features of our proposed models are that explicit periodic and aperiodic signals are taken as input, and external periodic/aperiodic decomposition is not needed in training. Experiments using a singing voice corpus show that our proposed structure improves the naturalness of the generated waveform. We also show that the speech waveforms with a pitch outside of the training data range can be generated with more naturalness.
Augmented Reality Search to Improve Searching Using Augmented Reality

In the current scenario we are facing the issue of real view which is object deal with image or in virtual world for such kind of difficulties the Augmented Reality has came into existence (AR). This paper deal with Augmented Reality Search (ARS). In this Augmented Reality Search (ARS) just user have to make the voice command and the Augmented Reality Search (ARS) will provide you real view of that object. Consider real world scenario where a student searched for NIT Bangalore then it will show the real view of that campus.
Local path planning of mobile robot based on self-adaptive dynamic window approach

Abstract Dynamic Window Approach (DWA) is widely used in the local path planning of mobile robot. DWA algorithm has the advantages of simple planning algorithm and good realtime performance. However, the DWA exists unreachable target point, unreasonable path planning in the dense obstacles and it cannot ensure both speed and security. Therefore, an online parameter self-adaptive DWA algorithm is proposed to automatically adjust the weight of the objective function on the basis of the size of alternative velocity space and the distance between the mobile robot to the target point. By adapting to the dynamic changes of the environment, the optimal running speed and reasonable path of mobile robot can be obtained. The simulation verified that the mobile robot can pass through the dense obstacles area and reach the target point safely and reliably. And the problems that the robot might move around the dense obstacles and result in an unsmooth path are avoided. and the operational efficiency of the mobile robot is ensured simultaneously.