Reviewerly/exHarmony huggingface dataset:
Application of blockchain technology in banking: a systematic literature review

Blockchain is one of the latest disruptive innovations that got recognition with the rise of cryptocurrency, especially bitcoin. Blockchain got traction from people when Bitcoin was introduced in the market, and since then, the technology has been used for several purposes, the most common being banking. The objective of this study will be to provide insights into how blockchain is employed in the banking sector. Four databases were used for this study. These databases include Web of Science, Dimensions, EBSCOhost, and ProQuest. After filtering the database, we retrieved 146 articles to explore the impact of blockchain on banking. Secondly, we conducted a bibliometric analysis of these filtered articles to identify common themes, country of study, and the years when blockchain in banking became prominent. The conclusion includes the implications of the study for further research purposes. This study extends the previous study and provides insights for future studies.
Validating an Automatic Item Generation System Based on a Cognitive Diagnostic Model

Cognitive diagnostic assessments (CDAs) require a large number of itemsto measure the target attributes with high precision. An automatic itemgeneration (AIG) system would help to reduce the cost and effort of itemwriting in CDAs. This study aimed to develop a valid AIG system for CDAsin linear equations of mathematics by designing an AIG system andexamining two aspects of a generated CDA in cognitive diagnosticmodeling: (a) the M-matrix, which specifies the set of attributesrequired by each item model and (b) the item discrimination index, whichis computed from item parameters in the deterministic input,noisy-and-gate (DINA) model. First, we compared an original M-matrix totwo alternative M-matrices by using information criteria, posteriorpredictive model checks, and item discrimination indices. Second, weexamined the magnitude and variability of the item discriminationindices for every item model. No substantially large differences werefound among the results from all of the M-matrices. The discriminationindices tended to be high in items that measured major attributes, andthe variabilities of the indices were small within each item model,except for a few item models. These findings indicate the validity ofour M-matrix and AIG system. Furthermore, they suggest ways to improvethe AIG system. Research limitations and how future studies can help toenhance the AIG system are discussed.
Information-Theoretic Principal Variable Selection Criterion and Its Properties

In order to reduce a large number of original variables to a smaller one that still contains most of the correlation information in statistical data without an external criterion, we propose ``the information-theoretic principal variable (iPV) selection criterion", from the perspective of Kullback—Leibler divergence. In addition, from the viewpoint of statistical parameters, we show that (a) the iPV selection criterion has a stopping rule, and (b) if a set of non-selected variables by the stopping rule is characterized as the independent set then the value of the iPV selection criteria is zero.
Breast cancer detection using deep learning and cnn-based model

The second-most dangerous cancer in the world is breast cancer. Not just in India, but all around the world, breast cancer is the primary cause of death for women. According to the USA in 2011, out of eight one woman had cancer. Inappropriate breast cell division can result in benign or malignant breast cancer. Consequently, this is how breast cancer progresses. Therefore, it is crucial to detect the breast cancer at the early stage. By doing this, many lives can be saved and the sickness can be adequately treated while also being treated as a very serious condition. Breast cancer is most dangerous disease and at present it treated as global disease. Invasive breast cancer will likely affect 246,660 women in the USA in 2016, and 40,450 women will likely pass away from the disease. Mammography continues to be labor-intensive and has acknowledged drawbacks despite its success as a tool for detecting breast cancer, including low sensitivity in women with dense breast tissue. The development of neural networks has been used to breast histopathology images during the past ten years to help radiologists operate more accurately and efficiently. The goal of this study is to use the most recent convolution neural network (CNN) expertise to images of breast histopathology. The first section of the research examines conventional Computer Assisted Detection (CAD) utilising machine learning and a more current CNN-based model for Breast Histopathology Images.
ТВОРЧЕСКАЯ ПАЛИТРА ЮРИЯ ГЕВОРКЯНА / Yuri Gevorgyan's Creative Palette

May 11, 2021 marked the 90th anniversary of the composer, educator, musical-social figure, and simply the wonderful person Yury Aleksander Gevorgyan. The article outlines Gevorgyan's portrait as a composer, public figure and teacher. Alongside with his creative and social activity, for more than 40 years, Yury Aleksander Gevorgyan, was also involved in educational activity. From 1960 to 2002 he taught at Kh. Abovyan Pedagogical Institute (Armenian State Pedagogical University). The composer’s main works are anlysed.
From Learning to Read to Reading to Learn: School Libraries, Literacy and Guided Inquiry

School libraries are about the future. They are about the development of knowledgeable and knowing young people; young people who have the ability to read the word and the world, and who can live their lives as thinking, informed, knowledgeable and productive citizens of an increasingly inter-connected world. They are about young people who have the knowledge, skills, attitudes and values to invest wisely in confidently shaping their own futures and their lives as family, community and workplace members. Reading, Knowing and Doing are the multiple faces of the future global citizens that we nurture in our schools. Reading, Knowing, Doing, as the multiple faces of literacy, are the multiple faces of quality school libraries. And Reading, Knowing and Doing are at the heart of informed, in-tune, and in-touch school librarians committed to providing the best opportunities for our students to learn to use their minds well.
FORMATION OF VOCAL-PEDAGOGICAL CULTURE OF THE FUTURE MUSIC TEACHER

The problem of professional training of the future music teacher in the lessons of voice production is considered and vocal and technical techniques are proposed, which are used by the author of the Sttudy in practical work with Students. Adhering to the vocal-pedagogical principles of Professor OStap Darchuk from the Lviv Vocal School, where the author received training, and utilizing his advice, the article emphasizes the significance of musical-vocal reading based on linguiStic support, on the “character of sound” in the resonance point, and in a specific rhythm and tempo. Such reading helps develop a clear, articulate diction in the singer – the future music teacher – promoting uniformity in voice production, good projection, and the agility of sound. In doing so, the aim is to Strive for the moSt complete conveyance of the meaning of each word and the overall content of the piece. Musicalvocal declamation is also employed – a technical approach to the vocal-creative pronunciation of words with all their grammatical and logical accents, expressing the emotions contained in them. The article emphasizes the importance of underStanding the role of breathing in voice production, maStering basic types of breathing exercises (speaking, reciting poems, delivering speeches, etc.). A series of exercises for “breathing gymnaStics” is proposed in the work. Therefore, summarizing the material presented above, it is worth noting that the main goal of this article is to highlight certain vocal-technical techniques that collectively form the methodology for developing the voice of future music teachers. Since the process of shaping the elements of pedagogical maStery in a music teacher occurs gradually and takes into account the individual approach to each Student, the techniques should be selected based on what can be moSt beneficial at each Stage of the Student’s education and upbringing. Therefore, the research focuses on revealing techniques that can be recommended for work at the initial Stage of building the vocal apparatus of the Student. The proposed techniques, as described by the article’s author, are widely applied in her pedagogical work in educating future music teachers.
THE METHOD OF RELATIVE SOLMIZATION IN UKRAINIAN AND FOREIGN MUSIC PEDAGOGY: THEORETICAL ASPECT

The article provides a historical overview of the use of the method of relative solmization, which is most effective at the initial stage of the development of children’s musical abilities, in Ukrainian and foreign music pedagogy. An attempt was made to systematically analyse the relative system of solmization, which was one of the factors in raising the level of school music education in Ukraine in the second half of the 20th century. The ways of revival and use of this method in the modern musical and aesthetic practice of secondary schools of Ukraine are revealed. It has been proven that the method of relative solmization in various variants and in combination with the absolute method is widely used in the practice of mass and special music education. Analysing the history of the music education development in Ukraine, we can assert that the method of the relative solmization, in various variations and combined with the absolute method, is widely applied in the practice of both mass and specialized music education. Some principles and methods of well-known contemporary music-educational concepts were known and developed in Ukraine long before their use abroad. The popularity of relative solmization depends on objective socio-historical changes and trends in the development of the musical community’s interest in vocal or instrumental music education. Analysing various adaptations of the relative solmization method within the context of national musical cultures allows us to conclude that the majority of the world musicians and educators cannot imagine the process of the general music education without using this method, especially in the early stages of developing children’s musical abilities based on national children’s folklore.
METHODIC OF FORMATION OF A DIDACTIC CONCEPTTERMINOLOGICAL SYSTEM “SOCIO-CULTURAL ACTIVITY”

Based on the use of the activity approach in humanitarian Studies, the methodic foundations of the formation of the didactic concept-terminological syStem “socio-cultural activity” were formulated and subStantiated. The classification of the main concepts of the proposed terminological syStem was carried out, in particular, the basic and derived concepts were highlighted. Based on the terminological analysis of scientific sources, author’s definitions of main concepts such as: person, activity, behavior, society, space are proposed. Based on the principles of terminological work, basic concepts are expanded into derived concepts of the proposed didactic syStem, such as: human activity, social activity, cultural activity, sociocultural activity, sociocultural space, sociocultural sphere, sociocultural syStem, sociocultural complex, sociocultural cluSter, sociocultural induStry, etc. The author’s vision of Structuring spheres of socio-cultural activity based on the selection of their types and forms is proposed. The components of the socio-cultural space at different taxonomic levels from global to local, as well as at the level of cooperation of various taxonomic subjects are subStantiated. Basic and related spheres of socio-cultural activity are identified and Structured by species, and their subject components at the macro-, meso-, micro- and nano-levels of socio-cultural analysis are determined. We can State that the didactic concept-terminological syStem “socio-cultural activity” is based on the categories: person, activity, society, space and is described through the concretization of such general scientific concepts as: space – a syStem of relations and connections; sphere – a set (plurality) of constituent components diStinguished by various characteriStics; syStem – a combination of elements and their connections; a complex is a system with a high density of connections. In the future, the proposed syStem can be used for the preparation of educational and methodological publications, in particular, as the basic conception of the educational manual “Socio-cultural activity”, as well as for the formation of terminological and reference publications and the creation of electronic training courses on the MOODLE platform, in particular, the filling of their components such as “Keywords” and “Glossary”.
Lessons Learned from FIPSE Projects II - Northwestern University

This archived section of the book Lessons Learned from FIPSE PRojects II focuses on a project to teach music theory to students in a new way, rather than in a fragmented fashion. This project, by creating a multi-media, real-time instructional system, enabled students to integrate the visual, aural, kinesthetic, affective and chronological components of musical comprehension.
Distributed Sparse Class-Imbalance Learning and Its Applications

In the present work, the study on class imbalance problems in a <i>distributed</i> setting exploiting sparsity structure in the data has been carried out. We formulate the class-imbalance learning problem as a cost-sensitive learning problem with <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula> regularization. The cost-sensitive loss function is a cost-weighted smooth hinge loss. The resultant optimization problem is minimized within the <i>Distributed Alternating Direction Method of Multiplier</i> (DADMM) framework. We partition the data matrix across samples. This operation splits the original problem into a distributed <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math></inline-formula> regularized smooth loss minimization and a <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula> regularized squared loss minimization. <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math></inline-formula> regularized subproblem is solved via Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and random coordinate descent method in parallel at multiple processing nodes using <i>MPI</i> whereas <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula> regularized problem is just a simple soft-thresholding operation. We show, empirically, that the distributed solution approximates the centralized solution on many benchmark data sets. The centralized solution is obtained via Cost-Sensitive Stochastic Coordinate Descent (CSSCD). Empirical results on small and large-scale benchmark datasets show some promising avenues to further investigate the real-world applications of the proposed algorithms such as anomaly detection, class-imbalance learning, etc. To the best of our knowledge, ours is the first work to study class-imbalance in a <i>distributed</i> environment on large-scale <i>sparse</i> data.
Sustainable Reform of "Introductory Dynamics" Driven by a Community of Practice

The Strategic Instructional Initiatives Program (SIIP) is a college-wide effort to promote the sustainable adoption of evidence-
Object-Based Video Coding by Visual Saliency and Temporal Correlation

When a disaster occurs, video communication is an effective way to disseminate large quantities of important information. However, video coding standards such as High Efficiency Video Coding (HEVC) compress entire videos, whatever the contents are; at low bit rates, the quality of significant objects deteriorates. In this paper, an object-based video coding method is proposed to address this problem. The proposed method extracts objects on the basis of visual saliency and temporal correlation between frames. Subsequently, we execute pre-processing which degrades the background quality before encoding the video with HEVC. This method can reduce the bit rate while preserving target object quality. Experimental comparison with HEVC demonstrates the superior performance of the proposed method.
Incorporating Discrete Constraints Into Random Walk-Based Graph Matching

Graph matching is a fundamental problem in theoretical computer science and artificial intelligence, and lays the foundation for many computer vision and machine learning tasks. Approximate algorithms are necessary for graph matching due to its NP-complete nature. Inspired by the usage in network-related tasks, random walk is generalized to graph matching as a type of approximate algorithm. However, it may be inappropriate for the previous random walk-based graph matching algorithms to utilize continuous techniques without considering the discrete property. In this paper, we propose a novel random walk-based graph matching algorithm by incorporating both continuous and discrete constraints in the optimization process. Specifically, after interpreting graph matching by random walk, the continuous constraints are directly embedded in the random walk constraint in each iteration. Further, both the assignment matrix (vector) and the pairwise similarity measure between graphs are iteratively updated according the discrete constraints, which automatically leads the continuous solution to the discrete domain. Comparisons on both synthetic and real-world data demonstrate the effectiveness of the proposed algorithm.
A Class-Incremental Learning Method Based on One Class Support Vector Machine

Abstract Class-incremental learning refers to the problem that the number of classes increases dynamically in the training stage. In this paper, a method based on one class support vector machine (OCSVM) is proposed for class incremental learning. The basic idea of the proposed method is that one class model is used to learn the distribution of new class, and then the knowledge learned and previous models are used for class incremental learning. The proposed method does not need to store all training data and makes full use of the learned information, so it has lower memory cost and time consumption. Also, the proposed method can build model when only one class is available. Compared with OC2MC, CIL, NN, the proposed method also achieves competitive results on different datasets.
PALO bounds for reinforcement learning in partially observable stochastic games

A partially observable stochastic game (POSG) is a general model for multiagent decision making under uncertainty. Perkins’ Monte Carlo exploring starts for partially observable Markov decision process (POMDP) (MCES-P) integrates Monte Carlo exploring starts (MCES) into a local search of the policy space to offer an elegant template for model-free reinforcement learning in POSGs. However, multiagent reinforcement learning in POSGs is tremendously more complex than in single agent settings due to the heterogeneity of agents and discrepancy of their goals. In this article, we generalize reinforcement learning under partial observability to self-interested and cooperative multiagent settings under the POSG umbrella. We present three new templates for multiagent reinforcement learning in POSGs. MCES for interactive POMDP (MCESIP) extends MCES-P by maintaining predictions of the other agent’s actions based on dynamic beliefs over models. MCES for multiagent POMDP (MCES-MP) generalizes MCES-P to the canonical multiagent POMDP framework, with a single policy mapping joint observations of all agents to joint actions. Finally, MCES for factored-reward multiagent POMDP (MCES-FMP) has each agent individually mapping joint observations to their own action. We use probabilistic approximate locally optimal (PALO) bounds to analyze sample complexity, thereby instantiating these templates to PALO learning. We promote sample efficiency by including a policy space pruning technique and evaluate the approaches on six benchmark domains as well as compare with the state-of-the-art techniques, which demonstrates that MCES-IP and MCES-FMP yield improved policies with fewer samples compared to the previous baselines.
Fundamental noisy multiparameter quantum bounds

Abstract Quantum multiparameter estimation involves estimating multiple parameters simultaneously and can be more precise than estimating them individually. Our interest here is to determine fundamental quantum limits to the achievable multiparameter estimation precision in the presence of noise. We first present a lower bound to the estimation error covariance for a noisy initial probe state evolving through a noiseless quantum channel. We then present a lower bound to the estimation error covariance in the most general form for a noisy initial probe state evolving through a noisy quantum channel. We show conditions and accordingly measurements to attain these estimation precision limits for noisy systems. We see that the Heisenberg precision scaling of 1/ N can be achieved with a probe comprising N particles even in the presence of noise. In fact, some noise in the initial probe state or the quantum channel can serve as a feature rather than a bug, since the estimation precision scaling achievable in the presence of noise in the initial state or the channel in some situations is impossible in the absence of noise in the initial state or the channel. However, a lot of noise harms the quantum advantage achievable with N parallel resources, and allows for a best precision scaling of $${\bf{1}}/\sqrt{{\boldsymbol{N}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mn>1</mml:mn> <mml:mo>/</mml:mo> <mml:msqrt> <mml:mi>N</mml:mi> </mml:msqrt> </mml:math> . Moreover, the Heisenberg precision limit can be beaten with noise in the channel, and we present a super-Heisenberg precision limit with scaling of 1/ N 2 for optimal amount of noise in the channel, characterized by one-particle evolution operators. Furthermore, using γ -particle evolution operators for the noisy channel, where γ &gt; 1, the best precision scaling attainable is 1/ N 2 γ , which is otherwise known to be only possible using 2 γ -particle evolution operators for a noiseless channel.
Practical quantum oblivious transfer with a single photon

Quantum oblivious transfer (QOT) is an essential cryptographic primitive. But unconditionally secure QOT is known to be impossible. Here we propose a practical QOT protocol, which is perfectly secure against dishonest sender without relying on any technological assumption. Meanwhile, it is also secure against dishonest receiver in the absence of long-term quantum memory and complicated collective measurements. The protocol is extremely feasible, as it can be implemented using currently available Mach-Zehnder interferometer, and no quantum memory, collective measurements nor entanglement are needed for honest participants. More importantly, comparing with other practical QOT schemes, our protocol has an unbeatable efficiency since it requires the transmission of a single photon only.
Exact Passive-Aggressive Algorithms for Ordinal Regression Using Interval Labels

In this paper, we propose exact passive-aggressive (PA) online algorithms for learning to rank. The proposed algorithms can be used even when we have interval labels instead of actual labels for examples. The proposed algorithms solve a convex optimization problem at every trial. We find exact solution to those optimization problems to determine the updated parameters. We propose support class algorithm (SCA) which finds the active constraints using the KKT conditions of the optimization problems. These active constrains form support set which determines the set of thresholds that need to be updated. We derive update rules for PA, PA-I and PA-II. We show that the proposed algorithms maintain the ordering of the thresholds after every trial. We provide the mistake bounds of the proposed algorithms in both ideal and general settings. We also show experimentally that the proposed algorithms successfully learn accurate classifiers using interval labels as well as exact labels. Proposed algorithms also do well compared to other approaches.