Reviewerly/exHarmony huggingface dataset:
The Presumption of a Creative Nature (Originality) of Copyright Objects

The paper is devoted to the doctrinal meaning and practical significance of the presumption of the creative nature (originality) of copyright objects. This presumption is not directly enshrined in Russian law, but it follows from the systemic interpretation of the rules dedicated to notion of the author. A citizen who created a work by his creative work is recognized as the author. The laws of many countries contain the presumption of originality of works, but its interpretations are diametrically different. At the present time, in the conditions of an increasingly accelerating and complicating civil turnover accompanied by the information revolution, legal and technical substantive approaches to the category of originality (as a synonym for creativity) as a common and only prerequisite for the protection of works by copyright and the continental copyright system have gradually begun to converge. At the same time, domestic judicial practice still unreasonably ignores the doctrine of substantial similarity of works based on the presumption of originality. The author proposes an authentic classification of disputes concerning the originality of works, the basis of which is the number of objects involved in the dispute. The author builds a coordinate system, the criterion of which is the degree of change of the original work: identical copying — non-identical copying — processing — free creation of another original work. At the same time, the author emphasizes that a copy, even significantly different from the original, does not cease to be a copy. In legal terms, identical and non-identical copying constitutes reproduction that requires the consent of the author or copyright holder of the original work. A necessary sign of processing is the purpose of the author of the changes to expand the possibilities of using the original work; processing also requires the consent of the author or copyright holder with respect to the original work. Non-identical copying and reworking should be distinguished from creating a new work using an unprotected content of the original authentic work.
Generating Prime Numbers Using Genetic Algorithms

Genetic algorithms are well-known and frequently used heuristic methods for solving optimization problems. In modern cryptography the generation of large primes has an important role in the implementation of public-key cryptosystems such as RSA. In general the prime number generation starts from the random number. If the generated random number can pass a specified probabilistic primality test the random number is tentatively considered as a prime number and applied to a public-key cryptosystem. This paper investigates the application of genetic algorithms for generating large prime numbers that have special significance in cryptography. An introduction to the theory of prime numbers and the methods used to check the primality of a large number are shown. Moreover the implementation of a genetic algorithm for generating prime numbers with a convenient representation and genetic operators is presented. We compare the efficiency of the existing method for generating prime numbers and genetic algorithms techniques and we show that GA can be a viable option to generate large prime numbers.
Research on Integrated Automatic Control of Electrical Engineering Based on Multi-Objective Ant Colony Algorithm

This paper introduces a kind of electrical engineering integrated automatic control system based on multi-objective ant colony algorithm. Firstly, it introduces the power control automation of electrical engineering, including functional modularization, automatic feedback and power analysis. Secondly, the comprehensive automatic control system based on electrical engineering is introduced, including power transmission system and product automation. Finally, the multiobjective ant colony algorithm and scheduling problem applied to electrical automation are introduced.
New Requirements Format for Higher Economic Education in the Era of Digitalization

The article studies the relations of educational standards in the enlarged group of Economics with the recommended professional standards. The assessment of the possibilities of professional requirements unification in the conditions of digitalization and transition to a mixed type of training is carried out. The new format of teaching students, including remotely using digital technologies, requires a more careful selection of training profiles (programs) within each direction. Different approaches to the formation of training profiles are considered, the authors of which argue both “for” and “against” the unification of curriculums. Comparing the types of professional activities within a given training group can help reduce the list of related activities and reduce the administrative costs of training qualified personnel without losing the quality of education.
Archeofinds. A Geodatabase for the Archiving, Management, and Analysis of Ancient World Artifacts

The reconstruction of ancient civilizations requires the analysis of the material manifestations that have come down to us. This complex process involves the collection, comparison, and joint analysis of multiple types of data that can benefit from the availability, accessibility, and implementation through the collective contribution of digital datasets structured and homologated, according to shared standards. Archeofinds is a geodatabase specifically designed to provide support for the processes of information generation and sharing, produced through the collection, management, and contextualized analysis of multiple types of data, relating to multiple classes of ancient world artifacts.
Learning Rule Optimization and Comparative Evaluation of Accelerated Self-Organizing Maps for Industrial Applications

The emergence of low latency and high bandwidth 5G networks, alongside localized computation and data storage of edge computing are enabling real-time applications in industrial settings, such as smart grid, smart cities, and smart factories. The resolution, frequency and variety of data streams generated by such applications are not effectively processed and analysed by contemporary machine learning algorithms. This challenge is further complicated by the unlabelled and non-deterministic nature of the data streams. Hardware accelerated machine learning has been proposed to address some of these challenges but limited work has been published on unsupervised learning from unlabelled data. In this paper, we extend the hardware accelerated Self Organizing Map (SOM) algorithm by optimizing the learning rule for computational efficiency, followed by a comparative empirical evaluation with two other variants, tri-state SOM and integer SOM. We have used two datasets representative of real-time industrial applications in 5G networks and smart grids, for this evaluation.
Inspection Method for Appearance of Underwater Structure of Wharf

This paper pre-trains the adaptive super-resolution model based on meta-transfer learning through external image data sets, so that the model can learn the prior information of image reconstruction; random Gaussian sampling is used to randomize the images in the external image data set The down-sampling of parameters makes the training data in the meta-migration learning process contain multi-task information; down-sampling the target low-resolution image to be reconstructed to obtain low-resolution sub-images as training data to train the model; relying on the twin neural network and The model is trained in confrontation, and the model parameters are adjusted to complete the training of the model by comparing the difference between the low-resolution image and the low-resolution sub-image; the model is used to reconstruct the super-resolution image on the target image. Finally, the feasibility of the method is verified through experiments.
Labelling Unknown Class Samples

In a practical world, any living being survives through life long learning process. In computation, generic machine learning models are close-set recognition oriented. To create life long machine learning model, algorithms need to deal with the open-set recognition process. In this work, we have proposed a framework to recognize unknown samples of an unknown class and label them with an appropriate class label. We design decision criteria based on the knowledge of the existing two known classes and applied them to the pool of samples containing unknown samples. Experimental results show that our approach provides similar accuracy as the normal closed-set classification process, and thus, it establishes the correctness of our approach.
Design and Development of Collaborative Approach for Integrity Auditing and Data Recovery based on Fingerprint Identification for Secure Cloud Storage

In a Leading field of Information Technology moreover make information Security a unified piece of it. To manage security, Authentication assumes a significant part. Biometric is the physical unique identification as well as Authentication for third party. We are proposed the Security model for preventing many attacks so we are used Inner most layer as a 3DES (Triple Encryption standard) Cryptography algorithm that is providing 3-key protection as 64-bit And the outer most layer used the MD5 (Message Digest) Algorithm. i. e. Providing 128 – bit protection. As well as we are using Fingerprint Identification as a physical Security that used in third party remote integrity auditing, and remote data integrity auditing is proposed to ensure the uprightness of the information put away in the cloud. Data Storage of cloud services has expanded paces of acknowledgment because of their adaptability and the worry of the security and privacy levels. The large number of integrity and security issues that arise depends on the difference between the customer and the service provider in the sense of an external auditor. The remote data integrity auditing is at this point prepared to be viably executed. In the meantime, the proposed scheme is depends on identity-based cryptography, which works on the convoluted testament the executives. The safety investigation and the exhibition assessment show that the planned property is safe and productive.
A Study on the Integration Method of Sports Practice and Video Games

It is difficult for recreational players to continue practicing sports, which they know is important to improve their technique and get more enjoyment. Therefore, we propose utilizing a concept of “toolification of games”, which enables players to perceive the effect of practicing while playing a game. Few papers have focused on sports training while playing a video game, but some sports players benefit from a training strategy similar to playing particular video games. This benefit suggests the possibility of using a video game for sports training. The proposed method will contribute to making monotonous training more enjoyable by “improving their sports skill while playing a game.” In this study, we focused on badminton training and playing Tetris together. When practicing badminton, the player is often required to hit the shuttlecock to different parts of the court anywhere they want to. This way of practicing is similar to a general strategy used when playing Tetris—the player attempts to distribute Tetriminos (block units in the Tetris game) to every row without bias. This paper describes experimental results from badminton training practice using Tetris compared with practice using conventional visual feedback methods.
Payload Estimation for Hydraulic Excavators using a Depth Camera

We propose a payload estimation system for hydraulic excavators. The proposed system achieves the following advantages: (1) it can be externally mounted on existing excavators, (2) it is relatively inexpensive, and (3) it achieves high accuracy. To realize these advantages, we install a depth camera externally on the inside of an excavator’s arm and use a method to determine the depth of the soil in the bucket from a bird’s eye view. Moreover, we utilize kernel ridge regression, which is a machine learning technique, to achieve highly accurate estimation. Furthermore, we enhance the method for small- scale training datasets by combining it with geometrical volume calculation.
DARIAH Pathfinder to Data Management Best Practices in the Humanities

This DARIAH Guide brings together tools, videos, short articles and other training materials that might be relevant when reflecting on your data management processes both in the immediate context of your research and in their broader disciplinary context. Its aim is to equip you with tools and practical advice, but more importantly, with conceptual twists that will help you to establish ethically committed, optimal and as open as possible research and data management workflows.
Oracle Bone Inscriptions Extraction by Using Weakly Supervised Instance Segmentation under Deep Network

Oracle-bone inscriptions (OBIs), the oldest hieroglyphs in China, were mainly carved on cattle scapulars and tortoise shells, as well as other animal bones. However, automatically extracting OBI characters is a rather complex task due to their differences in character size, orientation, alignment and noisy background. Conventional techniques like Laplacian operation, gradient-edge, or connected component, cannot obtain satisfying results. Therefore, in this paper, instance segmentation methods under deep convolutional neural network were exploited to extract OBIs automatically. More specifically, a SOTA weakly supervised instance segmentation model was introduced to solve this problem, considering that the pixel-level annotation is notoriously time-consuming compared to the bounding boxes annotation, which is extremely serious for the annotation of OBI images because annotators' lack of domain knowledge. The model was trained by 3228 oracle rubbing images and were tested on 312 ones. Results demonstrated that this method can provide a feasible way to automatically extract OBIs from rubbing images (as shown in Fig. 1).
Power Grid Data Confirmation Model Based on Behavior Characteristics

The power grid has high requirements for data security, and data security audit technology is facing challenges. Because the server in the power grid operating environment is considered untrustworthy and does not have the authority to obtain the secret key, the encrypted data cannot be parsed and the data processing ability of the data center is restricted. In response to the above problems, the power grid database encryption system was designed, and the access control module and the encryption module that should be written based on SQL statements were explained. The database encryption system was developed using the Java language and deployed in the cloud environment. Finally, the method was proved by experiments. feasibility.
Emergence and growth of mobile money in modern India: A study on the effect of mobile money

In contemporary India, mobile payments startedwith the onset of delivery services known as Immediate PaymentServices (IMPS) started by the National Payment Corporation ofIndia, setting the growth cycle in motion. Mobile Money hasgrown significantly, especially post demonetization, enabled by‘smart devices’ such as smartphones, smartwatches, smart cards,toll payment devices, and voice-enabled products deploying theInternet of Things (IoT). The ease and simplicity of paymentsthrough IoT devices are set to change the shopping experienceand consequently retail permanently. The government of India'sDigital India program is further likely to transform the entirenation into a digitally enabled society as well as a knowledgeeconomy. Mobile money is becoming an essential tool forfinancial inclusion to unite the unbanked communities of ruralIndia. Internet was introduced in India in 1995, and the firstwave of e-commerce began soon after that. Artificial Intelligence(AI) and smart solutions are noticeable in the growth of mobilepayments in India in dual ways, firstly in reducing fraud in highrisk mobile payments and secondly in improved Peer to Peer(P2P) Payment Platforms. Mobile payments through AI solutionsis just one illustration of technology that is broadly used today,and that has already started advancing the FinTech industryforward for the Fourth Industrial Revolution. This embarks on asound period of digital inclusion leading to mobile and internetconnectivity, with the overall growth of Information andCommunication Technology (ICT) networks in India. A dynamicforce for next-generation e-commerce is the fast-multiplication ofmobile phones, handheld computers, mobile devices and PDAs.Mobile commerce demonstrates a sea change in consumerbehavior in India. This study aims to examine the growth andeffect of mobile payments in India, where mobile penetration isthe second highest in the world, after China. The impact ofCOVID-19 pandemic on the digital payments segment in Indiaare noteworthy.. Like other economies of the world, the adverseeffects of the pandemic are pervading to significant sectors of theIndian economy including retail trade, aviation, hospitality,entertainment industry, restaurants, clubs and entertainmentparks. All of these sectors are experiencing the burden of theslowdown. In contrast, the digital payments sector comprising ofgroceries, online money transfer, Over The Top - OTT streamingmedia services, online gaming, online education is experiencing aboost. The government and RBI have asked banks to encouragetheir clients to use digital payment methods as a preventivemeasure against the COVID-19 pandemic. CITATIONM. Jakhiya, M. Mittal Bishnoi and H. Purohit, "Emergence and Growth of Mobile Money in Modern India: A Study on the Effect of Mobile Money," 2020 Advances in Science and Engineering Technology International Conferences (ASET), 2020, pp. 1-10, doi: 10.1109/ASET48392.2020.9118375.
Generative Facial Prior for Large-Factor Blind Face Super-Resolution

Abstract Most existing face super-resolution (SR) methods are developed based on an assumption that the degradation is fixed and known (e.g., bicubic down sampling). However, these methods suffer a severe performance drop in various unknown degradations in real-world applications. Previous methods usually rely on facial priors, such as facial geometry prior or reference prior, to restore realistic face details. Nevertheless, low-quality inputs cannot provide accurate geometric priors while high-quality references are often unavailable, which limits the use of face super-resolution in real-world scenes. In this work, we propose GPLSR which used the rich priors encapsulated in the pre-trained face GAN network to perform blind face super-resolution. This generative facial priori is introduced into the face super-resolution process through channel squeeze-and-excitation spatial feature transformation layer (SE-SFT), which makes our method achieve a good balance between realness and fidelity. Moreover, GPLSR can restores facial details with single forward pass because of powerful generative facial prior information. Extensive experiment shows that when the magnification factor is 16, this method achieves better performance than existing techniques in both synthetic and real datasets.
Protection of SVM model with secret key from unauthorized access

In this paper, we propose a block-wise image transformation method with a secret key for support vector machine (SVM) models. Models trained by using transformed images offer a poor performance to unauthorized users without a key, while they can offer a high performance to authorized users with a key. The proposed method is demonstrated to be robust enough against unauthorized access even under the use of kernel functions in a facial recognition experiment.
Voting Based Navigation System Fault Detection for Aircraft Position Information During Final Approach

This paper presents a simple and so easily implementable threshold and up-down counter-based voting algorithm for the fault detection of aircraft position information during landing. Easy implementation is inevitable in case of small unmanned aircraft due to the size weight and power consumption constraints. Most of the development is based-on real flight test data collected in frame of the VISION EU H2020 research project on a 4m wingspan aircraft. The aircraft’s position flight measurements were provided by an on-board GPS unit with SBAS correction and an on-board data-based inertial-barometric-camera sensor fusion ESKF algorithm. ILS was simulated as the aircraft and airport were not equipped with it. SBAS, ILS and ESKF errors were all artificially added to the nominal flight and simulated data. Pairwise comparison of position information is the basis of fault detection. Category I precision approach requirements are considered to tune and test the fault-detection algorithm first, by the selection of thresholds and then by the fine tuning of up-down counters to reduce false alarms. Both integrity risk and time-to-alert requirements are satisfied only the continuity risk resulted below the expected but this does not mean needless abortion of landings.
Revamp of Sentiment Analysis

Today digital reviews plays a vital part in influencing the customer. E-commerce companies provide a platform for consumers to share their thoughts and comments, thus it provides an insight about the performance of the product to the company as well as to buyers. In order to make it as useful one classifications of review is needed. Opinion mining is also known as sentiment analysis which in general is a process of extracting subjective information from the data collected. Machine learning provides the better insights by automatically analyzing the product review and separating them into classes and labels. Opinion mining is an artificial intelligence tool and its research is very useful for determining the sentiment of comments. A feed forward neural network classifier is used to determine the sentiment tendency of the comment. The proposed revamp of sentiment analysis approach is compared to RNN and CNN approaches. The result is displayed in the form of chart which has higher precision. Thus this technique is helpful on comments analysis.
Adaptive Unscented Kalman Filter-Based State Estimation With Applications To SI Engines

With a rapid increase in the vehicular industry to ensure maximum efficiency and lower fuel consumption in the vehicles, advanced control strategies are required. Such strategies need precise information of intake manifold pressure, engine speed, and fuel flow rate. Due to the engine placement and operation, sensors can not be placed to measure all the variables. This issue is addressed in this paper and a new adaptive Unscented Kalman filter (UKF) is proposed to estimate these variables. The novelty lies in the new adaptive laws that are designed to update the process noise and measurement noise covariances within the constrained augmented state-based UKF (CASUKF). Another contribution lies in the new combination of the novel adaptive laws, and CASUKF. In the literature, variants of the UKF either adapt the process noise and measurement noise covariances on the standard UKF or implement CASUKF with constant values of the process noise and measurement noise covariances. The algorithm is implemented for the nonlinear mean value spark-ignition engine model. Simulation results are shown to provide the effectiveness of the algorithm in comparison to other variants of the UKF.