Reviewerly/exHarmony huggingface dataset:
Towards a More Reproducible Biomedical Research Environment: Endorsement and Adoption of the FAIR Principles

The FAIR guiding Principles for scientific data management and stewardship are a fundamental enabler for digital transformation and transparent research. They were designed with the purpose of improving data quality, by making it Findable, Accessible, Interoperable and Reusable. While these principles have been endorsed by both data owners and regulators as key data management techniques, their translation into practice in quite novel. The recent publication of FAIR metrics that allow for the evaluation of the degree of FAIRness of a data source, platform or system is a further booster towards their adoption and practical implementation. We present in this paper an overview of the adoption and impact of the FAIR principles in the area of biomedical and life-science research. Moreover, we consider the use case of biomedical data discovery platforms and assess the degree of FAIR compatibility of three such platforms. This assessment is guided by the FAIR metrics.
A Retrieval Method for 3D CAD Assembly Models Using 3D Radon Transform and Spherical Harmonic Transform

We present a retrieval method for 3D CAD assemblies consisted of multiple components. The proposed method distinguishes not only shapes of 3D CAD assemblies but also layouts of their components. Similarity between two assemblies is computed from feature quantities of the components constituting the assemblies. In order to make the similarity robust to translation and rotation of an assembly in 3D space, we use the 3D Radon transform and the spherical harmonic transform. We show that this method has better retrieval precision and efficiency than targets for comparison by experimental evaluation.
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning

Recently, large-scale pre-trained language models have demonstrated impressive performance on several commonsense-reasoning benchmark datasets. However, building machines with commonsense to compose realistically plausible sentences remains challenging. In this paper, we present constrained text generation task, CommonGen associated with benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given set of common concepts (e.g., {dog, frisbee, catch, throw}); the task is to generate coherent sentence describing an everyday scenario using these concepts (e.g., a man throws frisbee and his dog catches it). The CommonGen task is challenging because it inherently requires 1) relational reasoning with background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations. Our dataset, constructed through combination of crowdsourced and existing caption corpora, consists of 79k commonsense descriptions over 35k unique concept-sets. Experiments show that there is large gap between state-of-the-art text generation models (e.g., T5) and human performance. Furthermore, we demonstrate that the learned generative commonsense reasoning capability can be transferred to improve downstream tasks such as CommonsenseQA by generating additional context.
USING THE WEB-TECHNOLOGIES IN THE PROCESS OF PSYCHOLOGICAL AND PEDAGOGICAL TRAINING OF FUTURE PRIMARY SCHOOL TEACHERS

The article deals with the problems of professional and pedagogical education, in particular the question of using web-technologies in the process of psychological and pedagogical preparation of future primary school teachers. The author describes a number of topical problems that can be solved by introducing elements of a blended learning system using cloud technologies (providing access to various educational resources; intensifying the system of professional and pedagogical education; developing the student's creative and intellectual abilities by means of cloud technologies; communicative activities based on common educational and professional interests etc.). The article presents the results of an online questionnaire of students in the specialty "Primary education" to determine the degree of using the web-applications in the process of psychological and pedagogical training and their dimension of interest in using cloud technologies in future professional activity. The basic directions of improvement in psychological and pedagogical preparation of future primary school teachers with using web-technologies (combination of traditional and online learning; using the web-quests, Google-services, E-textbooks, multimedia presentations, etc. ; development of critical and creative thinking, cognitive motivation; optimization of independent work at the expense of mobility, flexibility and availability of cloud services; constant professional growth of academic in order to master high the level of digital literacy) were characterized. The ways of introduction the cloud services in the educational process of high school (Google Sites, Google Classroom, Google Docs, Google Charts, Canva, Piktochart, Scratch) were highlighted. The system of principles of effective organization the psychological and pedagogical training of future primary school teachers with help of web-technologies were considered.
Reducing Smart Contract Runtime Errors on Ethereum

A variety of applications can be implemented on blockchains by writing smart contracts, but failed transactions caused by runtime errors waste computation, storage, and fees. In this article, we propose a new way to reduce runtime errors on Ethereum, a popular blockchain platform.
A Comprehensive Survey of Grammar Error Correction

Grammar error correction (GEC) is an important application aspect of natural language processing techniques. The past decade has witnessed significant progress achieved in GEC for the sake of increasing popularity of machine learning and deep learning, especially in late 2010s when near human-level GEC systems are available. However, there is no prior work focusing on the whole recapitulation of the progress. We present the first survey in GEC for a comprehensive retrospect of the literature in this area. We first give the introduction of five public datasets, data annotation schema, two important shared tasks and four standard evaluation metrics. More importantly, we discuss four kinds of basic approaches, including statistical machine translation based approach, neural machine translation based approach, classification based approach and language model based approach, six commonly applied performance boosting techniques for GEC systems and two data augmentation methods. Since GEC is typically viewed as a sister task of machine translation, many GEC systems are based on neural machine translation (NMT) approaches, where the neural sequence-to-sequence model is applied. Similarly, some performance boosting techniques are adapted from machine translation and are successfully combined with GEC systems for enhancement on the final performance. Furthermore, we conduct an analysis in level of basic approaches, performance boosting techniques and integrated GEC systems based on their experiment results respectively for more clear patterns and conclusions. Finally, we discuss five prospective directions for future GEC researches.
Multi-turn dialogue-oriented pretrained question generation model

Abstract In recent years, teaching machines to ask meaningful and coherent questions has attracted considerable attention in natural language processing. Question generation has found wide applications in areas such as education (testing knowledge) and chatbots (enhancing interaction). Following previous studies on conversational question generation, we propose a pretrained, encoder–decoder model that can incorporate the semantic information from both passage and hidden conversation representations. We adopt BERT as the encoder to combine external text and dialogue history, and we design a multi-head attention-based decoder to incorporate the semantic information from both text and hidden dialogue representations into the decoding process, thereby generating coherent questions. Experiments with conversational question generation and document-grounded dialogue response generation tasks indicate that the proposed model is superior to baseline models in terms of both standard metrics and human evaluations.
Imperfect Distributed Quantum Phase Estimation

In the near-term, the number of qubits in quantum computers will be limited to a few hundreds. Therefore, problems are often too large and complex to be run on quantum devices. By distributing quantum algorithms over different devices, larger problem instances can be run. This distributing however, often requires operations between two qubits of different devices. Using shared entangled states and classical communication, these operations between different devices can still be performed. In the ideal case of perfect fidelity, distributed quantum computing is a solution to achieving scalable quantum computers with a larger number of qubits. In this work we consider the effects on the output fidelity of a quantum algorithm when using noisy shared entangled states. We consider the quantum phase estimation algorithm and present two distribution schemes for the algorithm. We give the resource requirements for both and show that using less noisy shared entangled states results in a higher overall fidelity.
Digital curation practices in institutional repositories in South India: a study

Purpose The purpose of this study was to identify the digital curation practices in institutional repositories (IRs) in South India. Design/methodology/approach A voluntary survey was conducted among the IR managers of 23 South Indian IRs, and the response rate was 87%. Findings This study found that the active participation of South Indian IRs was only seen in a few digital curation activities. However, of the 33 digital curation activities analyzed, the active participation of repositories was only seen in ten digital curation activities. The performance of preservation activities was extremely low, and disagreements were recorded by the survey participants toward several digital curation activities. The most disagreed digital curation activities were emulation and cease data curation. All the participants had assigned metadata and allowed file downloads in their repositories. Raman Research Institute had provided a good number of digital curation services in their IR. Originality/value This is an in-depth study investigating the digital curation practice currently underway in South Indian IRs, and the researcher could not find similar studies in this niche.
Research on neural network model for new energy industry economy based on particle swarm optimization

This article has been retracted, and the online PDF has been watermarked "RETRACTED". A retraction notice is available at https://doi.org/10.3233/JIFS-219218.
Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning

Planning and reinforcement learning are two key approaches to sequential decision making. Multi-step approximate real-time dynamic programming, a recently successful algorithm class of which AlphaZero [Silver et al., 2018] is an example, combines both by nesting planning within a learning loop. However, the combination of planning and learning introduces a new question: how should we balance time spend on planning, learning and acting? The importance of this trade-off has not been explicitly studied before. We show that it is actually of key importance, with computational results indicating that we should neither plan too long nor too short. Conceptually, we identify a new spectrum of planning-learning algorithms which ranges from exhaustive search (long planning) to model-free RL (no planning), with optimal performance achieved midway.
Wavelet transform on regression trend curve and its application in financial data

In this paper, wavelet transform on a regression curve is investigated by using length-preserving projection and its application in financial data is also discussed. First, properties of wavelet filters on the regression trend curves are studied and two-scale equation of wavelet function is deduced on the regression trend curves. Second, the decomposition and reconstruction algorithm of discrete wavelet transform on regression trend curves is derived. Finally, two examples in financial data are given for discussion, based on decomposition and reconstruction algorithms on regression trend curves. Some new research interpretations are presented in dealing with financial data such as “volatility on regression growth trend”, “error on regression growth trend”, and so on.
An Architecture for Predictive Maintenance of Railway Points Based on Big Data Analytics

Massive amounts of data produced by railway systems are a valuable resource to enable Big Data analytics. Despite its richness, several challenges arise when dealing with the deployment of a big data architecture into a railway system. In this paper, we propose a four-layers big data architecture with the goal of establishing a data management policy to manage massive amounts of data produced by railway switch points and perform analytical tasks efficiently. An implementation of the architecture is given along with the realization of a Long Short-Term Memory prediction model for detecting failures on the Italian Railway Line of Milano - Monza - Chiasso.
Drug Trafficking on Darkmarkets: How Cryptomarkets are Changing Drug Global Trade and the Role of Organized Crime

Drug trafficking on darknet based marketplaces has become a highly concerning topic in law enforcement activities, recently. Even though Darkmarkets represent only a tiny fraction of the global drug trade, they are changing the drug markets social networks, introducing a new paradigm of the link between vendors and buyers of drugs. The aim of this study is to critically review the darkmarkets’ ecosystem and the previous literature regarding these new marketplaces, trying to investigate how the drug trade is changing with these new technologies, and the role of organised crime (OC) in these new illegal markets. And trying to understand how and whether is it involved OC on these cyber drug markets and the chain behind them. Despite opinions of part of the academy, the results show that there are no empirical evidences of direct involvement of OC as vendors in darkmarkets. However, there are evidences of an indirect role of OC in darknet drug trafficking, as supplier of illegal drugs to the online-vendors.
Parallel Corpus Filtering via Pre-trained Language Models

Web-crawled data provides a good source of parallel corpora for training machine translation models. It is automatically obtained, but extremely noisy, and recent work shows that neural machine translation systems are more sensitive to noise than traditional statistical machine translation methods. In this paper, we propose a novel approach to filter out noisy sentence pairs from web-crawled corpora via pre-trained language models. We measure sentence parallelism by leveraging the multilingual capability of BERT and use the Generative Pre-training (GPT) language model as a domain filter to balance data domains. We evaluate the proposed method on the WMT 2018 Parallel Corpus Filtering shared task, and on our own web-crawled Japanese-Chinese parallel corpus. Our method significantly outperforms baselines and achieves a new state-of-the-art. In an unsupervised setting, our method achieves comparable performance to the top-1 supervised method. We also evaluate on a web-crawled Japanese-Chinese parallel corpus that we make publicly available.
Effective time- and frequency-domain techniques for interpreting seismic precursors in groundwater level fluctuations on Jeju Island, Korea

Abstract An effective method, involving time and frequency domains was developed to interpret seismic precursors by comparing groundwater-level fluctuations recorded immediately and long before the occurrence of a known earthquake. The proposed method, consisting of the pre-processing (3-point filtering, band-pass filtering, and spectrum analysis) and post-processing (weighted moving average method and histogram and spectrum analyses) stages, was applied to the groundwater-level time series measured at three monitoring wells on Jeju Island, South Korea, from 00:00 on 8 September 2016 to 00:00 on 22 September 2016. The Gyeongju earthquake (Mw 5.4) occurred at 20:32 on 12 September2016. The histogram analysis exhibited an accentuating bellshape as the total number of waveforms, including those caused by the earthquake, of the groundwater-level fluctuations increased. The weighted moving average analysis indicated that various abnormal waveforms with different periods occurred in the fluctuations approaching the occurrence of the earthquake. The periods of seismic precursors in the groundwater-level fluctuations were determined by spectrum analysis and varied among the monitoring wells. Seismic precursor responses attributable to the Gyeongju earthquake were identified at least 8 hours before the earthquake, and the method used in this study indicates its good potential to predict an impending earthquake.
SiameseCCR: a novel method for one‐shot and few‐shot Chinese CAPTCHA recognition using deep Siamese network

The research of CAPTCHA recognition is helpful to discover the security vulnerabilities in time and improve its safety. In comparison with digits and English letters, Chinese characters have many more categories which lead to the requirement of a large amount of training data. Therefore, this study proposes a novel method for one-shot and few-shot Chinese CAPTCHA recognition, using the deep Siamese network, based on the idea of template matching. In this method, the residual convolutional neural network branches are used for feature extraction of CAPTCHAs, a fully-connected layer is used for calculating the similarity of features, and a hard negative mining algorithm is designed to promote convergence. Experiments are done on a self-built small-scale Chinese CAPTCHA dataset. The results show that this proposed method can achieve higher accuracy on the known characters than traditional methods. For the brand-new characters, only one template is required to recognise them and the accuracy is close to known characters. To summarise, it is able to build a Chinese CAPTCHA recognition model with high accuracy and extensibility by using a small-scale dataset.
Secure multiparty quantum computation based on Lagrange unitary operator

Abstract As an important subtopic of classical cryptography, secure multiparty quantum computation allows multiple parties to jointly compute their private inputs without revealing them. Most existing secure multiparty computation protocols have the shortcomings of low computational efficiency and high resource consumption. To remedy these shortcomings, we propose a secure multiparty quantum computation protocol by using the Lagrange unitary operator and the Shamir ( t , n ) threshold secret sharing, in which the server generates all secret shares and distributes each secret share to the corresponding participant, in addition, he prepares a particle and sends it to the first participant. The first participant performs the Lagrange unitary operation on the received particle, and then sends the transformed particle to the next participant. Until the last participant’s computation task is completed, the transformed particle is sent back to the server. The server performs Lagrange unitary operation on the received particle by using a secret message, and then measures the transformed particle to obtain the sum of the calculations of multiple participants. Security analysis shows that the proposed protocol can resist intercept-measurement attack, intercept-resend attack, entanglement-swapping attack, entanglement-measurement attack and collusion attack. Performance comparison shows that it has higher computation efficiency and lower resource consumption than other similar protocols.
MILP‐based automatic differential search for LEA and HIGHT block ciphers

The authors use the mixed-integer linear programming (MILP) technique for the automatic search for differential characteristics of LEA and HIGHT ciphers. They show that the MILP model of the differential property of modular addition with one constant input can be represented with a much lesser number of linear inequalities compared to the general case. Benefiting from this model for HIGHT block cipher, they can achieve a reduction of 112r out of 480r in the total number of linear constraints for the MILP model of r -round of HIGHT. This saving accelerates the searching process of HIGHT about twice as fast. They enjoy the MILP model to investigate the differential effect of these ciphers and provide a more accurate estimation for the differential probability. Their observations show that despite HIGHT, LEA exhibits a strong differential effect. The results gained by this method improve/extend the previous results as follows. For LEA block cipher, they found more efficient 12- and 13-round differentials whose probabilities are better than the best previous 12- and 13-round differentials for a factor of about 26 and 27, respectively. In the case of HIGHT block cipher, they found new 12- and 13-round differentials, though with the same best-reported probabilities.
Regional case study of Sustainable Development Goals implementation: Informal education and third mission of universities

The aim of the study is to introduce practical experience and explore the possibilities of sustainable development ideology in the region and to involve youth activists in international processes of regional reflection. Objectives: research of possibilities of youth organizations and universities in realization of non-formal education for overcoming environmental problems in the region; development of practical experience in realization of environmental education for sustainable development (ESD); development of pedagogical technology for non-formal work; creation of pedagogical model of interaction of different segments of youth organizations. Methodology: creation of the World Water Forum youth model. Work directions: adaptation of the international agenda to the regional one (Baikal region) and creation of the pedagogical model of practice-oriented work of youth communities for implementation of the third mission of HEIs. Regional ministries, youth government of Irkutsk region, colleges and universities of the region were involved in the model development. As a result of the thematic groups’ work, an inventory of the region’s problems was made by the activities of each working group; lists of the most promising areas of work to improve the environmental and social situation in the region were compiled; and road maps to achieve the set goals for up to 5 years were developed.
MANAJEMEN PERPUSTAKAAN SEKOLAH DASAR

This study determined the management of the SD Negeri 10 Indralaya Utara library. Sources of data in this study were principals, teachers, librarians and students in SD Negeri 10 Indralaya Utara. Data collection techniques observation, interview, documentation. Data analysis techniques 1) data reduction, 2) data presentation, and 3) data verification. The results showed that the four management components carried out by the SD Negeri 10 Indralaya Utara Library Manager well. It is known 1) the management of the SD Negeri 10 Indralaya Utara Library good; 2) the management of the SD Negeri 10 Indralaya Utara Library in terms of organization good; 3) SD Negeri 10 Indralaya Utara Library Management in terms of deployment very good; 4) SD Negeri 10 Indralaya Utara Library Management in terms of supervision very good.