Reviewerly/exHarmony huggingface dataset:
The NISQ Analyzer: Automating the Selection of Quantum Computers for Quantum Algorithms

Quantum computing can enable a variety of breakthroughs in research and industry in the future. Although some quantum algorithms already exist that show a theoretical speedup compared to the best known classical algorithms, the implementation and execution of these algorithms come with several challenges. The input data determines, for example, the required number of qubits and gates of a quantum algorithm. A quantum algorithm implementation also depends on the used Software Development Kit which restricts the set of usable quantum computers. Because of the limited capabilities of current quantum computers, choosing an appropriate one to execute a certain implementation for a given input is a difficult challenge that requires immense mathematical knowledge about the implemented quantum algorithm as well as technical knowledge about the used Software Development Kits. In this paper, we present a concept for the automated analysis and selection of implementations of quantum algorithms and appropriate quantum computers that can execute a selected implementation with a certain input data. The practical feasibility of the concept is demonstrated by the prototypical implementation of a tool that we call NISQ Analyzer.
A Hitchhiker’s Guide to Structural Similarity

The Structural Similarity (SSIM) Index is a very widely used image/video quality model that continues to play an important role in the perceptual evaluation of compression algorithms, encoding recipes and numerous other image/video processing algorithms. Several public implementations of the SSIM and Multiscale-SSIM (MS-SSIM) algorithms have been developed, which differ in efficiency and performance. This "bendable ruler" makes the process of quality assessment of encoding algorithms unreliable. To address this situation, we studied and compared the functions and performances of popular and widely used implementations of SSIM, and we also considered a variety of design choices. Based on our studies and experiments, we have arrived at a collection of recommendations on how to use SSIM most effectively, including ways to reduce its computational burden.
Benchmarking Hamiltonian Noise in the D-Wave Quantum Annealer

Various sources of noise limit the performance of quantum computers by altering qubit states in an uncontrolled manner throughout computations and reducing their coherence time. In quantum annealers, this noise introduces additional fluctuations to the parameters defining the original problem Hamiltonian, such that they find the ground states of problems perturbed from those originally programmed. Here, we describe a method to benchmark the amount of noise affecting the programmed Hamiltonian of a quantum annealer. We show that a sequence of degenerate runs with the coefficients of the programmed Hamiltonian set to zero leads to an estimate of the noise spectral density affecting Hamiltonian parameters “in situ” during the quantum annealing protocol. The method is demonstrated in D-Wave's lower noise 2000 qubit device (DW_2000Q_6) and in its recently released 5000 qubit device (Advantage_system1.1). Our benchmarking of DW_2000Q_6 shows Hamiltonian noise dominated by the 1/f <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.7</sup> frequency dependence characteristic of flux noise intrinsic to the materials forming flux qubits. In contrast, Advantage_system1.1 is found to be affected by additional noise sources for low annealing times, with underlying intrinsic flux noise amplitudes 2-3 times higher than in DW_2000Q_6 for all annealing times.
Data-Efficient Domain Randomization With Bayesian Optimization

When learning policies for robot control, the required real-world data is typically prohibitively expensive to acquire, so learning in simulation is a popular strategy. Unfortunately, such polices are often not transferable to the real world due to a mismatch between the simulation and reality, called 'reality gap'. Domain randomization methods tackle this problem by randomizing the physics simulator (source domain) during training according to a distribution over domain parameters in order to obtain more robust policies that are able to overcome the reality gap. Most domain randomization approaches sample the domain parameters from a fixed distribution. This solution is suboptimal in the context of sim-to-real transferability, since it yields policies that have been trained without explicitly optimizing for the reward on the real system (target domain). Additionally, a fixed distribution assumes there is prior knowledge about the uncertainty over the domain parameters. In this paper, we propose Bayesian Domain Randomization (BayRn), a black-box sim-to-real algorithm that solves tasks efficiently by adapting the domain parameter distribution during learning given sparse data from the real-world target domain. BayRn uses Bayesian optimization to search the space of source domain distribution parameters such that this leads to a policy which maximizes the real-word objective, allowing for adaptive distributions during policy optimization. We experimentally validate the proposed approach in sim-to-sim as well as in sim-to-real experiments, comparing against three baseline methods on two robotic tasks. Our results show that BayRn is able to perform sim-to-real transfer, while significantly reducing the required prior knowledge.
Contact Tracing for Covid-19 — A Digital Inoculation against Future Pandemics

range of actions that alter their health trajectories can only help anyone who aspires to improve health -whether for whole populations or for the next patient in the waiting room.
Materials for emergent silicon-integrated optical computing

Progress in computing architectures is approaching a paradigm shift: traditional computing based on digital complementary metal-oxide semiconductor technology is nearing physical limits in terms of miniaturization, speed, and, especially, power consumption. Consequently, alternative approaches are under investigation. One of the most promising is based on a "brain-like" or neuromorphic computation scheme. Another approach is quantum computing using photons. Both of these approaches can be realized using silicon photonics, and at the heart of both technologies is an efficient, ultra-low power broad band optical modulator. As silicon modulators suffer from relatively high power consumption, materials other than silicon itself have to be considered for the modulator. In this Perspective, we present our view on such materials. We focus on oxides showing a strong linear electro-optic effect that can also be integrated with Si, thus capitalizing on new materials to enable the devices and circuit architectures that exploit shifting computational machine learning paradigms, while leveraging current manufacturing infrastructure. This is expected to result in a new generation of computers that consume less power and possess a larger bandwidth.
Quantum Natural Language Processing on Near-Term Quantum Computers

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Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled and unseen target domain, which is usually trained on data from both domains. Access to the source domain data at the adaptation stage, however, is often limited, due to data storage or privacy issues. To alleviate this, in this work, we target source free UDA for segmentation, and propose to adapt an “off-the-shelf” segmentation model pre-trained in the source domain to the target domain, with an adaptive batch-wise normalization statistics adaptation framework. Specifically, the domain-specific low-order batch statistics, i.e., mean and variance, are gradually adapted with an exponential momentum decay scheme, while the consistency of domain shareable high-order batch statistics, i.e., scaling and shifting parameters, is explicitly enforced by our optimization objective. The transferability of each channel is adaptively measured first from which to balance the contribution of each channel. Moreover, the proposed source free UDA framework is orthogonal to unsupervised learning methods, e.g., self-entropy minimization, which can thus be simply added on top of our framework. Extensive experiments on the BraTS 2018 database show that our source free UDA framework outperformed existing source-relaxed UDA methods for the cross-subtype UDA segmentation task and yielded comparable results for the cross-modality UDA segmentation task, compared with a supervised UDA methods with the source data.
Comparative Analysis of Deepfake Image Detection Method Using Convolutional Neural Network

Generation Z is a data-driven generation. Everyone has the entirety of humanity’s knowledge in their hands. The technological possibilities are endless. However, we use and misuse this blessing to face swap using deepfake. Deepfake is an emerging subdomain of artificial intelligence technology in which one person’s face is overlaid over another person’s face, which is very prominent across social media. Machine learning is the main element of deepfakes, and it has allowed deepfake images and videos to be generated considerably faster and at a lower cost. Despite the negative connotations associated with the phrase “deepfakes,” the technology is being more widely employed commercially and individually. Although it is relatively new, the latest technological advances make it more and more challenging to detect deepfakes and synthesized images from real ones. An increasing sense of unease has developed around the emergence of deepfake technologies. Our main objective is to detect deepfake images from real ones accurately. In this research, we implemented several methods to detect deepfake images and make a comparative analysis. Our model was trained by datasets from Kaggle, which had 70,000 images from the Flickr dataset and 70,000 images produced by styleGAN. For this comparative study of the use of convolutional neural networks (CNN) to identify genuine and deepfake pictures, we trained eight different CNN models. Three of these models were trained using the DenseNet architecture (DenseNet121, DenseNet169, and DenseNet201); two were trained using the VGGNet architecture (VGG16, VGG19); one was with the ResNet50 architecture, one with the VGGFace, and one with a bespoke CNN architecture. We have also implemented a custom model that incorporates methods like dropout and padding that aid in determining whether or not the other models reflect their objectives. The results were categorized by five evaluation metrics: accuracy, precision, recall, F1-score, and area under the ROC (receiver operating characteristic) curve. Amongst all the models, VGGFace performed the best, with 99% accuracy. Besides, we obtained 97% from the ResNet50, 96% from the DenseNet201, 95% from the DenseNet169, 94% from the VGG19, 92% from the VGG16, 97% from the DenseNet121 model, and 90% from the custom model.
Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter

The study proposes an improved Harris hawks optimization (IHHO) algorithm by integrating the standard Harris hawks optimization (HHO) algorithm and mutation-based search mechanism for developing a high-performance machine learning solution for predicting soil compression index. HHO is a newly introduced meta-heuristic optimization algorithm (MOA) used to solve continuous search problems. Compared to the original HHO, the proposed IHHO can evade trapping in local optima, which in turn raises the search capabilities and enhances the search mechanism relying on mutation. Subsequently, a novel meta-heuristic-based soft computing technique called ELM-IHHO was established by integrating IHHO and extreme learning machine (ELM) to estimate soil compression index. A sum of 688 consolidation test data was collected for this purpose from an ongoing dedicated freight corridor railway project. To evaluate the generalization capability of the proposed ELM-IHHO model, a detailed comparison between ELM-IHHO and other well-established MOAs, such as particle swarm optimization, genetic algorithm, and biogeography-based optimization integrated with ELM, was performed. Based on the outcomes, the ELM-IHHO model exhibits superior performance over the other MOAs in predicting soil compression index.
Survey Research in Software Engineering: Problems and Mitigation Strategies

Background: The need for empirical investigations in software engineering is growing. Many researchers nowadays, conduct and validate their solutions using empirical research. Survey is one empirical method which enables researchers to collect data from a large population. Main aim of the survey is to generalize the findings. Aims: In this study we aim to identify the problems researchers face during survey design, and mitigation strategies. Method: A literature review as well as semi-structured interviews with nine software engineering researchers were conducted to elicit their views on problems and mitigation strategies. The researchers are all focused on empirical software engineering. Results: We identified 24 problems and 65 strategies, structured according to the survey research process. The most commonly discussed problem was sampling, in particular the ability to obtain a sufficiently large sample. To improve survey instrument design, evaluation and execution recommendations for question formulation and survey pre-testing were given. The importance of involving multiple researchers in the analysis of survey results was stressed. Conclusions: The elicited problems and strategies may serve researchers during the design of their studies. However, it was observed that some strategies were conflicting. This shows that it is important to conduct a trade-off analysis between strategies.
Conclusive precision bounds for SU(1,1) interferometers

In this paper, we revisit the quantum Fisher information (QFI) calculation in SU(1,1) interferometer considering different phase configurations. When one of the input modes is a vacuum state, we show by using phase averaging, different phase configurations give same QFI. In addition, by casting the phase estimation as a two-parameter estimation problem, we show that the calculation of the quantum Fisher information matrix (QFIM) is necessary in general. Particularly, within this setup, the phase averaging method is equivalent to a two parameter estimation problem. We also calculate the phase sensitivity for different input states using QFIM approach.
Experimental Simultaneous Learning of Multiple Nonclassical Correlations

Nonclassical correlations can be regarded as resources for quantum information processing. However, the classification problem of nonclassical correlations for quantum states remains a challenge, even for finite-size systems. Although there exists a set of criteria for determining individual nonclassical correlations, a unified framework that is capable of simultaneously classifying multiple correlations is still missing. In this Letter, we experimentally explored the possibility of applying machine-learning methods for simultaneously identifying nonclassical correlations. Specifically, by using partial information, we applied an artificial neural network, support vector machine, and decision tree for learning entanglement, quantum steering, and nonlocality. Overall, we found that, for a family of quantum states, all three approaches can achieve high accuracy for the classification problem. Moreover, the run time of the machine-learning methods to output the state label is experimentally found to be significantly less than that of state tomography.
Autonomous multipartite entanglement engines

The generation of genuine multipartite entangled states is challenging in practice. Here we explore an alternative route to this task, via autonomous entanglement engines which use only incoherent coupling to thermal baths and time-independent interactions. We present a general machine architecture, which allows for the generation of a broad range of multipartite entangled states in a heralded manner. Specifically, given a target multiple-qubit state, we give a sufficient condition ensuring that it can be generated by our machine. We discuss the cases of Greenberger-Horne-Zeilinger, Dicke, and cluster states in detail. These results demonstrate the potential of purely thermal resources for creating multipartite entangled states useful for quantum information processing.
Modeling of Scientific Intercultural Communication of the Teaching Staff at Smart University

Today the concept of a smart university is not defined clearly enough. However, one of the basic requirements to its teaching staff is a demand for constant development of the intellectual potential. One of the factors making it possible to develop scientific professional mobility in the context of globalization is cross-cultural communications, as well as transindividual goals, the achievement of which makes it possible to estimate the self-development abilities. In higher education, the rate of publication activities reflecting the existence of publications by higher education institutions teachers in non-Russian sources in a foreign language is taken into account as accreditation factors. That is why the cross-cultural scientific communication as a compulsory competence of a higher education teacher is an important factor featured in the program of Russia’s integration into the world scientific environment. This leads to the need of handling the tasks of quick mastering of the cross-cultural scientific environment through different forms of scientific interaction. The goal of this article is to describe the mechanisms making it possible to develop scientific cross-cultural communication as one of the basic competences of the teaching staff in the context of a smart university development. It shows the set of scientific approaches, provisions, and conditions based on which it is possible to form the scientific cross-cultural communication competence as a personal characteristic of each member of the staff. The results of the practical experiment of many years are presented.
A Survey of Game Theoretic Approaches for Adversarial Machine Learning in Cybersecurity Tasks

Machine learning techniques are used extensively for automating various cybersecurity tasks. Most of these techniques use supervised learning algorithms that rely on training the algorithm to classify incoming data into categories, using data encountered in the relevant domain. A critical vulnerability of these algorithms is that they are susceptible to adversarial attacks by which a malicious entity called an adversary deliberately alters the training data to misguide the learning algorithm into making classification errors. Adversarial attacks could render the learning algorithm unsuitable for use and leave critical systems vulnerable to cybersecurity attacks. This article provides a detailed survey of the state‐of‐the‐art techniques that are used to make a machine learning algorithm robust against adversarial attacks by using the computational framework of game theory. We also discuss open problems and challenges and possible directions for further research that would make deep machine learning–based systems more robust and reliable for cybersecurity tasks.
Performance Analytical Comparison of Blockchain-as-a-Service (BaaS) Platforms

Both blockchain technologies and cloud computing are contemporary emerging technologies. While the application of Blockchain technologies is being spread beyond cryptocurrency, cloud computing is also seeing a paradigm shift to meet the needs of the 4th industrial revolution (Industry 4.0). New technological advancement, especially by the fusion of these two, such as Blockchain-as-a-Service (BaaS), is considered to be able to significantly generate values to the enterprises. This article surveys the current status of BaaS in terms of technological development, applications, market potentials and so forth. An evaluative judgement, comparing amongst various BaaS platforms, has been presented, along with the trajectory of adoption, challenges and risk factors. Finally, the study suggests standardisation of available BaaS platforms.
Health Analysis by Digital Doctor Using Deep Neural Network

Technology is an important aspect of everyone’s daily life. Health care is the improvement or maintenance of health where traditionally patients visit the hospitals after booking an appointment. However, it is not always feasible for the patient and doctor to present physically every time. The purpose of technology in health care is to provide better facilities and reduce hassles of the patients. This also makes the health care cost-effective as well as time-effective. The proposed idea is to create a web application to enable elementary conversations between the doctor and the patient. Real-time speech has been converted to text to comprehend the conversation by extracting significant information. This also reduces the overhead of saving the video call for future references and it will be available to patients on their profile. Keyword extraction has been performed using slot-filling technique. Slot-filling technique performs in spoken language understanding by filling the slots, i.e. set of arguments. The research work is proposed to build recurrent neural to implement slot-filling task. Recurrent neural networks take input as words and produce output as slot value of corresponding word. Report of significant information extracted is generated using this technique.
Calibrated deep attention model for 3D pose estimation in the wild

&lt;abstract&gt; &lt;p&gt;Three-dimensional human pose estimation is a key technology in many computer vision tasks. Regressing a 3D pose from 2D images is a challenging task, especially for applications in natural scenes. Recovering the 3D pose from a monocular image is an ill-posed problem itself; moreover, most of the existing datasets have been captured in a laboratory environment, which means that the model trained by them cannot generalize well to in-the-wild data. In this work, we improve the 3D pose estimation performance by introducing the attention mechanism and a calibration network. The attention model will capture the channel-wise dependence, so as to enhance the depth analysis ability of the model. The multi-scale pose calibration network adaptively learns body structure and motion characteristics, and will therefore rectify the estimation results. We tested our model on the Human 3.6M dataset for quantitive evaluation, and the experimental results show the proposed methods with higher accuracy. In order to test the generalization capability for in-the-wild applications, we also report the qualitative results on the natural scene Leeds Sports Pose dataset; the visualization results show that the estimated results are more reasonable than the baseline model.&lt;/p&gt; &lt;/abstract&gt;
A Linguistic Overview of WhatsApp Communication

Today, digital discourse has become a mode of communication that is both widespread and global, WhatsApp being one of the most popular Instant Messaging tools. WhatsApp-supported communication is thus gradually attracting the attention of researchers. The present volume seeks to contribute to this field of linguistics by providing a critical review of the state-of-the-art of WhatsApp studies. With this aim in mind, different types of sources were evaluated and the retrieved documents were classified into two main thematic domains. On the one hand, references that focused on WhatsApp linguistic characteristics were included: status notifications, multimodal elements such as emojis or memes and language variations, among others. On the other, works that described the use of WhatsApp to learn English as a foreign or second language (EFL/ESL) were reviewed. Based on this critical literature review, it was possible to detect relevant lines of future research.