Reviewerly/exHarmony huggingface dataset:
Fine-grained anomaly detection via multi-task self-supervision

Detecting anomalies using deep learning has become a major challenge over the last years, and is becoming increasingly promising in several fields. The introduction of self-supervised learning has greatly helped many methods including anomaly detection where simple geometric transformation recognition tasks are used. However these methods do not perform well on fine-grained problems since they lack finer features. By combining in a multi-task framework high-scale shape features oriented task with low-scale fine features oriented task, our method greatly improves fine-grained anomaly detection. It outperforms state-of-the-art with up to 31% relative error reduction measured with AUROC on various anomaly detection problems.
Application of a novel Anonymization Method for Electrocardiogram data

Chronos, a time series oriented anonymization method is presented in this paper. The method is tested on electrocardiogram time series in the MIT-BIH Arrhythmia Data Base and the Physikalisch TechnischeBundesanstalt (PTB) Diagnostic ECG, with empirical measures of the privacy protection provided by the method. The signal retention properties of the method are also tested graphically and computationally by training algorithms on the original and anonymized sets. The models trained on the anonymized data achieve a 3% accuracy difference compared to the original models. The method outputs samples that are similar to the original data while protecting the records by producing new synthetic data points.
Two-atom van der Waals forces with one atom excited: The identical-atom limit

We compute the conservative van-der-Waals forces between two atoms, one of which is initially excited, in the limit of identical atoms. Starting with the perturbative calculation of the interaction between two dissimilar atoms, we show that a time-dependent approach in the weak-interaction approximation is essential in considering the identical atoms limit in the perturbative regime. In this limit we find that, at leading order, the van-der-Waals forces are fully-resonant and grow linearly in time, being different upon each atom. The resultant net force upon the two-atom system is related to the directionality of spontaneous emission, which results from the violation of parity symmetry. In contrast to the usual stationary van-der-Waals forces, the time-dependent conservative forces cannot be written as the gradients of the expectation values of the interaction potentials, but as the expectation values of the gradients of the interaction potentials only.
Python Programming in PyPI for Translational Medicine

This is the world’s first tutorial article on Python Packaging for beginners and practitioners for translational medicine or medicine in general. This tutorial will allow researchers to demonstrate and showcase their tools on PyPI packages around the world. Nowadays, for translational medicine, researchers need to deal with big data. This paper describes how to build an executable Python Package Index (PyPI) code and package. PyPI is a repository of software for the Python programming language with 5,019,737 files and 544,359 users (programmers) as of 19 October 2021. First, programmers must understand how to scrape a dataset over the Internet; second, they must read the dataset file in csv format; third, build a program to compute the target values; fourth, convert the Python program to the PyPI package.; and fifth, upload the PyPI package. This paper depicts a covidlag executable package as an example for calculating the accurate case fatality rate (CFR) and the lag time from infection to death. You can install the covidlag by pip terminal command and test it. This paper also introduces deathdaily and scorecovid packages on PyPI Stats, which can inform how many users have downloaded the specified PyPI package. The usefulness and applicability of a developed tool can be verified by PyPI Stats with the number of downloaded users.
Hash Function Based on Controlled Alternate Quantum Walks With Memory (September 2021)

We propose a Quantum inspired Hash Function using controlled alternate quantum walks with Memory on cycles (QHFM), where the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$j$</tex-math></inline-formula> th message bit decides whether to run quantum walk with one-step memory or to run quantum walk with two-step memory at the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$j$</tex-math></inline-formula> th time step, and the hash value is calculated from the resulting probability distribution of the walker. Numerical simulation shows that the proposed hash function has near-ideal statistical performance and is at least on a par with the state-of-the-art hash functions based on quantum walks in terms of sensitivity of hash value to message, diffusion and confusion properties, uniform distribution property, and collision resistance property; and theoretical analysis indicates that the time and space complexity of the new scheme are not greater than those of its peers. The good performance of QHFM suggests that quantum walks that differ not only in coin operators but also in memory lengths can be combined to build good hash functions, which, in turn, enriches the construction of controlled alternate quantum walks.
Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors

Social network alignment aims at aligning person identities across social networks. Embedding based models have been shown effective for the alignment where the structural proximity preserving objective is typically adopted for the model training. With the observation that ``overly-close'' user embeddings are unavoidable for such models causing alignment inaccuracy, we propose a novel learning framework which tries to enforce the resulting embeddings to be more widely apart among the users via the introduction of carefully implanted pseudo anchors. We further proposed a meta-learning algorithm to guide the updating of the pseudo anchor embeddings during the learning process. The proposed intervention via the use of pseudo anchors and meta-learning allows the learning framework to be applicable to a wide spectrum of network alignment methods. We have incorporated the proposed learning framework into several state-of-the-art models. Our experimental results demonstrate its efficacy where the methods with the pseudo anchors implanted can outperform their counterparts without pseudo anchors by a fairly large margin, especially when there only exist very few labeled anchors.
An Adaptive Evolutionary Whale Optimization Algorithm

Compared with traditional swarm intelligence algorithm, whale optimization algorithm (WOA) has unique mechanism and simple parameters, with certain advantages, such as high search precision and strong generalization ability. For the shortcomings of whale optimization algorithm: slow convergence speed, and easily getting fallen into local optimum, this paper proposes an adaptive evolutionary optimization algorithm whales (Adaptive elite strategy whale optimization algorithm, AWOA), a method of searching for food and spiral update location introduced adaptive adjusting weighting function, abundant diversity of population while strengthening local optimization ability; Adaptive differential variation disturbance is introduced in the contraction enveloping stage to provide the searching power in the later stage and avoid falling into the local optimum. The simulation results show that the improved adaptive evolutionary whale algorithm enriches the diversity of the search population in the early stage and enhances the search power in the later stage. The proposed algorithm has advantages including: fast convergence speed, high optimization precision and stronger generalization ability.
How Agricultural Digital Innovation Can Benefit from Semantics: The Case of the AGROVOC Multilingual Thesaurus

AGROVOC is the multilingual thesaurus managed and published by the Food and Agriculture Organization of the United Nations (FAO). Its content is available in more than 40 languages and covers all the FAO&rsquo;s areas of interest. The structural basis is a resource description framework (RDF) and simple knowledge organization system (SKOS). More than 39,000 concepts identified by a uniform resource identifier (URI) and 800,000 terms are related through a hierarchical system and aligned to knowledge organization systems. This paper aims to illustrate the recent developments in the context of AGROVOC and to present use cases where it has contributed to enhancing the interoperability of data shared by different information systems.
Recent Advances in Trustworthy Explainable Artificial Intelligence: Status, Challenges and Perspectives

This is a survey paper on Explainable Artificial Intelligence (XAI).
Paragraph Similarity Matches for Generating Multiple-choice Test Items

Multiple-choice questions (MCQs) are widely used in knowledge assessment in educational institutions, during work interviews, in entertainment quizzes and games.Although the research on the automatic or semi-automatic generation of multiple-choice test items has been conducted since the beginning of this millennium, most approaches focus on generating questions from a single sentence.In this research, a state-of-the-art method of creating questions based on multiple sentences is introduced.It was inspired by semantic similarity matches used in the translation memory component of translation management systems.The performance of two deep learning algorithms, doc2vec and SBERT, is compared for the paragraph similarity task.The experiments are performed on the adhoc corpus within the EU domain.For the automatic evaluation, a smaller corpus of manually selected matching paragraphs has been compiled.The results prove the good performance of Sentence Embeddings for the given task.
Development of Digital Skills of Library Specialists in the Distant Format

Digitalization of the library sector requires library staff to develop digital competencies, information retrieval skills and new knowledge in the field of data processing. The purpose of the study was to identify the digital skills of specialists of public libraries and the opportunities for their development when mastering additional professional education programs with the use of distance learning technologies. Educational programs for advanced training of library specialists, implemented within the framework of the Federal project “Creative People” of the National project “Culture”, have become the basis for such work. The article analyses the experience of participation of the St. Petersburg State Institute of Culture in educational projects for professional development of practicing librarians using distance learning technologies, the topics of programs offered by the centres for continuing education and advanced training of creative and managerial personnel in the field of culture in 2019—2021. Programs of additional professional education give priority to the formation and development of digital skills in the following categories: information (information literacy), communication (communication and collaboration) and creative skills (creation of digital content). There is an obvious shortage of educational programs related to the formation of critical thinking of users of digital resources and information security. Communication skills of librarians participating in the Creative People project implemented in their willingness to undergo formal training remotely, in a positive attitude towards the distant forms of professional development, despite their labour intensiveness. It is established that in the structure of thematic preferences of library specialists for future training in the system of continuing professional education there prevail the topics related to the formation of information and communication competencies. The article notes that significant proportion of specialists who have mastered the advanced training program are not ready to clearly determine in what types of practical activities their knowledge and skills can be applied. The obtained results make it possible to correct the topics of educational programs of further professional education of library specialists, to clarify the list of priority didactic tasks and the tools of distance learning technologies used for their implementation.
QuantumCircuitOpt: An Open-source Framework for Provably Optimal Quantum Circuit Design

In recent years, the quantum computing community has seen an explosion of novel methods to implement non-trivial quantum computations on near-term hardware. An important direction of research has been to decompose an arbitrary entangled state, represented as a unitary, into a quantum circuit, that is, a sequence of gates supported by a quantum processor. It has been well known that circuits with longer decompositions and more entangling multi-qubit gates are error-prone for the current noisy, intermediate-scale quantum devices. To this end, there has been a significant interest to develop heuristic-based methods to discover compact circuits. We contribute to this effort by proposing QuantumCircuitOpt (QCOpt), a novel open-source framework which implements mathematical optimization formulations and algorithms for decomposing arbitrary unitary gates into a sequence of hardware-native gates. A core innovation of QCOpt is that it provides optimality guarantees on the quantum circuits that it produces. In particular, we show that QCOpt can find up to 57% reduction in the number of necessary gates on circuits with up to four qubits, and in run times less than a few minutes on commodity computing hardware. We also validate the efficacy of QCOpt as a tool for quantum circuit design in comparison with a naive brute-force enumeration algorithm. We also show how the QCOpt package can be adapted to various built-in types of native gate sets, based on different hardware platforms like those produced by IBM, Rigetti and Google. We hope this package will facilitate further algorithmic exploration for quantum processor designers, as well as quantum physicists.
M-learning: A Nexus for Adult Learners’ Motivation and Readiness to Learn in Federal Universities at Southwest, Nigeria

The aim of this study was to examine the perspective of adult learners on the relationship that exist between motivation to learn, readiness to learn and the use of m-learning for teaching and learning activities. In order to accomplish this, descriptive research survey design was adopted by this study. The population of the study comprised of adult students at the distance learning programmes of Federal Universities in Southwest, Nigeria. The sample for the study comprised of one hundred and twenty adult learners that were selected using multistage sampling techniques. A questionnaire was designed for data collection and validated through face and content validity while the reliability was done through Cronbach Alpha with coefficient 0.89. Collated data were analyzed through simple percentage and Pearson product moment correlation. The findings revealed that adult learners use mobile devices and there are significant relationships between motivation to learn, readiness to learn and the use of m-learning. It was recommended that m-learning should be formally introduced into adult education programme in Nigeria.
Research on the Online College English Teaching Mode from the Perspective of Autonomous Learning

In order to explore the online college English teaching mode from the perspective of autonomous learning and facilitate college students to better learn English independently as well as to master their English application skills, this article expounds the significance of online English teaching through theoretical analysis. At the same time, it expounds how to realize online English teaching in the perspective of autonomous learning, so as to improve the quality of English teaching and students’ English skills.
Private attacks in longest chain proof-of-stake protocols with single secret leader elections

Single Secret Leader Elections have recently been proposed as an improved leader election mechanism for proof-of-stake (PoS) blockchains. However, the security gain they provide has not been quantified. In this work, we present a comparison of PoS longest-chain protocols that are based on Single Secret Leader Elections (SSLE) - that elect exactly one leader per round - versus those based on Probabilistic Leader Elections (PLE) - where one leader is elected on expectation. Our analysis shows that when considering the private attack - the worst attack on longest-chain protocols [14] - the security gained from using SSLE is substantial: the settlement time is decreased by ~ 25% for a 33% or 25% adversary. Furthermore, when considering grinding attacks, we find that the security threshold is increased by 10% (from 0.26 in the PLE case to 0.36 in the SSLE case) and the settlement time is decreased by roughly 70% for a 20% adversary in the SSLE case.
Stripe Noise Removal from Remote Sensing Images

Remote sensing images are in many domains used, including geographic, military, urban planning and environmental surveillance, but they are somewhat limiting their application due of additional stripe noise. Clear images from stripe pictures may be easily predicted in most existing stream noise reduction algorithms without considering the underlying characteristics of strip noise that cause the structure to be destroyed. Thus a new strategy was suggested in this study from the point of view of the image breakdown. The inherent qualities of strip noise and image properties are taken into consideration. The suggested methodology combines regularization, group regulation and television regularization in a framework for picture decomposition, into a (TV). The first two terms are used to execute stripe noise qualities through statistical analyses and regularization of the TV should evaluate the portions of the smooth structures of the stripe-free image. In addition, an effective alternating minimization methodology is proposed to solve the picture decomposition model.
A Lévy Flight Based Firefly Algorithm for Multilevel Thresholding Image Segmentation

Abstract Traditional exhaustive search methods have high time complexity in solving multilevel threshold problems. To address this problem, a Levy flight based firefly algorithm called LFA is proposed. Otsu is regard as its objective function. A series of standard test images were used to evaluate the performance of the algorithm. The Peak signal to noise ratio(PSNR) and Structural similarity index (SSIM) are utilized to evaluate the segmented image. The experimental results show that the LFA is better than FA for multilevel thresholding image segmentation.
Application of Data Mining Technology in Financial Fraud Identification

In the era of big data, the identification of financial fraud has changed from the traditional case analysis stage to the data mining stage. The application of data mining technology is helpful in identifying the company's fraud in massive data. In the process of fraud identification, a variety of data mining methods can be used, such as statistical methods, classification, clustering and neural network construction. This paper analyzes the application of several kinds of data mining methods in the field of financial fraud, and finds that due to the different model building methods and sample selection in empirical research, the accuracy of each method has not yet been determined. In the future, with the development of accounting information system, the combination of data mining and text mining will become the inevitable trend of financial fraud identification.
Generating self-attention activation maps for visual interpretations of convolutional neural networks

In recent years, many interpretable methods based on class activation maps (CAMs) have served as an important judging basis for the predictions of convolutional neural networks (CNNs). However, these methods still suffer from the problems of gradient noise, weight distortion, and perturbation deviation. In this work, we present self-attention class activation map (SA-CAM) and shed light on how it uses the self-attention mechanism to refine the existing CAM methods. In addition to generating basic activation feature maps, SA-CAM adds an attention skip connection as a regularization item for each feature map which further refines the focus area of an underlying CNN model. By introducing an attention branch and constructing a new attention operator, SA-CAM greatly alleviates the limitations of the CAM methods. The experimental results on the ImageNet dataset show that SA-CAM can not only generate highly accurate and intuitive interpretation but also have robust stability in adversarial comparison with the state-of-the-art CAM methods.
Big Data Storage Tools Using NoSQL Databases and Their Applications in Various Domains: A Systematic Review

Over the past few years, data has been growing significantly due to the advent of new connected devices, availability of bandwidth, and the emergence of new applications which utilize cloud computing infrastructure in the data centers. This increased amount of data faces many problems in terms of storage, transmission, management, and processing, etc. Therefore, the term big data has gained significant attention from researchers in recent years. The rapidly growing quantity, velocity, and variety of data require more probable and logical tools for its storage. For this purpose, the industry is highly emphasizing the development of more viable tools for the storage of big data. The traditional big data storage tools are unsuccessful in storing an enormous amount of data. Hence, the structural modifications of management mechanisms of conventional storage systems such as SQL databases to NoSQL databases technology are necessary to cope up with drastically increasing requirements of big data storage. The primary objective of this paper is to concentrate exclusively on designing a road map for NoSQL big data storage technologies, evaluate current evidence, research progresses in NoSQL data storage systems and their applications in various domains. We conducted a systematic literature review (SLR) of various studies published in recent years. We propose a framework to classify selected articles on the basis of various factors such as motivations behind big data storage, NoSQL techniques used for storing big data, and significant applications of big data in different domains. Furthermore, we also discuss research issues and define an outline for future research in the big data storage domain for NoSQL databases.
Research and application of personalized learning under the background of artificial intelligence

The development of artificial intelligence technology brings opportunities for the innovation of personalized learning. Using tag, index and intelligent grabbing technology, learning resources can be quickly obtained from complex knowledge map; Using data analysis, network analysis, emotion analysis and visualization technology, the intelligent tutor can make plans, accurately push and evaluate for personalized learners; The application of virtual reality, augmented reality and hybrid reality technology to build personalized online learning classroom. This paper discusses the “Online + offline” small class mode of personalized learning under the background of artificial intelligence, and promotes the teaching reform from “teacher centered” to “student-centered”.