Reviewerly/exHarmony huggingface dataset:
Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining

Abstract We study the effects of two mechanisms which increase the efficacy of contact-tracing applications (CTAs) such as the mobile phone contact-tracing applications that have been used during the COVID-19 epidemic. The first mechanism is the introduction of user referrals. We compare four scenarios for the uptake of CTAs — (1) the p % of individuals that use the CTA are chosen randomly, (2) a smaller initial set of randomly-chosen users each refer a contact to use the CTA, achieving p % in total, (3) a small initial set of randomly-chosen users each refer around half of their contacts to use the CTA, achieving p % in total, and (4) for comparison, an idealised scenario in which the p % of the population that uses the CTA is the p % with the most contacts. Using agent-based epidemiological models incorporating a geometric space, we find that, even when the uptake percentage p % is small, CTAs are an effective tool for mitigating the spread of the epidemic in all scenarios. Moreover, user referrals significantly improve efficacy. In addition, it turns out that user referrals reduce the quarantine load. The second mechanism for increasing the efficacy of CTAs is tuning the severity of quarantine measures. Our modelling shows that using CTAs with mild quarantine measures is effective in reducing the maximum hospital load and the number of people who become ill, but leads to a relatively high quarantine load, which may cause economic disruption. Fortunately, under stricter quarantine measures, the advantages are maintained but the quarantine load is reduced. Our models incorporate geometric inhomogeneous random graphs to study the effects of the presence of super-spreaders and of the absence of long-distant contacts (e.g., through travel restrictions) on our conclusions.
Collapsed 3-Dimensional Alexandrov Spaces: A Brief Survey

We survey two recent developments in the topic of three-dimensional Alexandrov spaces: the topological classification of closed collapsed threedimensional Alexandrov spaces and the geometrization of sufficiently collapsed closed three-dimensional Alexandrov spaces.
Modeling INQNET’s time-bin qubit teleportation experiments using phase space methods

We apply phase space methods to simulate time-bin qubit teleportation experiments at the Caltech Quantum Network (CQNET) and Fermilab Quantum Network (FQNET) [1]. Our model includes all experimentally relevant parameters and contributions from the multi-photon events.
A Comparative Study of Various Approaches to Lossy Image Compression Process

An image is a 2D signal processed by the human visual system. The signal representing images are usually in analog form, but for processing, storage, transmission, and computing by computer applications, analog images are converted from analog to digital form. A digital image is a 2D array of pixels. Digital images are used in medical science, security techniques, and in transmission of data, for storing memories and for many purposes in human life. Digital image consumes storage in digital devices, as well as the size of image, may cause problem for transmission of digital image over a network, hence we need some compression method to reduce image size with respect to storage. This paper presents a comparative study of different type of lossy image compression approaches which varies on parameters such as mathematical transformation, quantization, and round-off factor (in n-MM method).
Multi-Object Detection and Tracking on Mobile Device Based on DeepLearning

Deep learning promotes the development of computer vision, but like R-CNN series models with accurate advantage and YOLO series models with speed advantage, which are heavily dependent on the powerful GPU. So it is difficult to deploy on mobile applications to achieve higher performance in mobile vision. In this study, we used the light weight MobileNet as the backbone to extract objects feature maps and referred to the training strategy of SSD and YOLO to achieve real-time multi-object detection. In addition, we carried out the multi-object tracking after detection automatically through processing some cached information like feature maps and motion position in a sequence of frames. Our proposed detection and tracking model achieved 80 categories detection at the speed of 12 FPS and up to 16 objects tracking simultaneously at the speed of over 30 FPS on iPhone 8.
EMRs with Blockchain: A Distributed Democratised Electronic Medical Record Sharing Platform

Medical data sharing needs to be done with the utmost respect for privacy and security. It contains intimate data of the patient and any access to it must be highly regulated. With the emergence of vertical solutions in healthcare institutions, interoperability across organisations has been hindered. The authors of this paper propose a blockchain based medical-data sharing solution, utilising Hyperledger Fabric to regulate access to medical data, and using the InterPlanetary File System for its storage. We believe that the combination of these two distributed solutions can enable patients to access their medical records across healthcare institutions while ensuring non-repudiation, immutability and providing data-ownership. It would enable healthcare practitioners to access all previous medical records in a single location, empowering them with the data required for the effective diagnosis and treatment of patients. Making it safe and straightforward, it would also enable patients to share medical data with research institutions, leading to the creation of reliable data sets, laying the groundwork required for the creation of personalised medicine.
Toward Conceptualizing Perplexity in Cybersecurity: An Exploratory Study

As cybersecurity threats evolve and cybersecurity teams update digital security infrastructures, employees often get perplexed by the threats and corresponding countermeasures. Drawing insight from a literature review and a qualitative exploratory study with 85 participants, this paper defines cybersecurity perplexity, a paradoxical psychological state that individuals experience when facing adverse cybersecurity conditions in the workplace (e.g., ambiguous or surprising security policies or awareness of cybersecurity threats). Cybersecurity perplexity has three interrelated dimensions: cybersecurity confusion, cybersecurity pressure, and cybersecurity response uncertainty. Central to cybersecurity perplexity is that individuals are confused and uncertain about how to respond, yet feel pressure to act, to adverse cybersecurity conditions. Our data suggests that perplexity merits researchers’ attention because it can arise from myriad cybersecurity conditions that individuals frequently encounter in the workplace. We contribute to the cybersecurity literature by providing a conceptualization of cybersecurity perplexity and initiating a discussion about this novel phenomenon encouraging future research.
Robust Sparse Low-rank Hypergraph Learning under Complex Noise

As a natural extension of the traditional graph model, hypergraph has been extensively exploited and applied in many tasks such as image clustering, classification, etc. The performance of these tasks highly depends on building an informative hypergraph to accurately and robustly formulate the underlying data correlation. Existing hypergraph construction methods can only be suitable for simple Gaussian or outlier noise assumptions, which cannot be applied to more complex noise scenario in practical applications. To address this challenge, we propose a robust hypergraph learning model by adopting the Mixture of Gaussians (MoG) noise modeling strategy. In particular, our model adopts low-rank representation and sparse representation simultaneously to construct an informative hypergraph. The Correlation among nodes and the weights of edges can be obtained by seeking a low-rank and sparse representation matrix. The so-obtained hypergraph can capture both the global mixture of subspaces structure (by low-rank) and the locally linear structure (by sparse) of the data. Furthermore, an efficient Expectation-Maximization-like optimization algorithm is designed to solve the proposed model. Finally, the superiority of our model is demonstrated by extensive experiments on image clustering.
An efficient resource deployment method for steam- based stochastic demands in distributed cloud platforms

It has been a consensus that deploying geographically dispersed stream-based online services into distributed cloud platforms has gained exceptional advantages. Globally visiting services make user requests characterised with dramatic fluctuation, which introduces stochastic demands for various resources. In order to maximise satisfied user requests and guarantee quality-of-service under given expense budget, efficient resource deployment becomes the key to this problem. We propose a stochastic demand oriented resource deployment method with more profits and less time complexity. Experiments using simulated and realistic data indicate that proposed method can outperform existing algorithms by increasing the weighted summation of satisfied demands up to 37%, fit for all scenarios with heterogeneous distributed cloud resources.
Block Formation for Storing Data on Information Security Incidents for Digital Investigations

Nowadays technologies such as Blockchain (BC) and the Internet of Things (IoT) can be heard everywhere. But because of the leap in the development of these technologies, there is a need to evaluate the existing approaches critically. One of the up-to-date tasks is to study information security (IS) incidents as a part of the IoT. Due to a large number of different manufacturers and options for technology implementation, it cannot be unambiguously concluded what choice will be better. The paper examines the related work in the area and proposes an approach to form the basis for storing data on IS incidents. For this purpose, the authors formulate a block structure for including in a chain for later use, for example, in computer forensics.
STUDY OF SATISFACTION OF STUDENTS FROM THE NON-PRESENCE FORM OF EMERGENCY STUDY

In the present study review is presented of the distance form of academic training of students from the Faculty of Education of Trakia University – Stara Zagora in music disciplines, which is carried out in a state of emergency. Emphasis is placed on the level of student satisfaction in terms of its pedagogical effectiveness.
High-fidelity quantum teleportation of time-bin qubits at telecommunication wavelength

Quantum teleportation is important for fundamental physics studies and quantum network technologies. We teleport time-bin qubits with high fidelity of 91 1% at telecom-wavelength using off-the-shelf fiber-based components and custom DAQ ± systems.
Relational Text-Type for Biological Sequences

DNA sequencers output very long biological data strings that we should persist in a database system. In this paper, we first propose a conceptual schema for representing the core biological information, which may be inferred from biological data and necessary function manipulations. We also present a possible extension of the relational text data type-specific to manipulate biological sequences and their derivatives.
Analysis of Polish Nasalized Vowels Based on Spatial Energy Distribution and Formant Frequency Measurement

In this paper, we discuss the results of the analysis of F1 and F2 frequency measurements in Polish nasalized vowels represented in writing by the graphemes ę and ą (realized before voiceless fricatives). The speech material included recordings of isolated word items provided by 20 adult native speakers of Polish (10 females and 10 males). According to the claims often presented in phonetic studies, the two vowels are phonetically realized as diphthongs composed of two subsequent stages of realization: an oral and a nasal stage. In our investigation, we refer to the results obtained by Lorenc et al. (cf. [13] and [14]) based on the analyses of spatial distribution of the acoustic field which indicate that the structure might be even more complex in certain cases and include three or even more stages. We measure formant frequencies within these stages using the stage timestamps obtained with a novel infrastructure composed of a multi-channel recorder with a circular microphone array. Among others, the results indicate that the two vowels differ significantly with regard to their internal structures as expressed by the number and types of the stages as well as frequency formant characteristics of those stages.
An Analysis of the Present Situation and Problems of English Classroom Instruction in Primary Schools

Adequate and appropriate classroom instruction language can stimulate students' learning initiative, attract students' attention, strengthen the interaction between teachers and students, and improve teaching efficiency. However, in the present primary school English classroom, there are some problems in Teachers' classroom instruction language, such as unclear directive language, too fast language speed, complex instruction language and non-standard use. In view of these problems, the author puts forward some suggestions, hoping to help primary school English teachers in improving classroom instruction.
Content Delivery Management Protocol for Mobile Computing Problems – A Innovative Approach

The concept of mobile computing has become a fascinating field of examination because of the headway of remote innovation. With the quickly expanding data transmission of remote organizations and the request of securing data anyplace, whenever, conveying substance to cell phones in a successful, effective, and customized way has been perceived as one of the significant capacities for empowering data on-request. The idea of 'client profile' is utilized for conveying tweaked substance to versatile clients. The client profiles live in an application worker, which incorporates clients' data advantages, properties of cell phones, and media inclinations. At the point when a remote application gets a data demand from a portable client, it gets pertinent substance from either organization information bases or different sources including the Internet-dependent on catchphrases indicated by the client, alter it dependent on clients' inclinations and organization condition and return it to the client. In different cases, a remote application may naturally multicast certain data to a gathering of clients who share the normal interest using the 'push' innovation. This report intends to investigate powerful approaches to empower the conveyance of customized substance to cell phones under the limitations forced by remote organizations and cell phones. The work introduced in this paper is the consequence of the course directed @ the eighth-semester level by the under-graduate understudy.
Fundamentals of contemporary semantics: An interview with Gennaro Chierchia

Roberta Pires de Oliveira interviews Gennaro Chierchia The interview surveys the main topics that constitute common-ground in contemporary semantics aiming at introducing a map to guide students’ education as linguists.
Diophantine Attack on Prime Power With Modulus $N=p^rq$

The importance of keeping information secret cannot be overemphasized especially in today,s digital world where eavesdroppers are rampant in our chanels of communication. This made the use of strong encryption schemes inevitable in order to safeguard the security of our system. RSA cryptosystem and its variants have been designed to provide confidentiality and integrity of data in our medium of communication. This paper reports new short decryption exponent attack on prime power with modulus $N=p^rq$ for $r\geq 2$ using continued fraction method which makes it vulnerable to Diophantine attack and breaks the security of the cryptosystem by factoring the modulus into its prime factors since the hardness relies on the integer factorization problem. The paper also shows that if the short decryption exponent $d
Two-Stages Multimedia Data Encryption and Compression via Joint Chaotic Compressive Sensing and Latent Vector Transmission

we propose a digital communication system in presence of noise and quantization guaranteeing two-levels, joint dimensionality reduction and security of multimedia data with the aid of compressive sensing theory and a stacked autoencoder architecture.
A Study on the Satisfaction Degree of High School Graduates with the New College Entrance Examination under the Influence of Multiple Factors

This paper discusses the opinions of high school graduates in Hangzhou on their satisfaction with college entrance examination under the influence of many factors. On the basis of the sample data collected from the questionnaire survey, the contingency table was used to determine the influencing factors on the satisfaction degree of high school graduates in choosing courses. On this basis, a binary logistic regression model was established to predict the satisfaction degree of high school graduates in choosing courses according to the equation.
Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles

In this paper, we propose a novel deep Q-network (DQN)-based edge selection algorithm designed specifically for real-time surveillance in unmanned aerial vehicle (UAV) networks. The proposed algorithm is designed under the consideration of delay, energy, and overflow as optimizations to ensure real-time properties while striking a balance for other environment-related parameters. The merit of the proposed algorithm is verified via simulation-based performance evaluation.
Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations

In recent years, parsing performance is dramatically improved on in-domain texts thanks to the rapid progress of deep neural network models.The major challenge for current parsing research is to improve parsing performance on out-of-domain texts that are very different from the indomain training data when there is only a small-scale out-domain labeled data.To deal with this problem, we propose to improve the contextualized word representations via adversarial learning and fine-tuning BERT processes.Concretely, we apply adversarial learning to three representative semi-supervised domain adaption methods, i.e., direct concatenation (CON), feature augmentation (FA), and domain embedding (DE) with two useful strategies, i.e., fused targetdomain word representations and orthogonality constraints, thus enabling to model more pure yet effective domain-specific and domain-invariant representations.Simultaneously, we utilize a large-scale target-domain unlabeled data to fine-tune BERT with only the language model loss, thus obtaining reliable contextualized word representations that benefit for the cross-domain dependency parsing.Experiments on a benchmark dataset show that our proposed adversarial approaches achieve consistent improvements, and fine-tuning BERT further boosts the parsing accuracy by a large margin.Our single model achieves the same state-of-the-art performance as the top submitted system in the NLPCC-2019 shared task, which uses ensemble models and BERT.
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks

Semi-supervised node classification on graph-structured data has many applications such as fraud detection, fake account and review detection, user's private attribute inference in social networks, and community detection. Various methods such as pairwise Markov Random Fields (pMRF) and graph neural networks were developed for semi-supervised node classification. pMRF is more efficient than graph neural networks. However, existing pMRF-based methods are less accurate than graph neural networks, due to a key limitation that they assume a heuristics-based constant edge potential for all edges. In this work, we aim to address the key limitation of existing pMRF-based methods. In particular, we propose to learn edge potentials for pMRF. Our evaluation results on various types of graph datasets show that our optimized pMRF-based method consistently outperforms existing graph neural networks in terms of both accuracy and efficiency. Our results highlight that previous work may have underestimated the power of pMRF for semi-supervised node classification.
Predicting the Quality of Translations Without an Oracle

Even though machine translation (MT) systems have shown promise for automatic translations, the quality of translations produced by MT systems is still far behind professional human translations (HTs), because of the complexity of grammar and word usage in natural languages. As a result, HTs are still commonly used in practice. Nevertheless, the quality of HTs is strongly depending on the skills and knowledge of translators. How to measure the quality of translations produced by MT systems and human translators in an automatic manner has faced a lot of challenges. The transitional way to manually checking the accuracy of translation quality by bilingual speakers is expensive and time-consuming. Therefore, we propose an unsupervised method to assess HTs and MTs quality without having access to any labelled data. We compare a range of methods which are able to automatically grade the quality of HTs and MTs, and observe that the Bidirectional Minimum Word Mover’s Distance (BiMWMD) obtains the best performance on both HTs and MTs dataset.
Automated Assessment of the Curliness of Collagen Fiber in Breast Cancer

The growth and spread of breast cancer are influenced by the composition and structural properties of collagen in the extracellular matrix of tumors. Straight alignment of collagen has been attributed to tumor cell migration, which is correlated with tumor progression and metastasis in breast cancer. Thus, there is a need to characterize collagen alignment to study its value as a prognostic biomarker. We present a framework to characterize the curliness of collagen fibers in breast cancer images from DUET (DUal-mode Emission and Transmission) studies on hematoxylin and eosin (H&E) stained tissue samples. Our novel approach highlights the characteristic fiber gradients using a standard ridge detection method before feeding into the convolutional neural network. Experiments were performed on patches of breast cancer images containing straight or curly collagen. The proposed approach outperforms in terms of area under the curve against transfer learning methods trained directly on the original patches. We also explore a feature fusion strategy to combine feature representations of both the original patches and their ridge filter responses.
Multi-parameter Regression of Photovoltaic Systems using Selection of Variables with the Method: Recursive Feature Elimination for Ridge, Lasso and Bayes

The research focuses on the application of regularization techniques in a multiparameter linear regression model to predict the DC voltage levels of a photovoltaic system from 14 variables. Two predictions were made, in the first prediction, all the variables were taken, 14 independent variable and one dependent variable; Shrinkage Regularization types were applied, as a variable selection method. In the second prediction we propose the use of semiautomatic methods, we used Recursive Feature Elimination (RFE) as a variable selection method and to obtained results. We applied the following Shrinkage regularization methods: Lasso, Ridge and Bayesian Ridge. The results were validated demonstrating: linearity, normality of error terms, non-self-correlation and homoscedasticity. In all cases the precision obtained is greater than 91.99%.