Reviewerly/exHarmony huggingface dataset:
Performing musicology: retrospective, methodological principles, looking to the future

The history of performing musicology is a vast panorama of traditions in the context of national schools and styles of certain eras. It provides a key to deciphering individual and widely used techniques. Her legacy helps not only to comprehend the process of the formation of the art of outstanding representatives of world musical performance, but also opens up unlimited prospects for the further development of performing skills and enriching it with new content. In this article, the author collects and studies materials on the topic of research. The purpose of this publication is an analysis of sources (the quality effects model), revealing the history of performing musicology and its key methods which give grounds for constructing hypotheses regarding the prospects of this direction in the context of world (including Ukraine`s art) culture. The author gives different types of performing musicology analysis including integrative, systematic and comparative methods. It's a base for the aim of this work which consists of the creation of hypotheses connected with the performing musicology`s future.
Research on Public Administration and Resource Allocation Based on Blockchain and Structured Occupational Therapy

In order to explore the public administration and resource allocation based on blockchain and structured occupational therapy, this paper takes the public affairs in the prevention and control of the Corona Virus Disease 2019 (COVID-19) epidemic as an example to conduct research. On the basis of summarizing and analyzing the previous published literatures, this study expounded the research status and significance of public administration and resource allocations; elaborated the development background, current status and future challenges of blockchain, and structured occupational therapy; introduced the methods and principles of data quality collaboration model and multiparty collaboration standard management; analyzed the case background of public administration and resource allocation in the prevention and control of the COVID-19 epidemic; discussed the public administration mechanism based on blockchain and structured occupational therapy; established a resource allocation method based on blockchain and structured occupational therapy; fathomed the role of the distributed ledger established by blockchain to increase the information symmetry of public administration activities; proposed a blockchain-established special machine trust for resource allocation; and finally, anatomized the data security sharing and access control mechanism based on blockchain and structured occupational therapy. The research results show that the public administration and resource allocation in this paper can effectively realize the data integration of the whole process and all departments and show the whole data and realize the traceability of the whole process. The blockchain revolutionizes the hierarchical leadership method of traditional resource allocation, shortens the distance between superiors and subordinates, makes information dissemination more fluent, and handles things more efficiently, making resource allocation ultimately form a flatter organization structure. In the original trust system of resource allocation, the blockchain and structured occupational therapy realizes the reconstruction of the trust system by preventing information tampering, using information encryption technology, and using information traceability technology. The results of this paper provide a reference for further research on the public administration and resource allocation based on blockchain and structured occupational therapy.
Quantitative Schlieren imaging based on fringe projection

An effective yet simple quantitative synthetic Schlieren imaging technique is presented in this paper. A multiplexed fringe pattern displayed on a common laptop screen is observed through a disturbed medium resulting in a change of the fringe pattern. Fourier analysis of such pattern provides high-resolution and almost real time measurement of rays’ deflection due to the medium refractive index variation. The developed technique was applied for measurement of a compressible fluid discharge from a narrow channel in a wind tunnel.
Planning for a Zero Trust Architecture

Zero trust is a set of cybersecurity principles used when planning and implementing an enterprise architecture.Input and cooperation from various stakeholders in an enterprise is needed in order for a zero trust architecture to succeed in improving the enterprise security posture.Some of these stakeholders may not be familiar with risk analysis and management.This document provides a quick overview of the NIST Risk Management Framework (NIST RMF) and how the NIST RMF can help in developing and implementing a zero trust architecture.
Research on problems and countermeasures in reading skills training of boys majoring in preschool education

At present, reading teaching is a very important content in preschool education teaching, which plays an important role in improving the comprehensive quality of students. But at present, there are still many problems in reading course for boys majoring in preschool education. Starting from boys’ own learning characteristics, this paper finds out the problems, explores the reasons, seeks countermeasures, and puts forward corresponding solutions.
Integration of Action Civics Strategic Intervention Materials (ACSIM) for Improved Learners’ Performance in Araling Panlipunan 8

The study aimed to integrate Action Civics Strategic Intervention Materials (ACSIM) in Araling Panlipunan 8 for improve learners’ performance at Dolores National High School. Specifically, it sought to: determine the profile of learner respondents; find out the perceived level of engagement on action civics activities; describe and interpret the pretest scores; identify the least mastered competencies; describe and interpret the posttest scores; and find out if there is a significant difference between the pretest and posttest mean scores of the learner respondents. Likewise, the researcher employed the descriptive method and designbased research in this study. Findings revealed that majority of the respondents were 14.38 years old; females; and, with the average family income of PhP.12, 667.30. The perceived level of engagement on action civics activities was found to be all together HIGH as to youth voice, youth expertise, learning by doing and reflection. The mean performance of the learner respondents’ scores in the pretest in AP 8 for Third Quarter before integration of ACSIM was found 16.05 with the verbal interpretation of AVERAGE. The Least Mastered Competencies in AP 8 for the Third Quarter were as follows: MELC # 3 - Nasusuri ang mga dahilan, kaganapan at epekto ng rebolusyong siyentipiko, enlightenment at industriyal; MELC # 4 - Naipaliliwanag ang kaugnayan ng rebolusyong pangkaisipan sa rebolusyong Amerikano at Pranses; MELC # 5 - Nasusuri ang dahilan, pangyayari at epekto ng ikalawang yugto ng kolonyalismo (Imperyalismo). The mean performance in the posttest in AP 8 for the Third Quarter after integration of ACSIM was found to be 25.03, with verbal interpretation of HIGH. More so, there was a significant difference between the pretest and posttest mean scores of the learners. It can be inferred that the integration of ACSIM has a large effect in improving the performance of learners in Araling Panlipunan 8. The pretest has served the purpose of a diagnostic test to identify the strengths and weaknesses of the learners. Lastly, subject teachers can make customized and user – friendly strategic intervention materials effectively.
Data Augmentation via Dependency Tree Morphing for Low-Resource Languages

Neural NLP systems achieve high scores in the presence of sizable training dataset. Lack of such datasets leads to poor system performances in the case low-resource languages. We present two simple text augmentation techniques using dependency trees, inspired from image processing. We crop sentences by removing dependency links, and we rotate sentences by moving the tree fragments around the root. We apply these techniques to augment the training sets of low-resource languages in Universal Dependencies project. We implement a character-level sequence tagging model and evaluate the augmented datasets on part-of-speech tagging task. We show that crop and rotate provides improvements over the models trained with non-augmented data for majority of the languages, especially for languages with rich case marking systems.
A Closer Look into Transformer-Based Code Intelligence Through Code Transformation: Challenges and Opportunities

Transformer-based models have demonstrated state-of-the-art performance in many intelligent coding tasks such as code comment generation and code completion. Previous studies show that deep learning models are sensitive to the input variations, but few studies have systematically studied the robustness of Transformer under perturbed input code. In this work, we empirically study the effect of semantic-preserving code transformation on the performance of Transformer. Specifically, 24 and 27 code transformation strategies are implemented for two popular programming languages, Java and Python, respectively. For facilitating analysis, the strategies are grouped into five categories: block transformation, insertion/deletion transformation, grammatical statement transformation, grammatical token transformation, and identifier transformation. Experiments on three popular code intelligence tasks, including code completion, code summarization and code search, demonstrate insertion/deletion transformation and identifier transformation show the greatest impact on the performance of Transformer. Our results also suggest that Transformer based on abstract syntax trees (ASTs) shows more robust performance than the model based on only code sequence under most code transformations. Besides, the design of positional encoding can impact the robustness of Transformer under code transformation. Based on our findings, we distill some insights about the challenges and opportunities for Transformer-based code intelligence.
ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking

We introduce ReFinED, an efficient end-to-end entity linking model which uses fine-grained entity types and entity descriptions to perform linking. The model performs mention detection, fine-grained entity typing, and entity disambiguation for all mentions within a document in a single forward pass, making it more than 60 times faster than competitive existing approaches. ReFinED also surpasses state-of-the-art performance on standard entity linking datasets by an average of 3.7 F1. The model is capable of generalising to large-scale knowledge bases such as Wikidata (which has 15 times more entities than Wikipedia) and of zero-shot entity linking. The combination of speed, accuracy and scale makes ReFinED an effective and cost-efficient system for extracting entities from web-scale datasets, for which the model has been successfully deployed. Our code and pre-trained models are available at https://github.com/alexa/ReFinED
An Integrated Framework for DevSecOps Adoption

Introduction of DevOps into the software development life cycle represents a cultural shift in the IT culture, amalgamating development and operations to improve delivery speed in a rapid and maintainable manner. At the same time, security threats and breaches are expected to grow as more enterprises move to new agile frameworks for rapid product delivery. Meanwhile, DevSecOps is a mindset change that revolutionizes software development by embedding security at each step of the software cycle, leading to resilient software. This paper discusses a framework organization can use to embed DevSecOps swiftly and efficiently into the general IT culture.
Smart City and Privacy Concerns During COVID-19: Lessons from Singapore, Malaysia, and Indonesia

Abstract Although smart technologies for managing urban problems have gained traction in Southeast Asia, citizens, experts, and policymakers continue to express concerns over data protection and security. Smart technologies, such as big data, artificial intelligence, and the Internet of Things (IoT), are perceived as enablers for cities to control air pollution, reduce traffic, streamline public services, and make energy use more efficient. Yet experts underline that smart technologies’ personal and behavioral data have not been adequately protected, thus bringing significant risk to individual privacy. However, the COVID-19 pandemic has further intensified the dialogue to address privacy concerns in the digital sphere. Drawing from the experience of COVID-19 tracing applications in Singapore, Malaysia, and Indonesia, this chapter finds that despite concerns about technical issues and accessibility, the practice of surveillance and the effectiveness of such technology adoption to fight COVID-19 remain in debate. The dialogue on data protection in the digital sphere has been more complex as there are contesting interests between the need to ensure public safety and the need to protect individual privacy.
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning

Safe reinforcement learning is extremely challenging--not only must the agent explore an unknown environment, it must do so while ensuring no safety constraint violations. We formulate this safe reinforcement learning (RL) problem using the framework of a finite-horizon Constrained Markov Decision Process (CMDP) with an unknown transition probability function, where we model the safety requirements as constraints on the expected cumulative costs that must be satisfied during all episodes of learning. We propose a model-based safe RL algorithm that we call Doubly Optimistic and Pessimistic Exploration (DOPE), and show that it achieves an objective regret $\tilde{O}(|\mathcal{S}|\sqrt{|\mathcal{A}| K})$ without violating the safety constraints during learning, where $|\mathcal{S}|$ is the number of states, $|\mathcal{A}|$ is the number of actions, and $K$ is the number of learning episodes. Our key idea is to combine a reward bonus for exploration (optimism) with a conservative constraint (pessimism), in addition to the standard optimistic model-based exploration. DOPE is not only able to improve the objective regret bound, but also shows a significant empirical performance improvement as compared to earlier optimism-pessimism approaches.
Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities

Combining additive models and neural networks allows to broaden the scope of statistical regression and extend deep learning-based approaches by interpretable structured additive predictors at the same time. Existing attempts uniting the two modeling approaches are, however, limited to very specific combinations and, more importantly, involve an identifiability issue. As a consequence, interpretability and stable estimation are typically lost. We propose a general framework to combine structured regression models and deep neural networks into a unifying network architecture. To overcome the inherent identifiability issues between different model parts, we construct an orthogonalization cell that projects the deep neural network into the orthogonal complement of the statistical model predictor. This enables proper estimation of structured model parts and thereby interpretability. We demonstrate the framework's efficacy in numerical experiments and illustrate its special merits in benchmarks and real-world applications.
TNT: Vision Transformer for Turbulence Simulations

Turbulence is notoriously difficult to model due to its multi-scale nature and sensitivity to small perturbations. Classical solvers of turbulence simulation generally operate on finer grids and are computationally inefficient. In this paper, we propose the Turbulence Neural Transformer (TNT), which is a learned simulator based on the transformer architecture, to predict turbulent dynamics on coarsened grids. TNT extends the positional embeddings of vanilla transformers to a spatiotemporal setting to learn the representation in the 3D time-series domain, and applies Temporal Mutual Self-Attention (TMSA), which captures adjacent dependencies, to extract deep and dynamic features. TNT is capable of generating comparatively long-range predictions stably and accurately, and we show that TNT outperforms the state-of-the-art U-net simulator on several metrics. We also test the model performance with different components removed and evaluate robustness to different initial conditions. Although more experiments are needed, we conclude that TNT has great potential to outperform existing solvers and generalize to additional simulation datasets.
MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably

In this work, we introduce the Gulliver multi-party computation model (GMPC). The GMPC model considers a single highly powerful party, called the server or Gulliver, that is connected to $n$ users over a star topology network (alternatively formulated as a full network, where the server can block any message). The users are significantly less powerful than the server, and, in particular, should have both computation and communication complexities that are polylogarithmic in $n$. Protocols in the GMPC model should be secure against malicious adversaries that may corrupt a subset of the users and/or the server. Designing protocols in the GMPC model is a delicate task, since users can only hold information about polylog(n) other users (and, in particular, can only communicate with polylog(n) other users). In addition, the server can block any message between any pair of honest parties. Thus, reaching an agreement becomes a challenging task. Nevertheless, we design generic protocols in the GMPC model, assuming that at most $\alpha<1/6$ fraction of the users may be corrupted (in addition to the server). Our main contribution is a variant of Feige's committee election protocol [FOCS 1999] that is secure in the GMPC model. Given this tool we show: 1. Assuming fully homomorphic encryption (FHE), any computationally efficient function with $O\left(n\cdot polylog(n)\right)$-size output can be securely computed in the GMPC model. 2. Any function that can be computed by a circuit of $O(polylog(n))$ depth, $O\left(n\cdot polylog(n)\right)$ size, and bounded fan-in and fan-out can be securely computed in the GMPC model without assuming FHE. 3. In particular, sorting can be securely computed in the GMPC model without assuming FHE. This has important applications for the shuffle model of differential privacy, and resolves an open question of Bell et al. [CCS 2020].
What if properties are owned by no one or everyone? foundation of blockchain enabled engineered ownership

Blockchain and smart contracts enable the network of hybrid autonomous human and machine agents.In this paper, we propose the concept of engineered ownership, a blockchain-based socio-technical governance system.A system of coded rules that defines the boundaries, shapes incentives and distributes rights among such autonomous agents.To lay a foundation for engineered ownership, we first study the nature of ownership by examining the concept of property.Shaped by history and ideologies, property rights are the most formalized and studied ownership system.We then untangle the layered structures and system design impacts of property by investigating three property rights theories.Finally, we derive from these learnings the system features of engineered ownership, identify related challenges, and present a roadmap towards a holistic theory of engineered ownership.
Representative Service Providers and Their Selection in Cloud Computing Domain; A Comprehensive Overview

The popularity of cloud computing is growing owing to its large data storage capacity and high computation power. It provides online, on-demand, scalable application solution, removes hardware and software barriers for non-specialist, rapidly integrates and deploys desired and necessary facilities, supports quick upgrading and addition of features. Users get benefitted with the selection of the appropriate cloud computing platform for their projects. Here, our paper provides a comprehensive overview of the services provided to the users by the most common cloud computing service providers. This paper could be used as a reference while selecting the best service provider based on the requirements of the projects.
Collision Forgery Attack on the AES-OTR Algorithm under Quantum Computing

In recent years, some general cryptographic technologies have been widely used in network platforms related to the national economy and people’s livelihood, effectively curbing network security risks and maintaining the orderly operation and normal order of society. However, due to the fast development and considerable benefits of quantum computing, the classical cryptosystem faces serious security threats, so it is crucial to analyze and assess the anti-quantum computing ability of cryptographic algorithms under the quantum security model, to enhance or perfect the design defects of related algorithms. However, the current design and research of anti-quantum cryptography primarily focus on the cryptographic structure or working mode under the quantum security model, and there is a lack of quantum security analysis on instantiated cryptographic algorithms. This paper investigates the security of AES-OTR, one of the third-round algorithms in the CAESAR competition, under the Q2 model. The periodic functions of the associated data were constructed by forging the associated data according to the parallel and serial structure characteristics of the AES-OTR algorithm in processing the associated data, and the periodic functions of the associated data were constructed multiple times based on the Simon quantum algorithm. By using the collision pair, two collision forgery attacks on the AES-OTR algorithm can be successfully implemented, and the period s is obtained by solving with a probability close to 1. The attacks in this paper caused a significant threat to the security of the AES-OTR algorithm.
A New String Edit Distance and Applications

String edit distances have been used for decades in applications ranging from spelling correction and web search suggestions to DNA analysis. Most string edit distances are variations of the Levenshtein distance and consider only single-character edits. In forensic applications polymorphic genetic markers such as short tandem repeats (STRs) are used. At these repetitive motifs the DNA copying errors consist of more than just single base differences. More often the phenomenon of “stutter” is observed, where the number of repeated units differs (by whole units) from the template. To adapt the Levenshtein distance to be suitable for forensic applications where DNA sequence similarity is of interest, a generalized string edit distance is defined that accommodates the addition or deletion of whole motifs in addition to single-nucleotide edits. A dynamic programming implementation is developed for computing this distance between sequences. The novelty of this algorithm is in handling the complex interactions that arise between multiple- and single-character edits. Forensic examples illustrate the purpose and use of the Restricted Forensic Levenshtein (RFL) distance measure, but applications extend to sequence alignment and string similarity in other biological areas, as well as dynamic programming algorithms more broadly.