Reviewerly/exHarmony huggingface dataset:
Non-fundamental, non-parametric Bitcoin forecasting

Bitcoin is the largest cryptocurrency in the world, but its lack of quantitative qualities makes fundamental analysis of its intrinsic value difficult. As an alternative valuation and forecasting method we propose a non-parametric model based on technical analysis. Using simple technical indicators, we produce point and density forecasts of Bitcoin returns with a feedforward neural network. We run several models over the full period of April 2011–March 2018, and four subsamples, and we find that backpropagation neural networks dominate various competing models in terms of their forecast accuracy. We conclude that the dynamics of Bitcoin returns is characterized by predictive local non-linear trends that reflect the speculative nature of cryptocurrency trading.
Multimodal Abstractive Summarization for How2 Videos

In this paper, we study abstractive summarization for open-domain videos.Unlike the traditional text news summarization, the goal is less to "compress" text information but rather to provide a fluent textual summary of information that has been collected and fused from different source modalities, in our case video and audio transcripts (or text).We show how a multi-source sequence-to-sequence model with hierarchical attention can integrate information from different modalities into a coherent output, compare various models trained with different modalities and present pilot experiments on the How2 corpus of instructional videos.We also propose a new evaluation metric (Content F1) for abstractive summarization task that measures semantic adequacy rather than fluency of the summaries, which is covered by metrics like ROUGE and BLEU.
Automatic Modulation Classification Using Compressive Convolutional Neural Network

The deep convolutional neural network has strong representative ability, which can learn latent information repeatedly from signal samples and improve the accuracy of automatic modulation classification (AMC). In this paper, a novel compressive convolutional neural network (CCNN) is proposed for AMC, where different constellation images, i.e., regular constellation images (RCs) and contrast enhanced grid constellation images (CGCs), are generated as network inputs from received signals. Moreover, a compressive loss constraint is proposed to train the CCNN, which aims at capturing high-dimensional features for modulation classification. Additionally, CCNN utilizes intra-class compactness and inter-class separability to enhance the classification and robustness performance for the different orders of modulations. The simulation results demonstrate that CCNN displays superior classification and robustness performance than existing AMC methods.
TAPAS: Train-Less Accuracy Predictor for Architecture Search

In recent years an increasing number of researchers and practitioners have been suggesting algorithms for large-scale neural network architecture search: genetic algorithms, reinforcement learning, learning curve extrapolation, and accuracy predictors. None of them, however, demonstrated highperformance without training new experiments in the presence of unseen datasets. We propose a new deep neural network accuracy predictor, that estimates in fractions of a second classification performance for unseen input datasets, without training. In contrast to previously proposed approaches, our prediction is not only calibrated on the topological network information, but also on the characterization of the dataset-difficulty which allows us to re-tune the prediction without any training. Our predictor achieves a performance which exceeds 100 networks per second on a single GPU, thus creating the opportunity to perform large-scale architecture search within a few minutes. We present results of two searches performed in 400 seconds on a single GPU. Our best discovered networks reach 93.67% accuracy for CIFAR-10 and 81.01% for CIFAR-100, verified by training. These networks are performance competitive with other automatically discovered state-of-the-art networks however we only needed a small fraction of the time to solution and computational resources.
BIRD: Engineering an Efficient CNF-XOR SAT Solver and Its Applications to Approximate Model Counting

Given a Boolean formula φ, the problem of model counting, also referred to as #SAT is to compute the number of solutions of φ. Model counting is a fundamental problem in artificial intelligence with a wide range of applications including probabilistic reasoning, decision making under uncertainty, quantified information flow, and the like. Motivated by the success of SAT solvers, there has been surge of interest in the design of hashing-based techniques for approximate model counting for the past decade. We profiled the state of the art approximate model counter ApproxMC2 and observed that over 99.99% of time is consumed by the underlying SAT solver, CryptoMiniSat. This observation motivated us to ask: Can we design an efficient underlying CNF-XOR SAT solver that can take advantage of the structure of hashing-based algorithms and would this lead to an efficient approximate model counter?&#x0D; The primary contribution of this paper is an affirmative answer to the above question. We present a novel architecture, called BIRD, to handle CNF-XOR formulas arising from hashingbased techniques. The resulting hashing-based approximate model counter, called ApproxMC3, employs the BIRD framework in its underlying SAT solver, CryptoMiniSat. To the best of our knowledge, we conducted the most comprehensive study of evaluation performance of counting algorithms involving 1896 benchmarks with computational effort totaling 86400 computational hours. Our experimental evaluation demonstrates significant runtime performance improvement for ApproxMC3 over ApproxMC2. In particular, we solve 648 benchmarks more than ApproxMC2, the state of the art approximate model counter and for all the formulas where both ApproxMC2 and ApproxMC3 did not timeout and took more than 1 seconds, the mean speedup is 284.40 – more than two orders of magnitude.&#x0D; Erratum: This research is supported in part by the National Research Foundation Singapore under its AI Singapore Programme (Award Number: [AISG-RP-2018-005])
An adaptive color image watermarking using RDWT-SVD and artificial bee colony based quality metric strength factor optimization

Image watermarking has emerged as a useful method for solving security issues like authenticity, copyright protection and rightful ownership of digital data. Existing watermarking schemes use either a binary or grayscale image as a watermark. This paper proposes a new robust and adaptive watermarking scheme in which both the host and watermark are the color images of the same size and dimension. The security of the proposed watermarking scheme is enhanced by scrambling both color host and watermark images using Arnold chaotic map. The host image is decomposed by redundant discrete wavelet transform (RDWT) into four sub-bands of the same dimension, and then approximate sub-band undergoes singular value decomposition (SVD) to obtain the principal component (PC). The scrambled watermark is then directly inserted into a principal component of scrambled host image, using an artificial bee colony optimized adaptive multi-scaling factor, obtained by considering both the host and watermark image perceptual quality to overcome the tradeoff between imperceptibility and robustness of the watermarked image. The hybridization of RDWT-SVD provides an advantage of no shift-invariant to achieve higher embedding capacity in the host image and preserving the imperceptibility and robustness by exploiting SVD properties. To measure the imperceptibility and robustness of the proposed scheme, both qualitative and quantitative evaluation parameters like peak signal to noise ratio (PSNR), structural similarity index metric (SSIM) and normalized cross-correlation (NC) are used. Experiments are performed against several image processing attacks and the results are analyzed and compared with other related existing watermarking schemes which clearly depict the usefulness of the proposed scheme. At the same time, the proposed scheme overcomes the major security problem of false positive error (FPE) that mostly occurs in existing SVD based watermarking schemes.
CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis

Aspect level sentiment classification is a finegrained sentiment analysis task.To detect the sentiment towards a particular aspect in a sentence, previous studies have developed various attention-based methods for generating aspect-specific sentence representations.However, the attention may inherently introduce noise and downgrade the performance.In this paper, we propose constrained attention networks (CAN), a simple yet effective solution, to regularize the attention for multiaspect sentiment analysis, which alleviates the drawback of the attention mechanism.Specifically, we introduce orthogonal regularization on multiple aspects and sparse regularization on each single aspect.Experimental results on two public datasets demonstrate the effectiveness of our approach.We further extend our approach to multi-task settings and outperform the state-of-the-art methods.
EarEcho

Smart wearable devices have recently become one of the major technological trends and been widely adopted by the general public. Wireless earphones, in particular, have seen a skyrocketing growth due to its great usability and convenience. With the goal of seeking a more unobtrusive wearable authentication method that the users can easily use and conveniently access, in this study we present EarEcho as a novel, affordable, user-friendly biometric authentication solution. EarEcho takes advantages of the unique physical and geometrical characteristics of human ear canal and assesses the content-free acoustic features of in-ear sound waves for user authentication in a wearable and mobile manner. We implemented the proposed EarEcho on a proof-of-concept prototype and tested it among 20 subjects under diverse application scenarios. We can achieve a recall of 94.19% and precision of 95.16% for one-time authentication, while a recall of 97.55% and precision of 97.57% for continuous authentication. EarEcho has demonstrated its stability over time and robustness to cope with the uncertainties on the varying background noises, body motions, and sound pressure levels.
Development of Augmented Reality-Based Interactive Multimedia to Improve Critical Thinking Skills in Science Learning

This study aimed to describe the development of augmented reality-based interactive multimedia to improve the critical thinking skills of elementary school teacher education students in learning earth structure and fisheries material science.This type of research was research and development by Sukmadinata consisting of preliminary studies, model development, and model testing.The research subjects were elementary school teacher education students at Sebelas Maret University, Indonesia.The technique of collecting data used tests, questionnaires, and observations.The collected data was then analyzed qualitatively and quantitatively.Qualitative data analysis used the interactive models of Miles and Huberman and quantitative data analysis using a paired sample t-test.The results of the study show that the development of media products determines indicators of learning, programming, display, and curriculum.The main field test obtained ρvalue = 0.002< 0.05, it can be decided that H0 rejected.Students think critically using augmented reality-based interactive multimedia better than before using it.At present, it is obtained interactive multimedia based on augmented reality that is feasible to use in accordance with the views of experts and users.
Localization of Deep Inpainting Using High-Pass Fully Convolutional Network

Image inpainting has been substantially improved with deep learning in the past years. Deep inpainting can fill image regions with plausible contents, which are not visually apparent. Although inpainting is originally designed to repair images, it can even be used for malicious manipulations, e.g., removal of specific objects. Therefore, it is necessary to identify the presence of inpainting in an image. This paper presents a method to locate the regions manipulated by deep inpainting. The proposed method employs a fully convolutional network that is based on high-pass filtered image residuals. Firstly, we analyze and observe that the inpainted regions are more distinguishable from the untouched ones in the residual domain. Hence, a high-pass pre-filtering module is designed to get image residuals for enhancing inpainting traces. Then, a feature extraction module, which learns discriminative features from image residuals, is built with four concatenated ResNet blocks. The learned feature maps are finally enlarged by an up-sampling module, so that a pixel-wise inpainting localization map is obtained. The whole network is trained end-to-end with a loss addressing the class imbalance. Extensive experimental results evaluated on both synthetic and realistic images subjected to deep inpainting have shown the effectiveness of the proposed method.
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks

In this paper, we study the implicit regularization of the gradient descent algorithm in homogeneous neural networks, including fully-connected and convolutional neural networks with ReLU or LeakyReLU activations. In particular, we study the gradient descent or gradient flow (i.e., gradient descent with infinitesimal step size) optimizing the logistic loss or cross-entropy loss of any homogeneous model (possibly non-smooth), and show that if the training loss decreases below a certain threshold, then we can define a smoothed version of the normalized margin which increases over time. We also formulate a natural constrained optimization problem related to margin maximization, and prove that both the normalized margin and its smoothed version converge to the objective value at a KKT point of the optimization problem. Our results generalize the previous results for logistic regression with one-layer or multi-layer linear networks, and provide more quantitative convergence results with weaker assumptions than previous results for homogeneous smooth neural networks. We conduct several experiments to justify our theoretical finding on MNIST and CIFAR-10 datasets. Finally, as margin is closely related to robustness, we discuss potential benefits of training longer for improving the robustness of the model.
Knowledge Graph Embedding via Graph Attenuated Attention Networks

Knowledge graphs contain a wealth of real-world knowledge that can provide strong support for artificial intelligence applications. Much progress has been made in knowledge graph completion, state-of-the-art models are based on graph convolutional neural networks. These models automatically extract features, in combination with the features of the graph model, to generate feature embeddings with a strong expressive ability. However, these methods assign the same weights on the relation path in the knowledge graph and ignore the rich information presented in neighbor nodes, which result in incomplete mining of triple features. To this end, we propose Graph Attenuated Attention networks(GAATs), a novel representation method, which integrates an attenuated attention mechanism to assign different weight in different relation path and acquire the information from the neighborhoods. As a result, entities and relations can be learned in any neighbors. Our empirical research provides insight into the effectiveness of the attenuated attention-based models, and we show significant improvement compared to the state-of-the-art methods on two benchmark datasets WN18RR and FB15k-237.
Semantic Information for Robot Navigation: A Survey

There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstraction in the representation of information. In contrast with the early years, when navigation relied on geometric navigators that interpreted the environment as a series of accessible areas or later developments that led to the use of graph theory, semantic information has moved robot navigation one step further. This work presents a survey on the concepts, methodologies and techniques that allow including semantic information in robot navigation systems. The techniques involved have to deal with a range of tasks from modelling the environment and building a semantic map, to including methods to learn new concepts and the representation of the knowledge acquired, in many cases through interaction with users. As understanding the environment is essential to achieve high-level navigation, this paper reviews techniques for acquisition of semantic information, paying attention to the two main groups: human-assisted and autonomous techniques. Some state-of-the-art semantic knowledge representations are also studied, including ontologies, cognitive maps and semantic maps. All of this leads to a recent concept, semantic navigation, which integrates the previous topics to generate high-level navigation systems able to deal with real-world complex situations.
Learning deformable registration of medical images with anatomical constraints

Deformable image registration is a fundamental problem in the field of medical image analysis. During the last years, we have witnessed the advent of deep learning-based image registration methods which achieve state-of-the-art performance, and drastically reduce the required computational time. However, little work has been done regarding how can we encourage our models to produce not only accurate, but also anatomically plausible results, which is still an open question in the field. In this work, we argue that incorporating anatomical priors in the form of global constraints into the learning process of these models, will further improve their performance and boost the realism of the warped images after registration. We learn global non-linear representations of image anatomy using segmentation masks, and employ them to constraint the registration process. The proposed AC-RegNet architecture is evaluated in the context of chest X-ray image registration using three different datasets, where the high anatomical variability makes the task extremely challenging. Our experiments show that the proposed anatomically constrained registration model produces more realistic and accurate results than state-of-the-art methods, demonstrating the potential of this approach.
Deep Learning Framework for Vehicle and Pedestrian Detection in Rural Roads on an Embedded GPU

Object detection, one of the most fundamental and challenging problems in computer vision. Nowadays some dedicated embedded systems have emerged as a powerful strategy for deliver high processing capabilities including the NVIDIA Jetson family. The aim of the present work is the recognition of objects in complex rural areas through an embedded system, as well as the verification of accuracy and processing time. For this purpose, a low power embedded Graphics Processing Unit (Jetson Nano) has been selected, which allows multiple neural networks to be run in simultaneous and a computer vision algorithm to be applied for image recognition. As well, the performance of these deep learning neural networks such as ssd-mobilenet v1 and v2, pednet, multiped and ssd-inception v2 has been tested. Moreover, it was found that the accuracy and processing time were in some cases improved when all the models suggested in the research were applied. The pednet network model provides a high performance in pedestrian recognition, however, the sdd-mobilenet v2 and ssd-inception v2 models are better at detecting other objects such as vehicles in complex scenarios.
IoT cyber risk: a holistic analysis of cyber risk assessment frameworks, risk vectors, and risk ranking process

Abstract Security vulnerabilities of the modern Internet of Things (IoT) systems are unique, mainly due to the complexity and heterogeneity of the technology and data. The risks born out of these IoT systems cannot easily fit into an existing risk framework. There are many cybersecurity risk assessment approaches and frameworks that are under deployment in many governmental and commercial organizations. Extending these existing frameworks to IoT systems alone will not address the new risks that have arisen in the IoT ecosystem. This study has included a review of existing popular cyber risk assessment methodologies and their suitability to IoT systems. National Institute of Standards and Technology, Operationally Critical Threat, Asset, and Vulnerability Evaluation, Threat Assessment &amp; Remediation Analysis, and International Standards Organization are the four main frameworks critically analyzed in this research study. IoT risks are presented and reviewed in terms of the IoT risk category and impacted industries. IoT systems in financial technology and healthcare are dealt with in detail, given their high-risk exposure. Risk vectors for IoT and the Internet of Medical Things (IoMT) are discussed in this study. A unique risk ranking method to rank and quantify IoT risk is introduced in this study. This ranking method initiates a risk assessment approach exclusively for IoT systems by quantifying IoT risk vectors, leading to effective risk mitigation strategies and techniques. A unique computational approach to calculate the cyber risk for IoT systems with IoT-specific impact factors has been designed and explained in the context of IoMT systems.
Border Collie Optimization

In recent times, several metaheuristic algorithms have been proposed for solving real world optimization problems.In this paper, a new metaheuristic algorithm, called the Border Collie Optimization is introduced.The algorithm is developed by mimicking the sheep herding styles of Border Collie dogs.The Border Collie's unique herding style from the front as well as from the sides is adopted successfully in this paper.In this algorithm, the entire population is divided into two parts viz., dogs and sheep.This is done to equally focus on both exploration and exploitation of the search space.The Border Collie utilizes a predatory move called eyeing.This technique of the dogs is utilized to prevent the algorithm from getting stuck into local optima.A sensitivity analysis of the proposed algorithm has been carried out using the Sobol's sensitivity indices with the Sobol g-function for tuning of parameters.The proposed algorithm is applied on thirty-five benchmark functions.The proposed algorithm provides very competitive results, when compared with seven state-of-the-art algorithms like Ant Colony optimization, Differential algorithm, Genetic algorithm, Grey-wolf optimizer, Harris Hawk optimization, Particle Swarm optimization and Whale optimization algorithm.The performance of the proposed algorithm is analytically and visually tested by different methods to judge its supremacy.Finally, the statistical significance of the proposed algorithm is established by comparing it with other algorithms by employing Kruskal-Wallis test and Friedman test.