Reviewerly/exHarmony huggingface dataset:
Bayesian Analysis of Cancer Data Using a 4-Component Exponential Mixture Model

Cancer is among the major public health problems as well as a burden for Pakistan. About 148,000 new patients are diagnosed with cancer each year, and almost 100,000 patients die due to this fatal disease. Lung, breast, liver, cervical, blood/bone marrow, and oral cancers are the most common cancers in Pakistan. Perhaps smoking, physical inactivity, infections, exposure to toxins, and unhealthy diet are the main factors responsible for the spread of cancer. We preferred a novel four-component mixture model under Bayesian estimation to estimate the average number of incidences and death of both genders in different age groups. For this purpose, we considered 28 different kinds of cancers diagnosed in recent years. Data of registered patients all over Pakistan in the year 2012 were taken from GLOBOCAN. All the patients were divided into 4 age groups and also split based on genders to be applied to the proposed mixture model. Bayesian analysis is performed on the data using a four-component exponential mixture model. Estimators for mixture model parameters are derived under Bayesian procedures using three different priors and two loss functions. Simulation study and graphical representation for the estimates are also presented. It is noted from analysis of real data that the Bayes estimates under LINEX loss assuming Jeffreys' prior is more efficient for the no. of incidences in male and female. As far as no. of deaths are concerned again, LINEX loss assuming Jeffreys' prior gives better results for the male population, but for the female population, the best loss function is SELF assuming Jeffreys' prior.
Speech Pattern Based Black-Box Model Watermarking for Automatic Speech Recognition

As an effective method for intellectual property (IP) protection, model watermarking technology has been applied on a wide variety of deep neural networks (DNN), including speech classification models. However, how to design a black-box watermarking scheme for automatic speech recognition (ASR) models is still an unsolved problem, which is a significant demand for protecting remote ASR Application Programming Interface (API) deployed in cloud servers. Due to conditional independence assumption and label-detection-based evasion attack risk of ASR models, the black-box model watermarking scheme for speech classification models cannot apply to ASR models. In this paper, we propose the first black-box model watermarking framework for protecting the IP of ASR models. Specifically, we synthesize trigger audios by spreading the speech clips of model owners over the entire input audios and labeling the trigger audios with the stego texts, which hides the authorship information with linguistic steganography. Experiments on the state-of-the-art open-source ASR system DeepSpeech demonstrate the feasibility of the proposed watermarking scheme, which is robust against five kinds of attacks and has little impact on accuracy.
Wind Turbine Detection on Sentinel-2 Images

ESA's Sentinel- 2 satellites have been in orbit for five years, acquiring huge amounts of data all over the world. They are a formidable tool for mass detection as they are freely available. Given their importance in the energetic transition and their spread over countries or continents, wind turbines are natural candidates for such studies. We propose an automatic wind turbine detector for low resolution satellite images based on an a contrario approach, exploiting the geometry of wind turbines' shadows and hubs. Our experiments show promising detection rates, improving the state of the art in the proposed conditions.
Hypothesis Testing for Hierarchical Structures in Cognitive Diagnosis Models

Cognitive Diagnosis Models (CDMs) are a special family of discrete latent variable models widely used in educational, psychological and social sciences. In many applications of CDMs, certain hierarchical structures among the latent attributes are assumed by researchers to characterize their dependence structure. Specifically, a directed acyclic graph is used to specify hierarchical constraints on the allowable configurations of the discrete latent attributes. In this paper, we consider the important yet unaddressed problem of testing the existence of latent hierarchical structures in CDMs. We first introduce the concept of testability of hierarchical structures in CDMs and present sufficient conditions. Then we study the asymptotic behaviors of the likelihood ratio test (LRT) statistic, which is widely used for testing nested models. Due to the irregularity of the problem, the asymptotic distribution of LRT becomes nonstandard and tends to provide unsatisfactory finite sample performance under practical conditions. We provide statistical insights on such failures, and propose to use parametric bootstrap to perform the testing. We also demonstrate the effectiveness and superiority of parametric bootstrap for testing the latent hierarchies over non-parametric bootstrap and the na\"ive Chi-squared test through comprehensive simulations and an educational assessment dataset.
Thermal entanglement of superconducting qubits for arbitrary interaction strength

We investigate the thermal entanglement in two superconducting qubits for arbitrary interaction strength and ground state frequencies. We calculate the concurrence of the system to quantify the thermal entanglement. We suggest a scheme, where an external tunable coupler qubit sandwich between two superconducting qubits generates entanglement. The behavior of concurrence is analyzed for three different cases, in which we consider the effects of the temperature, the qubit-qubit effective coupling strength, and the qubit frequencies on the thermal entanglement. What deserves mentioning here is that to achieve maximally entangled states, it is better to use two superconducting qubits with the same frequencies. We also note that for a given temperature, the thermal entanglement can be tuned by qubit internal capacitance and inductance.
On the need to use modern information and communication technologies in the system of mathematical training of a future mathematics teacher

This scientific article deals with discusses and analyzes the advantages of the use of information and communication technology in the preparation of future teachers of mathematics. And also shows the important role of information and communication technologies in the organization of independent and laboratory works and students of pedagogical institutes.
Cybersecurity and the new firm: surviving online threats

Purpose Nascent firms and startups are often subject to challenges that their more mature counterparts can avoid. While cybersecurity is an issue that all firms contend with, it is especially challenging for new entrepreneurial ventures who lack the resources and capabilities of established firms. The purpose of this paper is to seek to delve deeper into the cybersecurity and risk management needs of small firms and startups. Design/methodology/approach Extant literature and available tools are explored to develop a usable framework applicable to small firms and new entrepreneurial ventures. Findings The liabilities of newness and smallness make entrepreneurial ventures a unique context in which to study the significance of cybersecurity and data privacy risk management. The authors offer an overview of issues and potential solutions relevant to entrepreneurial ventures. Research limitations/implications While offering practical insights, the work is a theoretical framework. The framework will enable researchers to develop more nuanced theory when it comes to cybersecurity and data privacy risk management. Practical implications The framework illustrates four distinct contexts for cybersecurity and risk management when it comes to the needs of small firms and startups. Adoption levels are explained, and small business operators and entrepreneurs can thus use the framework to determine the most appropriate approach for their enterprise. Originality/value The authors develop a framework illustrating adoption of different security and risk management practices by entrepreneurial ventures based on their specific needs and context. The authors thus offer practical solutions for startups and nascent firms regarding cybersecurity and privacy management.
Plug&amp;Play subcarrier wave quantum key distribution with deep modulation

We report a quantum key distribution using subcarrier wave encoding in Plug&Play configuration. Our detailed study shows how subcarrier encoding operates in a presence of Rayleigh back-scattering, chromatic dispersion and other experimental negative factors that occur in Plug&Play configuration. We propose and experimentally demonstrate a novel regime for the subcarrier wave encoding, where simultaneous detection of two logical states in a single basis is combined with robustness and simplicity of the original protocol. This deep modulation regime shows reliable quantum communication with losses up to 20 dB and promises a twice increase in secret key generation rate in comparison with the original subcarrier wave quantum key distribution in one-way or Plug&Play configurations.
Transfer Learning for KiTS21 Challenge

Transfer learning has witnessed a recent surge of interest after proving successful in multiple applications. However, it highly relies on the quantity of annotated data. Constrained by the labor cost and expertise, it is hard to annotate sufficient organs and tumors at the voxel level for medical image segmentation. Consequently, most bench-mark datasets were collected for the segmentation of only one type of organ and/or tumor, and all task-irrelevant organs and tumors were annotated as the background. We aim to make use of these partially but plentifully labeled datasets to boost the segmentation performance of the annotation-limited KiTS21 segmentation task. To this end, we first construct a general medical image segmentation model that learns to segment these partially labeled organs or tumors. Then we transfer its pre-trained weights to a specific downstream task, i.e., KiTS21. The primary experiments demonstrate the effectiveness of the proposed transfer learning strategy. Our method achieves 0.890 Dice score, 0.805 SurfaceDice, and 0.822 Tumor Dice in the KiTS21 challenge.
On the capacity of deep generative networks for approximating distributions

We study the efficacy and efficiency of deep generative networks for approximating probability distributions. We prove that neural networks can transform a low-dimensional source distribution to a distribution that is arbitrarily close to a high-dimensional target distribution, when the closeness is measured by Wasserstein distances and maximum mean discrepancy. Upper bounds of the approximation error are obtained in terms of the width and depth of neural network. Furthermore, it is shown that the approximation error in Wasserstein distance grows at most linearly on the ambient dimension and that the approximation order only depends on the intrinsic dimension of the target distribution. On the contrary, when f-divergences are used as metrics of distributions, the approximation property is different. We show that in order to approximate the target distribution in f-divergences, the dimension of the source distribution cannot be smaller than the intrinsic dimension of the target distribution.
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Owing to a reduced solar background and low propagation losses in the atmosphere, the 2- to 2.5-$\mu$m waveband is a promising candidate for daylight quantum communication. This spectral region also offers low losses and low dispersion in hollow-core fibers and in silicon waveguides. We demonstrate for the first time near-maximally entangled photon pairs at 2.1 $\mu$m that could support device independent quantum key distribution (DIQKD) assuming sufficiently high channel efficiencies. The state corresponds to a positive secure-key rate (0.254 bits/pair, with a quantum bit error rate of 3.8%) based on measurements in a laboratory setting with minimal channel loss and transmission distance. This is promising for the future implementation of DIQKD at 2.1 $\mu$m.
Design of College English Halving Classroom Teaching System Based on Big Data

Abstract With the development of big data and the continuous advancement of education informatization, the single lecture-based classroom teaching model no longer meets the development requirements of the society for the development of talent education and training. Under the environment of big data technology, how to innovate teaching models and talent training models becomes the focus of the current classroom teaching reform. This article aims to study the design of the college English bisection classroom teaching system based on big data. On the basis of introducing the bisection classroom, the basic theory of teaching system design and the design principles of the bisection classroom teaching system, the theoretical analysis and top-level design of the system login module and the teaching system that adaptively integrates teaching resources according to the status of college students are carried out. Finally, an experimental test of the college English split-class teaching system based on big data has been carried out. The test results show that the system can realize the core of system management, teaching resource release and dynamic configuration.
React-bratus: Visualising React Component Hierarchies

We report on the development of react-bratus, a prototype providing developers with an interactive web-based component hierarchy visualization for React-based projects. We continuously evaluate react-bratus in multiple iterations with junior developers and find that component hierarchy visualizations are a promising tool for helping novice developers reverse engineer React-based applications.
Circular convolution-based feature extraction algorithm for classification of high-dimensional datasets

Abstract High-dimensional data analysis has become the most challenging task nowadays. Dimensionality reduction plays an important role here. It focuses on data features, which have proved their impact on accuracy, execution time, and space requirement. In this study, a dimensionality reduction method is proposed based on the convolution of input features. The experiments are carried out on minimal preprocessed nine benchmark datasets. Results show that the proposed method gives an average 38% feature reduction in the original dimensions. The algorithm accuracy is tested using the decision tree (DT), support vector machine (SVM), and K -nearest neighbor (KNN) classifiers and evaluated with the existing principal component analysis algorithm. The average increase in accuracy (Δ) is 8.06 for DT, 5.80 for SVM, and 18.80 for the KNN algorithm. The most significant characteristic feature of the proposed model is that it reduces attributes, leading to less computation time without loss in classifier accuracy.
Hybrid Cryptography for Cloud Security: Methodologies and Designs

Cloud computing is a trending information technology applied for storing and accessing information over the Internet. Probably, sensitive information is stored on remote servers that are not managed nor controlled by the customers. Therefore, potential attacks may be launched against stored information from either inside the cloud service provider or outsider attackers. Cryptography is the fundamental mechanism that provides enough level of security to the cloud. Hybrid cryptography endeavors to enhance security and performance by integrating more than one cryptographic algorithm. In our study, we conducted a survey on applied hybrid cryptographic models for data security in the cloud between 2013 and 2020. We have presented the design, the implementation methodology, limitations found, and the suggested applications for each proposal. We finalized this paper with a comparison summary table. We hope to make a scientific contribution to secure the cloud.
Bridging Art and Science: Phenomenon-Driven Theorizing

We present a model of phenomenon-driven theorizing that may guide researchers in bridging art and science as they engage in pure theory development. The model seeks to leverage activities related to discovery and prescience (conceiving the theory), imagination and logic (constructing the theory), as well as storytelling and scripting (communicating the theory). We illustrate the model through a reflective inquiry into our recent work on data network effects. The model represents a balanced take on theory development and is useful for tempering the natural tendency in academia to drift toward the science of theorizing at the expense of the art of theorizing.
A Re-classification of Information Seeking Tasks and Their Computational Solutions

This article presents a re-classification of information seeking (IS) tasks, concepts, and algorithms. The proposed taxonomy provides new dimensions to look into information seeking tasks and methods. The new dimensions include the number of search iterations, search goal types, and procedures to reach these goals. Differences along these dimensions for the information seeking tasks call for suitable computational solutions. The article then reviews machine learning solutions that match each new category. The paper ends with a review of evaluation campaigns for IS systems.
Comb-based photonic neural population for parallel and nonlinear processing

It is believed that neural information representation and processing relies on the neural population instead of a single neuron. In neuromorphic photonics, photonic neurons in the form of nonlinear responses have been extensively studied in single devices and temporal nodes. However, to construct a photonic neural population (PNP), the process of scaling up and massive interconnections remain challenging considering the physical complexity and response latency. Here, we propose a comb-based PNP interconnected by carrier coupling with superior scalability. Two unique properties of neural population are theoretically and experimentally demonstrated in the comb-based PNP, including nonlinear response curves and population activities coding. A classification task of three input patterns with dual radio-frequency (RF) tones is successfully implemented in a time-efficient manner, which allows the comb-based PNP to make effective use of the ultra-broad bandwidth of photonics for parallel and nonlinear processing.
Still No Free Lunches: The Price to Pay for Tighter PAC-Bayes Bounds

"No free lunch" results state the impossibility of obtaining meaningful bounds on the error of a learning algorithm without prior assumptions and modelling, which is more or less realistic for a given problem. Some models are "expensive" (strong assumptions, such as sub-Gaussian tails), others are "cheap" (simply finite variance). As it is well known, the more you pay, the more you get: in other words, the most expensive models yield the more interesting bounds. Recent advances in robust statistics have investigated procedures to obtain tight bounds while keeping the cost of assumptions minimal. The present paper explores and exhibits what the limits are for obtaining tight probably approximately correct (PAC)-Bayes bounds in a robust setting for cheap models.
State transfer with separable optical beams and variational quantum algorithms with classical light

Classical electromagnetic fields and quantum mechanics obey the principle of superposition alike. This opens up many avenues for simulation of a large variety of phenomena and algorithms, which have hitherto been considered quantum mechanical. In this paper, we propose two such applications. In the first, we introduce a new, to the best of our knowledge, class of beams, called “equivalent optical beams,” in parallel with equivalent states introduced in Phys. Rev. A 89 , 062110 ( 2014 ) PLRAAN 1050-2947 10.1103/PhysRevA.89.062110 . These beams have the same information content for all practical purposes. Employing them, we show how to transfer information from one degree of freedom of classical light to another, without need for classically entangled beams. Next, we show that quantum machine learning can be performed with OAM beams through the implementation of a quantum classifier circuit. We provide explicit protocols and explore the possibility of their experimental realization.
Cross-lingual citations in English papers: a large-scale analysis of prevalence, usage, and impact

Abstract Citation information in scholarly data is an important source of insight into the reception of publications and the scholarly discourse. Outcomes of citation analyses and the applicability of citation-based machine learning approaches heavily depend on the completeness of such data. One particular shortcoming of scholarly data nowadays is that non-English publications are often not included in data sets, or that language metadata is not available. Because of this, citations between publications of differing languages (cross-lingual citations) have only been studied to a very limited degree. In this paper, we present an analysis of cross-lingual citations based on over one million English papers, spanning three scientific disciplines and a time span of three decades. Our investigation covers differences between cited languages and disciplines, trends over time, and the usage characteristics as well as impact of cross-lingual citations. Among our findings are an increasing rate of citations to publications written in Chinese, citations being primarily to local non-English languages, and consistency in citation intent between cross- and monolingual citations. To facilitate further research, we make our collected data and source code publicly available.
How Internet of Things responds to the COVID-19 pandemic

The cornovirus disease (COVID-19) pandemic has had a severe impact on our daily lives. As a result, there has been an increasing demand for technological solutions to overcome such challenges. The Internet of Things (IoT) has recently emerged to improve many aspects of human's day-to-day activities and routines. IoT makes it easier to follow the safety guidelines and precautions provided by the World Health Organization (WHO). Prior reports have shown that the world nowadays may need more IoT facilities than ever before. However, little is known about the reaction of the IoT community towards defeating the COVID-19 pandemic, technologies being used, solutions being provided, and how our societies perceive the IoT means available to them. In this paper, we conduct an empirical study to investigate the IoT response to the COVID-19 pandemic. In particular, we study the characteristics of the IoT solutions hosted on a large online IoT community (i.e., Hackster.io) throughout the year of 2020. The study: (a) explores the proportion, types, and nations of IoT solutions/engineers that contributed to defeating COVID-19, (b) characterizes the complexity of COVID-19 IoT solutions, and (c) identifies how IoT solutions are perceived by the surrounding community. Our results indicate that IoT engineers have been actively working towards providing solutions to help their societies, especially in the most affected nations. Our findings (i) provide insights into the aspects IoT practitioners need to pay more attention to when developing IoT solutions for COVID-19 and to (ii) outlines the common IoT solutions and technologies available to humans to deal with the current challenges.