Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION FPGA BASED IMPLEMENTATION OF 128-BIT AES ALGORITHM USING VHDL

In current world of computations, data encryption is of prominent importance. Many algorithms were developed for data encryption and decryption to prevent hacking. The Advanced Encryption Standard (AES) is one of the data encryption techniques. Two famous kinds of hardware implementation techniques are pipelining and loop-unrolling techniques. In pipelining, registers are inserted between each combinational processing element so that each input data block can be processed simultaneously in each processing element. In this work, a pipelined implementation of AES encryption algorithm is developed. The number of rounds of AES-128 encryption is 10 and an architecture implementing this cipher is called fully pipelined, when all data blocks of 10 rounds can be processed simultaneously. In the loop-unrolling technique one or multiple rounds of the algorithm are processed in the same clock cycle. Here only one round of the algorithm is implemented as a combinational processing element and a data register is also used to store the result obtained in the previous clock cycle. In this work, a128-bit AES is implemented and for each round of AES encryption, a different sub-key is used as the round key, which is produced by the key schedule algorithm based on the loop-unrolled technique, to produce the required sub-key for each round is done. The no of I/O, Slices in the proposed work are 386, 229 respectively with Spartan6 fpga with a minimum period of 5.813ns,maximum Frequency of 172.031MHz, minimum input arrival time before clock of 4.823ns,maximum output required time after clock of 5.588ns, throughput of 2.2Gbps. The no of I/O, Slices are 387, 264 respectively with Artix7 fpga with a minimum period of 3.397ns, maximum Frequency of 294.366MHz, minimum input arrival time before clock of 1.649ns, maximum output required time after clock of 1.669ns, throughput of 3.77Gbps.
A New Insight into Entropy Based on the Fuzzy Operators, Applied to Useful Information Extraction from Noisy Time-Frequency Distributions

In this paper, we study the connections between generalized mean operators and entropies, where the mean value operators are related to the strictly monotone logical operators of fuzzy theory. Here, we propose a new entropy measure based on the family of generalized Dombi operators. Namely, this measure is obtained by using the Dombi operator as a generator function in the general solution of the bisymmetric functional equation. We show how the proposed entropy can be used in a fuzzy system where the performance is consistent in choosing the best alternative in the Multiple Attribute Decision-Making Problem. This newly defined entropy was also applied to the problem of extracting useful information from time-frequency representations of noisy, nonstationary, and multicomponent signals. The denoising results were compared to Shannon and Rényi entropies. The proposed entropy measure is shown to significantly outperform the competing ones in terms of denoising classification accuracy and the F1-score due to its sensitivity to small changes in the probability distribution.
Analysis of Context-Dependent Errors in the Medical Domain in Spanish: A Corpus-Based Study

This corpus-based study aimed to investigate the presence of context-dependent linguistic errors in a corpus of clinical reports. The data were taken from a corpus comprising more than 2 million words and made up of clinical reports from emergency medicine, intensive care unit, general surgery, and psychiatry. Quantitative and qualitative analyses were carried out. A language model based on n-grams was developed for the detection of errors, parameters for the selection of cases were defined, and a classification tool was implemented. The findings indicated that emergency medicine was the medical specialty with the highest number of context-dependent errors and that the most frequent type of error was omission of written accent. Furthermore, the analysis revealed the presence of errors of competence due to the incorrect application of the linguistic norm of Spanish, phenomena of phonetic similarity, and composition of words; it is also worth noting that performance errors occurred due to rapid typing on the keyboard. This study constituted the first analysis and creation of a typology of context-dependent errors for the medical domain in Spanish. It contributed to the design of a module based on linguistic knowledge that can be used for the development and improvement of automatic correction systems that, in turn, are used for data processing in medicine.
Feature extraction algorithm of an irregular small celestial body in a weak light environment

This study focuses on creating a crater-matching algorithm to improve the matching rate and address the phenomenon of insufficient feature extraction and mismatching of irregular celestial objects and crater edge information on the dim surface of celestial bodies images. These images were captured by the detector's navigation camera. In order to improve the brightness and clarity of the images, the target images were filtered, denoised, and image-enhanced using the bilateral filtering method and improved histogram equalization algorithm, successively. Then, the enhanced image was extracted and matched using the ORB feature point detection algorithm based on scale invariance, and the feature point mismatch was processed by the Hamming distance screening method. The simulation results revealed that the optimization algorithm effectively improved the imaging quality of the target image in dark and weak light environments, increased the number of feature points extracted, reduced the mismatch of effective feature point pairs, and improved the matching rate.
Clones of inductive superpositions of terms

&lt;abstract&gt;&lt;p&gt;A superposition is an operation of terms by which we substitute each variable within a term with other forms of terms. With more options of terms to be replaced, an inductive superposition is apparently more general than the superposition. This comes with a downside that it does not satisfy the superassociative property on the set of all terms of a given type while the superposition does. A derived base set of terms on which the inductive superposition is superassociative is given in this paper. A clone-like algebraic structure involving such base set and superposition is the main topic of this paper. Generating systems of the clone-like algebra are characterized and it turns out that the algebra is only free with respect to itself under the certain selections of fixed terms concerning its inductive superposition or the specific type of its base set.&lt;/p&gt;&lt;/abstract&gt;
Multi-task Label-wise Transformer for Chinese Named Entity Recognition

Benefiting from the improvement of positional encoding and the introduction of lexical knowledge, Transformer has achieved superior performance than the prevailing BiLSTM-based models in named entity recognition (NER) task. However, existing Transformer-based models for Chinese NER pay less attention to the information captured by the bottom layers of Transformer and the significance of representation subspace where each head of Transformer is projected. In this article, we propose M ulti- T ask L abel- W ise T ransformer (MTLWT). From a global perspective, we assign entity boundary prediction (EBP) and entity type prediction (ETP) tasks to the first two layers. In this way, we stimulate lower layers to participate more in constructing character representation. Besides, in each multi-head self-attention (MHSA) layer, we provide a specific focus for each individual head, making the head project into a significant subspace. Experiments on four datasets from different domains show that our proposed model achieves comparable performance with other state-of-the-art models. In particular, MTLWT outperforms the other frameworks without external knowledge on all the datasets.
Auditing of Outsourced Data Integrity - A Taxonomy

Introduction: Cloud has gained a huge number of consumers in today's environment due to its broad range of services. On the cloud server, data owners can store any type of data, and users can access it whenever they need it. Methods: Numerous challenges come with the ease of use of outsourced data. The accuracy and secrecy of data outsourced come at stake. Users have a low level of trust in cloud service providers since they can be deceptive at times. A cloud audit is an examination conducted by a data owner to assess and document the performance of their cloud vendor. Results: The goal of a cloud vendor audit is to determine how well they follow a set of predetermined controls. Conclusion: In the article, the authors present various schemes of cloud auditing, their categorizations, and merits and demerits along with the future directions of research in the domain.
NOx Emission Prediction Based on SSA-DELM via CFD and DCS Data Fusion

The objective of this study was to obtain NOx emission prediction model at the inlet selective catalytic reduction (SCR) reactors, which was the basis of combustion optimization and denitrification treatment. A deep extreme learning machine (DELM) optimized by the sparrow optimization algorithm (SSA) was adopted to establish the NOx model based on data fusion of Computational Fluid Dynamics (CFD) simulation and Distributed Control System (DCD). The mechanism analysis and XGBoost algorithm was used to select input variables. The results show that the XGBoost-SSA-DELM-based prediction model has high prediction accuracy with mean absolute error of 2.54 mg/m3. The results of this study have important implications for research on improving combustion efficiency and reducing pollutant emissions.
Ransomware as a Predator: Modelling the Systemic Risk to Prey

The accelerated pace with which companies, governments, and institutions embrace digital transformation is creating opportunities for economic prosperity, but also increases the threat landscape. Recent orchestrated cyber-attacks have revealed the unpredictability of the harm they can cause in our society, rendering the creation of new models that capture systemic risk more critical than ever. In this article, we model the behaviour of one of the most prominent cyber-attacks: ransomware; in particular, ransomware that propagates between organisations via the Internet. We draw concepts from epidemiological models of viral propagation to reason about policies that can reduce the systemic cyber-risk to the community. To achieve this, we present a compartment-based epidemiological model of predator-prey interactions and run simulations to validate the importance of defensive controls that reduce the propagation of ransomware. Our model suggests that with specific defensive controls in place, other response policies may also become more effective. A prey policy to not pay the ransom may improve the ability of the victim population to recover; while information-sharing may reduce the number of organisations compromised if certain conditions on the speed of threat-intelligence sharing practices are met. These results indicate the validity of the approach, which we believe could be extended to explore the impacts of a broad range of attacker and defender behaviours and characteristics of the digital environment on systemic risk.
The CUTE CANVAS: developing a design tool for planning strategic actions for institutional of digital competencies

This paper presents a process of development and validation of a methodological tool for supporting the co-design of strategic actions to enhance the digital competencies of educators in higher education. Considering that organisational strategy and change are often imperfectly reflected in structural arrangements in higher education institutions, this work proposes a novel approach to address this difficulty. In a four-iteration process with a design-based approach, a team of experts and stakeholders from six European countries has created and validated a CANVAS to design actions for fostering Digital Teaching Competences based on the DigCompEdu framework. The result is a methodological instrument that facilitates co-design across stakeholder groups within higher education institutions. Its goal is to enhance a strategic vision for participatory action by scaffolding institutional development processes, collaboratively defined, for short, medium- and long-term impacts. This work can also be a reference for other systematised innovation design approaches.
Keyframe image processing of semantic 3D point clouds based on deep learning

With the rapid development of web technologies and the popularity of smartphones, users are uploading and sharing a large number of images every day. Therefore, it is a very important issue nowadays to enable users to discover exactly the information they need in the vast amount of data and to make it possible to integrate their large amount of image material efficiently. However, traditional content-based image retrieval techniques are based on images, and there is a "semantic gap" between this and people's understanding of images. To address this "semantic gap," a keyframe image processing method for 3D point clouds is proposed, and based on this, a U-Net-based binary data stream semantic segmentation network is established for keyframe image processing of 3D point clouds in combination with deep learning techniques.
Interaction of educational process participants at the stage of primary music education

A modern children's music school provides training for children in two educational programs with different learning objectives. The most effective for the development of a child's musical abilities is considered to be training in pre-professional programs in the field of musical art. These programs involve preparing the most gifted children to continue their musical education. The chapter identifies and analyzes the features of the interaction of participants in the educational process that influence the formation of a psychologically favorable educational environment necessary for the development of musically gifted children: styles of parenting are considered, and practical experience in teaching theoretical disciplines in a children's music school is presented.
Dual'naia innovatsionnaia podgotovka konkurentnosposobnykh na rynke truda rabochikh kadrov i spetsialistov srednego zvena, vladeiushchikh tsifrovymi kompetentsiiami, vysokimi patrioticheskimi kachestvami, na osnove nepreryvnogo operezhaiushchego professional'nogo obrazovaniia i sokhraneniia liderstva kolledzha v SVO

Modern economic development of Russia determines the reforming of vocational education system and production in accordance with innovative technological equipment. The dual system is the most common form of personnel training which combines theoretical training (in Russia 40%, in Germany 20–25%) in educational institutions and the industrial training (75–80%) at enterprises. 96% of young people who completed dual training got a job at enterprises.
Freeze-and-mutate: abnormal sample identification for DL applications through model core analysis

Deep learning (DL) applications, representing an emerging form of new software, are gaining increasing popularity by their intelligent and adaptive services. However, their service reliability depends highly on the prediction accuracy of their internally-integrated DL models. In practice, DL models are often observed to suffer from ill predictions upon abnormal inputs (e.g., adversarial attacking samples, out-of-distribution (OOD) samples, and etc.), and this could easily lead to unexpected behaviors or even catastrophic consequences (e.g., system crash). One promising way to guard the application reliability is to reveal such abnormal inputs in time before they are fed to the DL models integrated in the concerned applications. Then remedy actions (e.g., discarding or fixing these inputs) can be done to protect applications from acting abnormally. Existing work addressed this revealing problem by either making sample distance-comparison based analysis or generating sufficient model mutants for comparative analysis. However, such treatments caused a restricted focus on samples only, while overlooking the DL models themselves, or had to analyze massive mutants, incurring non-negligible overheads to applications. In this article, we propose a novel approach, NetChopper, to conducting a core analysis on the target DL model, and then partitioning it into two parts, one associating closely with the training knowledge being the model core (expected to be important and thus stable), and the other being the remaining part (expected to be immaterial and thus changeable). Based on such partitioning, NetChopper proceeds to preserve (or freeze) the model core, but mutate the remaining part to produce only a small number of model mutants. Later, NetChopper becomes able to reveal abnormal inputs from normal ones by exploiting these model-relevant and light-weight mutants only. We experimentally evaluated NetChopper by widely-used DL subjects (e.g., MNIST+LeNet4, and CIFAR10+VGG16) and typical abnormal inputs (e.g., adversarial and OOD samples). The results reported NetChopper ’s promising AUROC scores in revealing the abnormal degrees of inputs, generally and stably outperforming, or comparably effective as, state-of-the-art techniques (e.g., mMutant, Surprise, and Mahalanobis), and also confirmed its high effectiveness and efficiency (with only marginal online overhead).
Equivalence of single-server and multiple-server blind quantum computation protocols

Because quantum computers are expensive, it is envisaged that individuals who want to utilize them would do so by delegating their calculations to someone who has a quantum computer. When quantum computer users delegate computations to quantum servers, they wish to keep information about their calculations hidden from the servers. The protocol of delegating a calculation while hiding information about the calculation from the server is called {\sl blind quantum computation protocol}. Prior research on single-server's blind quantum computation protocol required users to have quantum capabilities. Prior research on multiple-servers' blind quantum computation protocols required users to have just classical capabilities but imposed limits on the server-to-server communication. There are no known single-server blind quantum computation protocols with a classical user and multiple-servers blind quantum computation protocols that allows servers to communicate freely with each other. We show that the existence of these protocols is equivalence.
Lyapunov control of finite-dimensional quantum systems based on bi-objective quantum-behaved particle swarm optimization algorithm

This paper presents a Lyapunov control scheme to drive finite-dimensional closed and Markovian open quantum systems into any target pure state with as high fidelity and as little time as possible. The control law is established by a Lyapunov function with an unknown Hermitian operator, where the eigenvectors of the operator are constructed based on the condition that the LaSalle invariant set contains the desired target state and a set of optimal eigenvalues of the Hermitian operator is searched by the quantum-behaved particle swarm optimization (QPSO) algorithm. In particular, for open systems, when the denominator in the control law is quite small, we propose an improved QPSO algorithm with constraints to enhance the flexibility in implementation. Finally, numerical simulations are carried out on a five-level and a three-qubit closed quantum systems, and a five-level and a ten-level open quantum systems. Simulation results demonstrate that the proposed control scheme can achieve high-fidelity transfer to desired target states for high-dimensional closed and open quantum systems.
A framework for analyzing context-oriented programming languages

Context-aware systems keep on emerging as an intrinsic part of everyday activities. To cope with such situations, programming languages are extended to support the notion of context. Although context-oriented programming languages exist for over 15 years, they were tested for their suitability for developing context-aware systems only to a limited extent. In this paper, we propose a framework for analyzing the suitability of context-oriented languages from a wider viewpoint. Using this framework, we are able to examine context definition and activation, reasoning capabilities, process aspects of how to work with the language, and other pragmatic considerations. To demonstrate the use of the framework, we apply it to analyze three context-oriented programming languages: ServalCJ, Subjective-C, and COBPjs which represent the major Context-Oriented Programming themes. We evaluate the capabilities of each language using the purposed framework. Developers of context-oriented programming languages can use the framework to improve their languages and the associated development and supporting tools. Furthermore, such analysis can support users of context-oriented programming languages in deciding the language that best suits their needs.
Diverse single image generation with controllable global structure

Image generation from a single image using generative adversarial networks is quite interesting due to the realism of generated images. However, recent approaches need improvement for such realistic and diverse image generation, when the global context of the image is important such as in face, animal, and architectural image generation. This is mainly due to the use of fewer convolutional layers for mainly capturing the patch statistics and, thereby, not being able to capture global statistics very well. We solve this problem by using attention blocks at selected scales and feeding a random Gaussian blurred image to the discriminator for training. Our results are visually better than the state-of-the-art particularly in generating images that require global context. The diversity of our image generation, measured using the average standard deviation of pixels, is also better.
Prediction of 5th Generation Mobile Users Traffic Based on Multiple Machine Learning Models

The fast development of mobile communication technology and the maturing of the ICT (Information Communications Technology) industry are the causes of the rapid expansion of the mobile communication market, as represented by smartphones and smart applications. It is foreseeable that the speed of mobile communication network is getting faster, and most software needs traffic to run. Therefore, the traffic consumption is also increasing. To ensure the traffic demand of users, the mobile communication network will face challenges. In this paper, the data comes from the data platform openly published by operators, and the relevant factors are used to predict the traffic of mobile communication users’ 5G (5th Generation Mobile Communication Technology). SVM (Support Vector Machine), Logistic, and Decision-Tree-Classifier models are especially used to generate predictions. One or more of the evaluation markers is Mean Absolute Error (MAE), Mean Square Error (MSE), or Root Mean Square Error (RMSE). The experimental results show that using a variety of machine learning models can predict the demand of users’ 5G traffic more accurately. In addition, for operators, it can better carry out accurate traffic package marketing for different users.