Reviewerly/exHarmony huggingface dataset:
Testing Statistical Charts: What Makes a Good Graph?

It has been approximately 100 years since the very first formal experimental evaluations of statistical charts were conducted. In that time, technological changes have impacted both our charts and our testing methods, resulting in a dizzying array of charts, many different taxonomies to classify graphics, and several different philosophical approaches to testing the efficacy of charts and graphs experimentally. Once rare, charts and graphical displays are now everywhere—but do they help us understand? In this article we review the history of graphical testing across disciplines, discuss different direct approaches to testing graphics, and contrast direct tests with visual inference, which requires that the viewer determine both the question and the answer. Examining the past 100 years of graphical testing, we summarize best practices for creating effective graphics and discuss what the future holds for graphics and empirical testing of interactive statistical visualizations.
Quantum cryptography networks in support of path verification in service function chains

Quantum key distribution (QKD) is a physical technology that enables the secure generation of bit streams (keys) in two separated locations. This technology is designed to provide a solution for very secure (quantum-safe) key agreement, which is nowadays at risk due to advances in quantum computing. The recent demonstration of a QKD network in the metropolitan area of Madrid shows how these networks can be deployed in current production infrastructure by following existing networking paradigms, such as software-defined networking. In particular, a three-node QKD network is implemented on the metropolitan area network using existing infrastructure and coexisting with other data and control services. On the other hand, telecommunication networks are drastically changing the way services are architectured. Users of the operator’s infrastructure are moving from traditional connectivity services (e.g., virtual private networks) to a set of interconnected network functions, either physical or virtual, in the shape of service function chaining (SFC). However, SFC users do not have a method to validate that the traffic flow is appropriately forwarded across the nodes in the network, a situation that may lead to very critical security breaches (e.g., a security node or a firewall in the chain that is bypassed). This work presents a method for validating ordered proof-of-transit (OPoT) on top of the Madrid Quantum Network. We first provide a general description of the QKD network deployed in Madrid. Then, we describe an existing security protocol for PoT in packet networks, analyzing its issues and vulnerabilities. Finally, this work presents a protocol for alleviating the security breach found in this work and for providing OPoT in SFC. Finally, an example of the real implementation is shown, where nodes being part of the OPoT scheme are provisioned with QKD-derived keys.
Identity-Preserving Face Hallucination via Deep Reinforcement Learning

In this paper, we propose an identity-preserving face hallucination (IPFH) method via deep reinforcement learning. Most existing methods ultra-resolve facial visual information in guidance of appearance similarity which rarely attend to recovering the semantic property, undermining further face analysis (e.g., recognition). We present a visual-semantic hallucinator relying on deep reinforcement learning to adaptively repair local details for the restoration of both identity and appearance characteristics. Specifically, we first capture the facial global topology structure to roughly recover the visual information with the pixel-wise similarity constraint. To super-resolve more photo-realistic faces, we explore the contextual interdependency to reconstruct facial local textural details (e.g., over-smoothed edges) with the constraints of visual and identity similarity. In terms of the visual similarity constraint, we develop the dual domain network with bidirectional consistency on both HR domain and LR domain to improve the appearance quality. Moreover, we introduce the identity constraint to encourage hallucinated faces to satisfy the identity property. Experimental results on several benchmarks demonstrate our method achieves promising performance on the recovery of visual and semantic information.
Understanding the Effect of Developer Sentiment on Fix-Inducing Changes: An Exploratory Study on GitHub Pull Requests

Developer emotion or sentiment in a software development environment has the potential to affect performance, and consequently, the software itself. Sentiment analysis, conducted to analyze online collaborative artifacts, can derive effects of developer sentiment. This study aims to understand how developer sentiment is related to bugs, by analyzing the difference of sentiment between regular and Fix-Inducing Changes (FIC) - changes to code that introduce bugs in the system. To do so, sentiment is extracted from Pull Requests of 6 well known GitHub repositories, which contain both code and contributor discussion. Sentiment is calculated using a tool specializing in the software engineering domain: SentiStrength-SE. Next, FICs are detected from Commits by filtering the ones that fix bugs and tracking the origin of the code these remove. Commits are categorized based on FICs and assigned separate sentiment scores (-4 to +4) based on different preceding artifacts - Commits, Comments and Reviews from Pull Requests. The statistical result shows that FICs, compared to regular Commits, contain more positive Comments and Reviews. Commits that precede an FIC have more negative messages. Similarly, all the Pull Request artifacts combined are more negative for FICs than regular Commits.
Modeling and Connecting Asset Administrative Shells for Mini Factories

The supply-chain based (SCB) concept applied to Mini Factories is a very relevant approach for current manufacturing. SBC, allows a network-like interconnectivity between assets by setting up factories inside the premises of the principal manufacturer, allowing more efficient machines utilization. The aforesaid assets need a proper representation and it is there where the core of Industry 4.0 (I4.0) comes to the rescue. According to I4.0, every asset (e.g. a machine, software, sensor or actuator, etc.), should be modeled via an Asset Administrative Shell (AAS). AAS is a knowledge structure that provides a description of the asset, its technical functionality and its relationships to other assets. It can be viewed as the as the data model from where the digital twin stems from. In this paper we extend our previously presented methodology meant for creating Asset Administrative Shells in Mini Factories with a focus on connectivity. By using our proposed methodology, Model Factory @ARTC has been able to quickly connect and integrate diverse assets into our factory of the future, demonstrating some of the enhanced capabilities of Industry 4.0.
Classifier shared deep network with multi-hierarchy loss for low resolution face recognition

Face images in real Closed-Circuit Television (CCTV) are usually with low resolution, which remarkably deteriorates the performance of existing face recognition algorithms and hinders the application of face recognition. The main technical focus of this issue, matching between high-resolution (HR) and low-resolution (LR) face images has attracted significant attention. In order to better address this problem, we propose a Classifier Shared Deep Network with Multi-Hierarchy Loss (CS-MHL-Net) for low-resolution face recognition (LRFR) in this paper. Firstly, considering that contrastive loss and its variants are not conducive to the convergence of network and the reduction of discrepancy, a shared classifier between HR and LR face images is proposed to further narrow the domain gap between HR and LR by sharing the corresponding weights which can be seen as the class center. Secondly, to fully exploit intermediate features and loss constraints, we embed multi-hierarchy loss into intermediate layers, with the target of reducing the distances between HR and LR intermediate features after max pooling and avoiding the decreasing of accuracy caused by over-utilization of intermediate features. Experimental results on LFW and SCface demonstrate the effectiveness and superiority of the proposed method.
Co-training Based on Semi-Supervised Ensemble Classification Approach for Multi-label Data Stream

A large amount of data streams in the form of texts and images has been emerging in many real-world applications. These data streams often present the characteristics such as multi-labels, label missing and new class emerging, which makes the existing data stream classification algorithm face the challenges in precision space and time performance. This is because, on the one hand, it is known that data stream classification algorithms are mostly trained on all labeled single-class data, while there are a large amount of unlabeled data and few labeled data due to it is difficult to obtain labels in the real world. On the other hand, many of existing multi-label data stream classification algorithms mostly focused on the classification with all labeled data and without emerging new classes, and there are few semi-supervised methods. Therefore, this paper proposes a semi-supervised ensemble classification algorithm for multi-label data streams based on co-training. Firstly, the algorithm uses the sliding window mechanism to partition the data stream into data chunks. On the former w data chucks, the multi-label semi-supervised classification algorithm COINS based on co-training is used to training a base classifier on each chunk, and then an ensemble model with w COINS classifiers is generated ensemble model to adapt to the environment of data stream with a large number of unlabeled data. Meanwhile, a new class emerging detection mechanism is introduced, and the w+1 data chunk is predicted by the ensemble model to detect whether there is a new class emerging. When a new label is detected, the classifier is retrained on the current data chunk, and the ensemble model is updated. Finally, experimental results on five real data sets show that: as compared with the classical algorithms, the proposed approach can improve the classification accuracy of multi-label data streams with a large number of missing labels and new labels emerging.
Energy Aware Mission Planning for WMRs on Uneven Terrains

Field robotics applications typically require platforms to be deployed for extended periods of time, traversing large distances over often uneven terrains. To best leverage these platforms, it is important to know the energy costs of their operation, to maximise range and operating time. This is particularly the case for electrically powered robots, which share a single power source for actuation, sensing, and computation. In this paper we build upon our prior work—derivation of a physics-based power model for an omnidirectional wheeled mobile robot— to develop an energy-aware rough terrain motion planner, which enables efficient missions to be defined and carried out whilst considering the impact of the terrain slope and slippage characteristics on the energy cost of motion. We develop an effective pipeline for the generation of such missions on-the-fly, and compare the performance of the proposed energy-aware approach against a 3D Euclidean distance metric, experimentally verifying the increased energy efficiency of the resulting plans.
A Study on Facial Expression Recognition in Assessing Teaching Skills: Datasets and Methods

Facial expressions recognition (FER) is an important modality in the future assessing teaching skill system (ATS). The fundamental difference with FER is in its low resolution image, unique occlusion of veiled teachers, and varied lighting conditions. However, it is still able to solve the problem of variations in head poses. This study, overall, began with the collection of articles from Scopus, created the FER taxonomy class and mapped the use of datasets. After finding out which datasets were suitable for the image of expression in teaching, a deeper discussion about classifier-classifier (using the same CK+ dataset) was capable of solving problems (low resolution image, occlusion, variation in lighting conditions and head poses). Finally, from the discussion conducted, it is known the potential for modification of algorithms, appropriate datasets and research opportunities for future FER in ATS.
Off-Line Camera-Based Calibration for Optical See-Through Head-Mounted Displays

In recent years, the entry into the market of self contained optical see-through headsets with integrated multi-sensor capabilities has led the way to innovative and technology driven augmented reality applications and has encouraged the adoption of these devices also across highly challenging medical and industrial settings. Despite this, the display calibration process of consumer level systems is still sub-optimal, particularly for those applications that require high accuracy in the spatial alignment between computer generated elements and a real-world scene. State-of-the-art manual and automated calibration procedures designed to estimate all the projection parameters are too complex for real application cases outside laboratory environments. This paper describes an off-line fast calibration procedure that only requires a camera to observe a planar pattern displayed on the see-through display. The camera that replaces the user’s eye must be placed within the eye-motion-box of the see-through display. The method exploits standard camera calibration and computer vision techniques to estimate the projection parameters of the display model for a generic position of the camera. At execution time, the projection parameters can then be refined through a planar homography that encapsulates the shift and scaling effect associated with the estimated relative translation from the old camera position to the current user’s eye position. Compared to classical SPAAM techniques that still rely on the human element and to other camera based calibration procedures, the proposed technique is flexible and easy to replicate in both laboratory environments and real-world settings.
A Survey of Multi-Label Topic Models

Every day, an enormous amount of text data is produced. Sources of text data include news, social media, emails, text messages, medical reports, scientific publications and fiction. To keep track of this data, there are categories, key words, tags or labels that are assigned to each text. Automatically predicting such labels is the task of multi-label text classification. Often however, we are interested in more than just the pure classification: rather, we would like to understand which parts of a text belong to the label, which words are important for the label or which labels occur together. Because of this, topic models may be used for multi-label classification as an interpretable model that is flexible and easily extensible. This survey demonstrates the manifold possibilities and flexibility of the topic model framework for the complex setting of multi-label text classification by categorizing different variants of models.
Enhanced Adversarial Learning Based Video Anomaly Detection with Object Confidence and Position

Video anomaly detection is to identify the abnormal objects, positions and behaviours during the video sequences. It is an important but challenging problem in intelligent video surveillance. Nowadays, there is much concern about the generative adversarial networks (GAN) to detect anomalies which contains two parts: generator and discriminator. However, the two networks of this model are hard to train well at the same time in practical use. In this paper, we propose to exploit object detection to enhance the adversarial learning model and to improve classification method to distinguish anomalies in a semi-supervised manner. We also detect object position anomaly in our proposed model which can not be done in generative adversarial learning models separately. The proposed framework is evaluated on dataset UCSD Ped1 and Ped2 using two criteria: area under the curve (AUC) and equal error rate (EER). The results confirm that our proposed method can effectively improve object variety anomaly performance and detect object position anomaly and is also superior to the baseline. Our approach also achieves improved performance compared with recent state-of-the-art methods.
DroneVR: A Web Virtual Reality Simulator for Drone Operator

In recent years, Unmanned Aerial Vehicle (UAV) has been used extensively in various applications from entertainment, virtual tourism to construction, mining, agriculture. Navigation, path planning, and image acquisition are the main tasks in administering these aerial devices in accordance with real-time object tracking for affordable aerial vehicles. Aircraft crash is one of the most critical issues due to the uncontrolled environment and signal loss that cause the aerial vehicle to hit the buildings on its returning mode. Furthermore, real-time image processing, such as object tracking, has not yet been exploited for a low-cost aerial vehicle. This paper proposes a prototype embedded in a Web-based application called DroneVR to mitigate the aforementioned issues. The virtual reality environment was reconstructed based on the real-world fly data (OpenStreetMap) in which path planning and navigation were carried out. Gaussian Mixture Model was used to extract foreground and detect a moving object, Kalman Filter method was then applied to predict and keep track of object's motion. Perceived ease of use was investigated with a small sample size users to improve the simulator.
Introducing VR technology for increasing the digitalization of SMEs

One main discipline of Industry 4.0 (I4.0) is Virtual Reality (VR), which could contribute to increase product quality, decrease design and production costs and reduce the time needed to go from product concept to production. However, most of VR deployments are not very familiar to SMEs. There are various software applications that could be used to create Virtual Environment with specific scenarios usable for training SMEs' employees. This article presents two main VR platforms for the realization of virtual environments suitable for training users in the operation of LPWAN in Industry 4.0 workplace. It presents the main technological aspects of Virtual Reality, the available technologies, their potential, and their limitations for supporting the VR developers in creating VR environments that produce reliable/feasible simulations of a specific use case scenario. It presents how promising is the adoption and realization of VR into Industry 4.0.
A Dynamic Rectified Linear Activation Units

Deep neural network regression models produce substantial gains in big data prediction systems. Multilayer perceptron neural (MPL) networks have more various properties than single-layer feedforward neural networks. A deeper neural network is more intelligent and sophisticated, which is one of the main research directions. However, the disappearing gradient is the primary problem that restricts the research. The appropriate activation function is one of the effective methods for solving this problem. A bold idea about activation functions emerged: if the activation function is different in two adjacent training epochs, the probability of the same gradient value will be small. We proposed a novel activation function whose shape can be changed dynamically in training. Our experimental results show that this activation function with “dynamic” characteristics can effectively avoid the disappearing gradient and can make the multilayer perceptron neural networks deeper.
Differential-Evolution-Based Generative Adversarial Networks for Edge Detection

Since objects in natural scenarios possess various scales and aspect ratios, learning the rich edge information is very critical for vision-based tasks. Conventional generative adversarial networks (GANs) based methods for edge detection don't perform so well due to model collapse. In order to capture rich edge information and avoid model collapse as much as possible, we consider the learning of GANs as an evolutionary optimization and propose a novel method termed as differential-evolution-based generative adversarial networks (DEGAN) for richer edge detection. In particular, built upon GANs structure, we introduce an improved differential evolutionary algorithm to refine the input of generator, with fitness function evaluated by the discriminator. Experimental results on the well-known BSDS500 and NYUD benchmarks indicate our proposed DEGAN can achieve state-of-the-art performance while retaining a fast speed and validate its simplicity, effectiveness, and efficiency. The high quality of our results on edge detection with proposed DEGAN may promise to make other vision-based tasks work better.
A Study of Bug Resolution Characteristics in Popular Programming Languages

This paper presents a large-scale study that investigates the bug resolution characteristics among popular Github projects written in different programming languages. We explore correlations but, of course, we cannot infer causation. Specifically, we analyse bug resolution data from approximately 70 million Source Line of Code, drawn from 3 million commits to 600 GitHub projects, primarily written in 10 programming languages. We find notable variations in apparent bug resolution time and patch (fix) size. While interpretation of results from such large-scale empirical studies is inherently difficult, we believe that the differences in medians are sufficiently large to warrant further investigation, replication, re-analysis and follow up research. For example, in our corpus, the median apparent bug resolution time (elapsed time from raise to resolve) for Ruby was 4X that for Go and 2.5X for Java. We also found that patches tend to touch more files for the corpus of strongly typed and for statically typed programs. However, we also found evidence for a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lower</i> elapsed resolution time for bug resolution committed to projects constructed from statically typed languages. These findings, if replicated in subsequent follow on studies, may shed further empirical light on the debate about the importance of static typing.
Towards reliable digital forensics investigations through measurement science

Abstract Validation is the scientifically accepted methodology for demonstrating the accuracy and reliability of a process. Currently, digital forensics discipline does not conduct the sort of validation testing needed to develop statistical confidence in tool performance, or to generate the empirical data needed to bring objectivity to examiner conclusions. Recently recommended standards provide more reliable results, and tool testing programs provide a better understanding of tools' limitations. However, these efforts do not constitute testing over the full range of expected target device conditions, product functionality, and target device configurations which are needed for validation testing. Several major impediments to validation testing include a lack of validation methods, a lack of reference data, and a lack of the definitional precision and low‐level specificity needed to establish a system of measurement in digital forensics. This study attempts to identify the remaining needs that prevent comprehensive validation in digital forensics and identify potential solutions to address those needs. This article is characterized under: Digital and Multimedia Science &gt; Cyber Threat Intelligence Jurisprudence and Regulatory Oversight &gt; Interdisciplinary Collaboration