Reviewerly/exHarmony huggingface dataset:
A Danish terminological ontology of incident management in the field of disaster management

Abstract Lack of concept clarification based on concept modelling results in inappropriate term use and inefficient information and communication technology. In this contribution, we refer to the fire aboard the ship Scandinavian Star in 1990 and the terror attack at Utøya in 2011 as examples. We establish a terminological ontology for 46 Danish concepts included in an alphabetically organized term list on incident management by the Danish Emergency Management Agency (DEMA). Based on this ontology, the main aim is to demonstrate how terminological principles may form the basis for creating structured data, which may support a common understanding of domain‐specific key concepts which again is a prerequisite for successful and well‐functioning disaster management systems. The terminological method will be explained, focusing on terminological ontologies and terminological definitions reflecting the semantic structure of the domain. Further, we illustrate the use of technology for knowledge modelling and sharing, and we briefly describe how the results of terminology work may be stored in a term bank in order to support common understanding and efficient communication. Moreover, terminological ontologies may lay the foundation for data models for disaster management systems, technologies involving Artificial Intelligence, and building blocks of semantic technologies.
Space-efficient construction of compressed suffix trees

We show how to build several data structures of central importance to string processing by taking as input the Burrows-Wheeler transform (BWT) and using small extra working space. Let n be the text length and σ be the alphabet size. We first provide two algorithms that enumerate all LCP values and suffix tree intervals in O(nlog⁡σ) time using just o(nlog⁡σ) bits of working space on top of the input re-writable BWT. Using these algorithms as building blocks, for any parameter 0<ϵ≤1 we show how to build the PLCP bitvector and the balanced parentheses representation of the suffix tree topology in O(n(log⁡σ+ϵ−1⋅log⁡log⁡n)) time using at most nlog⁡σ⋅(ϵ+o(1)) bits of working space on top of the input re-writable BWT and the output. For example, we can build a compressed suffix tree from the BWT using just succinct working space (i.e. o(nlog⁡σ) bits) and Θ(nlog⁡σ+n(log⁡log⁡n)1+δ) time, for any constant δ>0. This improves the previous most space-efficient algorithms, which worked in O(n) bits and O(nlog⁡n) time. We also consider the problem of merging BWTs of string collections, and provide a solution running in O(nlog⁡σ) time and using just o(nlog⁡σ) bits of working space. An efficient implementation of our LCP construction and BWT merge algorithms uses (in RAM) as few as n bits on top of a packed representation of the input/output and process data as fast as 2.92 megabases per second.
How to Utilize My App Reviews? A Novel Topics Extraction Machine Learning Schema for Strategic Business Purposes

Acquiring knowledge about users’ opinion and what they say regarding specific features within an app, constitutes a solid steppingstone for understanding their needs and concerns. App review utilization helps project management teams to identify threads and opportunities for app software maintenance, optimization and strategic marketing purposes. Nevertheless, app user review classification for identifying valuable gems of information for app software improvement, is a complex and multidimensional issue. It requires foresight and multiple combinations of sophisticated text pre-processing, feature extraction and machine learning methods to efficiently classify app reviews into specific topics. Against this backdrop, we propose a novel feature engineering classification schema that is capable to identify more efficiently and earlier terms-words within reviews that could be classified into specific topics. For this reason, we present a novel feature extraction method, the DEVMAX.DF combined with different machine learning algorithms to propose a solution in app review classification problems. One step further, a simulation of a real case scenario takes place to validate the effectiveness of the proposed classification schema into different apps. After multiple experiments, results indicate that the proposed schema outperforms other term extraction methods such as TF.IDF and χ2 to classify app reviews into topics. To this end, the paper contributes to the knowledge expansion of research and practitioners with the purpose to reinforce their decision-making process within the realm of app reviews utilization.
Supporting Skill-based Flexible Manufacturing with Symbolic AI Methods

Many manufacturing processes ask for greater flexibility and transparency, which represent key features for constructing robust industrial cyber-physical systems. In the Industry 4.0 scenario, where factories are equipped with 'smart' machines, it is crucial to have robust AI methods that allow to completely automate assembly by quickly adapting to various production requests. This is complemented by making the system transparent, which gives understanding how the manufacturing process is carried out and helps build trust in such systems. To this end, we exploit semantic technologies to represent machines' capabilities and match them to production requests, to address the question of flexibility, as well as AI planning techniques to generate production sequences. We utilize OWL justifications, as a well established explanations technique in OWL ontologies, to explain the matching of machines' skills to product requests and the generated production sequences. We illustrate results from our methods for an experimental production facility.
Summary of Scene Text Detection and Recognition

In recent years, scene text recognition has received much attention, and has a wealth of application scenarios, such as: photo translation, image retrieval, scene understanding and so on. However, the text in the scene is also faced with many problems, such as: light changes, deformation text, text string recognition under background noise interference, text skew and degree of curvature, and a large number of artistic fonts. Solving the above problems will always be a challenging thing. This paper reviews some recent work on text detection, text recognition and end-to-end text detection and recognition, and finally looks forward to the future development direction.
Adversarial Example Attacks in the Physical World

Deep learning has made major breakthroughs in many problems that are difficult to solve by traditional algorithms, and has become a cutting-edge technology in the field of artificial intelligence. Deep neural network is the most widely used model in deep learning. However, recent research has shown that deep neural networks are vulnerable to adversarial examples, resulting from small-magnitude and carefully crafted perturbations added to the input that mislead the model. It will bring serious security risks to systems which based on deep learning. Therefore, adversarial example has attracted much attention in the field of artificial intelligence. Adversarial examples are generated in the digital world and extended to the physical world. This paper comprehensively investigates the attack work of adversarial examples in the physical world. Firstly, the related concepts of adversarial examples and typical generation algorithms are introduced, with the purpose of discussing the challenges of adversarial attacks in the physical world. Then, we enumerate the practical applications of adversarial examples in classification tasks and target detection tasks. Finally, we give the future research direction of adversarial example attack in the physical world. By reviewing the existing literature, it is helpful to improve the shortcomings of the existing works and propose more effective attack methods.
Interferometric sensing of the tilt angle of a Gaussian beam

We investigate interferometric techniques to estimate the deflection angle of an optical beam and compare them to the direct detection of the beam deflection. We show that quantum metrology methods lead to a unifying treatment for both single photons and classical fields. Using the Fisher information to assess the precision limits of the interferometric schemes, we show that the precision can be increased by exploiting the initial transverse displacement of the beam. This gain, which is present for both Sagnac and Mach-Zehnder-like configurations, can be considerable when compared to non-interferometric methods. In addition to the fundamental increase in precision, the interferometric schemes have the technical advantage that (i) the precision limits can be saturated by a sole polarization measurement on the field, and that (ii) the detection system can be placed at any longitudinal position along the beam. We also consider position-dependent polarization measurements, and show that in this case the precision increases with the propagation distance, as well as the initial transverse displacement.
Performance Evaluation of State-of-the-Art Edge Computing Devices for DNN Inference

Fast Deep Neural Network (DNN) inference is nowadays possible thanks to the recent significant advancements in computing platforms and DNN frameworks. The advent of the Internet of Things, among other factors, leads to the massive deployment of edge computing devices, whose features and performance are very diverse. In order to help designers identify the hardware platforms and DNNs that best suit their target embedded applications, this paper presents a DNN inference performance analysis for state-of-the-art edge devices. These include Graphical Processing Units, Tensor Processing Units and Field-Programmable Systems-on-Chip. Different versions of the MobileNet and Inception DNNs and different frameworks are considered in the analysis.
Counting calories without wearables: Device-free Human Energy Expenditure Estimation

Maintaining certain physical activity levels is important to prevent or delay the onset of many medical conditions such as diabetes, or mental health disorders. Traditional calorie estimation methods require wearing devices, such as pedometers, smart watches or smart bracelets, which continuously monitor user activity and estimate the energy expenditure. However, wearable devices may not be suitable for some patients due to the need for periodic maintenance, frequent recharging and having to wear it all the time. In this paper we investigate a feasibility of a device-free human energy expenditure estimation based on RF-sensing, which recognises coarse-grained user activity, such as walking, standing, sitting or resting by monitoring the impact of a person's activity on ambient wireless links. The calorie estimation is then based on Metabolic Equivalent concept that expresses the energy cost of an activity as a multiple of a person's basal metabolic rate using Harrison-Benedict model. The experimental evaluation using low cost IEEE 802.15.4 transceivers demonstrated that the approach estimated energy expenditure within an indoor environment within 7.4% to 41.2% range when compared to a FitBit Blaze bracelet.
Deep Learning approach for Minimizing Disease Spread using Face Identification and Contact Tracing

Convolutional Neural system arrangement for anticipating the likelihood of relationship of a person with others in a social affair is the main objective of this paper. The framework joins the convolutional neural system and AI calculations. The coordinated applications are face identification, face bunching and face identifier. This technique is fit for quick order and gives preferred execution over the eigenfaces approach. The DLIB calculation encodes the face into 128D exhibit and stores it in record taking care of framework. The framework accepts video as source and employments face acknowledgment Chinese murmur bunching calculation to group the essences of a person. In view of the information from the bunch results, the apriori/affiliation rule mining calculation finds the affiliated likelihood. The framework is coordinated with google cloud to give application-based interface which makes application programming interface calls. Association rule mining algorithm is used to find the confidence probabilities of a person with other individuals of the video clipping.
Sociality of hate: The transmission of victimization of LGBT+ people through social media

Hate crimes carry many emotional and psychological detriments for those who are targeted because of who they are. The harms associated with hate are commonly theorized in the context of those directly targeted. Using a victimological lens, I consider how the harms of a mass anti-LGBT+ shooting in Orlando, Florida were carried across social media, indirectly victimizing LGBT+ people in the North East of England. This article examines seven distinct interviews conducted post-Orlando from a wider sample of 32. LGBT+ participants were victimized vicariously by receiving news of the Orlando shooting. They utilized social media to organize vigils, stand in solidarity with LGBT+ Floridians, and share in the emotional distress caused by the shooting. The findings contribute to our understandings of hate crime as a communicative tool, by examining the role of social media in carrying the emotional harms associated with hate. Through these in-depth narratives, this article encourages a conversation about how hate crimes, transmitted across social media, can victimize people who share the victimized identity with the direct victims.
Named Entity Recognition with Context-Aware Dictionary Knowledge

Named entity recognition (NER) is an important task in the natural language processing field. Existing NER methods heavily rely on labeled data for model training, and their performance on rare entities is usually unsatisfactory. Entity dictionaries can cover many entities including both popular ones and rare ones, and are useful for NER. However, many entity names are context-dependent and it is not optimal to directly apply dictionaries without considering the context. In this paper, we propose a neural NER approach which can exploit dictionary knowledge with contextual information. We propose to learn context-aware dictionary knowledge by modeling the interactions between the entities in dictionaries and their contexts via context-dictionary attention. In addition, we propose an auxiliary term classification task to predict the types of the matched entity names, and jointly train it with the NER model to fuse both contexts and dictionary knowledge into NER. Extensive experiments on the CoNLL-2003 benchmark dataset validate the effectiveness of our approach in exploiting entity dictionaries to improve the performance of various NER models.
DO BETTER INSTITUTIONS BROADEN ACCESS TO SANITATION IN <scp>SUB‐SAHARA</scp> AFRICA?

Inadequate access to sanitation remains a persistent issue in sub‐Saharan African countries, affecting children, women, and workers. We employ dynamic panel estimation to uncover the empirical relationship between institutions and access to sanitation in sub‐Sahara Africa. We find that control of corruption, regulatory quality, and voice and accountability increase access to sanitation. Moreover, a dichotomy exists between rural and urban areas in that efficient corruption control, rule of law, and government effectiveness facilitate access to sanitation in rural areas. However, only voice and accountability matter in urban areas. These findings generate important policy implications in achieving universal access to sanitation. ( JEL D72, O55, O180, P16)
Approximate two‐sided tolerance intervals for normal mixture distributions

Summary Universal and individual two‐sided tolerance intervals that take the inherent structure of normal mixture distributions into account are introduced in this paper for the purpose of monitoring the overall population and specific subpopulations. On the basis of generalised fiducial inference, a Markov chain Monte Carlo sampler is proposed to generate realisations from the generalised fiducial distributions of unknown parameters for obtaining the required tolerance intervals. Based on the simulation results, it is shown that the proposed method can maintain the empirical coverage rates sufficiently close to the nominal level. In addition, a lake acidity monitoring study is used to illustrate the proposed method.
Volatility and Value at Risk of Crypto Versus Fiat Currencies

This paper examines the behavior of Bitcoin and Ripple compared to the three fiat currencies, EURUSD, GBPUSD and CNYUSD, by comparing their volatility and VaR during the period extending from March 01, 2016 to February 28, 2019. EWMA, GARCH (1, 1), GARCH (p, q) and EGARCH (1, 1) were used to forecast volatilities. EWMA model outperformed the rest of the models for all of the selected fiat and cryptocurrencies during the in-sample period and for EURUSD, GBPUSD and Bitcoin during the out-of-sample period. GARCH (p, q) was the optimal model for the CNYUSD and Ripple in the out-of-sample period. Bitcoin and Ripple exhibit an asymmetry in their volatility which is significantly higher than all the volatilities of the studied currencies. When estimated volatilities were compared to the implied volatility, the GARCH (1, 1), GARCH (6, 6) and EWMA were the optimal models for the EURUSD, CNYUSD and GBPUSD, respectively for the in-sample period. VaR results were accepted for the EURUSD, GBPUSD and Bitcoin at all confidence levels. For the CNYUSD, VaR measures underestimated the risk at the 99% confidence level unlike Ripple's VaR that was accepted at the 90% and 99% confidence levels. Our results suggest that Bitcoin and generally the cryptocurrencies market cannot act as alternatives to fiat currencies at the moment.
Quantum error correction using weak measurements

The standard quantum error correction protocols use projective measurements to extract the error syndromes from the encoded states. We consider the more general scenario of weak measurements, where only partial information about the error syndrome can be extracted from the encoded state. We construct a feedback protocol that probabilistically corrects the error based on the extracted information. Using numerical simulations of one-qubit error correction codes, we show that our error correction succeeds for a range of the weak measurement strength, where (a) the error rate is below the threshold beyond which multiple errors dominate, and (b) the error rate is less than the rate at which weak measurement extracts information. It is also obvious that error correction with too small a measurement strength should be avoided.
Color Image Encryption Algorithm for Medical Image by Mixing Chaotic Maps

Advances in wireless network technologies such as 5G mobile communications and the development of multimedia display devices have made it possible to transfer huge amounts of images over wired and wireless Internet or to store them on third parties. These technologies have removed barriers to distance between patients and medical staff by providing clinical health care at a distance. Also these made it easier to access health services and enabled the transmission of big data such as medical images. Therefore, the necessity of protecting various sensitive information included in the medical image is increasing. In this paper, we propose a color medical image encryption algorithm to secure medical images. The proposed encryption algorithm is based on programmable cellular automata, maximum length cell automata, and Chen chaotic system. Various experiments and statistical analyzes verify the safety of the proposed algorithm.
WITHDRAWN: Sports public service system based on FPGA and Convolutional Neural Network

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Data Analysis of Bitcoin Blockchain Network Nodes

With the development of the world economy and network technology, the security and anonymity of the Bitcoin blockchain are increasingly valued by people. At the same time, because Bitcoin does not have a unified regulatory agency, the Bitcoin blockchain has also brought a series of problems, such as drug transactions and online money laundering. Therefore, node information in the Bitcoin blockchain network needs to be collected and analyzed. Problematic transactions should be analyzed and the source should be traced accurately. This article mainly explains the basic technical principles and data structure of the Bitcoin blockchain, and summarizes the latest research on the data analysis of the Bitcoin blockchain network nodes in recent years. At the same time, relevant data for the last 100,000 blocks in the Bitcoin blockchain are collected and the recent changes in the data of the Bitcoin blockchain network nodes are showed in preparation for further analysis. Finally, the analysis of node data of the Bitcoin blockchain network is summarized and prospected.
Conditions for superdecoherence

Decoherence is the main obstacle to quantum computation. The decoherence rate per qubit is typically assumed to be constant. It is known, however, that quantum registers coupling to a single reservoir can show a decoherence rate per qubit that increases linearly with the number of qubits. This effect has been referred to as superdecoherence, and has been suggested to pose a threat to the scalability of quantum computation. Here, we show that superdecoherence is absent when the spectrum of the single reservoir is continuous, rather than discrete. The reason of this absence, is that, as the number of qubits is increased, a quantum register inevitably becomes susceptible to an ever narrower bandwidth of frequencies in the reservoir. Furthermore, we show that for superdecoherence to occur in a reservoir with a discrete spectrum, one of the frequencies in the reservoir has to coincide exactly with the frequency the quantum register is most susceptible to. We thus fully resolve the conditions that determine the presence or absence of superdecoherence. We conclude that superdecoherence is easily avoidable in practical realizations of quantum computers.
Identifying Software Cost Attributes of Software Project Management in Global Software Development

The management of global and distributed software projects is a very difficult task further complicated by the emergence of new challenges inherent in stakeholder dispersion. Software cost estimation plays a central role to face challenges in the context of Global Software Development (GSD). The objective of this study is to identify software cost attributes related to GSD context to present an integrative framework encompassing these attributes. Thirty cost attributes were identified using a Systematic Literature Review (SLR) and later compiled into a framework inspired by the Software Engineering Institute (SEI) taxonomy.