Reviewerly/exHarmony huggingface dataset:
Cognitive-inspired domain adaptation of sentiment lexicons

Sentiment lexicons are essential tools for polarity classification and opinion mining. In contrast to machine learning methods that only leverage text features or raw text for sentiment analysis, methods that use sentiment lexicons embrace higher interpretability. Although a number of domain-specific sentiment lexicons are made available, it is impractical to build an ex ante lexicon that fully reflects the characteristics of the language usage in endless domains. In this article, we propose a novel approach to simultaneously train a vanilla sentiment classifier and adapt word polarities to the target domain. Specifically, we sequentially track the wrongly predicted sentences and use them as the supervision instead of addressing the gold standard as a whole to emulate the life-long cognitive process of lexicon learning. An exploration-exploitation mechanism is designed to trade off between searching for new sentiment words and updating the polarity score of one word. Experimental results on several popular datasets show that our approach significantly improves the sentiment classification performance for a variety of domains by means of improving the quality of sentiment lexicons. Case-studies also illustrate how polarity scores of the same words are discovered for different domains.
Towards Real-Time Advancement of Underwater Visual Quality With GAN

Low visual quality has prevented underwater robotic vision from a wide range of applications. Although several algorithms have been developed, real time and adaptive methods are deficient for real-world tasks. In this paper, we address this difficulty based on generative adversarial networks (GAN), and propose a GAN-based restoration scheme (GAN-RS). In particular, we develop a multibranch discriminator including an adversarial branch and a critic branch for the purpose of simultaneously preserving image content and removing underwater noise. In addition to adversarial learning, a novel dark channel prior loss also promotes the generator to produce realistic vision. More specifically, an underwater index is investigated to describe underwater properties, and a loss function based on the underwater index is designed to train the critic branch for underwater noise suppression. Through extensive comparisons on visual quality and feature restoration, we confirm the superiority of the proposed approach. Consequently, the GAN-RS can adaptively improve underwater visual quality in real time and induce an overall superior restoration performance. Finally, a real-world experiment is conducted on the seabed for grasping marine products, and the results are quite promising. The source code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> [Online]. Available: https://github.com/SeanChenxy/GAN_RS..
An empirical study to investigate oversampling methods for improving software defect prediction using imbalanced data

Software defect prediction is important to identify defects in the early phases of software development life cycle. This early identification and thereby removal of software defects is crucial to yield a cost-effective and good quality software product. Though, previous studies have successfully used machine learning techniques for software defect prediction, these techniques yield biased results when applied on imbalanced data sets. An imbalanced data set has non-uniform class distribution with very few instances of a specific class as compared to that of the other class. Use of imbalanced datasets leads to off-target predictions of the minority class, which is generally considered to be more important than the majority class. Thus, handling imbalanced data effectively is crucial for successful development of a competent defect prediction model. This study evaluates the effectiveness of machine learning classifiers for software defect prediction on twelve imbalanced NASA datasets by application of sampling methods and cost sensitive classifiers. We investigate five existing oversampling methods, which replicate the instances of minority class and also propose a new method SPIDER3 by suggesting modifications in SPIDER2 oversampling method. Furthermore, the work evaluates the performance of MetaCost learners for cost sensitive learning on imbalanced datasets. The results show improvement in the prediction capability of machine learning classifiers with the use of oversampling methods. Furthermore, the proposed SPIDER3 method shows promising results.
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution With Large Factors

Recently, single-image super-resolution has made great progress owing to the development of deep convolutional neural networks (CNNs). The vast majority of CNN-based models use a pre-defined upsampling operator, such as bicubic interpolation, to upscale input low-resolution images to the desired size and learn non-linear mapping between the interpolated image and ground truth high-resolution (HR) image. However, interpolation processing can lead to visual artifacts as details are over-smoothed, particularly when the super-resolution factor is high. In this paper, we propose a Deep Recurrent Fusion Network (DRFN), which utilizes transposed convolution instead of bicubic interpolation for upsampling and integrates different-level features extracted from recurrent residual blocks to reconstruct the final HR images. We adopt a deep recurrence learning strategy and thus have a larger receptive field, which is conducive to reconstructing an image more accurately. Furthermore, we show that the multi-level fusion structure is suitable for dealing with image super-resolution problems. Extensive benchmark evaluations demonstrate that the proposed DRFN performs better than most current deep learning methods in terms of accuracy and visual effects, especially for large-scale images, while using fewer parameters.
Path planning of multiple autonomous marine vehicles for adaptive sampling using Voronoi-based ant colony optimization

In this paper, a hybrid Voronoi-based ant colony optimization (V-ACO) technique for multiple autonomous marine vehicles (AMVs) is proposed to solve adaptive ocean sampling problem. The Voronoi-based scheme utilizes Voronoi partition with tournament selection method that enables more Voronoi edges lie in higher scientific interest regions. This scheme is then combined with ant colony optimization (ACO) using modified heuristic function, to find collision-free optimal trajectories for multiple AMVs to collect ocean measurements. For comparison, conventional ACO, rapidly-exploring random tree star (RRT*) and Dijkstra’s algorithm are also applied and tested for adaptive ocean sampling. Results of simulation tests specifically highlight the effectiveness and robustness of the proposed V-ACO path planner in generating trajectories of multi-AMVs that maximize data collection for adaptive ocean sampling in high scientific interest areas while considering specified mission time, inter-vehicle and obstacles avoidance constraints. Furthermore, field experiments validate the capability of the proposed V-ACO path planner in finding optimal solutions for adaptive ocean sampling.
Chaos-Induced and Mutation-Driven Schemes Boosting Salp Chains-Inspired Optimizers

Salp swarm algorithm (SSA) is a newly developed meta-heuristic algorithm, which is mainly developed based on the swarming behavior of salps sailing and foraging in the ocean.An improved salp swarm-based optimizer is proposed in this paper to overcome the potential shortcomings of original SSA, including being easily trapped in local or deceptive optima and its slow convergence rates in dealing with some high-dimensional and multimodal landscapes.The designed variant is called CMSSA that combines two strategies simultaneously.First, a chaotic exploitative mechanism with ''shrinking'' mode is introduced into the basic SSA to improve the exploitative tendencies of the algorithm.Then, a combined mutation scheme is adapted to make full use of the strong intensification capabilities of Gaussian mutation and the strong exploratory leanings of Cauchy mutation.In addition, the embedded strategies can achieve a more stable equilibrium between the core searching patterns of the SSA, which are diversification and intensification.We thoroughly studied the optimization advantages of the improved CMSSA using several representative benchmark cases, including unimodal, multimodal, and fixed-dimension multimodal functions, and three well-regarded engineering cases.The obtained experimental results, statistical tests, and comparative simulations indicate that the exploratory and exploitative proclivities of the SSA and its convergence patterns are vividly improved.The results indicate that the proposed CMSSA is a promising algorithm and shows superior efficacy compared with other algorithms.
Using virtual reality in the classroom: preservice teachers’ perceptions of its use as a teaching and learning tool

Virtual reality (VR) platforms act as a potentially transformative tool in learning and teaching. The aim of this study was to examine pre-service teachers’ (PST) perceptions about VR, inclusive of their beliefs about its capacity to be used as a teaching and learning tool. A case-study, conducted at an urban university in Australia involved a sample of n = 41. Participants’ positive perceptions of VR in their teaching relate to its potential to engage learners, the immersive potential of the platform and the scope of VR to offer students experiences they might otherwise not have with other learning tools. Concerns expressed by PSTs include their relatively low self-efficacy to use VR in their teaching, monitoring-related matters, financial cost and implementing the technology in a safe and supportive way. There was a significant difference in PSTs’ amount of self-efficacy to teach using VR when compared to their overall confidence to use digital technologies. PSTs typically had greater awareness of the immersive and engagement potential of VR and less awareness about its potential to foster and promote collaborative learning. This paper contributes to an emerging discourse regarding the possible applications of VR in educational environments and particularly in relation teacher-educator contexts.
Alcoholism Identification Based on an AlexNet Transfer Learning Model

Aim: This paper proposes a novel alcoholism identification approach that can assist radiologists in patient diagnosis. Method: AlexNet was used as the basic transfer learning model. The global learning rate was small, at 10-4, and the iteration epoch number was at 10. The learning rate factor of replaced layers was 10 times larger than that of the transferred layers. We tested five different replacement configurations of transfer learning. Results: The experiment shows that the best performance was achieved by replacing the final fully connected layer. Our method yielded a sensitivity of 97.44%± 1.15%, a specificity of 97.41 ± 1.51%, a precision of 97.34 ± 1.49%, an accuracy of 97.42 ± 0.95%, and an F1 score of 97.37 ± 0.97% on the test set. Conclusion: This method can assist radiologists in their routine alcoholism screening of brain magnetic resonance images.
Towards Practical Differentially Private Convex Optimization

Building useful predictive models often involves learning from sensitive data. Training models with differential privacy can guarantee the privacy of such sensitive data. For convex optimization tasks, several differentially private algorithms are known, but none has yet been deployed in practice. In this work, we make two major contributions towards practical differentially private convex optimization. First, we present Approximate Minima Perturbation, a novel algorithm that can leverage any off-the-shelf optimizer. We show that it can be employed without any hyperparameter tuning, thus making it an attractive technique for practical deployment. Second, we perform an extensive empirical evaluation of the state-of-the-art algorithms for differentially private convex optimization, on a range of publicly available benchmark datasets, and real-world datasets obtained through an industrial collaboration. We release open-source implementations of all the differentially private convex optimization algorithms considered, and benchmarks on as many as nine public datasets, four of which are high-dimensional.
Data fusion in cyber-physical-social systems: State-of-the-art and perspectives

Cyber-Physical-Social systems (CPSSs) are the extension of Cyber-Physical systems (CPS), which seamlessly integrate cyber space, physical space and social space. CPSSs promote the information resource from single space to tri-space, so as to lead a revolution in data science (DS). This paper aims to provide a comprehensive review of data fusion in CPSSs for readers. We firstly analyze data collection and representation in CPSS and propose to use tensors to represent CPSS data, then a general definition of CPSS data fusion is proposed to clarify the concept of information fusion in CPSS. After that, some representative data fusion methods related to CPSS are reviewed. Furthermore, we propose a series of tensor based data fusion methods for CPSS data. Also, we review the design of data fusion frameworks and propose a comprehensive data fusion framework for CPSS. Some challenges and future works are discussed as well.
AdaResU-Net: Multiobjective adaptive convolutional neural network for medical image segmentation

Adapting an existing convolutional neural network architecture to a specific dataset for medical image segmentation remains a challenging task that requires extensive expertise and time to fine-tune the hyperparameters. Hyperparameter optimization approaches that automate the search have been proposed but have mainly focused on optimizing the segmentation performance. However, optimizing the network size is also important to prevent unnecessary and costly computational operations. In this paper, we present a multiobjective adaptive convolutional neural network (AdaResU-Net) for medical image segmentation that is able to automatically adapt to new datasets while minimizing the size of the network. The proposed AdaResU-Net is comprised of a fixed architecture that combines the structure of the state-of-the-art U-Net with a residual learning framework to improve information propagation and promote an efficient training. Then, a multiobjective evolutionary algorithm (MEA) that optimizes both segmentation accuracy and model size is proposed to evolve the AdaResU-Net networks with different hyperparameters. The presented model is tested on two publically available medical image datasets and compared with the U-Net. Results show that the AdaResU-Net achieves better segmentation performance with less than 30% the number of trainable parameters. Additionally, the MEA algorithm generated configurations that are smaller and perform better or equally well than configurations generated with a Bayesian hyperparameter optimization approach.
Irony detection via sentiment-based transfer learning

Irony as a literary technique is widely used in online texts such as Twitter posts. Accurate irony detection is crucial for tasks such as effective sentiment analysis. A text’s ironic intent is defined by its context incongruity. For example in the phrase “I love being ignored”, the irony is defined by the incongruity between the positive word “love” and the negative context of “being ignored”. Existing studies mostly formulate irony detection as a standard supervised learning text categorization task, relying on explicit expressions for detecting context incongruity. In this paper we formulate irony detection instead as a transfer learning task where supervised learning on irony labeled text is enriched with knowledge transferred from external sentiment analysis resources. Importantly, we focus on identifying the hidden, implicit incongruity without relying on explicit incongruity expressions, as in “I like to think of myself as a broken down Justin Bieber – my philosophy professor.” We propose three transfer learning-based approaches to using sentiment knowledge to improve the attention mechanism of recurrent neural models for capturing hidden patterns for incongruity. Our main findings are: (1) Using sentiment knowledge from external resources is a very effective approach to improving irony detection; (2) For detecting implicit incongruity, transferring deep sentiment features seems to be the most effective way. Experiments show that our proposed models outperform state-of-the-art neural models for irony detection.
Learning-Based Sampling for Natural Image Matting

The goal of natural image matting is the estimation of opacities of a user-defined foreground object that is essential in creating realistic composite imagery. Natural matting is a challenging process due to the high number of unknowns in the mathematical modeling of the problem, namely the opacities as well as the foreground and background layer colors, while the original image serves as the single observation. In this paper, we propose the estimation of the layer colors through the use of deep neural networks prior to the opacity estimation. The layer color estimation is a better match for the capabilities of neural networks, and the availability of these colors substantially increase the performance of opacity estimation due to the reduced number of unknowns in the compositing equation. A prominent approach to matting in parallel to ours is called sampling-based matting, which involves gathering color samples from known-opacity regions to predict the layer colors. Our approach outperforms not only the previous hand-crafted sampling algorithms, but also current data-driven methods. We hence classify our method as a hybrid sampling- and learning-based approach to matting, and demonstrate the effectiveness of our approach through detailed ablation studies using alternative network architectures.
Deep neural networks for single shot structured light profilometry

In 3D optical metrology, single-shot structured light profilometry techniques have inherent advantages over their multi-shot counterparts in terms of measurement speed, optical setup simplicity, and robustness to motion artifacts. In this paper, we present a new approach to extract height information from single deformed fringe patterns, based entirely on deep learning. By training a fully convolutional neural network on a large set of simulated height maps with corresponding deformed fringe patterns, we demonstrate the ability of the network to obtain full-field height information from previously unseen fringe patterns with high accuracy. As an added benefit, intermediate data processing steps such as background masking, noise reduction and phase unwrapping that are otherwise required in classic demodulation strategies, can be learned directly by the network as part of its mapping function.
CapSal: Leveraging Captioning to Boost Semantics for Salient Object Detection

Detecting salient objects in cluttered scenes is a big challenge. To address this problem, we argue that the model needs to learn discriminative semantic features for salient objects. To this end, we propose to leverage captioning as an auxiliary semantic task to boost salient object detection in complex scenarios. Specifically, we develop a CapSal model which consists of two sub-networks, the Image Captioning Network (ICN) and the Local-Global Perception Network (LGPN). ICN encodes the embedding of a generated caption to capture the semantic information of major objects in the scene, while LGPN incorporates the captioning embedding with local-global visual contexts for predicting the saliency map. ICN and LGPN are jointly trained to model high-level semantics as well as visual saliency. Extensive experiments demonstrate the effectiveness of image captioning in boosting the performance of salient object detection. In particular, our model performs significantly better than the state-of-the-art methods on several challenging datasets of complex scenarios.
Chainer

Software frameworks for neural networks play a key role in the development and application of deep learning methods. In this paper, we introduce the Chainer framework, which intends to provide a flexible, intuitive, and high performance means of implementing the full range of deep learning models needed by researchers and practitioners. Chainer provides acceleration using Graphics Processing Units with a familiar NumPy-like API through CuPy, supports general and dynamic models in Python through Define-by-Run, and also provides add-on packages for state-of-the-art computer vision models as well as distributed training.
RENAS: Reinforced Evolutionary Neural Architecture Search

Neural Architecture Search (NAS) is an important yet challenging task in network design due to its high computational consumption. To address this issue, we propose the Reinforced Evolutionary Neural Architecture Search (RENAS), which is an evolutionary method with reinforced mutation for NAS. Our method integrates reinforced mutation into an evolution algorithm for neural architecture exploration, in which a mutation controller is introduced to learn the effects of slight modifications and make mutation actions. The reinforced mutation controller guides the model population to evolve efficiently. Furthermore, as child models can inherit parameters from their parents during evolution, our method requires very limited computational resources. In experiments, we conduct the proposed search method on CIFAR-10 and obtain a powerful network architecture, RENASNet. This architecture achieves a competitive result on CIFAR-10. The explored network architecture is transferable to ImageNet and achieves a new state-of-the-art accuracy, i.e., 75.7% top-1 accuracy with 5.36M parameters on mobile ImageNet. We further test its performance on semantic segmentation with DeepLabv3 on the PASCAL VOC. RENASNet outperforms MobileNet-v1, MobileNet-v2 and NASNet. It achieves 75.83% mIOU without being pretrained on COCO.
Monocular Depth Estimation Using Relative Depth Maps

We propose a novel algorithm for monocular depth estimation using relative depth maps. First, using a convolutional neural network, we estimate relative depths between pairs of regions, as well as ordinary depths, at various scales. Second, we restore relative depth maps from selectively estimated data based on the rank-1 property of pairwise comparison matrices. Third, we decompose ordinary and relative depth maps into components and recombine them optimally to reconstruct a final depth map. Experimental results show that the proposed algorithm provides the state-of-art depth estimation performance.