Reviewerly/exHarmony huggingface dataset:
What are Data Insights to Professional Visualization Users?

While many visualization researchers have attempted to define data insights, little is known about how visualization users perceive them. We interviewed 23 professional users of end-user visualization platforms (e.g., Tableau and Power BI) about their experiences with data insights. We report on seven characteristics of data insights based on interviewees' descriptions. Grounded in these characteristics, we propose practical implications for creating tools that aim to automatically communicate data insights to users.
Blockchain Enabled Digital Government and Public Sector Services: A Survey

In this paper we carry out a survey of vision & white papers and reports from industry as well as private industry actors, along with academic literature, to understand how blockchain is being used for digital government and public services. The purpose of this survey is to explore which fundamental properties of blockchain technology are being harnessed, and how; putting it in perspective by reviewing technological alternatives when pertinent, and by also discussing the cautions one needs to take to use blockchains in a prudent and correct manner. The case studies discussed in this paper are thus not exhaustive, nor is any individual case discussed in great depth. Instead, we focus on capturing a wide spectrum of representative use cases to expose the efficacy as well as limitations of integrating blockchains in the government technology stack.
Interpretable Recommender System With Heterogeneous Information: A Geometric Deep Learning Perspective

Recommender systems (RS) are ubiquitous in digital space. This paper develops a deep learning-based approach to address three practical challenges in RS: complex structures of high-dimensional data, noise in relational information, and the black-box nature of machine learning algorithms. Our method—Multi-GraphGraph Attention Network (MG-GAT)—learns latent user and business representations by aggregating a diverse set of information from neighbors of each user (business) on a neighbor importance graph. MG-GAT out-performs state-of-the-art deep learning models in the recommendation task using two large-scale datasets collected from Yelp and four other standard datasets in RS. The improved performance highlights MG-GAT’s advantage in incorporating multi-modal features in a principled manner. The features importance, neighbor importance graph, and latent representations reveal business insights on predictive features and explainable characteristics of business and users. Moreover, the learned neighbor importance graph can be used in a variety of management applications, such as targeting customers, promoting new businesses, and designing information acquisition strategies. Our paper presents a quintessential big data application of deep learning models in management while providing interpretability essential for real-world decision-making.
Exploring cybersecurity-related emotions and finding that they are challenging to measure

Abstract This paper reports on a three-part investigation into people’s perceptions of cybersecurity, based on their lived experiences. We sought thereby to reveal issues located within the Johari grid’s “Blind Spot” quadrant. We utilized research methodologies from both the Arts and Science in order firstly to identify blind spot issues, and secondly to explore their dimensions. Our investigation confirmed a number of aspects that we were indeed aware of, when it came to people’s lived cybersecurity experiences. We also identified one particular blind spot issue: widespread, but not universal, negativity towards cybersecurity. We then carried out an investigation using a recognized methodology from psychology, as a first attempt to assess the nature of this negativity and to get a sense of its roots. What our initial experiment revealed was that scoping cybersecurity-related emotions is nontrivial and will require the formulation of new measurement tools. We conclude by reporting on the challenges, to inform researchers who plan to extend the research reported in this paper.
Game-Theoretic Approach to a Decision-Making Problem for Blockchain Mining

It is an important decision-making problem for a miner in the blockchain networks if he/she participates in the mining so that he/she earns a reward by creating a new block earlier than other miners. We formulate this decision-making problem as a noncooperative game, because the probability of creating a block depends not only on one's own available computational resources, but also those of other miners. Through theoretical and numerical analyses, we show a hysteresis phenomenon of Nash equilibria depending on the reward and a jump phenomenon of miner decisions by a slight change in reward. We also show that the reward for which miners decide not to participate in the mining becomes smaller as the number of miners increases.
An Infrared-Visible Image Registration Method Based on the Constrained Point Feature

It is difficult to find correct correspondences for infrared and visible image registration because of different imaging principles. Traditional registration methods based on the point feature require designing the complicated feature descriptor and eliminate mismatched points, which results in unsatisfactory precision and much calculation time. To tackle these problems, this paper presents an artful method based on constrained point features to align infrared and visible images. The proposed method principally contains three steps. First, constrained point features are extracted by employing an object detection algorithm, which avoids constructing the complex feature descriptor and introduces the senior semantic information to improve the registration accuracy. Then, the left value rule (LV-rule) is designed to match constrained points strictly without the deletion of mismatched and redundant points. Finally, the affine transformation matrix is calculated according to matched point pairs. Moreover, this paper presents an evaluation method to automatically estimate registration accuracy. The proposed method is tested on a public dataset. Among all tested infrared-visible image pairs, registration results demonstrate that the proposed framework outperforms five state-of-the-art registration algorithms in terms of accuracy, speed, and robustness.
Learning Node Representations from Noisy Graph Structures

Learning low-dimensional representations on graphs has proved to be effective in various downstream tasks. However, noises prevail in real-world networks, which compromise networks to a large extent in that edges in networks propagate noises through the whole network instead of only the node itself. Whereas existing methods tend to focus on preserving structural properties, the robustness of the learned representations against noises is generally ignored. In this paper, we propose a novel framework to learn noise-free node representations and eliminate noises simultaneously. Since noises are often unknown on real graphs, we design two generators, namely a graph generator and a noise generator, to identify normal structures and noises in an unsupervised setting. On the one hand, the graph generator serves as a unified scheme to incorporate any useful graph prior knowledge to generate normal structures. We illustrate the generative process with community structures and power-law degree distributions as examples. On the other hand, the noise generator generates graph noises not only satisfying some fundamental properties but also in an adaptive way. Thus, real noises with arbitrary distributions can be handled successfully. Finally, in order to eliminate noises and obtain noise-free node representations, two generators need to be optimized jointly, and through maximum likelihood estimation, we equivalently convert the model into imposing different regularization constraints on the true graph and noises respectively. Our model is evaluated on both real-world and synthetic data. It outperforms other strong baselines for node classification and graph reconstruction tasks, demonstrating its ability to eliminate graph noises.
Estimation of software quality parameters for hybrid agile process model

Abstract This study aims to motivate a new hybrid agile methodology is a combination of agile models from Scrum, Extreme Programming, and Lean Software Development. The main aim of the hybrid agile model is the timely delivery of projects to clients with high quality at a reduced rate. But the main difficulty in hybrid agile model to effectively reflect the software quality attributes. Basically, the failure of a software project is mainly not because of inefficiency of functional features but due to inefficiency of quality attributes, like performance, reliability and effective usability. The work presents the introduction of Hybrid Agile Quality Parameter Analysis (HAQPE) that is a quality attribute driven agile development method. The outcome of developed quality attribute HAQPE was evaluated through hybrid agile process assessment by applying it to a commercial project of software industry. The results demonstrated that the developed quality attribute model is more efficient than the legacy agile development process.
A Study of Unmanned Path Planning Based on a Double-Twin RBM-BP Deep Neural Network

Addressing the shortcomings of unmanned path planning, such as significant error and low precision, a path-planning algorithm based on the whale optimization algorithm (WOA)-optimized double-blinking restricted Boltzmann machine-back propagation (RBM-BP) deep neural network model is proposed. The model consists mainly of two twin RBMs and one BP neural network. One twin RBM is used for feature extraction of the unmanned path location, and the other RBM is used for the path similarity calculation. The model uses the WOA algorithm to optimize parameters, which reduces the number of training sessions, shortens the training time, and reduces the training errors of the neural network. In the MATLAB simulation experiment, the proposed algorithm is superior to several other neural network algorithms in terms of training errors. The comparison of the optimal path under the simulation of complex road conditions shows the superior performance of this algorithm. To further test the performance algorithm introduced in this paper, a flower bed, computer room and other actual scenarios were chosen to conduct path-planning experiments for unmanned paths. The results show that the proposed algorithm has obvious advantages in path selection, reducing the running time and improving the running efficiency. Therefore, it has definitive practical value in unmanned driving.
Support for scholars coping with online harassment: an ecological framework

This article contributes to understanding the phenomenon of online harassment of scholars with a focus on examining the effectiveness of support for coping with online harassment. We collected data from 182 participants of an online survey examining scholars' experiences with online harassment. Bronfenbrenner's ecological model was used to examine the relationship between coping and support. We found that scholars used numerous engagement and disengagement coping strategies, and that the effectiveness of support for such strategies varied across genders. These findings have implications for practice and research, especially as they relate to equity and safety for scholars operating in online spaces.
Application of Differential Evolution Algorithm to Optimal Control Problem with State Variable Constraints

The tenacity of this paper was to test and search for an efficient solution by solving optimal control problems that are expected to obtain from local solutions. The goal was achieved by studying and considering the performance of an evolutionary method like differential evolution (DE) algorithms. The main objective function considered in this paper is the performance index. This index is minimized using DE while obtaining the best position of a manipulator model with a single link. The simulation results indicate a significant reduction in performance index in comparison to an existing method.
Efficient Super-Resolution Using MobileNetV3

Deep learning methods for super-resolution (SR) have been dominating in terms of performance in recent years. Such methods can potentially improve the digital zoom capabilities of most modern mobile phones, but are not directly applicable on device, due to hardware constraints. In this work, we adapt MobileNetV3 blocks, shown to work well for classification, detection and segmentation, to the task of super-resolution. The proposed models with the modified MobileNetV3 block are shown to be efficient enough to run on modern mobile phones with an accuracy approaching that of the much heavier, state-of-the-art (SOTA) super-resolution approaches.
Outdoor illumination estimation via all convolutional neural networks

When inserting a virtual object into an outdoor image, the recovery of sun orientation has a critical effect on the fusion of virtual objects and real scenes. For the problem of sun orientation estimation from a single outdoor image, a new end-to-end learning method is proposed in this paper. A luminance channel of sky region is introduced into the input to enhance the extracted image features. A network only consists of convolutional layers is designed to improve the ability of extracting image features. Pruning and quantization are used to compress the network, resulting in a large reduction in the number of network parameters and the storage space, only with a slight loss of precision.
On Quantum Channels and Operations Preserving Finiteness of the von Neumann Entropy

We describe the class (semigroup) of quantum channels mapping states with finite entropy into states with finite entropy. We show, in particular, that this class is naturally decomposed into three convex subclasses, two of them are closed under concatenations and tensor products. We obtain asymptotically tight universal continuity bounds for the output entropy of two types of quantum channels: channels with finite output entropy and energy-constrained channels preserving finiteness of the entropy.
Disaster management using unmanned aerial vehicles

Effective disaster management is one of the most important challenges faced on the global scale. The inaccessibility of large parts of terrain despite the urgent need of support immediately following a disaster poses one of the major hindrances during the management process. Over the years, unmanned aerial vehicles (UAVs) are increasingly being used to provide aid in many disaster management operations and help increase the effectiveness of a disaster response. This chapter highlights the various uses of UAVs in disaster management applications. This has been done by grouping the existing and possible applications of UAVs based on the various roles played by them during disaster preparedness and management, namely, coverage and information gathering, resource dispatch and survivor rescue, and communication. The chapter also provides insight into the various coverage algorithms, sensing systems, and communication architectures which UAVs can potentially use in disaster management. In addition to these, the core hardware requirements of the UAVs are discussed as well as the challenges that need to be overcome in order to improve their role in disaster management operations.
Improving the performance of Machine Learning Algorithms for TOR detection

The Onion Router (TOR) networks provide anonymity, in terms of identity and location, to the Internet users by encrypting traffic multiple times along the path and routing it via an overlay network of servers. Although TOR was initially developed as a medium to maintain users' privacy, cyber criminals and hackers take advantage of this anonymity, and as a result, many illegal activities are carried out using TOR networks. With the ever-changing landscape of Internet services, traditional traffic analysis methods are not efficient for analyzing encrypted traffic and there is a need for alternative methods for analyzing TOR traffic. In this paper, we develop a machine learning model to identify whether a given network traffic is TOR or nonTOR. We use the ISCX2016 TOR-nonTOR dataset to train our model and perform random oversampling and random undersampling to remove data imbalance. Furthermore, to improve the efficiency of our classifiers, we use k-fold cross-validation and Grid Search algorithms for hyperparameter tuning. Results show that we achieve more than 90% accuracy with random sampling and hyperparameter tuning methods.
A cross-layer approach towards developing efficient embedded Deep Learning systems

With the evolution of Smart Cyber–Physical Systems (CPS) and Internet-of-Things (IoT), the number of connected (intelligent) devices is increasing at an exponential rate, and so as the data being produced by them. To process this gigantic amount of data, algorithms are being proposed that can extract information from it without much human intervention. Deep Learning (DL) is one of the fields which has recently shown significant potential towards this direction, as it allows computational models (deep neural networks) composed of multiple processing layers to learn autonomously to interpret data with the help of the back-propagation algorithm. However, state-of-the-art deep neural networks (DNNs) are highly resource-hungry. To enable the use of DNNs in resource constraint mobile devices, several optimization techniques have been proposed at different abstraction layers of the computing stack. In this paper, we highlight the main categories of the optimization techniques and present a cross-layer methodology for developing performance/energy efficient DNN-based systems. At the software level, we employ a structured pruning technique along with the quantization of inputs and network parameters to reduce the computational complexity and memory requirements for the DNN inference process. At the hardware level, exploiting the error resilience characteristics of the DNNs, we make use of functional approximations in the arithmetic modules to further improve the efficiency of DNN-based systems.
A Labeled Transactions-Based Dataset on the Ethereum Network

A few datasets of blockchain networks are available to be used in evaluating intrusion detection systems, and some of the proposed detection systems are evaluated as self-generated blockchain transactions' datasets. These blockchain datasets use an unsuitable representation, which mainly depends on transaction format, and they contain non-qualified transactions' features that lead to increased false alarm rate if the detection system is deployed in real blockchain networks. Further, due to authors' copyright and privacy constraints, most of the existing blockchain datasets are unavailable to be used by other researchers. The paper aims to provide a benchmark dataset of transactions-based dataset of Ethereum network for the tuning, assessing, and comparisons of any newly proposed intrusion detection system used in Blockchain networks. The proposed datasets setup is based on a real Ethereum network and ensures abnormal transaction exposure. The proposed blockchain transactions' dataset will be publicly available and represented using a set of transactions-based features. The requirements of reliable and valid datasets have been met in the proposed transactions-based dataset to ensure its worthiness to be used by other researchers in the same field.
Feature Selection for Zero-Shot Gesture Recognition

Existing classification techniques assign a predetermined categorical label to each sample and cannot recognize the new categories that might appear after the training stage. This limitation has led to the advent of new paradigms in machine learning such as zero-shot learning (ZSL). ZSL aims to recognize unseen categories by having a high-level description of them. While deep learning has pushed the limits of ZSL for object recognition, ZSL for temporal problems such as unfamiliar gesture recognition (ZSGL) remain unexplored. Previous attempts to address ZSGL were focused on the creation of gesture attributes, attribute-based datasets, and algorithmic improvements, and there is little or no research concerned with feature selection for ZSGL problems. It is indisputable that deep learning has obviated the need for feature engineering for the problems with large datasets. However, when the data is scarce, it is critical to leverage the domain information to create discriminative input features. The main goal of this work is to study the effect of three different feature extraction techniques (raw features, engineered features, and deep learning features) on the performance of ZSGL. Next, we propose a new approach for ZSGL that jointly minimizes the reconstruction loss, semantic and classification losses. Our methodology yields an unseen class accuracy of (38%) which parallels the accuracies obtained through state-of-the-art approaches.
Accelerate Non-unit Stride Convolutions with Winograd Algorithms

While computer vision tasks target increasingly challenging scenarios, the need for real-time processing of images rises as well, requiring more efficient methods to accelerate convolutional neural networks. For unit stride convolutions, we use FFT-based methods and Winograd algorithms to compute matrix convolutions, which effectively lower the computing complexity by reducing the number of multiplications. For non-unit stride convolutions, we usually cannot directly apply those algorithms to accelerate the computations. In this work, we propose a novel universal approach to construct the non-unit stride convolution algorithms for any given stride and filter sizes from Winograd algorithms. Specifically, we first demonstrate the steps to decompose an arbitrary convolutional kernel and apply the Winograd algorithms separately to compute non-unit stride convolutions. We then present the derivation of this method and proof by construction to confirm the validity of this approach. Finally, we discuss the minimum number of multiplications and additions necessary for the non-unit stride convolutions and evaluate the performance of the decomposed Winograd algorithms. From our analysis of the computational complexity, the new approach can benefit from 1.5x to 3x fewer multiplications. In our experiments in real DNN layers, we have acquired around 1.3x speedup (Told /Tnew) of the Winograd algorithms against the conventional convolution algorithm in various experiment settings.
Simultaneous Decoherence and Mode Filtering in Quantum Channels: Theory and Experiment

Quantum networks require robust quantum channels for fast and reliable entanglement distribution over long distances. As quantum communication technology matures, it moves towards utilizing actual fibers and free-space optical channels; hence, there is a growing need for physical models describing decoherence. The primary challenge is to concisely account for numerous elements distributed along a lengthy optical path. We approach this by starting with an analytical model of a channel with just two lumped elements, one representing decoherence and the other representing mode filtering. Interestingly, we find that, while the order and relative orientation of the two elements produce a wealth of different biphoton states, the amount of entanglement in all those states is exactly the same. Then, we conduct experiments that implement this channel and verify our analytical findings. Finally, we expand our analysis to the most general fiber polarization channel, comprising a statistically significant number of arbitrarily oriented elements. We show that, over an ample range of parameters, our two-element analytical model is quite accurate in describing the fiber channel, which makes it an effective tool for gaining insights into channel decoherence.