Reviewerly/exHarmony huggingface dataset:
Does Mode of Digital Contact Tracing Affect User Willingness to Share Information? A Quantitative Study

Digital contact tracing can limit the spread of infectious diseases. Nevertheless, there remain barriers to attaining sufficient adoption. In this study, we investigate how willingness to participate in contact tracing is affected by two critical factors: the modes of data collection and the type of data collected. We conducted a scenario-based survey study among 220 respondents in the United States (U.S.) to understand their perceptions about contact tracing associated with automated and manual contact tracing methods. The findings indicate a promising use of smartphones and a combination of public health officials and medical health records as information sources. Through a quantitative analysis, we describe how different modalities and individual demographic factors may affect user compliance in providing four key pieces of information to contact tracing.
Practical underwater quantum key distribution based on decoy-state BB84 protocol

Polarization encoding quantum key distribution has been proven to be a reliable method to build a secure communication system. It has already been used in an inter-city fiber channel and near-Earth atmosphere channel, leaving an underwater channel the last barrier to conquer. Here we demonstrate a decoy-state BB84 quantum key distribution system over a water channel with a compact system design for future experiments in the ocean. In the system, a multiple-intensity modulated laser module is designed to produce the light pulses of quantum states, including signal state, decoy state, and vacuum state. Classical communication and synchronization are realized by wireless optical transmission. Multiple filtering techniques and wavelength division multiplexing are further used to avoid cross talk of different lights. We test the performance of the system and obtain a final key rate of 245.6 bps with an average quantum bit error rate of 1.91% over a 2.4 m water channel, in which the channel attenuation is 16.35 dB. Numerical simulation shows that the system can tolerate up to 21.7 dB total channel loss and can still generate secure keys in 277.9 m Jerlov type I ocean channel.
A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation. II. Generalization and Python Implementation

We propose a generalization of our previous KDE (kernel density estimation) method for estimating luminosity functions (LFs). This new upgrade further extend the application scope of our KDE method, making it a very flexible approach which is suitable to deal with most of bivariate LF calculation problems. From the mathematical point of view, usually the LF calculation can be abstracted as a density estimation problem in the bounded domain of $\{Z_1f_{\mathrm{lim}}(z) \}$. We use the transformation-reflection KDE method ($\hatϕ$) to solve the problem, and introduce an approximate method ($\hatϕ_{\mathrm{1}}$) based on one-dimensional KDE to deal with the small sample size case. In practical applications, the different versions of LF estimators can be flexibly chosen according to the Kolmogorov-Smirnov test criterion. Based on 200 simulated samples, we find that for both cases of dividing or not dividing redshift bins, especially for the latter, our method performs significantly better than the traditional binning method $\hatϕ_{\mathrm{bin}}$. Moreover, with the increase of sample size $n$, our LF estimator converges to the true LF remarkably faster than $\hatϕ_{\mathrm{bin}}$. To implement our method, we have developed a public, open-source Python Toolkit, called \texttt{kdeLF}. With the support of \texttt{kdeLF}, our KDE method is expected to be a competitive alternative to existing nonparametric estimators, due to its high accuracy and excellent stability. \texttt{kdeLF} is available at \url{http://github.com/yuanzunli/kdeLF} with extensive documentation available at \url{http://kdelf.readthedocs.org/en/latest~}.
Learning software requirements syntax: An unsupervised approach to recognize templates

Requirements are textual representations of the desired software capabilities. Many templates have been used to standardize the structure of requirement statements such as Rupps, EARS, and User Stories. Templates provide a good solution to improve different Requirements Engineering (RE) tasks since their well-defined syntax facilitates the different text processing steps in RE automation researches. However, many empirical studies have concluded that there is a gap between these RE researches and their implementation in industrial and real-life projects. The success of RE automation approaches strongly depends on the consistency of the requirements with the syntax of the predefined templates. Such consistency cannot be guaranteed in real projects, especially in large development projects, or when one has little control over the requirements authoring environment. In this paper, we propose an unsupervised approach to recognize templates from the requirements themselves by extracting their common syntactic structures. The resultant templates reflect the actual syntactic structure of requirements; hence it can recognize both standard and non-standard templates. Our approach uses techniques from Natural Language Processing and Graph Theory to handle this problem through three main stages (1) we formulate the problem as a graph problem, where each requirement is represented as a vertex and each pair of requirements has a structural similarity, (2) We detect main communities in the resultant graph by applying a hybrid technique combining limited dynamic programming and greedy algorithms, (3) finally, we reinterpret the detected communities as templates. Our experiments show that the suggested approach can detect templates that follow well-known standards with a 0.90 F1-measure. Moreover, the approach can detect common syntactic features for non-standard templates in more than 73.5% of the cases. Our evaluation indicates that these results are robust regardless of the number and the length of the processed requirements.
Transforming supply chain network and logistics using blockchain - a survey

The supply chain is the fastest growing sector of business technology. As it grows day by day, the difficulty of handling vast amount of transaction records thus increases. To effectively manage the supply chain, the participants in the system need to record the price, quantity, location, date, and other pertinent details. In addition to that the asymmetric information transfer in the production process, limitations of the quality monitoring, losses from the counterfeit market and the self-interests of supply chain participants are the major challenges affecting the supply chain management system. Even a small change in the production process can have an enormous smash on the financial growth, trust of the supply chain system. This survey aims to study how blockchain technology helps supply chain networks to overcome these challenges. This survey discusses the current status of blockchain technology and its potential benefits. This survey also discusses various implementations on blockchain-based supply chain networks and how those implementation frameworks solve the challenges in the traditional supply chain system.
Recognizing Arabic Handwritten Literal Amount Using Convolutional Neural Networks

Currently, deep learning techniques have become the core of recent research in pattern recognition domain and especially for the handwriting recognition field where the challenges for the Arabic language are stilling. Despite their high importance and performances, for the best of our acknowledge, deep learning techniques have not been investigated in the context of Arabic handwritten literal amount recognition. The main aim of this paper is to investigate the effect of several Convolutional Neural Networks CNNs based on the proposed architecture with regularization parameters for such context. To achieve this aim, the AHDB database was used where very promising results were obtained outperforming the previous works on this database.
A Critical Analysis of Offline Evaluation Decisions Against Online Results: A Real-Time Recommendations Case Study

Offline evaluation has a widespread use in the development of recommender systems. In order to perform offline evaluation, an Information Retrieval practitioner has to make several decisions, such as, choosing metrics, train–test split strategies, true positives, how to account for biases and others. These decisions have been debated for many years and they are still open to debate today. In this work, we will trial and discuss different decisions that can be taken during offline evaluation for recommender systems. We will then compare their outcome against the results of AB tests performed in an e-commerce production system, which we consider to be the gold standard for evaluation. This is done to verify in an empirical manner which decisions present corroborate with the outcome of the results of an AB test.
RA V-Net: deep learning network for automated liver segmentation

Objective.Segmenting liver from CT images is the first step for doctors to diagnose a patient's disease. Processing medical images with deep learning models has become a current research trend. Although it can automate segmenting region of interest of medical images, the inability to achieve the required segmentation accuracy is an urgent problem to be solved.Approach.Residual Attention V-Net (RA V-Net) based on U-Net is proposed to improve the performance of medical image segmentation. Composite Original Feature Residual Module is proposed to achieve a higher level of image feature extraction capability and prevent gradient disappearance or explosion. Attention Recovery Module is proposed to add spatial attention to the model. Channel Attention Module is introduced to extract relevant channels with dependencies and strengthen them by matrix dot product.Main results.Through test, evaluation index has improved significantly. Lits2017 and 3Dircadb are chosen as our experimental datasets. On the Dice Similarity Coefficient, RA V-Net exceeds U-Net 0.1107 in Lits2017, and 0.0754 in 3Dircadb. On the Jaccard Similarity Coefficient, RA V-Net exceeds U-Net 0.1214 in Lits2017, and 0.13 in 3Dircadb.Significance.Combined with all the innovations, the model performs brightly in liver segmentation without clear over-segmentation and under-segmentation. The edges of organs are sharpened considerably with high precision. The model we proposed provides a reliable basis for the surgeon to design the surgical plans.
Quantum Pin Codes

We introduce quantum pin codes: a class of quantum CSS codes. Quantum pin codes are a generalization of quantum color codes and Reed-Muller codes and share a lot of their structure and properties. Pin codes have gauge operators, an unfolding procedure and their stabilizers form so-called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> -orthogonal spaces meaning that the joint overlap between any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> stabilizer elements is always even. This last feature makes them interesting for devising magic-state distillation protocols, for instance by using puncturing techniques. We study examples of these codes and their properties.
A Generative Model for Anomaly Detection in Time Series Data

Machine and deep learning models are receiving increasing attention in smart manufacturing to optimize processes or to identify anomalous behavior. To be able to compute time series data in generative networks is a challenging task and becomes more attractive to the present date, as there are a lot of use cases. Also, creating novel audio files or detecting failures in an industrial environment gains importance. In this paper, the generator of a conditional Generative Adversarial Network (GAN) is fed directly with high-frequency data. Its encoder-decoder structure is able to learn a representation of the signal. The code is kept sparse by an additional regularization net during training. Comparing the code of the input and the reconstructed signal allows the calculation of an anomaly score for each sample and to classify the input as normal or anomalous.
A Framework for creating a Safety and Security Management System (SSMS)

Safety and security risks to critical infrastructure organizations are well known, and incidents in both fields have taken place. To help critical infrastructure organizations manage these areas, safety and security standards have been created. The main aim of this paper is to present a framework that has been created to manage both safety and security by providing guidance on how to create a Safety and Security Management System (SSMS). The framework identifies and remediates conflicts and issues between IT, OT, safety, and security. While also creating processes that can combine safety and security compliance to standards to reduce duplication of work and allow one process to manage both areas. A survey was carried out to understand if the framework would be of use to organizations and to better understand the issues users have with managing safety and security and how they manage conflicts that can occur. The survey showed key areas of concern for organizations and how the framework can be of use to them. It identified six themes from the research and identified improvements opportunities for the framework that can be implemented.
Chinese text sentiment analysis based on transformer model

As an important research area of natural language processing, text sentiment analysis has been widely used in opinion processing and product review analysis. Recurrent neural networks have the characteristics of temporal order, cannot directly extract the contextual semantic features of sentences, and cannot be computed in parallel; convolutional neural networks can be computed in parallel, but have the disadvantages of huge computation and long training time. To address the above problems, this paper proposes a Light-Transformer model based on word vector representation and positional embedding, and modifies the structure of the Transformer to keep only the Encoder module, and finally the extracted sentence features are input to two fully connected layers for classification. Experimental results on the NLPCC2014 Task2 dataset show that the method improves the classification accuracy by 0.3%-1.0% compared to traditional methods such as LSTM, CNN, etc., and the number of model parameters is greatly reduced compared to other Transformer-based models with close accuracy.
IRR Hygiene in the RPKI Era

The Internet Route Registry (IRR) and Resource Public Key Infrastructure (RPKI) both emerged as different solutions to improve routing security in the Border Gateway Protocol (BGP) by allowing networks to register information and develop route filters based on information other networks have registered. RPKI is a crypto system, with associated complexity and policy challenges; it has seen substantial but slowing adoption. IRR databases often contain inaccurate records due to lack of validation standards. Given the widespread use of IRR for routing security purposes, this inaccuracy merits further study. We study IRR accuracy by quantifying the consistency between IRR and RPKI records, analyze the causes of inconsistency, and examine which ASes are contributing correct IRR information. In October 2021, we found ROAs for around 20% of RADB IRR records, and a consistency of 38% and 60% in v4 and v6. For RIPE IRR, we found ROAs for 47% records and a consistency of 73% and 82% in v4 and v6. For APNIC IRR, we found ROAs for 76% records and a high consistency of 98% and 99% in v4 and v6. For AFRINIC IRR, we found ROAs for only 4% records and a consistency of 93% and 97% in v4 and v6.
Gene Expression Data Analysis Using Feature Weighted Robust Fuzzy -Means Clustering<i />

Clustering of gene expression data has been proven to be very useful in various applications, i.e., identifying the natural structure inherent in gene expression, understanding gene functions, mining relevant information from noisy data, and understanding gene regulation.In all these applications, genes, i.e., features, play a crucial role in characterizing them into different groups.These features may be relevant, irrelevant, or redundant, but they have different contributions during the clustering process.This paper presents a novel approach by considering the effect of features during the clustering process.In the proposed method, the fuzzy c-means the objective function is modified using a weighted Euclidean distance between the features with a monotonically decreasing function.The monotonically decreasing function helps control the features' contribution during the clustering process to partition the data into more relevant clusters.The proposed approach is validated, and performance is presented in various clustering performance measures on the different standard datasets.These clustering performance measures have also been compared with multiple state-of-the-art methods.
Why Did You Not Compare with That? Identifying Papers for Use as Baselines

We propose the task of automatically identifying papers used as baselines in a scientific article. We frame the problem as a binary classification task where all the references in a paper are to be classified as either baselines or non-baselines. This is a challenging problem due to the numerous ways in which a baseline reference can appear in a paper. We develop a dataset of 2, 075 papers from ACL anthology corpus with all their references manually annotated as one of the two classes. We develop a multi-module attention-based neural classifier for the baseline classification task that outperforms four state-of-the-art citation role classification methods when applied to the baseline classification task. We also present an analysis of the errors made by the proposed classifier, eliciting the challenges that make baseline identification a challenging problem.
Azure Functions

There is a plethora of books that will explain to you the most intricate details of working with Azure functions, so in this book we will try to keep it to the necessary minimum and refer you to Rahul Sawhney’s excellent book, Beginning Azure Functions (Apress, 2019), for the details.
Face recognition with Raspberry Pi using deep neural networks

Identifying a person through photography is difficult when dealing with different conditions such as light, colour and image cleanliness. Old facial recognition methods are not applicable due to the poor performance they have shown when dealing with a lot of data and under different conditions. However, with deep neural networks we can create systems that achieve high facial recognition efficiency from digital photography. The new deep learning methods have enabled the accuracy of person identification through digital photography to be very high. Knowing the face through software systems remains a separate problem. Facial recognition systems enable the person to be identified through digital photography and deal with large amounts of digital imaging. The aim of the research is the proposal and experimental implementation of deep neural networks, which allows face recognition from digital photography a solution based on Raspberry Pi. In addition to the efficiency of this system in different areas, this implementation is cost effective.
Standardization of Education Content for Future Engineers Based on Ontologies (by an Example of Railway Transport)

An increase in volume and complexity of engineering knowledge in the digitalization of high technology and knowledge-intensive industries including railway transport constantly raise the level of employers’ entry requirements for competences of their future employees. While recognising the potential of an ontological approach to modernise engineering education, many researchers comment that ontology-based education technologies are still insufficiently used in university practice. The primary novelty of this article lies in a number of conceptual provisions supported by the authors. Firstly, the content of education courses within engineering education programmes should be standardised through ontologies at the level of concepts and their relations. Secondly, subject ontologies harmonised with the standards for physical objects through a system of similar concepts can be considered as standards in the development of education courses. Thirdly, the ontological model of an education course has certain advantages over traditional formats for content representation (text, hypertext), as it is more systematic and available for a process of critical re-thinking. Fourthly, an education course ontology establishes the method of forming a system of professional concepts among future railway engineers. In order to translate the idea about standardisation of an education course via its concepts and relations into practice, a piece of ontology for the Artificial Intelligence Systems Course has been developed. The top-down method was applied to construct this ontology.
Definition-Augmented Jointly Training Framework for Intention Phrase Mining

We propose to mine intention phrases from large numbers of queries, for enabling rich query interpretation that identifies both query intentions and associated intention types. We formalize the notion of intention phrase as a sequence of keywords and an intention type, propose its three criteria (relevance, completeness, and clarity), and identify two key challenges. To handle the criterion modeling challenge, we design a jointly training framework with a sequence labeling model and a clarity classification model. To untangle the data sparsity challenge, we are the first to leverage definitions to learn the embeddings of words, discover a new data source (dictionary), and develop the definition-augmented encoder to generate good semantic representations for words and sentences. Our experiments over three large corpora (hotel, tourism, and product domains) verify the advantage of our model over baselines.
Spatio-Temporal Modelling Using Extreme Learning Machine

This chapter combines the developments of Chaps. 5 and 6 by using ELM to model each spatial coefficient map obtained from the EOF decomposition and extend the UQ to the spatio-temporal prediction. The modelling variance of the spatio-temporal prediction is estimated by reusing all component ELM variances obtained by the method presented in the previous chapter. The prediction variance is estimated by interpolating the expected squared residuals with a second model. However, this second modelling is performed on log-transformed data to ensure positive variance estimation, which generates the need to know the transformed variable’s variance. This issue is solved thanks again to ELM variance estimation. An application on the MeteoSwiss wind speed data is presented, providing an estimation of the power potential of aeolian energy in rural areas of Switzerland.