Reviewerly/exHarmony huggingface dataset:
Multi-layer quantum search and inclusion of NP into BQP

In this work, we present a multi-layer quantum search method that generates an exponential speedup of the standard Grover's algorithm. As direct applications, any NP problems can be solved efficiently on a quantum circuit with only polynomial gate complexity. In particular, such multi-layer search can solve the factoring problem with an exponential speedup, providing an alternative to Shor's algorithm. Our results show that the exponential speedup of quantum circuits is ubiquitous, and Grover's search is much more powerful than that has been demonstrated. With no contradiction to the quadratic optimality of single-layer query complexity, the great potential of Grover's search is fully released by such multi-layer search design.
Perception of Librarians towards Use of Social Media for Information Service Delivery: A Conceptual Paper

This study conceptualizes how librarians perceive social media in library and information service delivery. The study consults and review previous research on the use of social media to provide service delivery by librarians by librarians. Social media encourages librarians to create new knowledge, ideas, and services and share it across their community of users. Service delivery is the primary function of any university library. Due to the difficult nature of traditional library services, libraries now embrace and adopt some social media tools for service delivery. This research highlighted the opportunities offer by social media to improve service delivery, the study also discuss the categorization of social media, types of social media, applicability of social media, challenges face by librarians when using social media and strategies on incorporating social media when using service delivery. Based on the conceptual review carried out, majority of studies reveals that librarians use social media and ICT in information service library and the most use form of social media is the Facebook, WhatApps and twitter, but, however, they do encounter obstacles in terms when using the social media. The study concludes that librarians actually use social media for library information service delivery, but yet there is a lot of challenges for proper use of social media in library and information delivery. Therefore, the study recommends that university libraries should incorporate more social media to provide effective services to their clients and adequate finance should be provided regularly by the university management to improve the use of social media in information service delivery and finally librarians should render more proactive and more quality social media services to meet the information needs of clients
Historical Information Science within a New Interdisciplinary Field: the Academic Symposium Addressing the 15th Anniversary of the Historical Information Science Department of Moscow University

The article discusses the scientific symposium addressing the 15th anniversary of the Historical Information Science Department &amp;ndash; a part of Lomonosov Moscow University History Faculty. The authors analyze the reports and notes that they are dominated by the spatial approach to historical phenomena and processes. The reports addressed virtual 3D reconstruction of historical and cultural heritage objects as well as the application of geographic information systems and technologies to historical studies. All the conclusions were actively debated by the symposium participants. The methodological basis of the article is the information approach, the use of description and analysis methods. The authors briefly characterize the results of the latest studies applying digital technologies and review the development of historical studies based on mathematics and computer science methods. Herewith, emphasis is laid on the Moscow school of historical information science, primarily the studies carried out at the Historical Information Science Department of Lomonosov Moscow University History Faculty.
On the Learnability of Possibilistic Theories

We investigate learnability of possibilistic theories from entailments in light of Angluin's exact learning model. We consider cases in which only membership, only equivalence, and both kinds of queries can be posed by the learner. We then show that, for a large class of problems, polynomial time learnability results for classical logic can be transferred to the respective possibilistic extension. In particular, it follows from our results that the possibilistic extension of propositional Horn theories is exactly learnable in polynomial time. As polynomial time learnability in the exact model is transferable to the classical probably approximately correct model extended with membership queries, our work also establishes such results in this model.
From Dependency to Causality: A Machine Learning Approach.

The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference. Recent results in the ChaLearn cause-effect pair challenge have shown that causal directionality can be inferred with good accuracy also in Markov indistinguishable configurations thanks to data driven approaches. This paper proposes a supervised machine learning approach to infer the existence of a directed causal link between two variables in multivariate settings with n > 2 variables. The approach relies on the asymmetry of some conditional (in) dependence relations between the members of the Markov blankets of two variables causally connected. Our results show that supervised learning methods may be successfully used to extract causal information on the basis of asymmetric statistical descriptors also for n gt; 2 variate distributions.
Privacy-Preserving Biomedical Database Queries with Optimal Privacy-Utility Trade-Offs

Sharing data across research groups is an essential driver of biomedical research. In particular, biomedical databases with interactive query-answering systems allow users to retrieve information from the database using restricted types of queries. For example, medical data repositories allow researchers designing clinical studies to query how many patients in the database satisfy certain criteria, a workflow known as cohort discovery. In addition, genomic “beacon” services allow users to query whether or not a given genetic variant is observed in the database, a workflow we refer to as variant lookup. While these systems aim to facilitate the sharing of aggregate biomedical insights without divulging sensitive individual-level data, they can still leak private information about the individuals through the query answers. To address these privacy concerns, existing studies have proposed to perturb query results with a small amount of noise in order to reduce sensitivity to underlying individuals [1, 2]. However, these existing efforts either lack rigorous guarantees of privacy or introduce an excessive amount of noise into the system, limiting their effectiveness in practice.
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Adversarial Autoencoder and Multi-Task Semi-Supervised Learning for Multi-stage Process

In selection processes, decisions follow a sequence of stages. Early stages have more applicants and general information, while later stages have fewer applicants but specific data. This is represented by a dual funnel structure, in which the sample size decreases from one stage to the other while the information increases. Training classifiers for this case is challenging. In the early stages, the information may not contain distinct patterns to learn, causing underfitting. In later stages, applicants have been filtered out and the small sample can cause overfitting. We redesign the multi-stage problem to address both cases by combining adversarial autoencoders (AAE) and multi-task semi-supervised learning (MTSSL) to train an end-to-end neural network for all stages together. The AAE learns the representation of the data and performs data imputation in missing values. The generated dataset is fed to an MTSSL mechanism that trains all stages together, encouraging related tasks to contribute to each other using a temporal regularization structure. Using real-world data, we show that our approach outperforms other state-of-the-art methods with a gain of 4x over the standard case and a 12% improvement over the second-best method.
Deep Encoder-Decoder Neural Network for Fingerprint Image Denoising and Inpainting

Fingerprint image denoising is a very important step in fingerprint identification. to improve the denoising effect of fingerprint image,we have designs a fingerprint denoising algorithm based on deep encoder-decoder network,which encoder subnet to learn the fingerprint features of noisy images.the decoder subnet reconstructs the original fingerprint image based on the features to achieve denoising, while using the dilated convolution in the network to increase the receptor field without increasing the complexity and improve the network inference speed. In addition, feature fusion at different levels of the network is achieved through the introduction of residual learning, which further restores the detailed features of the fingerprint and improves the denoising effect. Finally, the experimental results show that the algorithm enables better recovery of edge, line and curve features in fingerprint images, with better visual effects and higher peak signal-to-noise ratio (PSNR) compared to other methods.
Learning to Learn Morphological Inflection for Resource-Poor Languages

We propose to cast the task of morphological inflection - mapping a lemma to an indicated inflected form - for resource-poor languages as a meta-learning problem. Treating each language as a separate task, we use data from high-resource source languages to learn a set of model parameters that can serve as a strong initialization point for fine-tuning on a resource-poor target language. Experiments with two model architectures on 29 target languages from 3 families show that our suggested approach outperforms all baselines. In particular, it obtains a 31.7% higher absolute accuracy than a previously proposed cross-lingual transfer model and outperforms the previous state of the art by 1.7% absolute accuracy on average over languages.
Multi-cloud DRaaS using OpenStack Keystone Federation

Disaster recovery (DR) for the distributed workload and data in multi-cloud environments is very complex and challenging. There exist point tools for failover from one cloud to another cloud with active/passive data replications or with simple backup of private cloud data to public cloud(s). However, these point solutions are not integrated with the cloud environment. In this paper, we demonstrate how cost-effective DR process/solution can be accomplished with our Federated Cloud Services Framework (middleware), which is built upon OpenStack [16], by leveraging existing OpenStack functionalities. As our solution leverages the functionality of the federated cloud environment, it generalizes easily to multi-cloud scenarios, and is better integrated with the cloud infrastructure. We also compare our solution to current DR process/solutions available in the market.
Deep learning-based techniques to enhance the precision of phrase-based statistical machine translation system for Indian languages

The paper focuses on improving the existing phrase-based statistical machine translation (PB-SMT) system by integrating deep learning knowledge to it. In this paper, a deep learning-based PB-SMT system for Indian languages is developed, so as to improve the conditional probability of the phrase-table and replaced the neural probabilistic language model with the existing back off algorithm of n-gram language model to improve the performance of language model. It is shown that the deep feature-based PB-SMT is better than the standard PB-SMT system. It is shown the significance of integrating manually created dictionaries that has been trained as separate translational model can enhance the result of statistical machine translation system when decoding. For automatic evaluation, it is shown that RIBES being a better evaluation metric for Indian languages compared to BLEU, a standard one.
Semantics-Reconstructing Hashing for Cross-Modal Retrieval

Retrieval on Cross-modal data has attracted extensive attention as it enables fast searching across various data sources, such as texts, images and videos. As one of the typical techniques for cross-model searching, hashing methods project features with high dimension into short-length hash codes, thus effectively improving storage and retrieval efficiency. Recently, many efforts have been made to widely study supervised methods with promising performance. However, there still remain some problems. Conventionally, hash codes and projection functions are learnt by preserving the pairwise similarities between data items, which neglects the discriminative property of class associated with each data item. Most of the existing methods that utilise class labels also undertake the binary codes learning under a classification frame. The relations between binary codes and labels have not been well considered. To tackle these problems, we propose a shallow supervised hash learning method – Semantics-reconstructing Cross-modal Hashing (SCH), which reconstructs semantic representation and learns the hash codes for the entire dataset jointly. For the semantic reconstruction, the learned semantic representation is projected back into label space, extracting more semantic information. By leveraging reconstructed semantic representations, the hash codes are learnt by considering the underlying correlations between labels, hash codes and original features, resulting in a further performance improvement. Moreover, SCH learns the hash codes and functions without relaxing the binary constraints simultaneously, therefore, it further reduces the quantization errors. In addition, the linear computational complexity of its training makes it practicable to big data. Extensive experiments show that the proposed SCH can perform better than the state-of-the-art baselines.
The Whole Truth about Partial Truth Tables

Partial truth tables have two salient virtues.First, like whole truth tables, they are algorithmic (i.e., effective).If you construct them correctly, you will get an answer to your question whether a particular argument is valid; whether a particular proposition is tautologous, self-contradictory, or contingent; or whether a particular set of propositions is consistent.Second, they are less time-consuming and tedious to construct than whole truth tables.No partial truth table has more than three rows, and many have only one.A whole truth table, by contrast, may have as many as 32, 64, 128, or 256 rows (or more).In this essay, I explain what a partial truth table is and show how such a table is constructed.I then apply the partial-truth-table technique successively to arguments, individual propositions, and sets of two or more propositions.I conclude by evaluating the most widely used logic textbooks, showing what they do well and where they fall short.
A Doubly Regularized Linear Discriminant Analysis Classifier with Automatic Parameter Selection

Linear discriminant analysis (LDA) based classifiers tend to falter in many practical settings where the training data size is smaller than, or comparable to, the number of features. As a remedy, regularized LDA (RLDA) methods have been proposed. However, the classification performance of these methods vary depending on the size of training and test data. In this paper, we propose a doubly regularized LDA classifier that we denote as R2LDA. In the proposed R2LDA approach, two regularization operations are carried out; one involving only the training data set, while the other also includes the given test data sample. The proposed R2LDA algorithm, unlike the classical RLDA techniques, caters for errors due to training data as well as the possible noise in the test data. Choosing the two regularization parameters in R2LDA can be automated through existing methods based on least squares (LS). Particularly, we show that a constrained perturbation regularization approach (COPRA) is well suited for the regularization parameter selection task needed for the proposed R2LDA classifier. Results obtained from both synthetic and real data demonstrate the consistency and effectiveness of the proposed R2LDA-COPRA classifier, especially in scenarios involving noisy test data.
L2 Reading Comprehension and Lexical Usage in Writing by Summarizing Instruction

This study aimed to examine how explicit summarizing instruction and practices can influence reading comprehension and lexical usage in writing by Korean EFL learners in classroom contexts. For the study, the value of summarizing in L2 learning and lexical usage in writing were discussed. The results showed that learners could significantly better understand reading passages than before the summarizing intervention. In lexical diversity, overall learners could use more diverse and advanced lexical items in post-compositions. The average TTR (type/token ratio) for three word lists, the 2nd 1000 word list, academic word list (AWL), and not-in-the-list words (NLW), increased significantly, whereas that of the 1st 1000 list decreased slightly in the post-writings. In terms of lexical sophistication measured by the total number of tokens in both AWL and NLW, learners showed significantly higher degrees of AWL, with slightly more in NLW in their post-writings. This positive effect would appear to be attributed to integrated summarizing processes with reading source texts about different topics, while deleting unimportant information and focusing on key words, phrases or sentence level comprehension for bottom-up processes, which was followed by output activities that transferred those expressions into their writing through sentence combination, paraphrasing, syntactic transformation and invention with dictionary use, teacher modelling and feedback.
PREPARATION OF THE EDUCATIONAL ENVIRONMENT FOR AN INCLUSIVE PROCESS IN THE INSTITUTIONS OF GENERAL SECONDARY EDUCATION

This article is devoted to approaches to the targeted and consistent preparation of the educational environment for inclusive learning in educational institutions. The author reveals the limited and controversial nature of the concept of «inclusive educational environment» in Ukrainian regulatory and educational materials; the author presents international guidelines, according to which the educational environment acquires such characteristics as: free, stimulating, supportive, developing for all students. The author carefully analyses the content and structural components of the classroom management strategy (object spatial, organizational-semantic, socio-psychological), aimed at positive transformation of the educational environment for the success of each participant in the educational process. The author presents research on the implementation of the «Classroom Management» strategy in the educational process, in which seven educational institutions took part. The aim of the research was to carry out positive transformations in the educational environment as the basis for the implementation of effective inclusive education. According to expert assessments of the teaching staff, the changes achieved have significantly improved the start of inclusive education. In turn, the team of organizers and experts of this research noted the particular success of two schools, the teaching teams of which showed sincere interest in the proposed approach and joined the preparatory work not as consumers, but as real partners, with enthusiasm, creative proposals, a desire to transform the environment for the better and achieve greater success in student learning and development. The results obtained showed the effectiveness of the «Classroom Management» strategy, which positively transform the educational environment and contribute to the development of a competent inclusive process.
Evolutionary Algorithm for Content-Based Image Search

Content-based image retrieval systems attempt to provide a means of searching for images in large repositories without using any information other than that contained in the image itself, usually in the form of low-level descriptors. Since these descriptors do not accurately represent the semantics of the image, evaluating the perceptual similarity between two images based only on them is not a trivial task. This paper describes an effective method for image recovery based on evolutionary computing techniques. The results are compared with those obtained by the classical approach of the movement of the query point and the rescheduling of the axes and by a technique based on self-organizing maps, showing a remarkably higher performance in the repositories.
Online Teaching of Structural Chemistry Course Based on “1+2” Platform

Since the outbreak of novel coronavirus pneumonia (NCP) at the beginning of this year, Chinese government acted immediately and made some regulations to control the epidemic NCP and promote the recovery of society and industry including education.Chinese universities stayed on the online courses without suspension on teaching and learning.Herein, we report online teaching of structural chemistry course based on the teaching mode of online live broadcast platform, online course resources and management platforms ("1 + 2" mode).This may promote online teaching, and take advantage of online live broadcast and massive open online courses, which achieves good teaching and learning experiences on structural chemistry course.
ВПЛИВ СТРУКТУРИ ПІЗНАВАЛЬНОЇ АКТИВНОСТІ НА АКАДЕМІЧНУ УСПІШНІСТЬ СТУДЕНТІВ-ПСИХОЛОГІВ

Cognitive activity of psychology students is an important link in the formation of cognitive needs, when the constant acquisition and updating of professional information becomes a necessity, which leads to intense independent, active and creative work to obtain the necessary knowledge. Thus, the purpose of the article is to determine the features of the structure of cognitive activity as a factor in the success of students’ learning activities.Materials and methods of investigation: theoretical – analysis, comparison and generalization of scientific literature according to the problem of research; empirical – “The method of research the signs of cognitive activity of university students” by Y. Y. Zhukov, analysis of academic progress of students; mathematical data processing – quantitative and qualitative data analysis, correlation analysis, t-Student criterion. Statistical analysis was conducted by using the STATISTICA software package. Cognitive activity research of the first-year students revealed its instability that means the possibility of active influence on it. Some students negatively evaluate the following signs: regulatory sign (students complain about inattention and distraction; they admit that they do not prepare for classes and do not search for their own ways of solving task, so doing them only to the teacher’s instructions); conscious interest (students use only compulsory literature, they are not inclined to ask the teacher further, and find the additional material unnecessary); the effectiveness of organization of the process of solving learning problems (students miss classes, not ready for credit, do not use tools for self-organization); super-situational activity (the students do not consider necessary to do additional non-compulsory tasks, attend voluntary classes and study additional specialized literature); comfortable learning activities (students complain about poor performance and non-satisfying activities). So these signs need attention and correction. A decisive factor affecting students’ academic progress is the high “regulatory trait” and “super-situational activity”. The basis for enhancing cognitive activity of students-psychologists can use such components as: “independence in solving educational problems” and “integration of educational material in life experience”.