Reviewerly/exHarmony huggingface dataset:
Insulator Contamination Perception Based on Feature Fusion of Infrared Image and Meteorological Parameters

Polluted insulators seriously threaten the safe and stable operation of power grids, which attaches great significance to insulator contamination perception. Among the present methods, the non-contact approaches based on infrared images have gradually been widely used, as they are much more safe and are of low cost. However, the thermal effect of insulators is largely affected by meteorological conditions, which makes the infrared image-based methods less accurate. To solve the above problem, we take infrared image and meteorological parameters including humidity and temperature as input, and propose a feature fusion model to perceive insulator contamination in different weather conditions. Firstly, different feature extraction networks are used to perform feature extraction on the two types of data; secondly, the two features are concatenated to fuse together; thirdly, further feature extraction is performed and contamination is classified according to the pollution severity. Case studies show that the proposed method can better explore the relationship between humidity, temperature and pollution level of the insulators, thus can better separate the contamination grades and outperform the conventional infrared image based methods.
DCINN: Deformable Convolution and Inception Based Neural Network for Tattoo Text Detection Through Skin Region

Identifying Tattoo is an integral part of forensic investigation and crime identification. Tattoo text detection is challenging because of its freestyle handwriting over the skin region with a variety of decorations. This paper introduces Deformable Convolution and Inception based Neural Network (DCINN) for detecting tattoo text. Before tattoo text detection, the proposed approach detects skin regions in the tattoo images based on color models. This results in skin regions containing Tattoo text, which reduces the background complexity of the tattoo text detection problem. For detecting tattoo text in the skin regions, we explore a DCINN, which generates binary maps from the final feature maps using differential binarization technique. Finally, polygonal bounding boxes are generated from the binary map for any orientation of text. Experiments on our Tattoo-Text dataset and two standard datasets of natural scene text images, namely, Total-Text, CTW1500 show that the proposed method is effective in detecting Tattoo text as well as natural scene text in the images. Furthermore, the proposed method outperforms the existing text detection methods in several criteria.
Wikinegata

Databases about general-world knowledge, so-called knowledge bases (KBs), are important in applications such as search and question answering. Traditionally, although KBs use open world assumption, popular KBs only store positive information, but withhold from taking any stance towards statements not contained in them. In this demo, we show that storing and presenting noteworthy negative statements would be important to overcome current limitations in various use cases. In particular, we introduce the Wiki neg ata portal, a platform to explore negative statements for Wikidata entities, by implementing a peer-based ranking method for inferring interesting negations in KBs. The demo is available at http://d5demos.mpi-inf.mpg.de/negation.
Seven Pain Points of Threat Analysis and Risk Assessment in the Automotive Domain

Delivering new features connected to the Internet while enforcing security in vehicles has pushed the automotive industry into turmoil. The automotive industry will inevitably face seven pain points while adapting their threat analysis and risk assessment practices.
Recurrent Convolutional Neural Network MSER-Based Approach for Payable Document Processing

A tremendous amount of vendor invoices is generated in the corporate sector. To automate the manual data entry in payable documents, highly accurate Optical Character Recognition (OCR) is required. This paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash disbursement. For text localization, the maximally stable extremal region is used, which extracts a word or digit chunk from an invoice. This chunk is later passed to the deep learning model, which performs text recognition. The deep learning model utilizes both convolution neural networks and long short-term memory (LSTM). The convolution layer is used for extracting features, which are fed to the LSTM. The model integrates feature extraction, modeling sequence, and transcription into a unified network. It handles the sequences of unconstrained lengths, independent of the character segmentation or horizontal scale normalization. Furthermore, it applies to both the lexicon-free and lexicon-based text recognition, and finally, it produces a comparatively smaller model, which can be implemented in practical applications. The overall superior performance in the experimental evaluation demonstrates the usefulness of the proposed model. The model is thus generic and can be used for other similar recognition scenarios.
The Availability of a Hidden Real Reference Affects the Plausibility of Position-Dynamic Auditory AR

This study examines the plausibility of Auditory Augmented Reality (AAR) realized with position-dynamic binaural synthesis over headphones. An established method to evaluate the plausibility of AAR asks participants to decide whether they are listening to the virtual or real version of the sound object. To date, this method has only been used to evaluate AAR systems for seated listeners. The AAR realization examined in this study instead allows listeners to turn to arbitrary directions and walk towards, past, and away from a real loudspeaker that reproduced sound only virtually. The experiment was conducted in two parts. In the first part, the subjects were asked whether they are listening to the real or the virtual version, not knowing that it was always the virtual version. In the second part, the real versions of the scenes where the loudspeaker actually reproduced sound were added. Two different source positions, three different test stimuli, and two different sound levels were considered. Seventeen volunteers, including five experts, participated. In the first part, none of the participants noticed that the virtual reproduction was active throughout the different test scenes. The inexperienced listeners tended to accept the virtual reproduction as real, while experts distributed their answers approximately equally. In the second part, experts could identify the virtual version quite reliably. For inexperienced listeners, the individual results varied enormously. Since the presence of the headphones influences the perception of the real sound field, this shadowing effect had to be considered in the creation of the virtual sound source as well. This requirement still limits test methods considering the real version in its ecological validity. Although the results indicate that the availability of a hidden real reference leads to a more critical evaluation, it is crucial to be aware that the presence of the headphones slightly distorts the reference. This issue seems more vital to the plausibility estimates achieved with this evaluation method than the increased freedom in motion.
Do data breaches damage reputation? Evidence from 45 companies between 2002 and 2018

Abstract While data breaches have become more common, there is little evidence that companies that incur them experience a persistent decline in financial performance or security prices. Using new firm-level data between 2002 and 2018, this paper finds that firms experience a 26–29% increase in reputational intangible capital following an average data breach. However, the largest and most salient breaches are associated with a 5–9% decline in reputational intangible capital following a data breach. These effects are concentrated among firms in consumer-facing industries: smaller (larger) data breaches are associated with more positive (negative) effects. These results suggest that current regulatory guidance may not provide complete incentives for firms to invest in cybersecurity capabilities, particularly for small- to medium-sized breaches.
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples

Despite the fact that deep reinforcement learning (RL) has surpassed human-level performances in various tasks, it still has several fundamental challenges. First, most RL methods require intensive data from the exploration of the environment to achieve satisfactory performance. Second, the use of neural networks in RL renders it hard to interpret the internals of the system in a way that humans can understand. To address these two challenges, we propose a framework that enables an RL agent to reason over its exploration process and distill high-level knowledge for effectively guiding its future explorations. Specifically, we propose a novel RL algorithm that learns high-level knowledge in the form of a finite reward automaton by using the L* learning algorithm. We prove that in episodic RL, a finite reward automaton can express any non-Markovian bounded reward functions with finitely many reward values and approximate any non-Markovian bounded reward function (with infinitely many reward values) with arbitrary precision. We also provide a lower bound for the episode length such that the proposed RL approach almost surely converges to an optimal policy in the limit. We test this approach on two RL environments with non-Markovian reward functions, choosing a variety of tasks with increasing complexity for each environment. We compare our algorithm with the state-of-the-art RL algorithms for non-Markovian reward functions, such as Joint Inference of Reward machines and Policies for RL (JIRP), Learning Reward Machine (LRM), and Proximal Policy Optimization (PPO2). Our results show that our algorithm converges to an optimal policy faster than other baseline methods.
Tour Planning Design for Mobile Robots Using Pruned Adaptive Resonance Theory Networks

The development of intelligent algorithms for controlling autonom- ous mobile robots in real-time activities has increased dramatically in recent years. However, conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories. The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory (PPART) neural network for effectively managing the touring process of autonomous mobile robots in real-time. The proposed system is implemented using the AlphaBot platform, and the performance of the system is evaluated according to the obstacle prediction accuracy, path detection accuracy, time-lapse, tour length, and the overall accuracy of the system. The proposed system provide a very high obstacle prediction accuracy of 99.61%. Accordingly, the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.
End-to-End Approach for Recognition of Historical Digit Strings

The plethora of digitalised historical document datasets released in recent years has rekindled interest in advancing the field of handwriting pattern recognition. In the same vein, a recently published data set, known as ARDIS, presents handwritten digits manually cropped from 15.000 scanned documents of Swedish churches' books that exhibit various handwriting styles. To this end, we propose an end-to-end segmentation- free deep learning approach to handle this challenging ancient handwriting style of dates present in the ARDIS dataset (4-digits long strings). We show that with slight modifications in the VGG-16 deep model, the framework can achieve a recognition rate of 93.2%, resulting in a feasible solution free of heuristic methods, segmentation, and fusion methods. Moreover, the proposed approach outperforms the well-known CRNN method (a model widely applied in handwriting recognition tasks).
Capsule Network Distributed Learning with Multi-Access Edge Computing for the Internet of Vehicles

For realizing an intelligent transport system, a vast amount of raw image data is required to train various intelligent applications on the Internet of Vehicles (IoV). A capsule network performs well in the computer vision area with fewer model parameters compared to convolutional neural networks. Due to the small-scale model, multi-access edge computing (MEC) devices can support online training for the whole capsule network model. In this article, we propose a novel framework for MEC-based capsule network (CapsMEC) distributed learning for IoV applications. Capsules in CapsMEC are specially designed to train in a collaborative way, which relieves the network pressure in MEC and the time-consuming training time of the traditional capsule network. Experimental results prove the effectiveness of the proposed framework.
Route planning of mixed ant colony algorithm based on Dubins path

Nowadays, with the rapid development of UAV and robot technology, the route planning of UAV and robot has become an inevitable problem. How to design a high precision and high reliability algorithm to guide the UAV or robot to the target point with the optimal path and avoid obstacles and threats in the path has become a hot research topic. In this paper, the grid system is used to divide the map into two values to determine the passable area and the threat area. In the route planning, the pheromone concentration and heuristic information are set based on the ant colony algorithm, and the heuristic factor is improved on the basis of the traditional ant colony algorithm. Thus, the improved algorithm can solve a feasible path and speed up the convergence speed. At the same time, Dubins curve is used to curve the solution path, so that the path can meet the requirements of flight curvature of UAV. In addition, this paper compares the difference between the improved ant colony algorithm and the traditional ant colony algorithm, and tests the influence of different parameters in the hybrid ant colony algorithm on the algorithm effect.
<i>P</i>-CURVING AS A SAFEGUARD AGAINST <i>P</i>-HACKING IN SLA RESEARCH

Abstract It is important to be able to identify research results likely to have been arrived at by means of “ p -hacking,” a common term for research and reporting practices (such as the selective reporting of results) that are biased toward finding p &lt; α. This paper discusses and demonstrates “ p -curving,” a means of checking a set of primary studies within a specific research stream for signs of p -hacking. A salient feature of p -curving is that it is based entirely on significant p -values. Because of the potential usefulness of p -curving and because it has been little used by SLA researchers, a case study illustrates the construction and analysis of a p -curve as a complement to meta-analysis. The focal p -curve in this study relates to published (quasi)experimental studies that addressed the research hypothesis that for low and middle proficiency learners L1 glosses facilitate vocabulary learning during reading better than L2 glosses do.
Event Relation Reasoning Based on Event Knowledge Graph

Natural language text contains numerous event-based, and a large number of semantic relations exist between events. Event relations express the event rationality logic and reveal the evolution process of events, which is of great significance for machines to understand the text and the construction of event-based knowledge base. Event relation discovery includes extracting event relation from text and obtaining event relation by reasoning. Event relation extraction focuses on the recognition of explicit relations, while event relation reasoning can also discover implicit relations, which is more meaningful and more difficult. In this paper, we propose a model combining LSTM and attention mechanism for event relation reasoning, which uses the attention mechanism to dynamically generate event sequence representation according to the type of relation and predicts the event relation. The macro-F1 value in the experimental result reaches 63.71%, which shows that the model can effectively discover implicit event-event relation.
IT’S ABOUT ENJOYING THE VIRTUAL EXPERIENCE: THE ROLE OF ENJOYMENT AND ENGAGEMENT IN THE ADOPTION OF VIRTUAL REALITY IN MARKETING EDUCATION

Digital technologies are becoming more embedded and prevalent in everyday life. One leading technology, Virtual Reality (VR), is well-positioned to extend its impact past the gaming industry and move deeper into education, particularly in university settings. Embracing digital technologies is essential in keeping marketing education relevant. Based on the technology acceptance model, this mixed-methods study investigates the role of perceived engagement and enjoyment in adopting VR as a new technology and its influence on satisfaction and perceived learning effectiveness. The paper provides empirical evidence supporting the importance of student enjoyment in the adoption of VR. Engagement, when enjoyable, is also an important contributor to the adoption of new technology. With increasing pressure placed on universities to quickly implement digital pedagogy applications, enjoyment is critical in allowing students to more easily adopt VR applications and to feel satisfied that they can learn effectively as an integral part of their educational experience.
Examination of the Consumers’ Expectations Regarding Company’s Contribution to Ontological Security

Security is considered a basic human need, according to Maslow’s hierarchy; however, it is hard to define exactly what the term means. Globalization, the digital environment, bring new dimensions and increasingly diverse interpretations of security to life. These new approaches, while reducing the consistency of the concept, allow an understanding of individual attitudes towards security. In recent years, several researchers have analyzed the psychological dimension of security at a personal level and ontological security in relation to citizens, residents, employees and students; however, based on a review of research history, there is still a scientific gap with regard to the perspective of customers and consumers. Accepting the fact that market actors are increasingly taking their share of creating a secure environment, in our empirical study we focus on corporate involvement and contribution through an examination of consumers’ perceptions of security. Corporate security is originally a field supporting the smooth operation of the organization, but nowadays it has become an important element of corporate strategy and also a factor of competitiveness. Keeping that in mind, our objective is to get a picture of how consumers judge the contribution of companies to the general sense of security in Hungary, what security-related tasks they expect from the corporate actors, and how they feel about a company’s security-related performance. In our study, we develop the ontological security model of consumers, into which we integrate—next to the individual socio-psychological features and the macro-environmental elements—the dimension of the assessment of corporate performance, the possible components of which are analyzed during the quantitative primary research.
Inflection reflection: images in mirrors whose curvature changes sign

Abstract Mirrors that are convex in some places and concave in others can generate images of extended objects (such as the viewer’s face) that are curiously distorted and often topologically disrupted. Understanding these images involves the caustics of the family of rays emitted by each point of the object, and the totality of all such families constituting the rays from all points of the object. The general theory is illustrated by the simplest mirror with an inflection, whose profile is a cubic function. Simulations, and observations with a flexible plastic mirror, show how the image changes as the viewer moves relative to the mirror.
Collaborative Knowledge-Enhanced Recommendation with Self-Supervisions

Knowledge-enhanced recommendation (KER) aims to integrate the knowledge graph (KG) into collaborative filtering (CF) for alleviating the sparsity and cold start problems. The state-of-the-art graph neural network (GNN)–based methods mainly focus on exploiting the connectivity between entities in the knowledge graph, while neglecting the interaction relation between items reflected in the user-item interactions. Moreover, the widely adopted BPR loss for model optimization fails to provide sufficient supervisions for learning discriminative representation of users and items. To address these issues, we propose the collaborative knowledge-enhanced recommendation (CKER) method. Specifically, CKER proposes a collaborative graph convolution network (CGCN) to learn the user and item representations from the connection between items in the constructed interaction graph and the connectivity between entities in the knowledge graph. Moreover, we introduce the self-supervised learning to maximize the mutual information between the interaction- and knowledge-aware user preferences by deriving additional supervision signals. We conduct comprehensive experiments on two benchmark datasets, namely Amazon-Book and Last-FM, and the experimental results show that CKER can outperform the state-of-the-art baselines in terms of recall and NDCG on knowledge-enhanced recommendation.
Constructing qudits from infinite-dimensional oscillators by coupling to qubits

Quantum harmonic oscillators are promising resources for quantum computation and quantum error correction, owing to the infinite number of available states and their ubiquitous presence in nature in various realizations, but accessing and manipulating the entire state space requires an infinite amount of energy. In this paper, the authors provide a fully analytical construction to systematically close off any finite-dimensional subspace of an oscillator and further achieve universal control on it, by coupling to a qubit.
A new hierarchy framework for feature engineering through multi‐objective evolutionary algorithm in text classification

Summary Sentiment classification is a field of sentiment analysis concerned with analyzing opinions, emotions, evaluations, and attitudes regarding a special topic like a product, an organization, a person, or an incident. With the growth of user‐generated content on the Web, this field gained great importance in online reviews. With a wide range of reviews, customers cannot read all reviews. Considering the increasing rate of electronic documents and the urgent need manually mine for keywords that are hard and time‐consuming, doing the same automatically is of high demand. A new framework proposed here to mine and classify users' comments based on mining keywords by applying the sequence pattern mining through the Separation‐Power concept, a multi‐objective evolutionary algorithm based on decomposition with four objectives, and a neural network as the final classifier. Some modifications are made on multi‐objective evolutionary algorithm based on decomposition and Apriori algorithms to improve the text classification efficiency. To evaluate the proposed framework, three datasets applied; which compared with the two methods to measure accuracy, precision, recall, and error‐index. The results indicate that this framework provides a better outcome than its counterparts with 99.45 precision, 99.34 accuracy, 99.48 recall, and 99.28% f‐measure.