Reviewerly/exHarmony huggingface dataset:
Optimal supplier of single-error-type entanglement via coherent-state transmission

Compared with entanglement with multiple types of noise, entanglement including only one type of error is a favorable fundamental resource not only for quantum communication but also for distributed quantum computation. We consider protocols that present single-error-type entanglement for distant qubits via coherent-state transmission over a lossy channel. Such a protocol is regarded as a subroutine to serve entanglement for larger protocol to yield a final output, such as ebits or pbits. In this paper, we provide a subroutine protocol which achieves the global optimal for typical jointly convex yield functions monotonically nondecreasing with respect to the singlet fraction, such as an arbitrary convex function of a singlet fraction, two-way distillable entanglement, and two-way distillable key. Entanglement generation based on remote nondestructive parity measurement protocol [K. Azuma et al., Phys. Rev. A 85, 062309 (2012)] is identified as such an optimal subroutine.
LeKAN: Extracting Long-tail Relations via Layer-Enhanced Knowledge-Aggregation Networks

Long-tailed relation extraction is a crucial task in the information extraction field for extracting the long-tailed, imbalanced relation between two annotated entities based on related context. Although many works have been devoted to distinguishing valid instances from noisy data and have achieved promising performance, such studies still have critical defects: works based on nonhierarchical relations ignore the correlations among the relations, and those based on hierarchical relations neglect the hierarchy of the relation structure, which is unbalanced and causes difficulty in extracting data-poor classes. In this paper, a novel layer-enhanced knowledge aggregation network, named LeKAN, is presented to classify the relations between two annotated entities from text, especially long-tailed relations, which are very common in various corpora. Inspired by the election mechanism, we aggregate the ancestors of long-tailed relation classes into new relation representations to prevent the long-tailed relations from being ignored. Specifically, we use GraphSAGE to learn the relational knowledge from an existing knowledge graph via class embedding. Moreover, we aggregate the acquired relational knowledge into the LeKAN by layer-enhanced knowledge-aggregating attention mechanism. Comprehensive experimental results demonstrate that the new method yields considerable improvement over other relation extraction methods on a large-scale benchmark dataset with a long-tailed distribution.
Communication activity of a digital society teacher

The paper examines the stages of society informatization (automation, informatization and digitalization) in historical retrospect and their impact on education. It also defines changes in the activity of a teacher in a digital society and education digitalization: specifics of the teachers activity in a digital society due to changes in the object (virtual learner), subject (digitized data, digital traces of the students educational activities) and tools of activity (digital technologies and platforms, video conferencing, information processing technologies); the peculiarity of the teachers activity due to the teachers remote interaction with students (online and offline communication); difficulties and problems of the teachers communication interaction with students. The theoretical conclusions are supported by the results of a pilot study in which teachers of educational institutions of higher and secondary vocational education participated. The empirical data obtained revealed difficulties affecting the quality of knowledge transfer in online and offline communications, problems of information flow management, as well as a tendency to reduce problems related to compliance with the rules of network ethics.
Balanced Prioritized Experience Replay

As an important technique of deep reinforcement learning, prioritized experience replay (PER) helps agents to learn and update strategies through past experience. However, PER does not change the situation that the historical information learned by the agent and stored in the replay buffer is fixed and unchanging. On the contrary, to learn a good strategy efficiently, agents often require the latest interactive information. In this paper, we propose a new method, Balance Prioritized Experience Replay (BPER), for properly prioritizing experience so as to replay recent important transitions more frequently, and therefore learn more efficiently. We incorporate BPER into Twin Delayed Deep Deterministic policy gradient algorithm (TD3) to generate a new policy gradient algorithm called TD3-BPER. The experimental results show that TD3-BPER can achieve better performance than original TD3 in several environments, such as HalfCheetah, Walker2d, Ant, and Swimmer.
HpGAN: Sequence Search With Generative Adversarial Networks

Sequences play an important role in many engineering applications. Searching sequences with desired properties has long been an intriguing but also challenging research topic. This article proposes a novel method, called HpGAN, to search desired sequences algorithmically using generative adversarial networks (GANs). HpGAN is based on the idea of zero-sum game to train a generative model, which can generate sequences with characteristics similar to the training sequences. In HpGAN, we design the Hopfield network as an encoder to avoid the limitations of GAN in generating discrete data. Compared with traditional sequence construction by algebraic tools, HpGAN is particularly suitable for complex problems which are intractable by mathematical analysis. We demonstrate the search capabilities of HpGAN in two applications: 1) HpGAN successfully found many different mutually orthogonal complementary sequence sets (MOCSSs) and optimal odd-length binary Z-complementary pairs (OB-ZCPs) which are not part of the training set. In the literature, both MOCSSs and OB-ZCPs have found wide applications in wireless communications and 2) HpGAN found new sequences which achieve a four-times increase of signal-to-interference ratio-benchmarked against the well-known Legendre sequences-of a mismatched filter (MMF) estimator in pulse compression radar systems. These sequences outperform those found by AlphaSeq.
Scientific Exploration of Conceptual and Algorithmic Terminologies of Machine Learning

At beginning, machine learning (ML) was only the endeavor of some computer geeks who wanted a program that can learn to play games by itself and the field of statistics which never employed any approaches based on computations. Now, it has developed into an independent field of study that has provided us base to develop self-learning functions. ML has also developed various algorithms for interpretation of text, email filtering, recognizing pattern, and several different commercial aspects. It also led to a different field of research such as the field of data mining which identifies hidden and non-hidden regularities and irregularities in so fast-growing data and artificial intelligence which advanced machine learning algorithm like neural networks to create a program that can closely mimic human beings. The research proposed here throws light on explaining various conceptual terminologies and expansion of machine learning from where it has started; among the major popular machine learning algorithms, some are discussed here that are being used today and for future scope of machine learning.
Selecting training samples for ovarian cancer classification via a semi-supervised clustering approach

Machine learning techniques have shown great promise in digital pathology. However, a major bottleneck is the difficulty of annotating necessary amount of tissue to deal with several variability factors, namely chemical fixation, sample slicing, or staining. Usually, models are trained using sets of annotated small image patches, but then, the number of required patches may increase exponentially and yet they must represent such variability. This paper presents a method for automatic sample selection to train a classifier for ovarian cancer by integrating a novel soft clustering strategy. The method starts by classifying a large set of patches with a previously trained classifier and divide patches from the cancer class as highly and moderately confident. An unsupervised selection of moderately confident patches by a Probabilistic Latent Semantic Analysis (PLSA), picks samples from <i>relevant and meaningful</i> groups with maximum within-group variance. A new model is re-trained using the highly confident patches together with patches obtained from the associated PLSA. This strategy outperforms a model trained with a larger set of annotated patches while the training times and the number of samples are much more smaller. The strategy was evaluated in a set of patches from 18 patients with Serous Ovarian Cancer, obtaining a reduction of 54.62% in the training time and 73.66% in the number of samples, while recall rate improved from 0.69 to 0.73.
An abnormal data identification algorithm based on mutual checking between PMU and SCADA

With the development of the smart grid, the data center generates a large amount of abnormal data while obtaining massive multi-source data. Based on the analysis of PMU and SCADA measurement data, this paper proposes an abnormal data identification algorithm based on measurement data checked reciprocally. According to the difference between PMU and SCADA, the probability density function based on the t-distribution is obtained, and the confidence interval of the mean of the two sources data is obtained with 95% confidence. Nodes outside this range are regarded as abnormal nodes. In order to improve the robustness of the algorithm in forecasting samples containing abnormal data, the p-MCC-ELM algorithm is proposed. Forecasting the PMU time series through p-MCC ELM, determine whether the source of the abnormal data nodes is PMU based on the error comparison, and finally determine the abnormal data location. Based on actual PMU and SCADA measurement data, the method proposed in this paper is verified, and the calculation results show that the proposed method can accurately identify the location and source of abnormal data nodes.
Using Natural Language Processing Techniques to Assess the Attitudes of Nursing Students During the COVID-19 Pandemic

COVID-19 pandemic has a significant impact in the world. It has led to different measures taken by governments to prevent its spread, such as school closures, employment of e-learning, and complete lockdowns. Together with the pandemic, these measures also affected many people, economically and psychologically. This chapter assesses the attitudes of undergraduate nursing students in Turkey during the COVID-19 pandemic, using automatic natural language processing (NLP) techniques. NLP is a branch of artificial intelligence, and it facilitates automatic analysis of natural language texts. Machine translation and sentiment analysis are among significant NLP techniques. Data collection is performed using an online questionnaire, filled by 101 students from three different universities in Turkey. Machine translation is used to translate responses of the students to English, and then sentiment analysis is performed on these translations. The sentiment analysis results can be used by related nursing educators and health professionals.
A Morpheme-Based Paradigm for the Ontological Analysis of Modern Greek Derivational Morphology

Morphology is the linguistic field that investigates the minimal meaningful units within words and their interactive processes. In coping with the ontological representation of Modern Greek (MG) derivational morphology the morpheme-based or lexicalist paradigm was tested due to the highly productive concatenative nature of the language. Following this, a specific domain ontological model, the MMoOn was chosen to assess MG morpheme-based morphological representation while being prepared to incorporate other formation approaches when required by the lexical data. Among others, MMoOn was chosen because of its targeted morphological character, its conceptual granularity, the covering of derivational aspects of morphology, its elasticity of embedding different inflectional language data models and its reference to previous frameworks. Accordingly, the model was appropriately extended for the MG language schema and tested towards a very productive MG derivational pattern revealing its high dynamics of representation and usability as a computed lexical inventory that semantically interlinks its entries.
Discrepancy-Guided Domain-Adaptive Data Augmentation

Data augmentation has been observed playing a crucial role in achieving better generalization in many machine learning tasks, especially in unsupervised domain adaptation (DA). It is particularly effective on visual object recognition tasks as images are high-dimensional with an enormous range of variations that can be simulated. Existing data augmentation techniques, however, are not explicitly designed to address the differences between different domains. Expert knowledge about the data is required, as well as manual efforts in finding the optimal parameters. In this article, we propose a novel domain-adaptive augmentation method by making use of a state-of-the-art style transfer method and domain discrepancy measurement. Specifically, we measure the discrepancy between source and target domains, and use it as a guide to augment the original source samples using style transferred source-to-target samples. The proposed domain-adaptive augmentation method is data and model agnostic that can be easily incorporated with state-of-the-art DA algorithms. We show empirically that, by using this domain-adaptive augmentation, we are able to gradually reduce the discrepancy between the source and target samples, and further boost the adaptation performance using different DA algorithms on three popular domain adaption datasets.
A Survey of Motion Planning Algorithms Based on Fast Searching Random Tree

The article summarizes the improvement of fast search random tree path planning algorithm in recent years. Firstly, the article explains the model of the original fast search random tree; secondly, the advantages and disadvantages of the RRT algorithm is discussed based on the simulation experiments.As a comparison, the improved RRT-based algorithm is introduced. Finally, the article summarizes the current researching challenges and proposes relevant suggestions and ideas.
Application of a hybrid approach in project management of creation and development of a smart University

The rapid development and implementation of the ideas of Industry 4.0 have an impact on the development of all spheres of human activity, and, first of all, on education. “A way to transform into smart people for the digital economy.” This will create a modern infrastructure in Russia for the development of the concept of society. The article proposes to use project management as a tool for the creation and smart development of a university. Particular attention is paid to a hybrid approach to project management, which combines the advantages of classic and flexible technologies. The methodological support and information infrastructure of project management of a smart university is considered, which will simplify and accelerate the process of managing management decisions, make it more visible for the members of the project team, project office and university management. The conceptual development of project management is proposed, which can be applied in the activities of project offices to manage a group of projects for the creation and development of an intellectual university. This model of a hybrid approach to project management of the creation and intellectual development of the university is adaptive. As a practical support for a hybrid approach to the practical use of the possibilities of using the financial and information infrastructure of an intellectual university. The experience of project management in the creation and development of smart is considered on the example of the Bauman Moscow State Technical University and Togliatti State University.
A Dependency-Aware Utterances Permutation Strategy to Improve Conversational Evaluation

The rapid growth in the number and complexity of conversational agents has highlighted the need for suitable evaluation tools to describe their performance. The main evaluation paradigms move from analyzing conversations where the user explores information needs following a scripted dialogue with the agent. We argue that this is not a realistic setting: different users ask different questions (and in a diverse order), obtaining distinct answers and changing the conversation path. We analyze what happens to conversational systems performance when we change the order of the utterances in a scripted conversation while respecting temporal dependencies between them. Our results highlight that the performance of the system widely varies. Our experiments show that diverse orders of utterances determine completely different rankings of systems by performance. The current way of evaluating conversational systems is thus biased. Motivated by these observations, we propose a new evaluation approach based on dependency-aware utterance permutations to increase the power of our evaluation tools.
Robust Perturbation for Visual Explanation: Cross-Checking Mask Optimization to Avoid Class Distortion

Along with the outstanding performance of the deep neural networks (DNNs), considerable research efforts have been devoted to finding ways to understand the decision of DNNs structures. In the computer vision domain, visualizing the attribution map is one of the most intuitive and understandable ways to achieve human-level interpretation. Among them, perturbation-based visualization can explain the "black box" property of the given network by optimizing perturbation masks that alter the network prediction of the target class the most. However, existing perturbation methods could make unexpected changes to network predictions after applying a perturbation mask to the input image, resulting in a loss of robustness and fidelity of the perturbation mechanisms. In this paper, we define class distortion as the unexpected changes of the network prediction during the perturbation process. To handle that, we propose a novel visual interpretation framework, Robust Perturbation, which shows robustness against the unexpected class distortion during the mask optimization. With a new cross-checking mask optimization strategy, our proposed framework perturbs the target prediction of the network while upholding the non-target predictions, providing more reliable and accurate visual explanations. We evaluate our framework on three different public datasets through extensive experiments. Furthermore, we propose a new metric for class distortion evaluation. In both quantitative and qualitative experiments, tackling the class distortion problem turns out to enhance the quality and fidelity of the visual explanation in comparison with the existing perturbation-based methods.
Dirmap: Web Application Vulnerability Detection Platform Based on Script Code

Web vulnerability detection technology is a very important approach to detect and ensure security in web application. However, there are limitation associated with the existing web application vulnerability detection methods in terms of variations in the detection capabilities. In this paper, a Script Code-based web application vulnerability detection platform named Dirmap is designed and implemented to address the aforementioned gap. The aim is to accurately and efficiently detect information leakage in existing web applications. The Dirmap (1) provides the web graphical management interface and data analysis display function based on the flash framework; (2) supports four detection patterns, including dictionary detection, pure explosion detection, crawler dynamic dictionary and fuzzy tag detection; (3) provides many functions of recursively scanning options, automatic detection of false leak pages, automatic processing of duplicate results, etc. The experimental results show that Dirmap can accurately and effectively detect information leakage vulnerabilities. Thus, promoting the overall security of web application systems. In addition, prevent hackers from using vulnerabilities to obtain unauthorized information.
Effects of a Computational Thinking Intervention Program on Executive Functions in Children Aged from 10 to 11

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<i>Info-Commit</i>: Information-Theoretic Polynomial Commitment

We introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Info-Commit</i> , an information-theoretic protocol for polynomial commitment and verification. With the help of a trusted initializer, a succinct commitment to a private polynomial <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> is provided to the user. The user then queries the server to obtain evaluations of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> at several inputs chosen by the user. The server provides the evaluations along with proofs of correctness which the user can verify against the initial commitment. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Info-Commit</i> has four main features. Firstly, the user is able to detect, with high probability, if the server has responded with evaluations of the same polynomial initially committed to. Secondly, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Info-Commit</i> provides rigorous privacy guarantees for the server: upon observing the initial commitment and the response provided by the server to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> evaluation queries, the user only learns <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(m^{2})$ </tex-math></inline-formula> symbols about the coefficients of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> . Thirdly, the verifiability and the privacy guarantees are unconditional regardless of the computational power of the two parties. Lastly, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Info-Commit</i> is doubly-efficient in the sense that in the evaluation phase, the user runs in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\sqrt {d})$ </tex-math></inline-formula> time and the server runs in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(d)$ </tex-math></inline-formula> time, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d-1$ </tex-math></inline-formula> is the degree of the polynomial <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> .