Reviewerly/exHarmony huggingface dataset:
Secure Electricity Trading and Incentive Contract Model for Electric Vehicle Based on Energy Blockchain

As a neoteric high-tech product, electric vehicles (EVs) can effectively solve the problems of energy shortages and environmental pollution.On the one hand, EV can relieve the peak load of a smart grid and improve the electricity system operation.On the other hand, EV's electricity trading information can provide useful data for vehicle management departments to electricity scheduling.However, hackers can easily obtain data from the central database to simulate both parties involved, which leads to the receiver getting unauthorized information.For these challenges, we propose a novel secure electricity trading and incentive contract model based on the basic rules of China's electricity market.The digital signature technology adopts elliptic curve bilinear pairing to guarantee the reliability and integrity of the transaction information.Energy blockchain is utilized for encryption and distributed storage of energy data with the possession of tamper-proof and traceability.The consistency part of the data block applies a practical Byzantine fault-tolerant (PBFT) algorithm, which not only increases transaction throughput but also reduces transmission delay.The incentive contract based on revenue rewards can promote the benign interaction of EVs.The security analysis reveals that this scheme can achieve better results.Compared with other schemes, our scheme saves about 64.55% of the communication overhead and validates the same number of signed messages in a shorter time.Incentive contracts based on game theory can facilitate EV electricity trading through energy coin rewards.This mechanism makes EV more willing and active to participate in transactions that guarantee the activity and stability of the network.
Color image zero-watermarking based on fast quaternion generic polar complex exponential transform

As one promising solution, zero-watermarking techniques have been proposed to enhance the image visual quality and applied to protect the intellectual property rights of the medical images, remote sensing images and military images. Owing to their favorable image description capability and geometric invariance, moments and moment invariants have become a popular tool for the zero-watermarking. However, two issues of the moments-based zero-watermarking methods should be addressed: First, most of them ignore the analysis and experiment on discriminability, resulting in a high false positive ratio; Second, direct computation of the moments from their definition is inefficient, numerically unstable and inaccurate, which severely affects the performances of these moments-based methods. To overcome the two challenges, in this paper, we present a Fast Quaternion Generic Polar Complex Exponential Transform (FQGPCET) based color image zero-watermarking algorithm. We first propose a novel computation strategy, i.e. FGPCET, to solve the moments computing problems. We then show that it is possible to generate a robust and discriminative image feature, by mixing the low-order QGPCET moments/coefficients. And finally, we develop a new color image zero-watermarking approach using FQGPCET and asymmetric tent map. Theoretical analysis and experimental results show that the proposed zero-watermarking algorithm achieves a good trade-off between robustness and discriminability, and has certain superiority in terms of security, capacity and time complexity.
On cognitive preferences and the plausibility of rule-based models

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.
Convenience Theory of Cryptocurrency Crime: A Content Analysis of U.S. Federal Court Decisions

This article examines cryptocurrency cases decided in the U.S. District and Circuit Courts to determine the applicability of Gottschalk's convenience theory of white-collar crime to cryptocurrency crime litigation and to empirically analyze whether the conditions under which cryptocurrency offenses occurred show support for the convenience theory. Analysis of U.S. federal district and circuit court case law involving cryptocurrency crimes and fraud indicates support for the convenience theory of white-collar crime. Defendants in various schemes were motivated by financial gain, either for the company or for personal use. Their roles and positions in the businesses allowed them access to resources that helped them perpetrate fraud through the following mechanisms: (1) operating front companies; (2) relationship building by defendants; (3) over representing profits that investors would obtain from purchases of virtual currencies, representing that cryptocurrencies were safe and reliable investments when they were risky, and overestimating abilities and capacities to provide services promised to investors in securities fraud; (4) breaching fiduciary duties to their clients and corporate stockholders by misappropriating profits for their own personal gain; and (5) engaging in dark web transactions that guaranteed anonymity. Defendants also employed various neutralization techniques to justify their crimes.
Adaptive total variation L1 regularization for salt and pepper image denoising

In this article, we propose an adaptive total variation (TV) regularization model for salt and pepper denoising in digital images. The adaptive TV denoising method is developed based on the general regularized image restoration model with L1 fidelity for handling salt and pepper noise model. An estimation for regularization parameter is also proposed based on the characteristics of the salt and pepper noise. We implement the proposed adaptive TV-L1 regularization model efficiently for image denoising using the primal dual gradient method. In the experiments, the full-reference image quality assessment metrics are used for evaluating denoising quality across various noise levels in different synthetic and real images. The denoising results are compared to other similar salt and pepper image denoising methods and our results indicate we obtain artifact free edge preserving restorations.
A Cloud-Based Framework for Machine Learning Workloads and Applications

In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models.
Discrete Computational Neural Dynamics Models for Solving Time-Dependent Sylvester Equation With Applications to Robotics and MIMO Systems

In this article, a neural dynamics model is constructed and investigated for solving time-dependent Sylvester equation with matrix inversion involved in the solving process. Besides, to eliminate the matrix inversion in the model, the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno method is leveraged to construct a new model. Moreover, the global convergence performance and the effectiveness of the two discrete computational models are testified by providing theoretical analyses and numerical experiments with comparisons to the existing solutions, respectively. Two applications to robotics and the multiple-input multiple-output system are given to elucidate the feasibility of the proposed models for solving time-dependent Sylvester equation.
Breast Cancer Detection Based on Deep Learning Technique

Breast cancer is the most common cancer among Malaysian women and roughly one in 19 women at risk of breast cancer in Malaysia. The number of breast cancer cases is steadily growing especially with increasing number of ageing population. The screening practice using mammography needs to be better and potentially efficient. There is always room for advancement when it comes to medical imaging. Early detection of cancer can reduce the risk of deaths for cancer patients. The objective of this paper is to compare the breast cancer detection with two model networks of deep learning technique. The overall process involves image preprocessing, classification and performance evaluation. In this paper, we evaluate the performance of deep learning model network which are VGG16 and ResNet50 to classify between normal tumor and abnormal tumor using IRMA dataset. The result show that VGG16 produces the better result with 94% compared to ResNet50 with 91.7% in term of accuracy.
An integrated 6DoF video camera and system design

Designing a fully integrated 360° video camera supporting 6DoF head motion parallax requires overcoming many technical hurdles, including camera placement, optical design, sensor resolution, system calibration, real-time video capture, depth reconstruction, and real-time novel view synthesis. While there is a large body of work describing various system components, such as multi-view depth estimation, our paper is the first to describe a complete, reproducible system that considers the challenges arising when designing, building, and deploying a full end-to-end 6DoF video camera and playback environment. Our system includes a computational imaging software pipeline supporting online markerless calibration, high-quality reconstruction, and real-time streaming and rendering. Most of our exposition is based on a professional 16-camera configuration, which will be commercially available to film producers. However, our software pipeline is generic and can handle a variety of camera geometries and configurations. The entire calibration and reconstruction software pipeline along with example datasets is open sourced to encourage follow-up research in high-quality 6DoF video reconstruction and rendering 1 .
Meta-GNN

Heterogeneous Information Networks (HINs) comprise nodes of different types inter-connected through diverse semantic relationships. In many real-world applications, nodes in information networks are often associated with additional attributes, resulting in Attributed HINs (or AHINs). In this paper, we study semi-supervised learning (SSL) on AHINs to classify nodes based on their structure, node types and attributes, given limited supervision. Recently, Graph Convolutional Networks (GCNs) have achieved impressive results in several graph-based SSL tasks. However, they operate on homogeneous networks, while being completely agnostic to the semantics of typed nodes and relationships in real-world HINs.
A Heuristic Neural Network Structure Relying on Fuzzy Logic for Images Scoring

Traditional deep learning methods are suboptimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always presents unclear boundaries (normal-more likely normal-probably normal). Thus, human error is common when annotating images. Differing from existing methods that focus on modifying kernel structure of neural networks, this article proposes a dominant fuzzy fully connected layer (FFCL) for breast imaging reporting and data system (BI-RADS) scoring and validates the universality of this proposed structure. This proposed model aims to develop complementary properties of scoring for semantic paradigms, while constructing fuzzy rules based on analyzing human thought patterns, and to particularly reduce the influence of semantic conglutination. Specifically, this semantic-sensitive defuzzifier layer projects features occupied by relative categories into semantic space, and a fuzzy decoder modifies probabilities of the last output layer referring to the global trend. Moreover, the ambiguous semantic space between two relative categories shrinks during the learning phases, as the positive and negative growth trends of one category appearing among its relatives were considered. We first used the Euclidean distance to zoom in the distance between the real scores and the predicted scores, and then employed two sample t test method to evidence the advantage of the FFCL architecture. Extensive experimental results performed on the curated breast imaging subset of digital database of screening mammography dataset show that our FFCL structure can achieve superior performances for both triple and multiclass classification in BI-RADS scoring, outperforming the state-of-the-art methods.
Spatio-Temporal Representation With Deep Neural Recurrent Network in MIMO CSI Feedback

In multiple-input multiple-output (MIMO) systems, it is crucial of utilizing the available channel state information (CSI) at the transmitter for precoding to improve the performance of frequency division duplex (FDD) networks. One of the main challenges is to compress a large amount of CSI in CSI feedback transmission in massive MIMO systems. In this letter, we propose a deep learning (DL)-based approach that uses a deep recurrent neural network (RNN) to learn temporal correlation and adopts depthwise separable convolution to shrink the model. The feature extraction module is also elaborately devised by studying decoupled spatio-temporal feature representations in different structures. Experimental results demonstrate that the proposed approach outperforms existing DL-based methods in terms of recovery quality and accuracy, which can also achieve remarkable robustness at low compression ratio (CR).
Toward ML-centric cloud platforms

Exploring the opportunities to use ML, the possible designs, and our experience with Microsoft Azure.
A low-cost dual-axis solar tracking system based on digital logic design: Design and implementation

Nowadays, photovoltaic (PV) systems have been being installed rapidly all over the world. However, the installations typically employ a fixed flat-plate system, using pre-specified angles acquired by geographical positioning. Hence, the power generated by PV systems decreases significantly, according to the variation of solar irradiation in the progress of a day and the seasons in a year. In order to increase the solar power generation, this paper proposes the design and implementation of a low-cost automatic dual-axis solar tracker system. The tracking system is designed as a closed-loop control based active tracking system, employing Light Dependent Resistor (LDR) sensors as the inputs of the system. The tracking strategy utilizes a digital logic design of the sensors’ participation implemented in a pseudo-azimuthal system to simply rotate around the primary (north-south) axis and the secondary (east-west) axis. In the experiment, the proposed tracking system and the fixed flat-plate system were evaluated for the comparative study. The experimental results show that the proposed tracking system increased the efficiency of electrical energy by averagely 44.89%, using the proposed system compared with the fixed flat-plate system. Finally, the cost analysis is provided for the proposed tracking system.
Weaponizing white thymos: flows of rage in the online audiences of the alt-right

The alt-right is a growing radical right-wing network that is particularly effective at mobilizing emotion through digital communications. Introducing ‘white thymos’ as a framework to theorize the role of rage, anger, and indignation in alt-right communications, this study argues that emotive communication connects alt-right users and mobilizes white thymos to the benefit of populist radical right politics. By combining linguistic, computational, and interpretive techniques on data collected from Twitter, this study demonstrates that the alt-right weaponizes white thymos in three ways: visual documentation of white victimization, processes of legitimization of racialized pride, and reinforcement of the rectitude of rage and indignation. The weaponization of white thymos is then shown to be central to the culture of the alt-right and its connectivity with populist radical right politics.
Shades of Knowledge-Infused Learning for Enhancing Deep Learning

Deep Learning has already proven to be the primary technique to address a number of problems. It holds further promise in solving more challenging problems if we can overcome obstacles, such as the lack of quality training data and poor interpretability. The exploitation of domain knowledge and application semantics can enhance existing deep learning methods by infusing relevant conceptual information into a statistical, data-driven computational approach. This will require resolving the impedance mismatch due to different representational forms and abstractions between symbolic and statistical AI techniques. In this article, we describe a continuum that comprises of three stages for infusion of knowledge into the machine/deep learning architectures. As this continuum progresses across these three stages, it starts with shallow infusion in the form of embeddings, and attention and knowledge-based constraints improve with a semideep infusion. Toward the end reflecting deeper incorporation of knowledge, we articulate the value of incorporating knowledge at different levels of abstractions in the latent layers of neural networks. While shallow infusion is well studied and semideep infusion is in progress, we consider Deep Infusion of Knowledge as a new paradigm that will significantly advance the capabilities and promises of deep learning.
Software‐assisted decision support in digital histopathology

Abstract Tissue diagnostics is the world of pathologists, and it is increasingly becoming digitalised to leverage the enormous potential of personalised medicine and of stratifying patients, enabling the administration of modern therapies. Therefore, the daily task for pathologists is changing drastically and will become increasingly demanding in order to take advantage of the development of modern computer technologies. The role of pathologist has rapidly evolved from exclusively describing the morphology and phenomenology of a disease, to becoming a gatekeeper for novel and most effective treatment options. This is possible based on the retrieval and management of a wide range of complex information from tissue or a group of cells and associated meta‐data. Intelligent and self‐learning software solutions can support and guide pathologists to score clinically relevant decisions based on the accurate and robust quantification of multiple target molecules or surrogate biomarker as companion or complimentary diagnostics along with relevant spatial relationships and contextual information from digital H&amp;E and multiplexed images. With the availability of multiplex staining techniques on a single slide, high‐resolution image analysis tools, and high‐end computer hardware, machine and deep learning solutions now offer diagnostic rulesets and algorithms that still require clinical validation in well‐designed studies. Before entering the clinical practice, the ‘human factor’ pathologist needs to develop trust in the output coming from the ‘digital black box of computational pathology’, including image analysis solutions and artificial intelligence algorithms to support critical clinical decisions which otherwise would not be available. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley &amp; Sons, Ltd.
A Methodology Combining Cosine Similarity with Classifier for Text Classification

Text Classification has received significant attention in recent years because of the proliferation of digital documents and is widely used in various applications such as filtering and recommendation. Consequently, many approaches, including those based on statistical theory, machine learning, and classifier performance improvement, have been proposed for improving text classification performance. Among these approaches, centroid-based classifier, multinomial naïve bayesian (MNB), support vector machines (SVM), convolutional neural network (CNN) are commonly used. In this paper, we introduce a cosine similarity-based methodology for improving performance. The methodology combines cosine similarity (between a test document and fixed categories) with conventional classifiers such as MNB, SVM, and CNN to improve the accuracy of the classifiers, and then we call the conventional classifiers with cosine similarity as enhanced classifiers. We applied the enhanced classifiers to famous datasets – 20NG, R8, R52, Cade12, and WebKB – and evaluated the performance of the enhanced classifiers in terms of the confusion matrix’s accuracy; we obtained outstanding results in that the enhanced classifiers show significant increases in accuracy. Moreover, through experiments, we identified which of two considered knowledge representation techniques (word count and term frequency-inverse document frequency (TFIDF)) is more suitable in terms of classifier performance.
A comprehensive survey of prominent cryptographic aspects for securing communication in post-quantum IoT networks

Internet of Things (IoT) ideates smart and inter-connected things capable of sharing their perceptions through the Internet. These devices are different from conventional Internet-connected devices in the sense that these are able to perform skill-full things on their own with minimal or no human interaction. Unfortunately, with the advent of amalgamate technologies, security has become a major concern for IoT networks. Recent efforts include re-inventing cryptographic solutions through the use of light-weight operations. However, after witnessing the growth of quantum computers, it can be inferred that the cryptographic techniques based on mathematical problems are not reliable enough. Therefore, there is need to develop solutions that can easily resist the adversarial effects and are suitable for the post-quantum world. In this paper, we perform in-depth analysis over the role of post-quantum cryptographic techniques for securing IoT networks and also explore ongoing research efforts in the field. In addition, we discuss the open research challenges and future research directions in the field.