Reviewerly/exHarmony huggingface dataset:
EFFORTS OF THE COMMITTEE FOR GUARDIANSHIP OF RUSSIAN ICON PAINTING TO PRESERVE RUSSIAN ICON TRADITIONS AT THE BEGINNING OF THE 20TH CENTURY

The Orthodox culture attracts the attention of wide circles today. The canonical Orthodox icon is worshiped not as a material object, but as a refl ection of a religious image. The Russian philosopher, theologian Pavel Florensky noted that an icon is a vision of the other world outlined by an icon painter, a kind of window in another world for a believer. The spiritual life of society is impossible without the spiritual activity of the people. Of particular importance in this case are cultural and educational institutions, whose activity is aimed at educating people. Cultural transformations at the beginning of the 20th century are ambiguously refl ected in modern scientifi c and journalistic literature. In this regard, there is a need to turn to the historical aspects of the turn of the 20th century. The article highlights the activities of the Committee for Guardianship of Russian Icon Painting. The authors of the paper describe the concern of the Committee for the preservation of the traditions of Russian icon painting and the diffi culties experienced by the Committee in the preparation of educated icon painters. The publishing activity of the Committee is shown and its initiatives are presented.
ORCHESTRATION AS A TEXTUAL INTERPRETATION OF A WORK (BASED ON V. PANCHENKO’S ARRANGEMENT OF THE CLAVIER OF E.-T.-A. HOFFMAN’S OPERA “UNDINE” FOR SOLOISTS, CHORUS AND SYMPHONY ORCHESTRA)

The continuing development of the composing technique is associated with the interpretation of timbre and other expressive possibilities of instruments and voices. At the same time, the displacement of concert performances of symphony orchestras and the tradition of creating “classical” orchestration by the practice of chamber-ensemble musicmaking and electronic musical creativity become topical problems. The question arises: has the attitude to the orchestration of music of the 18th-19th centuries changed today? What compromises are permissible, and what goes beyond the designated type of creative work? Do the ethical requirements to the composer’s text interpretation decrease when referring to the works of little-known authors? And if correctness and responsibility to the creator of the composition and the audience are great, then the role of research and analytical analysis multiplied by the mastery of the orchestration technology and talent compensate for the need to adapt the score to the proposed conditions of the work’s performance.
LED array microscopy system correction methodwith comprehensive error parameters optimizedby phase smoothing criterion

LED array microscopy is a novel computational imaging technique that can achieve two-dimensional (2D) phase imaging and three-dimensional (3D) refractive index imaging with both high resolution and a large field of view. Although its experimental setup is simple, the errors caused by LED array position and light source central wavelength obviously decrease the quality of reconstructed results. To solve this problem, comprehensive error parameters optimized by the phase smoothing criterion are put forward in this paper. The central wavelength error and 3D misalignment model with six freedom degree errors of LED array are considered as the comprehensive error parameters when the spatial positional and optical features of arbitrarily placed LED array are unknown. Phase smoothing criterion is also introduced to the cost function for optimizing comprehensive error parameters to improve the convergence results. Compared with current system correction methods, the simulation and experimental results show that the proposed method in this paper has the best reconstruction accuracy, which can be well applied to an LED array microscope system with unknown positional and optical features of the LED array.
Dados Bibliográficos definições e interlocuções com as tecnologias e a inovação

Based on the principles that bibliographic data have an intrinsic relationship with innovation and technological advances, the objectivesare: 1) to conceptualize the term “bibliographic data” and related terms; 2) to establish relationships between bibliographic data, technologies, and innovation; and 3) to present the evolution of bibliographic data throughout history. To achieve these objectives, a mapping of Brazilian literature was carried out, from which 45 documents were identified, including articles, theses, and dissertations, which address the technological evolution of Descriptive Cataloging, its products, and standardization instruments. Based on the reading of these documents and the theoretical references used by them, the evolution of bibliographic data throughout history was traced, based on their relationship with the main technological milestones of each period. It is concluded that technological advances have influenced and continue to influence bibliographic data, reflecting and impacting storage formats, standardization instruments and the structure of these data, which need to become more complex and granular to meet the informational needs of users in the current technological context.
Digital Morphology: Bridging the Final Gap in Automated Haematology Testing

Null.
Practical Asynchronous Distributed Key Generation: Improved Efficiency, Weaker Assumption, and Standard Model

Distributed key generation (DKG) allows bootstrapping threshold cryptosystems without relying on a trusted party, nowadays enabling fully decentralized applications in blockchains and multiparty computation (MPC). While we have recently seen new advancements for asynchronous DKG (ADKG) protocols, their performance remains the bottleneck for many applications, with only one protocol being implemented (DYX+ ADKG, IEEE S&P 2022). DYX+ ADKG relies on the Decisional Composite Residuosity assumption (being expensive to instantiate) and the Decisional Diffie-Hellman assumption, incurring a high latency (more than 100s with a failure threshold of 16). Moreover, the security of DYX+ ADKG is based on the random oracle model (ROM) which takes hash function as an ideal function; assuming the existence of random oracle is a strong assumption, and up to now, we cannot find any theoretically-sound implementation. Furthermore, the ADKG protocol needs public key infrastructure (PKI) to support the trustworthiness of public keys. The strong models (ROM and PKI) further limit the applicability of DYX+ ADKG, as they would add extra and strong assumptions to underlying threshold cryptosystems. For instance, if the original threshold cryptosystem works in the standard model, then the system using DYX+ ADKG would need to use ROM and PKI. In this paper, we design and implement a modular ADKG protocol that offers improved efficiency and stronger security guarantees. We explore a novel and much more direct reduction from ADKG to the underlying blocks, reducing the computational overhead and communication rounds of ADKG in the normal case. Our protocol works for both the low-threshold and high-threshold scenarios, being secure under the standard assumption (the well-established discrete logarithm assumption only) in the standard model (no trusted setup, ROM, or PKI).
Examining the Potential of Cryptocurrencies as An Asset Class-An Empirical Study

The market for virtual currencies in India rose from USD 926 a million during 2020 to USD 15 billion in January 2023. As reported by regulatory bodies, cryptocurrencies popularity has grown by eight hundred percent just this time last year. This study aims to find the effect of utility, user-friendliness of investment and effect of risk in trade on investors behavior (IB) with regard to cryptocurrencies. Digital currencies are an innovative kind of asset that has arisen. Because of its exceptional growth in value, Generation Y are turning to digital currencies as a means of investing. The Technology Acceptance Model is used in this article to investigate cryptocurrency uptake. A structured closed ended questionnaires are used to collect the responses. A user’s opinion is collected based on three parameters: utility, convenience with application, and safety. For collecting information, 185 investors were consulted by provided with questionnaires that they can complete the filling of responses by themselves. The respondents were chosen according to their readiness to participate in the survey. Convergent and discriminant Validity, reliability analysis and SEM model were used to analyze the data to find the results. The utility, utility, user-friendliness of investment and management of risk in trade are all having a major impact on behavioral patterns among investors while engage with cryptocurrency, according to the findings. This study contribution will help firms to comprehend users’ sensitivities with regard to cryptocurrencies and set various variables that influence its acceptance while delivering digital currency services to ease investment in better manner.
Blockchain-Based Searchable Asymmetric Encryption Scheme in Cloud Environment

Searchable Symmetric Encryption is used in encrypted databases for precise queries. The most brutal attack has demonstrated that facilitating forward privacy is a critical security goal. Asymmetric searchable encryption enables searches carried out over ciphertexts via delegation and trapdoors by the data owner. Asymmetric Searchable Encryption introduces in this work for a blockchain-based keyword search system. It allows decentralized storage searches using an authorized keyword set. The ASE has based on a unique primitive that guarantees keyword authorization and searches. Explore the possibilities of Ethereum’s smart contracts, the blockchain technology that provides a different pattern for reliability with effective processing. Locating encrypted cloud services was among the most frequent issues. This paper creates a novel public-key encryption that searches on the blockchain system that uses asymmetric key encryption to accomplish protected and well-organized keyword searches. The smart contract replaces the central search cloud server in AKE (Asymmetric Key Encryption), making it a searchable distributed public encryption scheme. It has a high search efficiency while reducing the number of computation-intensive operations.
Towards A Blockchain-Based Revenue Distribution Mechanism for Electrical Generators

Renewable energy generators have increased in number in recent years to meet the demand for the green transition. However, the inconsistent nature of the output from these sources, coupled with fluctuation in demand and market spot prices produces significant financial risk for renewable generators. An inflexible market structure exacerbates these effects, and renewable generators may benefit from a remodelled revenue distribution system. A potential solution to these problems could lie in distributed ledger technology, specifically blockchain. Advances in this technology have given rise to smart contracts-autonomous, immutable and transparent computer programs that reside on the blockchain network-that can enable new business models. This paper looks to utilise advances in blockchain smart contract technology to create a novel system for managing revenue streams. The paradigm suggests tokenising the right to a generator's regular market remuneration, creating a new financial tool for stakeholders. A blockchain program consisting of a smart contract is developed, serving as an investigation into the granular workings of such a system. This smart contract manages generators and stakeholders, while directing the movement of tokens and currency. The code is deployed to the blockchain, and tested with simulated transactions to demonstrate the effectiveness of the mechanism. Further development of such a solution, specifically revenue sharing between grouped generators, could offer significant stability to financial models for renewable energy generators.
An Analysis of Elliptic Curve Cryptography Secret Keys For Use In Cloud Workload Balancing And Security Provisioning

Cloud computing becomes increasingly popular that delivers dynamic scalable infrastructure for different applications, platforms and file/data storage. In cloud, security and resource sharing are major concerns which obstruct the growth of cloud computing. Many researchers have designed methods like Homomorphic Encryption, Fully Homomorphic Encryption (FHE), secure multiplication protocol, max-min algorithm, Ant Colony optimization (ACO), etc., for enhancing security and cloud-based services. The major problem existed in these methods was computation complexity, lower level of security, high execution time and resource failure. To overcome these problems, we concentrate on fine grained security and resource provisioning in cloud platform. For security, Homomorphic encryption scheme is performed based on Elliptic Curve secret key analysis which produces security against intruders and third party. This encryption scheme enhances cloud security based on secret key sharing between cloud users and data owners to securely access the data. Simultaneously, resource sharing is also involved which maintains multiple users and schedules their jobs into resources without failure. Independent clustering is designed for dynamically balancing user’s workload with significant parameters. Clustering process is mainly involved in separating workload and reducing time to allocate the resources. Our proposed environment is simulated with CloudSim which evaluates the proposed security scheme in terms of computation time, data protection rate, encryption/decryption time and the improvements in workload balancing is studied with execution time and VM migration.
Analysis of Net Present Worth of Solar Business Models in India

In India, the energy business is significantly affected by the combination of increasing energy demand and limited Non-replenishable energy sources. Photovoltaic solar power facilities have the potential to playa significant role in the energy sector. When setting up solar power plants, it is crucial to carefully consider their financial aspects. Solar business models assist in evaluating both the financial and other non-technical aspects of the solar industry. This paper presents a comparative study analyzing the cost-benefit aspects of existing solar business models operating at the secondary distribution level. In order to establish an accurate assumption of profit, all relevant influencing factors in the solar industry must be considered. The current analysis takes into account all conceivable factors such as inflation, depreciation, and tax breaks that impact the profit margin of a solar photovoltaic power plant at the secondary distribution level.
Ancient coins' surface inspection with web-based neural RTI visualization

The use of neural encodings has the potential to replace the commonly used polynomial fitting in the analysis of artwork surface based on Reflectance Transformation Imaging (RTI), as it has proved to result in more compact encoding with better relight quality, but it is still not widely used due to the lack of efficient implementations available to practitioners. In this work, we describe an optimized system to encode/decode neural relightable images providing interactive visualization in a web interface allowing multi-layer visualization and annotation. To develop it, we performed several experiments testing different decoder architectures and input processing pipelines, evaluating the quality of the results on specific benchmarks to find the optimal tradeoff between relighting quality and efficiency. A specific decoder has been then implemented for the web and integrated into an advanced visualisation tool. The system has been tested for the analysis of a group of ancient Roman bronze coins that present scarce readability and varying levels of preservation and that have been acquired with a multispectral light dome. Their level of corrosion and degradation, which in some cases hinders the recognition of the images, numerals, or text represented on them, makes the system testing particularly challenging and complex. Testing on such a real case scenario, however, enables us to determine the actual improvement that this new RTI visualization tool can offer to numismatists in their ability to identify the coins.
Character alignment methods for dialect-to-standard normalization

This paper evaluates various character alignment methods on the task of sentence-level standardization of dialect transcriptions.We compare alignment methods from different scientific traditions (dialectometry, speech processing, machine translation) and apply them to Finnish, Norwegian and Swiss German dialect datasets.In the absence of gold alignments, we evaluate the methods on a set of characteristics that are deemed undesirable for the task.We find that trained alignment methods only show marginal benefits to simple Levenshtein distance.On this particular task, eflomal outperforms related methods such as GIZA++ or fast_align by a large margin.
Adverbs, Surprisingly

This paper begins with the premise that adverbs are neglected in computational linguistics.This view derives from two analyses: a literature review and a novel adverb dataset to probe a stateof-the-art language model, thereby uncovering systematic gaps in accounts for adverb meaning.We suggest that using Frame Semantics for characterizing word meaning, as in FrameNet, provides a promising approach to adverb analysis, given its ability to describe ambiguity, semantic roles, and null instantiation.
Low-dimensional feature enhanced camouflaged object detection

Camouflaged Object Detection (COD) is a challenging vision task that aims at locating objects having high similarity in appearance with its background. Existing COD methods are highly dependent on the high-dimensional feature of camouflaged object while neglecting details within the low-dimensional feature. In this paper, we highlight lowdimensional information to enlarge the discrepancies between objects and their surroundings. We propose a CNN-based network ShallowNet with enhanced low-dimensional feature to retain more differences in the process of encoding. The proposed approach shows enhanced effectiveness both qualitatively and quantitatively compared with existing COD methods regarding various datasets.
Modal parameter estimation of turbulence response based on self-attention generative model

Modal parameter estimation of turbulence response is an important aspect of flutter test data processing. Using the modal parameter estimation results of turbulence response and the damping extrapolation curve, the flutter boundary of aircrafts can be predicted. However, owing to the randomness of atmospheric turbulence excitation, modal parameter estimation of the turbulence response has a challenge. This study analyses the turbulence response and calculates the corresponding impulse response using the seq-to-seq self-attention generative network. The encoder performs feature compression of the turbulence response, whereas the multi-head self-attention structure in the middle layer extracts the time series features. Up-sampling is performed based on the decoder to obtain the impulse response, and modal parameter estimation of the turbulence response is achieved. The self-attention generative model is validated using simulation data and the turbulence response is obtained by the wind tunnel test and the flutter flight test. The results demonstrate the feasibility and engineering applicability of the seq-to-seq generative model.
A smart brain controlled wheelchair based on TGAM

A brain-controlled wheelchair system based on TGAM module is proposed, which can improve people quality of life which suffering from severe movement disorders. The TGAM is used as the EEG signals acquisition and processing module. The EEG data is transmitted to the micro-controller through the Bluetooth module. The data is validated and the concentration parameter is parsed, the concentration value is converted into the speed parameter of the wheelchair, and the key state is converted into the wheelchair movement direction parameter, to control the wheelchair movement according to the user's real-time concentration. The test results show that the TGAM module can accurately collect EEG signals, and the micro-controller can analyze the concentration data, and control the wheelchair's forward, backward and turn through the motor. The intelligent wheelchair is simple, easy to operate, and stable in function. It can be operated only through the user's concentration, providing a new convenient wheelchair control mode for people with walking difficulties.
3D Hand Shape and Pose Estimation based on 2D Hand Keypoints

We present a method for simultaneous 3D hand shape and pose estimation on a single RGB image frame. Specifically, our method fits the MANO 3D hand model to 2D hand keypoints. Fitting is achieved based on a novel 2D objective function that exploits anatomical joint limits, combined with shape regularization on the MANO hand model, jointly optimizing the 3D shape and pose of the hand in a single frame. In a series of quantitative experiments on well-established datasets annotated with ground truth, we show that it is possible to obtain reconstructions that are competitive and, in some cases, superior to existing 3D hand pose estimation approaches.
Traceable In-Air Signature 3D Restoration Record Structure and In-Air Dominant Hand Biometric Based on Dynamic Time Warping Algorithm

The portable device in-air signature is developing at a fast speed for the past decades because of the advantage of being remote and traceless, but it has gained limited application in the business field. Lacking supervision during the enrollment phase is one of the security risks for in-air signature recognition. We propose a traceable in-air signature structure based on 3D signature restoration for dominant and non-dominant hand in-air signatures. We designed a dominant hand signature recognition model based on the dynamic time warping (DTW) algorithm to reduce the security threat during the enrollment phase of the in-air signature. By applying the DTW model to, in total, 80 in-air signature dominant and non-dominant hand samples collected through the self-designed application, the model can achieve 100% accuracy if more than 6 training samples are taken for each subject and 0% EER for the available participants.
Elite Frequent-Utility Pattern Mining in Hadoop Environments

With the extensive research of skyline frequent-utility pattern mining in the field of data mining, various algorithms emerge in an endless stream, but because these algorithms are limited by the size of the data set, traditional pattern algorithms do not perform well in medium and large databases. Therefore, this paper proposed the EFUP_MR algorithm by adding thresholds and applied it to the hadoop platform. It can run in medium and large databases, solve the problem of low efficiency in the traditional pattern, and because of the threshold setting, it can help users obtain the required elite itemsets more accurately. Finally, through experiments in different types of data sets, the proposed algorithm performance in larger datasets is proved.