Reviewerly/exHarmony huggingface dataset:
Deployment and application of lamp website architecture based on docker network

Server cluster and security experiments and research should be done on personal computers. If virtual machines such as VMware or VBox are used for experiments, it will consume a lot of computer resources and it is not easy to complete the experiments. It has the advantages of less resources, second level startup, rapid deployment, application isolation, easy migration, ensuring the consistency of development and testing production environment and multi-user environment. This paper gives the website architecture of docker network and gives a concrete experiment. According to the test results, the website deployed based on docker network has high access efficiency and security. Finally, the content of further research is given.
Optimization for resource management in heterogeneous spark clusters

Spark, as a large-scale data processing framework based on in-memory computing, is widely used in various big data processing scenarios. However, its existing resource scheduling strategy does not consider different types of applications and real-time utilization of cluster resources, resulting in low efficiency of both cluster resource usage and big data applications. To solve above problems, this paper proposes a strategy named Resource Scheduling for Heterogeneous Spark clusters (RSFHS). Firstly, the application is classified into CPU-intensive and IO-intensive according to the difference of resources consumed by the application; secondly, by monitoring the utilization of various resources of cluster nodes in real time, the remaining available resources of nodes are calculated, and then the nodes with strong processing capability and consistence with the type of applications are scheduled and allocated first. Experiments show that compared with Spark's existing resource scheduling strategy, RSFHS strategy can decrease the average execution time of applications by 6.5% ~12.26% and optimize the scheduling of different types of applications.
Aligning Semantic Interoperability Frameworks with the FOXS Stack for FAIR Health Data

Abstract Background FAIR Guiding Principles present a synergy with the use cases for digital health records, in that clinical data need to be found, accessible within a range of environments, and data must interoperate between systems and subsequently reused. The use of HL7 FHIR, openEHR, IHE XDS, and SNOMED CT (FOXS) together represents a specification to create an open digital health platform for modern health care applications. Objectives To describe where logical FOXS components align to the European Open Science Cloud Interoperability Framework (EOSC-IF) reference architecture for semantic interoperability. This should provide a means of defining if FOXS aligns to FAIR principles and to establish the data models and structures that support longitudinal care records as being fit to underpin scientific research. Methods The EOSC-IF Semantic View is a representation of semantic interoperability where meaning is preserved between systems and users. This was analyzed and cross-referenced with FOXS architectural components, mapping concepts, and objects that describe content such as catalogues and semantic artifacts. Results Majority of conceptual Semantic View components were featured within the FOXS architecture. Semantic Business Objects are composed of a range of elements such as openEHR archetypes and templates, FHIR resources and profiles, SNOMED CT concepts, and XDS document identifiers. Semantic Functional Content comprises catalogues of metadata that were also supported by openEHR and FHIR tools. Conclusions Despite some elements of EOSC-IF being vague (e.g., FAIR Digital Object), there was a broad conformance to the framework concepts and the components of a FOXS platform. This work supports a health-domain-specific view of semantic interoperability and how this may be achieved to support FAIR data for health research via a standardized framework.
Control Flow and Advanced Features

In this chapter, we will discuss the different alert boxes available. Then we will learn about the control flow aspects in Streamlit. We will also discuss how to change the default flow of the Streamlit application with the methods provided. Finally, we will learn advanced features including changing the default configuration of Streamlit and improving the performance using caching techniques.
Quantum Classification

In supervised learning, a general classification problem is defined as the assignment of labels to new data instances given a training set of already labelled (classified) data. In this section we introduce some quantum algorithms to make predictions on labels of previously unseen data instances: two examples of quantum distance based classifiers, a quantum versions of the k-nearest neighbors algorithm, and the quantum support vector machine. Moreover, we overview a quantum-inspired classifier that is an algorithm based on the quantum formalism but devised for classical machines.
QML Toolkit

Once described some quantum algorithms that are important to devise quantum machine learning (QML) schemes, in this chapter we list some notions and technical tools that are widely used in QML. The quantum random access memory (QRAM) is a model for the efficient retrieval of quantum states encoding data, a procedure of Hamiltonian simulation is crucial for some QML algorithms that we describe in the following, the SWAP test and the quantum calculation of distances are important tools adopted in several quantum algorithms for classification and clustering.
Quantum Pattern Recognition

In this chapter we introduce the quantum implementation of an associative memory based on a modification of the Grover algorithm. Then we review the application of the quantum Fourier transform to pattern recognistion and an adiabatic algorithm to retrieve binary patterns from a quantum memory.
Relevant Quantum Algorithms

We are in position to give a short but satisfactory overview on some well known quantum algorithms that are relevant as subroutines in machine learning schemes. In particular, the quantum Fourier transform is a quantum implementation of the discrete Fourier transform [Co94], Grover’s algorithm and amplitude amplification are quantum search algorithms in an unsorted database [Gr96, BH97], the phase estimation algorithm allows to estimate the eigenvalue corresponding to an eigenvector of a unitary operator [CN00] with remarkable quantum machine learning applications.
Basics of Quantum Computing

Quantum computing is a type of computation where quantum phenomena, such as state superposition and entanglement, are exploited to perform calculations. It is the most prominent application of quantum information theory and delivers algorithms to solve efficiently some problems which are hard for classical computers. This chapter is focused on the fundamentals of quantum computing like the abstract notion of a universal quantum computer and the circuit model for quantum computations. There is an overview on adiabatic quantum computing which provides a notion of analog quantum computer. Quantum annealers, as specific-purpose quantum machines, are also introduced.
Concluding Remarks

In this volume on the foundations of quantum machine learning (QML), we started from fundamentals of quantum mechanics and quantum computation to list some applications of well known quantum algorithms to devise machine learning schemes. In several cases, a quantum speedup over classical machine learning algorithms can be theoretically proved, however an effective implementation of QML algorithms is currently complicated on real quantum hardware. Nevertheless, for some QML schemes the situation is particularly tricky, also from the theoretical point of view, like quantum perceptrons and neural networks, where the advantage of a quantum implementation is not yet clarified. In general, we can conclude that a rather common shortcoming of several QML models is the need of a training phase performed updating parameters by means of classical optimization techniques. So, many QML proposals offer a quantum execution of a model but often the training remains a classical procedure.
Analysis of the Impact of Project Activities on the Development of Patriotic Education of Students

The author of the article points out the relevance of the patriotic theme, which is a priority in the educational work of Vavilov University. The problem of the influence of project activities on the development of patriotic education of student youth is considered by the example of the implementation of the cultural and educational project «Patriotic theme in the songs of Soviet composers». This method, using end–to-end technologies of the digital economy – virtual and augmented reality, has proved its effectiveness in the purposeful process of developing patriotic education of student youth.
Quantum Neural Networks

Artificial neural networks, or simply neural networks, are a class of machine learning models defined by a structure of interconnected nodes, called neurons, roughly resembling a biological brain. A neural network is characterized by the way its neurons are connected and by the weights of the connections. The weights are optimized during the training in order to accomplish a desired task. The term quantum neural networks may refer to artificial neurons implemented on quantum computers that can be combined in nets with a direct analogy to the classical networks but also to architectures constructed by variational quantum circuits that can be trained by backpropagation. The advantages of quantum neural networks with respect to classical counterparts are not yet clarified. However, there are some theoretical and experimental results suggesting that quantum neural networks can outperform classical networks, for instance a quantum feedforward neural network implemented in terms of a variational circuit is able to achieve a higher value of effective dimension (that is a figure of merit to quantify the generalization capability of a model) with respect to classical feedforward networks [Ab21]. An example of possible advantage of quantum neural networks is provided by simulations of a quantum Boltzmann machine where quantum effects enable better performances with respect to the classical version [Am18]. Anyway, proving a clear and definitive supremacy of quantum neural networks over the classical counterparts is currently an open issue which motivates the nowadays intese research activity on this topic.
Conclusion and Outlook

Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, bioinformatics, smart grid, online education, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature learning (LFL) is one of the most popular representation learning methods tailored for high dimensional and incomplete (HDI) data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing HDI data lies in addressing the uncertainty problem caused by their incomplete characteristics and some outliers (e.g., noises).
Supplemental Material: Large-scale basement mobilization endows the giant Carlin-type gold mineralization in the Youjiang Basin, South China: Insights from mercury isotopes

&lt;p&gt;Text S1: Mercury isotopic composition of terrestrial and marine reservoirs. Figure S1: Plots of δ202Hg versus ∆199Hg (a), and ∆201Hg versus ∆199Hg (b) of terrestrial and marine reservoirs. Text S2: Two endmember mixing model of mercury isotope. Table S1: Descriptions of the Precambrian basement rocks collected in this study from the periphery the Youjiang basin of South China. Table S2: THg and Hg isotopic compositions of ore-associated sulfides of the Carlin-type gold deposits in the Youjiang basin of South China. Table S3: THg and Hg isotopic compositions of Precambrian basement rocks from the periphery of the Youjiang basin, respectively. Table S4: THg and Hg isotopic compositions of the Permian E’meishan plume-related mafic rocks from the interior of the Youjiang basin, respectively.&lt;/p&gt;
Self-Supervised Learning in Hebrew–Model to Practice Framework

In this paper, we present the current state-of-the-art models for Automatic Speech Recognition due to a self-supervised training implemented on Hebrew language. The motivation behind using self-supervised learning is that even though we wouldn't probably get the accuracy rates as if we choose a supervised learning, we still can achieve amazing results with relatively low amount of data. This way of training allows us to train a model on unlabeled data (or to use a pre-trained model, which is always more accessible. It’s goal in the first unsupervised phase is to learn some good representations from raw audio samples, which can be useful for speech recognition tasks, without using any label data. Then, the model can be fine-tuned on a particular dataset for a specific purpose. It means that our involvement really occurs in the last layers of the model. This kind of training proved to be very powerful. We present complete framework from model to practice with simulations and training model and present an impressive result on Hebrew.
https://journals.scholarpublishing.org/index.php/AIVP/issue/view/425

In this paper, we prove the security of the LM05’s two-way quantum key distribution (TWQKD) under the assumption that detector devices in both Alice’s and Bob’s sides are controlled by eavesdroppers. Using a relatively simple method, more practical, we give an analytical proof that LM05’s TWQKD is measurement-device-independent (MDI) security in both the quantum forward channel and the quantum backward channel. This is the first work shows that TWQKD has fully MDI characteristic, thus makes it immune to all possible detector-device-attacks in two-quantum-way in actual implementations.
Theoretical analysis of biases in TLS encryption scheme Chacha 128

Chacha is a software-oriented stream cipher designed by Bernstein (2008). Chacha is a Salsa variant that is eSTREAM project's finalist candidate. Google added Chacha and a message authentication code to their transport layer security (TLS) and datagram TLS (DTLS) protocols in 2016. Chacha has become an area of interest for cryptanalysis since its adoption by Google. Almost all the existing cryptanalysis is experimental. Experimental cryptanalysis identifies vulnerable areas of a cipher, whereas theoretical analysis helps in the development of possible countermeasures. Differential cryptanalysis is a cryptanalytic technique that helps in discovering distinguishers on stream ciphers. Recently, Dey and Sarkar (2021) have theoretically explored the reason behind distinguishers in Salsa and Chacha 256 stream cipher. Motivated by this work, we have theoretically analysed differential attacks on Chacha 128 (Chacha 256 variant) up to four rounds and we have the bias probabilities. Our theoretical analysis results match the experimental results.
An Adaptive Partitioning and Multi-Granularity Network for Video-Based Person Re-Identification

Person re-identification (Re-ID) is a key technology used in the field of intelligent surveillance. The existing Re-ID methods are mainly realized by using convolutional neural networks (CNNs), but the feature information is easily lost in the operation process due to the down-sampling structure design in CNNs. Moreover, CNNs can only process one local neighbourhood at a time, which makes the global perception of the network poor. To overcome these shortcomings, in this study, we apply a pure transformer to a video-based Re-ID task by proposing an adaptive partitioning and multi-granularity (APMG) network framework. To enable the pure transformer structure better at adapting to the Re-ID task, we propose a new correlation-adaptive partitioning (CAP) of feature embedding modules that can adaptively partition person images according to structural correlations and thus retain the structure and semantics of local feature information in the images. To improve the Re-ID performance of the network, we also propose a multi-granularity (MG) module to better capture people feature information at different levels of granularity. We performed validation trials on three video-based benchmark datasets. The results show that the network structure based on the pure transformer can adapt to Re-ID tasks well, and our APMG network outperforms other state-of-the-art methods.
Learning the visual memorability of images with feedback-based training

Memorability is an image property that is remarkably consistent across viewers, as people tend to remember and forget the same images. In spite of this consistency, however, people have poor ability to predict what they will later remember (Bainbridge et al., 2013, Isola et al., 2014). Additionally, the specific factors that influence image memorability are still largely unknown. In this experiment, we tested whether participants receiving training could over time learn the difference between memorable and forgettable images. Participants (N=100) on the online platform Prolific viewed a sequence of randomly ordered face images, for a total of 180 images. Stimuli included an equal number of highly memorable and forgettable images as computed by prior memory experiments, with both image categories matched for demographic characteristics such as age, gender, and ethnicity. On each trial, participants were shown a new face and were asked whether they thought the face would be remembered or forgotten by most people. Immediately following their response, they were told whether they had answered correctly or incorrectly. Participants started at an average accuracy of 61%, suggesting some initial ability to distinguish between memorable and forgettable faces. When looking at subject performance across time, we observed a significant increase in accuracy over the first half of trials (90 trials). This trend was strongest in participants who started the task with low accuracy, yet participants with initial above average performance did not show additional improvement, suggesting a ceiling effect. Overall, these results suggest that with repeated exposure and feedback, people may be able to increasingly learn the difference between memorable and forgettable face images, a distinction which has thus far been opaque. Further investigation of the specific stimuli that drive learning and comparison with other image categories may offer insight into the mysteries behind why we remember what we do.
FaReT 2.0: An updated free and open-source toolkit of three-dimensional models and software to study face perception

Previous research on face perception has mostly used photographs of faces as stimuli, which do not allow researchers to have full control over the presented stimuli. This is severely limiting to psychophysical research, which requires very precise control over every aspect of stimuli. We originally developed the Face Research Toolkit (FaReT) to help alleviate this problem by using the MakeHuman framework to help generate stimuli with complete control over meaningful features. We have since added features to FaReT to expand its usefulness for face researchers. Previously, videos of faces changing from one expression to another were created by having all of the facial features progress at the same rate. Now we add a motion model in which each facial feature can change more quickly or more slowly in different sections of the animation. For example, FaReT can be set up to allow the eyebrows to rise very quickly as the mouth opens very slowly. All of this can be done without manipulating vertices: instead, it is done by simply specifying the groups of features (i.e., eyebrows, mouth, eyes, etc) and two motion parameters. Furthermore, FaReT can now be used to generate stimuli for classification image experiments, used to determine what features people are looking at to make discerning judgments. Random noise patterns that people classify as a target face feature (e.g., happy) are generated in the meaningful parameter space of MakeHuman, which makes obtained classification images lower-dimensional and more interpretable than published alternatives. FaReT can now also be used to help visualize classification images and other face parameter vectors by rendering a heat map directly onto a face. Finally, we improve FaReT’s database with a new algorithm and plugin that randomly generates a virtually infinite number of completely novel and realistic face models.
Understanding the invariances of visual features with separable subnetworks

Visual feature detectors that are useful for high-level semantic tasks must often be invariant to differences in the input space, but how such invariant feature detectors are constructed through image-computable operations is a fundamental and poorly understood challenge. Deep convolutional neural networks have the potential to provide insight into this puzzle, as invariant feature tuning often emerges in the latent spaces of such networks — but, how? Here we present a novel pruning method we call 'feature splitting', which can split a single CNN feature into multiple, sparse subnetworks, each of which only preserves its tuning response to a selection of inputs. We focus on polysemantic units, which respond strongly and selectively to seemingly unrelated semantic categories (e.g., monkey faces and written text) as a case study for splitting a feature across its invariance structure. While a few examples of polysemantic units have been characterized in DNNs, here we develop a data-driven method for identifying polysemantic units in the network. Then, we extract multiple sparse subnetworks, each of which only preserves the feature’s response to a targeted subset of image patches (e.g., to monkey faces, or to written text). In such instances, we find our feature-splitting algorithm returns highly separable subnetworks, with few shared weights between them. These findings indicate that the tuning of polysemantic units draws largely on highly distinct image filtering processes, acting as an ‘or’ gate by summing the outputs of these processes. Broadly, these feature-splitting methods introduce a principled approach for dissecting a wide range of invariance structures necessary for high-level feature detection (e.g. over units that respond to both profile and frontal views of faces, or to objects presented at different scales in the image), isolating the separable and shared computations underlying invariance.
Conversion of transverse momentum correlation of photon pairs into polarization entanglement by using wavefront-splitting interference

The transverse momentum correlation of photon pairs generated by noncollinear spontaneous parametric down-conversion with type-I phase matching is transformed into polarization entanglement. A robust wavefront-splitting interferometer scheme is used, due to which the phase of the entangled state does not require active stabilization. Frequency-degenerate signal and idler photons are separated and coupled into single-mode optical fibers. The concept is demonstrated in a compact and modular setup. A straightforward alignment process for producing any of the Bell states is presented. The entangled source has an output photon flux of $8.33\ifmmode\pm\else\textpm\fi{}1.27$ kcps/mW normalized to pump power (150 mW), and $17\ifmmode\pm\else\textpm\fi{}3%$ heralding ratio. The measured state fidelity with respect to the negative anticorrelated Bell state is $0.951\ifmmode\pm\else\textpm\fi{}0.004$.