Reviewerly/exHarmony huggingface dataset:
Learning Channel-Wise Interactions for Binary Convolutional Neural Networks

In this paper, we propose a channel-wise interaction based binary convolutional neural network learning method (CI-BCNN) for efficient inference. Conventional methods apply xnor and bitcount operations in binary convolution with notable quantization error, which usually obtains inconsistent signs in binary feature maps compared with their full-precision counterpart and leads to significant information loss. In contrast, our CI-BCNN mines the channel-wise interactions, through which prior knowledge is provided to alleviate inconsistency of signs in binary feature maps and preserves the information of input samples during inference. Specifically, we mine the channel-wise interactions by a reinforcement learning model, and impose channel-wise priors on the intermediate feature maps through the interacted bitcount function. Extensive experiments on the CIFAR-10 and ImageNet datasets show that our method outperforms the state-of-the-art binary convolutional neural networks with less computational and storage cost.
Neural Detection of Semantic Code Clones Via Tree-Based Convolution

Code clones are similar code fragments that share the same semantics but may differ syntactically to various degrees. Detecting code clones helps reduce the cost of software maintenance and prevent faults. Various approaches of detecting code clones have been proposed over the last two decades, but few of them can detect semantic clones, i.e., code clones with dissimilar syntax. Recent research has attempted to adopt deep learning for detecting code clones, such as using tree-based LSTM over Abstract Syntax Tree (AST). However, it does not fully leverage the structural information of code fragments, thereby limiting its clone-detection capability. To fully unleash the power of deep learning for detecting code clones, we propose a new approach that uses tree-based convolution to detect semantic clones, by capturing both the structural information of a code fragment from its AST and lexical information from code tokens. Additionally, our approach addresses the limitation that source code has an unlimited vocabulary of tokens and models, and thus exploiting lexical information from code tokens is often ineffective when dealing with unseen tokens. Particularly, we propose a new embedding technique called position-aware character embedding (PACE), which essentially treats any token as a position-weighted combination of character one-hot embeddings. Our experimental results show that our approach substantially outperforms an existing state-of-the-art approach with an increase of 0.42 and 0.15 in F1-score on two popular code-clone benchmarks (OJClone and BigCloneBench), respectively, while being more computationally efficient. Our experimental results also show that PACE enables our approach to be substantially more effective when code clones contain unseen tokens.
Blockchain-Based Access Control Model to Preserve Privacy for Personal Health Record Systems

Personal health record system (PHR system) stores health-related information of an individual. PHR system allows the data owner to manage and share his/her data with selected individuals. The originality or tamper resistance feature is crucial for PHR system because of the irreversible consequence of incorrect information. Blockchain technology becomes a potential solution due to its immutability and irreversibility properties. Unfortunately, some technical impediments such as limited storage, privacy concern, consent irrevocability, inefficient performance, and energy consumption exist. This work aims to handle these blockchain drawbacks and propose a blockchain-based PHR model. The proposed model is built using the blockchain technology to support a tamper resistance feature. Proxy reencryption and other cryptographic techniques are employed to preserve privacy. Features of the proposed model include fine-grained and flexible access control, revocability of consent, auditability, and tamper resistance. A detailed security analysis shows that the proposed model is provably secure for privacy and tamper resistance. The performance analysis shows that the proposed model achieves a better overall performance compared with the existing approach in the literature. Thus the proposed model is more suitable for the PHR system usage.
Deep view synthesis from sparse photometric images

The goal of light transport acquisition is to take images from a sparse set of lighting and viewing directions, and combine them to enable arbitrary relighting with changing view. While relighting from sparse images has received significant attention, there has been relatively less progress on view synthesis from a sparse set of "photometric" images---images captured under controlled conditions, lit by a single directional source; we use a spherical gantry to position the camera on a sphere surrounding the object. In this paper, we synthesize novel viewpoints across a wide range of viewing directions (covering a 60° cone) from a sparse set of just six viewing directions. While our approach relates to previous view synthesis and image-based rendering techniques, those methods are usually restricted to much smaller baselines, and are captured under environment illumination. At our baselines, input images have few correspondences and large occlusions; however we benefit from structured photometric images. Our method is based on a deep convolutional network trained to directly synthesize new views from the six input views. This network combines 3D convolutions on a plane sweep volume with a novel per-view per-depth plane attention map prediction network to effectively aggregate multi-view appearance. We train our network with a large-scale synthetic dataset of 1000 scenes with complex geometry and material properties. In practice, it is able to synthesize novel viewpoints for captured real data and reproduces complex appearance effects like occlusions, view-dependent specularities and hard shadows. Moreover, the method can also be combined with previous relighting techniques to enable changing both lighting and view, and applied to computer vision problems like multiview stereo from sparse image sets.
Progressive Teacher-Student Learning for Early Action Prediction

The goal of early action prediction is to recognize actions from partially observed videos with incomplete action executions, which is quite different from action recognition.Predicting early actions is very challenging since the partially observed videos do not contain enough action information for recognition.In this paper, we aim at improving early action prediction by proposing a novel teacherstudent learning framework.Our framework involves a teacher model for recognizing actions from full videos, a student model for predicting early actions from partial videos, and a teacher-student learning block for distilling progressive knowledge from teacher to student, crossing different tasks.Extensive experiments on three public action datasets show that the proposed progressive teacher-student learning framework can consistently improve performance of early action prediction model.We have also reported the state-of-the-art performances for early action prediction on all of these sets.
Migrating Knowledge between Physical Scenarios Based on Artificial Neural Networks

Deep learning is known to be data-hungry, which hinders its application in many areas of science when data sets are small. Here, we propose to use transfer learning methods to migrate knowledge between different physical scenarios and significantly improve the prediction accuracy of artificial neural networks trained on a small data set. This method can help reduce the demand for expensive data by making use of additional inexpensive data. First, we demonstrate that, in predicting the transmission from multilayer photonic film, the relative error rate is reduced by 50.5% (23.7%) when the source data comes from 10-layer (8-layer) films and the target data comes from 8-layer (10-layer) films. Second, we show that the relative error rate is decreased by 19.7% when knowledge is transferred between two very different physical scenarios: transmission from multilayer films and scattering from multilayer nanoparticles. Next, we propose a multitask learning method to improve the performance of different physical scenarios simultaneously in which each task only has a small data set. Finally, we demonstrate that the transfer learning framework truly discovers the common underlying physical rules instead of just performing a certain way of regularization.
PointFlowNet: Learning Representations for Rigid Motion Estimation From Point Clouds

Despite significant progress in image-based 3D scene flow estimation, the performance of such approaches has not yet reached the fidelity required by many applications. Simultaneously, these applications are often not restricted to image-based estimation: laser scanners provide a popular alternative to traditional cameras, for example in the context of self-driving cars, as they directly yield a 3D point cloud. In this paper, we propose to estimate 3D motion from such unstructured point clouds using a deep neural network. In a single forward pass, our model jointly predicts 3D scene flow as well as the 3D bounding box and rigid body motion of objects in the scene. While the prospect of estimating 3D scene flow from unstructured point clouds is promising, it is also a challenging task. We show that the traditional global representation of rigid body motion prohibits inference by CNNs, and propose a translation equivariant representation to circumvent this problem. For training our deep network, a large dataset is required. Because of this, we augment real scans from KITTI with virtual objects, realistically modeling occlusions and simulating sensor noise. A thorough comparison with classic and learning-based techniques highlights the robustness of the proposed approach.
Denoising Adversarial Autoencoders

Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabeled data to learn useful representations for inference. Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabeled input data from a latent representation space. More robust representations may be produced by an autoencoder if it learns to recover clean input samples from corrupted ones. Representations may be further improved by introducing regularization during training to shape the distribution of the encoded data in the latent space. We suggest denoising adversarial autoencoders (AAEs), which combine denoising and regularization, shaping the distribution of latent space using adversarial training. We introduce a novel analysis that shows how denoising may be incorporated into the training and sampling of AAEs. Experiments are performed to assess the contributions that denoising makes to the learning of representations for classification and sample synthesis. Our results suggest that autoencoders trained using a denoising criterion achieve higher classification performance and can synthesize samples that are more consistent with the input data than those trained without a corruption process.
Trick Me If You Can: Human-in-the-Loop Generation of Adversarial Examples for Question Answering

Adversarial evaluation stress-tests a model’s understanding of natural language. Because past approaches expose superficial patterns, the resulting adversarial examples are limited in complexity and diversity. We propose human- in-the-loop adversarial generation, where human authors are guided to break models. We aid the authors with interpretations of model predictions through an interactive user interface. We apply this generation framework to a question answering task called Quizbowl, where trivia enthusiasts craft adversarial questions. The resulting questions are validated via live human–computer matches: Although the questions appear ordinary to humans, they systematically stump neural and information retrieval models. The adversarial questions cover diverse phenomena from multi-hop reasoning to entity type distractors, exposing open challenges in robust question answering.
Distributed multi-label feature selection using individual mutual information measures

Multi-label learning generalizes traditional learning by allowing an instance to belong to multiple labels simultaneously. This causes multi-label data to be characterized by its large label space dimensionality and the dependencies among labels. These challenges have been addressed by feature selection techniques which improve the final model accuracy. However, the large number of features along with a large number of labels call for new approaches to manage data effectively and efficiently in distributed computing environments. This paper proposes a distributed model to compute a score that measures the quality of each feature with respect to multiple labels on Apache Spark. We propose two different approaches that study how to aggregate the mutual information of multiple labels: Euclidean Norm Maximization (ENM) and Geometric Mean Maximization (GMM). The former selects the features with the largest L2-norm whereas the latter selects the features with the largest geometric mean. Experiments compare 9 distributed multi-label feature selection methods on 12 datasets and 12 metrics. Results validated through statistical analysis indicate that ENM is able to outperform the reference methods by maximizing the relevance while minimizing the redundancy of the selected features in constant selection time.
Machine Learning Classification Techniques for Breast Cancer Diagnosis

Breast cancer is one of the most widely spread disease and the second leading cause of cancer death among women. Breast cancer starts when malignant lumps which are cancerous begin to grow from the breast cells. Doctors may wrongly diagnose benign tumor (which is non-cancerous) as malignant tumor. There is need for a computer aided detection (CAD) systems which uses machine learning approach to provide accurate diagnosis of breast cancer. These CAD systems can aid in detecting breast cancer at an early stage. When, breast cancer is detected early enough, the survival rate increases because better treatment can be provided. This paper aims at investigating Support Vector Machine (using radial basis kernel), Artificial Neural Networks and Naïve Bayes using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset. The focus of this paper is to integrate these machine learning techniques with feature selection/feature extraction methods and compare their performances to identify the most suitable approach. The goal is combining the advantages of dimensionality reduction and machine learning. This paper proposed a hybrid approach for breast cancer diagnosis by reducing the high dimensionality of features using linear discriminant analysis (LDA), and then applying the new reduced feature dataset to Support Vector Machine. The proposed approach obtained an accuracy of 98.82%, sensitivity of 98.41%, specificity of 99.07% and area under the receiver operating characteristic curve of 0.9994.
gllvm: Fast analysis of multivariate abundance data with generalized linear latent variable models in <scp>r</scp>

Abstract There has been rapid development in tools for multivariate analysis based on fully specified statistical models or ‘joint models’. One approach attracting a lot of attention is generalized linear latent variable models (GLLVMs). However, software for fitting these models is typically slow and not practical for large datasets. The r package gllvm offers relatively fast methods to fit GLLVMs via maximum likelihood, along with tools for model checking, visualization and inference. The main advantage of the package over other implementations is speed, for example, being two orders of magnitude faster, and capable of handling thousands of response variables. These advances come from using variational approximations to simplify the likelihood expression to be maximized, automatic differentiation software for model‐fitting (via the TMB package) and careful choice of initial values for parameters. Examples are used to illustrate the main features and functionality of the package, such as constrained or unconstrained ordination, including functional traits in ‘fourth corner’ models, and (if the number of environmental coefficients is not large) make inferences about environmental associations.
TraVeLGAN: Image-To-Image Translation by Transformation Vector Learning

Interest in image-to-image translation has grown substantially in recent years with the success of unsupervised models based on the cycle-consistency assumption. The achievements of these models have been limited to a particular subset of domains where this assumption yields good results, namely homogeneous domains that are characterized by style or texture differences. We tackle the challenging problem of image-to-image translation where the domains are defined by high-level shapes and contexts, as well as including significant clutter and heterogeneity. For this purpose, we introduce a novel GAN based on preserving intra-domain vector transformations in a latent space learned by a siamese network. The traditional GAN system introduced a discriminator network to guide the generator into generating images in the target domain. To this two-network system we add a third: a siamese network that guides the generator so that each original image shares semantics with its generated version. With this new three-network system, we no longer need to constrain the generators with the ubiquitous cycle-consistency restraint or any other autoencoding regularization. As a result, the generators can learn mappings between more complex domains that differ from each other by more than just style or texture. We demonstrate our model by mapping between high-resolution, arbitrarily chosen classes from the Imagenet dataset completely without pre-processing such as cropping, centering, or filtering unrepresentative images.
Efficient multi-population outpost fruit fly-driven optimizers: Framework and advances in support vector machines

The original fruit fly algorithm (FOA) in simple structure is easy to understand, but it has a slow convergence rate and tends to be trapped in the local optimal solutions. In order to improve the convergence rate and efficacy of FOA, two new mechanisms are integrated with the exploratory and exploitative strategies of the original FOA: the outpost mechanism and the multi-population mechanism. The outpost mechanism consists of two parts: greedy selection and Gaussian mutation, which is mainly used to improve the convergence rate of the algorithm. The multi-swarm mechanism divides the population of agents into several sub-swarms and selects several individuals from sub-swarm with a random probability. Then, the selected individuals are remapped into the feature space to expand the exploratory capabilities. To illustrate the performance of the proposed method, a comprehensive set of benchmark functions, including the unimodal, multimodal, and composition functions were chosen for testing tasks. Also, the proposed MOFOA is compared against the state-of-the-art improved FOA algorithms and other well-known swarm-based methods. The experimental results have shown that MOFOA can outperform all the competitors involved in this study in terms of convergence speed and solution quality in a significant manner. Furthermore, MOFOA is also employed to optimize two critical parameters of the support vector machine (SVM) for classification tasks. The results demonstrate that the proposed MOFOA can also achieve a better performance than other swarm-based methods in dealing with the optimization of the SVM in dealing with several financial datasets.
SGDNet

We propose an end-to-end saliency-guided deep neural network (SGDNet) for no-reference image quality assessment (NR-IQA). Our SGDNet is built on an end-to-end multi-task learning framework in which two sub-tasks including visual saliency prediction and image quality prediction are jointly optimized with a shared feature extractor. The existing multi-task CNN-based NR-IQA methods which usually consider distortion identification as the auxiliary sub-task cannot accurately identify the complex mixtures of distortions exist in authentically distorted images. By contrast, our saliency prediction sub-task is more universal because visual attention always exists when viewing every image, regardless of its distortion type. More importantly, related works have reported that saliency information is highly correlated with image quality while this property is fully utilized in our proposed SGNet by training the model with more informative labels including saliency maps and quality scores simultaneously. In addition, the outputs of the saliency prediction sub-task are transparent to the primary quality regression sub-task by providing a kind of spatial attention masks for a more perceptually-consistent feature fusion. By training the whole network with the two sub-tasks together, more discriminant features can be learned and a more accurate mapping from feature representations to quality scores can be established. Experimental results on both authentically and synthetically distorted IQA datasets demonstrate the superiority of our SGDNet, as compared to the state-of-the-art approaches.
Distillation-Based Training for Multi-Exit Architectures

Multi-exit architectures, in which a stack of processing layers is interleaved with early output layers, allow the processing of a test example to stop early and thus save computation time and/or energy. In this work, we propose a new training procedure for multi-exit architectures based on the principle of knowledge distillation. The method encourages early exits to mimic later, more accurate exits, by matching their probability outputs. Experiments on CIFAR100 and ImageNet show that distillation-based training significantly improves the accuracy of early exits while maintaining state-of-the-art accuracy for late ones. The method is particularly beneficial when training data is limited and also allows a straight-forward extension to semi-supervised learning, i.e. make use also of unlabeled data at training time. Moreover, it takes only a few lines to implement and imposes almost no computational overhead at training time, and none at all at test time.
Hybrid neural networks for big data classification

Two new hybrid neural architectures combining morphological neurons and perceptrons are introduced in this paper. The first architecture, called Morphological - Linear Neural Network (MLNN) consists of a hidden layer of morphological neurons and an output layer of classical perceptrons has the capability of extracting features. The second architecture, called Linear-Morphological Neural Network (LMNN) is composed of one or several perceptron layers as a feature extractor, it is then followed by an output layer of morphological neurons for non-linear classification. Both architectures are trained by stochastic gradient descent. One of the main contributions of this paper is to show that the morphological layer offers a greater capacity to extract features than the perceptron layer. This claim is supported both theoretically and experimentally. We prove that the morphological layer possesses a greater capacity per computation unit to segment the 2D input space than the perceptron layer. In other words, adding more hyper-boxes produces more response regions than adding hyperplanes. From an empirical point of view, we test the two new models on 25 standard datasets at low dimensionality and one big data dataset. The result is that MLNN requires a lesser number of learning parameters than the other tested architectures while achieving better accuracies.