Reviewerly/exHarmony huggingface dataset:
Color medical image lossless watermarking using chaotic system and accurate quaternion polar harmonic transforms

With the increasingly widespread use of medical images in medical institutions and networks, the copyright protection of medical images has become increasingly urgent. Since traditional embedded watermarking technology degrades the quality of the original images, it is not suitable for medical images. In addition, most medical watermarking algorithms are aimed at grayscale images; therefore, a smaller number of color medical image watermarking algorithms are available. In this paper, we propose a lossless watermarking scheme for color medical image copyright protection based on a chaotic system and quaternion polar harmonic transforms (QPHTs), which is a quaternion orthogonal moment method. In watermark embedding, we compute the QPHTs for the original color medical image and select accurate coefficients to construct a zero-watermark image. Then, the zero-watermark image is stored in the intellectual property right (IPR) database. Experimental results show that the proposed scheme is robust to geometric attacks and common attacks and that it has better performance than similar lossless watermarking schemes. In addition, this paper details the selection of accurate coefficients for QPHTs and compares stability with different choices of unit pure quaternions.
A Fast Non-Negative Latent Factor Model Based on Generalized Momentum Method

Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) is an efficient algorithm for building an NLF model on an HiDS matrix, yet it suffers slow convergence. A momentum method is frequently adopted to accelerate a learning algorithm, but it is incompatible with those implicitly adopting gradients like SLF-NMU. To build a fast NLF (FNLF) model, we propose a generalized momentum method compatible with SLF-NMU. With it, we further propose a single latent factor-dependent non-negative, multiplicative and momentum-incorporated update algorithm, thereby achieving an FNLF model. Empirical studies on six HiDS matrices from industrial application indicate that an FNLF model outperforms an NLF model in terms of both convergence rate and prediction accuracy for missing data. Hence, compared with an NLF model, an FNLF model is more practical in industrial applications.
Class-specific attribute weighted naive Bayes

Due to its easiness to construct and interpret, along with its good performance, naive Bayes (NB) is widely used to address classification problems in real-world applications. In order to alleviate its conditional independence assumption, a mass of attribute weighting approaches have been proposed. However, almost all these approaches assign each attribute a same (global) weight for all classes. In this paper, we call them the general attribute weighting and argue that for NB attribute weighting should be class-specific (class-dependent). Based on this premise, we propose a new paradigm for attribute weighting called the class-specific attribute weighting, which discriminatively assigns each attribute a specific weight for each class. We call the resulting model class-specific attribute weighted naive Bayes (CAWNB). CAWNB selects class-specific attribute weights to maximize the conditional log likelihood (CLL) objective function or minimize the mean squared error (MSE) objective function, and thus two different versions are created, which we denote as CAWNBCLL and CAWNBMSE, respectively. Extensive empirical studies show that CAWNBCLL and CAWNBMSE all obtain more satisfactory experimental results compared with NB and other existing state-of-the-art general attribute weighting approaches. We believe that for NB class-specific attribute weighting could be a more fine-grained attribute weighting approach than general attribute weighting.
An alternative SMOTE oversampling strategy for high-dimensional datasets

In this work, the Synthetic Minority Over-sampling Technique (SMOTE) approach is adapted for high-dimensional binary settings. A novel distance metric is proposed for the computation of the neighborhood for each minority sample, which takes into account only a subset of the available attributes that are relevant for the task. Three variants for the distance metric are explored: Euclidean, Manhattan, and Chebyshev distances, and four different ranking strategies: Fisher Score, Mutual Information, Eigenvector Centrality, and Correlation Score. Our proposal was compared with various oversampling techniques on low- and high-dimensional datasets with the presence of class-imbalance, including a case study on Natural Language Processing (NLP). The proposed oversampling strategy showed superior results on average when compared with SMOTE and other variants, demonstrating the importance of selecting the right attributes when defining the neighborhood in SMOTE-based oversampling methods.
An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis

An edge detection is important for its reliability and security which delivers a better understanding of object recognition in the applications of computer vision, such as pedestrian detection, face detection, and video surveillance. This paper introduced two fundamental limitations encountered in edge detection: edge connectivity and edge thickness, those have been used by various developments in the state-of-the-art. An optimal selection of the threshold for effectual edge detection has constantly been a key challenge in computer vision. Therefore, a robust edge detection algorithm using multiple threshold approaches (B-Edge) is proposed to cover both the limitations. The majorly used canny edge operator focuses on two thresholds selections and still witnesses a few gaps for optimal results. To handle the loopholes of the canny edge operator, our method selects the simulated triple thresholds that target to the prime issues of the edge detection: image contrast, effective edge pixels selection, errors handling, and similarity to the ground truth. The qualitative and quantitative experimental evaluations demonstrate that our edge detection method outperforms competing algorithms for mentioned issues. The proposed approach endeavors an improvement for both grayscale and colored images.
A Blockchain Framework for Securing Connected and Autonomous Vehicles

Recently, connected vehicles (CV) are becoming a promising research area leading to the concept of CV as a Service (CVaaS). With the increase of connected vehicles and an exponential growth in the field of online cab booking services, new requirements such as secure, seamless and robust information exchange among vehicles of vehicular networks are emerging. In this context, the original concept of vehicular networks is being transformed into a new concept known as connected and autonomous vehicles. Autonomous vehicular use yields a better experience and helps in reducing congestion by allowing current information to be obtained by the vehicles instantly. However, malicious users in the internet of vehicles may mislead the whole communication where intruders may compromise smart devices with the purpose of executing a malicious ploy. In order to prevent these issues, a blockchain technique is considered the best technique that provides secrecy and protection to the control system in real time conditions. In this paper, the issue of security in smart sensors of connected vehicles that can be compromised by expert intruders is addressed by proposing a blockchain framework. This study has further identified and validated the proposed mechanism based on various security criteria, such as fake requests of the user, compromise of smart devices, probabilistic authentication scenarios and alteration in stored user's ratings. The results have been analyzed against some existing approach and validated with improved simulated results that offer 79% success rate over the above-mentioned issues.
Emerging Technology and Business Model Innovation: The Case of Artificial Intelligence

Artificial intelligence (AI) has been altering industries as evidenced by Airbnb, Uber and other companies that have embraced its use to implement innovative new business models. Yet we may not fully understand how this emerging and rapidly advancing technology influences business model innovation. While many companies are being made vulnerable to new competitors equipped with AI technology, this study attempts to focus on the proactive side of the use of AI technology to drive business model innovation. Describing AI technology as the catalyst of business model innovation, this study sheds light on contingent factors shaping business model innovation initiated by the emerging technology. This study first provides a brief overview of AI, current issues being tackled in developing AI and explains how it transforms business models. Our case study of two companies that innovated their business models using AI shows its potential impact. We also discuss how executives can create an innovative AI-based culture, which rephrases the process of AI-based business model innovation. Companies that successfully capitalize on AI can create disruptive innovation through their new business models and processes, enabling them to potentially transform the global competitive landscape.
A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare

Recently, human healthcare from body sensor data has been getting remarkable research attentions by a huge range of human-computer interaction and pattern analysis researchers due to its practical applications such as smart health care systems. For example, smart wearable-based behavior recognition system can be used to assist the rehabilitation of patients in a smart clinic to improve the rehabilitation process and to prolong their independent life. Although there are many ways of using distributed sensors to monitor vital signs and behavior of people, physical human action recognition via body sensors provides valuable data regarding an individual's functionality and lifestyle. In this work, we propose a body sensor-based system for behavior recognition using deep Recurrent Neural Network (RNN), a promising deep learning algorithm based on sequential information. We perform data fusion from multiple body sensors such as electrocardiography (ECG), accelerometer, magnetometer, etc. The extracted features are further enhanced via kernel principal component analysis (KPCA). The robust features are then used to train an activity RNN, which is later used for behavior recognition. The system has been compared against the conventional approaches on three publicly available standard datasets. The experimental results show that the proposed approach outperforms the available state-of-the-art methods.
Predicting Future Frames Using Retrospective Cycle GAN

Recent advances in deep learning have significantly improved the performance of video prediction, however, top-performing algorithms start to generate blurry predictions as they attempt to predict farther future frames. In this paper, we propose a unified generative adversarial network for predicting accurate and temporally consistent future frames over time, even in a challenging environment. The key idea is to train a single generator that can predict both future and past frames while enforcing the consistency of bi-directional prediction using the retrospective cycle constraints. Moreover, we employ two discriminators not only to identify fake frames but also to distinguish fake contained image sequences from the real sequence. The latter discriminator, the sequence discriminator, plays a crucial role in predicting temporally consistent future frames. We experimentally verify the proposed framework using various real-world videos captured by car-mounted cameras, surveillance cameras, and arbitrary devices with state-of-the-art methods.
Gradient-Based Attribution Methods

The problem of explaining complex machine learning models, including Deep Neural Networks, has gained increasing attention over the last few years. While several methods have been proposed to explain network predictions, the definition itself of explanation is still debated. Moreover, only a few attempts to compare explanation methods from a theoretical perspective has been done. In this chapter, we discuss the theoretical properties of several attribution methods and show how they share the same idea of using the gradient information as a descriptive factor for the functioning of a model. Finally, we discuss the strengths and limitations of these methods and compare them with available alternatives.
Blockchain and its implications for accounting and auditing

Purpose Technological developments such as blockchain seem to be the next step in a digital era and might reshape the way we do business. They are expected to have an impact on both business and society in the next few decades. This paper aims to provide general insights into blockchain technology and the extent to which it might transform the accounting system. Design/methodology/approach Analysing the previous literature, the paper provides a general overview of this phenomenon, identifying pending technical as well as non-technical issues that will have to be addressed for the full potential of blockchain technology to be embraced. The paper also proposes ways in which the information quality dimension might be improved. Findings The paper identifies the pending challenges for blockchain, such as scalability, flexibility, a suitable architecture and cybersecurity. Additionally, to integrate blockchain technology fully into a real accounting ecosystem, a consensus between regulators, auditors and other parties is needed. Originality/value A general overview of this new phenomenon, as well as a summary of how the quality of accounting information might be improved, is provided. Given that it features elements such as decentralization and transparency, blockchain certainly has the potential to improve information and accounting quality.
An efficient double adaptive random spare reinforced whale optimization algorithm

Whale optimization algorithm (WOA) is a newly developed meta-heuristic algorithm, which is mainly based on the predation behavior of humpback whales in the ocean. In this paper, a reinforced variant called RDWOA is proposed to alleviate the central shortcomings of the original method that converges slowly, and it is easy to fall into local optimum when dealing with multi-dimensional problems. Two strategies are introduced into the original WOA. One is the strategy of random spare or random replacement to enhance the convergence speed of this algorithm. The other method is the strategy of double adaptive weight, which is introduced to improve the exploratory searching trends during the early stages and exploitative behaviors in the later stages. The combination of the two strategies significantly improves the convergence speed and the overall search ability of the algorithm. The advantages of the proposed RDWOA are deeply analyzed and studied by using typical benchmark examples such as unimodal, multi-modal, and fixed multi-modal functions, and three famous engineering design problems. The experimental results show that the exploratory and exploitative tendencies of WOA and its convergence mode have been significantly improved. The RDWOA developed in this paper is a promising improved WOA variant, and it has better efficacy compared to other state-of-the-art algorithms.
A survey of word embeddings for clinical text

Representing words as numerical vectors based on the contexts in which they appear has become the de facto method of analyzing text with machine learning. In this paper, we provide a guide for training these representations on clinical text data, using a survey of relevant research. Specifically, we discuss different types of word representations, clinical text corpora, available pre-trained clinical word vector embeddings, intrinsic and extrinsic evaluation, applications, and limitations of these approaches. This work can be used as a blueprint for clinicians and healthcare workers who may want to incorporate clinical text features in their own models and applications.
Learned Video Compression

We present a new algorithm for video coding, learned end-to-end for the low-latency mode. In this setting, our approach outperforms all existing video codecs across nearly the entire bitrate range. To our knowledge, this is the first ML-based method to do so. We evaluate our approach on standard video compression test sets of varying resolutions, and benchmark against all mainstream commercial codecs in the low-latency mode. On standard-definition videos, HEVC/H.265, AVC/H.264 and VP9 typically produce codes up to 60% larger than our algorithm. On high-definition 1080p videos, H.265 and VP9 typically produce codes up to 20% larger, and H.264 up to 35% larger. Furthermore, our approach does not suffer from blocking artifacts and pixelation, and thus produces videos that are more visually pleasing. We propose two main contributions. The first is a novel architecture for video compression, which (1) generalizes motion estimation to perform any learned compensation beyond simple translations, (2) rather than strictly relying on previously transmitted reference frames, maintains a state of arbitrary information learned by the model, and (3) enables jointly compressing all transmitted signals (such as optical flow and residual). Secondly, we present a framework for ML-based spatial rate control - a mechanism for assigning variable bitrates across space for each frame. This is a critical component for video coding, which to our knowledge had not been developed within a machine learning setting.
Indices Matter: Learning to Index for Deep Image Matting

We show that existing upsampling operators can be unified using the notion of the index function. This notion is inspired by an observation in the decoding process of deep image matting where indices-guided unpooling can often recover boundary details considerably better than other upsampling operators such as bilinear interpolation. By viewing the indices as a function of the feature map, we introduce the concept of 'learning to index', and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data and are used to guide the pooling and upsampling operators, without extra training supervision. At the core of this framework is a flexible network module, termed IndexNet, which dynamically generates indices conditioned on the feature map. Due to its flexibility, IndexNet can be used as a plug-in applying to almost all off-the-shelf convolutional networks that have coupled downsampling and upsampling stages. We demonstrate the effectiveness of IndexNet on the task of natural image matting where the quality of learned indices can be visually observed from predicted alpha mattes. Results on the Composition-1k matting dataset show that our model built on MobileNetv2 exhibits at least 16.1% improvement over the seminal VGG-16 based deep matting baseline, with less training data and lower model capacity. Code and models have been made available at: https://tinyurl.com/IndexNetV1.
Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey

Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a black box model due to their multilayer nonlinear structure, Deep Neural Networks are often criticized as being non-transparent and their predictions not traceable by humans. Furthermore, the models learn from artificially generated datasets, which often do not reflect reality. By basing decision-making algorithms on Deep Neural Networks, prejudice and unfairness may be promoted unknowingly due to a lack of transparency. Hence, several so-called explanators, or explainers, have been developed. Explainers try to give insight into the inner structure of machine learning black boxes by analyzing the connection between the input and output. In this survey, we present the mechanisms and properties of explaining systems for Deep Neural Networks for Computer Vision tasks. We give a comprehensive overview about the taxonomy of related studies and compare several survey papers that deal with explainability in general. We work out the drawbacks and gaps and summarize further research ideas.
Blockchain for power systems: Current trends and future applications

Today, the blockchain is synonymous of technological innovation, being recognized among the 10 top strategy technologies in 2018 by the consulting company Gartner, it is more and more adopted in different sectors. However, the initial enthusiasm around this technology is going beyond the peak of inflated expectations, towards more stable applications in money transactions, cryptocurrencies and Digital Commodity Exchanges. Essentially, misguided efforts, the overuse of blockchain, and the Bitcoin's price drop have been the main reasons for this decay in expectations. Nevertheless, the exploitation of the blockchain technology in the power systems area appears largely underexplored, furthermore, the relation to the physical asset makes the blockchain application more complex but also more reliable and related to measurable benefits. The most common applications in the power systems area relate to the energy market. When the blockchain technology is indeed applied to the energy field, the term energy blockchain is used. This article aims to propose a wide perspective about the application of the blockchain technology in the power systems area, clarifying some technical aspects concerning this promising technology, the features and applications developed so far, while focusing on the future of innovative applications in the electrical energy sector.
AR-based interaction for human-robot collaborative manufacturing

Industrial standards define safety requirements for Human-Robot Collaboration (HRC) in industrial manufacturing. The standards particularly require real-time monitoring and securing of the minimum protective distance between a robot and an operator. This paper proposes a depth-sensor based model for workspace monitoring and an interactive Augmented Reality (AR) User Interface (UI) for safe HRC. The AR UI is implemented on two different hardware: a projector-mirror setup and a wearable AR gear (HoloLens). The workspace model and UIs are evaluated in a realistic diesel engine assembly task. The AR-based interactive UIs provide 21–24% and 57–64% reduction in the task completion and robot idle time, respectively, as compared to a baseline without interaction and workspace sharing. However, user experience assessment reveal that HoloLens based AR is not yet suitable for industrial manufacturing while the projector-mirror setup shows clear improvements in safety and work ergonomics.