Reviewerly/exHarmony huggingface dataset:
DIGITAL TRANSFORMATION OF HIGHER EDUCATION INSTITUTIONS IN THE CONTEXT OF DIGITALIZATION OF SOCIETY: CHALLENGES AND PROSPECTS

The purpose of the article is to reveal the theoretical foundations of the process of digital transformation of higher education institutions in the conditions of digitalization of society; to determine and scientifically substantiate the organizational and pedagogical conditions for the formation of digital competence of future teachers. Research methods: analysis, synthesis and systematization of scientific literature on pedagogy, psychology, encyclopedic and reference, educational and methodological publications, information resources of the Internet - to clarify, compare and contrast approaches in the direction of the outlined problem. The article reveals and scientifically substantiates the theoretical foundations of the process of digital transformation of higher education institutions in the context of digitalization society, what it the reorientation of the modern teacher to the deep awareness of new, competitively oriented requirement for his professional activity: readiness for maximum use of digital tools, which increases the effectiveness of the educational process; implementation of remote educational innovations based on new opportunities digital technologies; mastering new teaching methods; creation digital space – climate with powerful potential for providing educational activities of the individual. Development of digital literacy of the society states identified as one of the priority tasks on the way to accelerated development of the digital economy, and digital competence is one of the dominative for almost every modern person to be successful in competitively oriented life creation and self-realization in the digital society. One of the challenges of the digitalized society is the readiness of the modern teacher to the digital transformation of the education process successfully and systematically applying high-tech achievement in professional activity. It is proved that information and communication technologies are aimed, first of all, for the development and improvement of the competences of future teachers, giving them competitive advantages: speed, namely, the dynamism of the cognitive activities, motivation (encouraging applicants for higher pedagogical education to independent mastering knowledge of the new); availability of information, which simplifies mastering the learning process; interdisciplinary content. Determined: successful formation of digital competence of future teachers can be effective in the implementation of organizational and pedagogical conditions, in particular: a set of stable motives in future teachers that express the orientation of the individual to master the digital competence; designing a digital educational environment through implementation of ICT, which is aimed at the successful solution of professional situation and tasks with high productivity; organization of optimal communicative interaction of all participant in the educational process on the subject-subject principles in the digital space.
A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful

We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms can produce computationally unfaithful posterior approximations. Our results show that all benchmarked algorithms -- (Sequential) Neural Posterior Estimation, (Sequential) Neural Ratio Estimation, Sequential Neural Likelihood and variants of Approximate Bayesian Computation -- can yield overconfident posterior approximations, which makes them unreliable for scientific use cases and falsificationist inquiry. Failing to address this issue may reduce the range of applicability of simulation-based inference. For this reason, we argue that research efforts should be made towards theoretical and methodological developments of conservative approximate inference algorithms and present research directions towards this objective. In this regard, we show empirical evidence that ensembling posterior surrogates provides more reliable approximations and mitigates the issue.
Democratizing Neural Machine Translation with OPUS-MT

This paper presents the OPUS ecosystem with a focus on the development of open machine translation models and tools, and their integration into end-user applications, development platforms and professional workflows. We discuss our on-going mission of increasing language coverage and translation quality, and also describe on-going work on the development of modular translation models and speed-optimized compact solutions for real-time translation on regular desktops and small devices.
Analysis of Blind Spot in Heavy Vehicles Driving Using VIKOR Method

A blind spot is a phase of road out of doors the driving force’s view that cannot be visible via rear home windows or home windows. Vehicle height and period, huge blind spots All motors have blind spots. These are regions or zones on either facet of the car that aren’t visible to the driver when considered in the rearview or facet view reflect. If there are blind spots, a driving force must always turn his head and test the automobiles earlier than converting lanes. Look in the replicate. Blind spots may be large enough to without difficulty block another vehicle, motorbike, bicycle owner or pedestrian out of your view. The “blind spot” is not seen to the motive force in the windshield across the vehicle. They are in all automobiles, however there are four massive blind spots on 1/2 vehicles. What brought about the blind spot to seem at the back of the trailer? In each of our eyes The size of a pin There is little useful blind spot. In this small region, wherein The optic nerve passes through the surface of the retina, No photosynthesis. In the absence of photosynthetic cells that stumble on light, it creates a blind spot. On this page you will discover 18 comparable words, antonyms, linguistic expressions and associated words for blind spot, namely: lack of notion, invisibility, weak spot, oversight, and membrane in one’s eye, obstruction, blindness, mistakes, and look before changing path. Look for blind spots for your rearview replicate and vehicles close to your shoulders. Be aware of the dangers – appearance beyond the car in front of you. The small vicinity inside the retina that does not experience mild is called the blind spot. The blind spot is the area in which the nerve endings within the retina input the optic nerve. Due to the lack of visual receptors which includes stems and cones on this region, snap shots that fall on this location cannot be detected. The blind spot is an area inside your range of vision which you cannot see precisely, however you really want to look. For instance, whilst you drive a car, the region in the back of your shoulders is regularly the blind spot. What we need to focus on here is the mental (or metaphorical) blind spot, which is defined as “ones but often unknown prejudice or lack of knowledge” or “tendency to ignore anything particularly”. It is difficult or unpleasant. Some human beings see blind spots as weak. “What are the blind spots of your leadership?” Your approach has to be to respond absolutely to the weak spot you understand when you ask and to explain how you can decrease its effect. It is crucial to offer a solution that suggests which you are self-aware and may overcome your weaknesses. the final value, strongly preferred got first rank and equally preferred got fifth rank.
Shapley values for cluster importance

Abstract This paper proposes a novel approach to explain the predictions made by data-driven methods. Since such predictions rely heavily on the data used for training, explanations that convey information about how the training data affects the predictions are useful. The paper proposes a novel approach to quantify how different data-clusters of the training data affect a prediction. The quantification is based on Shapley values, a concept which originates from coalitional game theory, developed to fairly distribute the payout among a set of cooperating players. A player’s Shapley value is a measure of that player’s contribution. Shapley values are often used to quantify feature importance, ie. how features affect a prediction. This paper extends this to cluster importance, letting clusters of the training data act as players in a game where the predictions are the payouts. The novel methodology proposed in this paper lets us explore and investigate how different clusters of the training data affect the predictions made by any black-box model, allowing new aspects of the reasoning and inner workings of a prediction model to be conveyed to the users. The methodology is fundamentally different from existing explanation methods, providing insight which would not be available otherwise, and should complement existing explanation methods, including explanations based on feature importance.
Towards a general framework of Randomized Benchmarking incorporating non-Markovian Noise

The rapid progress in the development of quantum devices is in large part due to the availability of a wide range of characterization techniques allowing to probe, test and adjust them. Nevertheless, these methods often make use of approximations that hold in rather simplistic circumstances. In particular, assuming that error mechanisms stay constant in time and have no dependence in the past, is something that will be impossible to do as quantum processors continue scaling up in depth and size. We establish a theoretical framework for the Randomized Benchmarking protocol encompassing temporally-correlated, so-called non-Markovian noise, at the gate level, for any gate set belonging to a wide class of finite groups. We obtain a general expression for the Average Sequence Fidelity (ASF) and propose a way to obtain average gate fidelities of full non-Markovian noise processes. Moreover, we obtain conditions that are fulfilled when an ASF displays authentic non-Markovian deviations. Finally, we show that even though gate-dependence does not translate into a perturbative term within the ASF, as in the Markovian case, the non-Markovian sequence fidelity nevertheless remains stable under small gate-dependent perturbations.
BookSum: A Collection of Datasets for Long-form Narrative Summarization

The majority of available text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases. While relevant, such datasets will offer limited challenges for future generations of text summarization systems. We address these issues by introducing BookSum, a collection of datasets for long-form narrative summarization. Our dataset covers source documents from the literature domain, such as novels, plays and stories, and includes highly abstractive, human written summaries on three levels of granularity of increasing difficulty: paragraph-, chapter-, and book-level. The domain and structure of our dataset poses a unique set of challenges for summarization systems, which include: processing very long documents, non-trivial causal and temporal dependencies, and rich discourse structures. To facilitate future work, we trained and evaluated multiple extractive and abstractive summarization models as baselines for our dataset.
M3ST: Mix at Three Levels for Speech Translation

How to solve the data scarcity problem for end-to-end speech-to-text translation (ST)? It's well known that data augmentation is an efficient method to improve performance for many tasks by enlarging the dataset. In this paper, we propose Mix at three levels for Speech Translation (M^3ST) method to increase the diversity of the augmented training corpus. Specifically, we conduct two phases of fine-tuning based on a pre-trained model using external machine translation (MT) data. In the first stage of fine-tuning, we mix the training corpus at three levels, including word level, sentence level and frame level, and fine-tune the entire model with mixed data. At the second stage of fine-tuning, we take both original speech sequences and original text sequences in parallel into the model to fine-tune the network, and use Jensen-Shannon divergence to regularize their outputs. Experiments on MuST-C speech translation benchmark and analysis show that M^3ST outperforms current strong baselines and achieves state-of-the-art results on eight directions with an average BLEU of 29.9.
Sustainable Development: A Semantics-aware Trends for Movies Recommendation System using Modern NLP

Abstract Recommendation systems are an important feature in the proposed virtual life, where users are often stuck with choices most of the time and need help to be able to find what they are looking for. In this work, contentbased techniques have been employed in the proposed recommender system in two ways, a deep review for content and features contents such as (cast, crew, keywords, and genres) has been conducted. A preprocessing stage using TF-IDF and CountVectorizer methods have been employed efficiently to prepare the dataset for any similarity measurements. Cosine similarity algorithm has been employed as well with and without sigmoid and linear kernals. The achieved result proves that similarities between movies using TF-IDF with - Cosine similarity (sigmoid kernel) overcomes the TF-IDF with - Cosine similarity (linear_kernel) and Cosine similarity with CountVectorizer in collaborative filtering. The accuracy values of different machine learning models are validated with K-fold Cross Validator techniques. The performance evaluation has been measured using ROOT Mean Square Error and Mean Absolute Error. Five Machine learning algorithms (NormalPredictor, SVD, KNNBasic (with k=20 and K=10), KNNBasic (with sim_options), and NMF (in several rating scales)). Accuracies are finally been validated with 3 folds from each validator. The best achieved RMSE and MAE scores are using SVD (RMSE = 90%) and (MAE = 69%), followed by KNNBasic (with sim_options, K= 20), NMF, KNNBasic (K=20), KNNBasic (K=10), ending with KNNBasic(sim_options, K= 10). Keywords: Recommendation System, Sustainable Development, Artificial Intelligence, Collaborative Filtering, Content-Based, Cosine Similarity, Movies Recommendation, NLP, Machine Learning Application.
Adapting Belief Propagation to Counter Shuffling of NTTs

The Number Theoretic Transform (NTT) is a major building block in recently introduced lattice based post-quantum (PQ) cryptography. The NTT was target of a number of recently proposed Belief Propagation (BP)-based Side Channel Attacks (SCAs). Ravi et al. have recently proposed a number of countermeasures mitigating these attacks.In 2021, Hamburg et al. presented a chosen-ciphertext enabled SCA improving noise-resistance, which we use as a starting point to state our findings. We introduce a pre-processing step as well as a new factor node which we call shuffle node. Shuffle nodes allow for a modified version of BP when included into a factor graph. The node iteratively learns the shuffling permutation of fine shuffling within a BP run.We further expand our attacker model and describe several matching algorithms to find inter-layer connections based on shuffled measurements. Our matching algorithm allows for either mixing prior distributions according to a doubly stochastic mix matrix or to extract permutations and perform an exact un-matching of layers. We additionally discuss the usage of sub-graph inference to reduce uncertainty and improve un-shuffling of butterflies.Based on our results, we conclude that the proposed countermeasures of Ravi et al. are powerful and counter Hamburg et al., yet could lead to a false security perception – a powerful adversary could still launch successful attacks. We discuss on the capabilities needed to defeat shuffling in the setting of Hamburg et al. using our expanded attacker model.Our methods are not limited to the presented case but provide a toolkit to analyze and evaluate shuffling countermeasures in BP-based attack scenarios.
Blockchain-Based Concept for Digital Transformation of Traceability Pyramid for Electrical Energy Measurement

Digital transformation of metrology is a holistic process that was started formally by the Joint Statement of Intent “On the digital transformation in the international scientific and quality infrastructure” signed by major metrology organisations in March 2022. With the digital transformation of metrology in motion, the questions of a seamless transition to digital representation while ensuring adherence to all the requirements of digital representation and maintaining a sustainable framework for future operations are just some of the challenges faced. To tackle these challenges, also within the concept of “more electrical world” (MEW), one technology is demonstrating high potential applicability as a possible candidate solution—blockchain technology, with its critical underlying properties (e.g., immutability, decentralisation, etc.) being fully compliant with the requirements of digital representation in metrology. Accordingly, this paper presents a blockchain-based concept for the digital transformation of the traceability pyramid for electrical energy measurement. The concept is developed in accordance with the goals of the Metroracle project. Based on the analyzed and presented state-of-the-art, the main contribution of the paper is the comprehensiveness of the concept, which encompasses the whole pyramid and describes all relevant processes and responsibilities of all stakeholders: measurement instrument (MI) owners, certificate issuers (National Accreditation Body (NAB), National laboratory (NL), Reference laboratory (RL)), MI manufacturers/developers, MI installers. The transformed pyramid is defined by Croatian metrology laws and regulations, but with smaller adjustments, it is applicable to other EU countries as well, and also to the traceability of other physical quantities, i.e., not to electrical energy only. Possible legal and technical issues are identified (amount of data, machine-readable standards and regulations, ensuring limited access, legal relevance of digital signature) and corresponding solutions presented, as well as further steps in our research and development within the Metroracle project.
A Particle Swarm Optimization Algorithm with Gradient Perturbation and Binary Tree Depth First Search Strategy

In this study, a particle swarm optimization (PSO) algorithm with a negative gradient perturbation and binary tree depth-first strategy (GB-PSO) is proposed. The negative gradient term accelerates particle optimization in the direction of decreasing the objective function value. To calculate the step size of this gradient term more easily, a method based on the ratio was proposed. In addition, a new PSO strategy is also proposed. Each iteration of PSO yields not only the current optimal solution of the group, but also the solution based on the 2-norm maximum. Under the current iteration solution of PSO, these two solutions are the children nodes. In the sense of the binary tree concept, the three solutions constitute the father-son relationship, and the solution generated throughout the entire search process constitutes the binary tree. PSO uses a traceable depth-first strategy to determine the optimal solution. Compared with the linear search strategy adopted by several algorithms, it can fully utilize the useful information obtained during the iterative process, construct a variety of particle swarm search paths, and prevent premature and enhance global optimization. The experimental results show that the algorithm outperforms some state-of-the-art PSO algorithms in terms of search performance.
Adaptive Stochastic Gradient Descent Method for Convex and Non-Convex Optimization

Stochastic gradient descent is the method of choice for solving large-scale optimization problems in machine learning. However, the question of how to effectively select the step-sizes in stochastic gradient descent methods is challenging, and can greatly influence the performance of stochastic gradient descent algorithms. In this paper, we propose a class of faster adaptive gradient descent methods, named AdaSGD, for solving both the convex and non-convex optimization problems. The novelty of this method is that it uses a new adaptive step size that depends on the expectation of the past stochastic gradient and its second moment, which makes it efficient and scalable for big data and high parameter dimensions. We show theoretically that the proposed AdaSGD algorithm has a convergence rate of O(1/T) in both convex and non-convex settings, where T is the maximum number of iterations. In addition, we extend the proposed AdaSGD to the case of momentum and obtain the same convergence rate for AdaSGD with momentum. To illustrate our theoretical results, several numerical experiments for solving problems arising in machine learning are made to verify the promise of the proposed method.
Secure Control of Networked Inverted Pendulum Visual Servo Systems Based on Active Disturbance Rejection Control

This paper investigates secure control of Networked Inverted Pendulum Visual Servo Systems (NIPVSSs) based on Active Disturbance Rejection Control (ADRC). Firstly, considering the network- and image-induced delays in conjuction with computational errors caused by image processing and image attacks, the model of NIPVSSs is established. Secondly, the limitations of the traditional Single-Input-Single-Output (SISO) ADRC used in NIPVSSs with disturbance are revealed. The limitations are that the ESO used in the traditional SISO ADRC brings large steady-state error, and the NLSEF used in the traditional SISO ADRC can achieve stable control of pendulum angle, but cannot achieve stable control of cart position. Thirdly, a new Single-Input-Multi-Output (SIMO) ADRC method is proposed for NIPVSSs with disturbance. In the new SIMO ADRC method, the new ESO is designed by introducing additional first and second derivatives of error to reduce the steady-state error. In addition, the new NLSEF is developed by taking both the calculated cart position and pendulum angle as inputs to achieve dual stable control of pendulum angle and cart position. Finally, combined with the designed ADRC parameter-tuning strategy, the results from simulation and real-world experiments confirm the effectiveness and feasibility of the proposed method.
Improving C4.5 Algorithm Accuracy With Adaptive Boosting Method For Predicting Students in Obtaining Education Funding

The level of accuracy in determining the prediction of the provision of educational funding assistance is very important for the education agency. The large number of data on prospective beneficiaries can be processed into information that can be used as decision support in determining eligibility for education funding assistance. The data processing is included in the field of data mining. One method that can be applied in predicting the feasibility of receiving aid funds is classification. There are several classification algorithms, one of which is a decision tree. The famous decision tree algorithm is C4.5. The C4.5 algorithm can be applied in classifying prospective recipients of educational aid funds. This study uses datasets from student data of SMK Al Fattah Kertosono. The purpose of this study is to increase the accuracy of the C4.5 algorithm by applying adaboost in classifying students who deserve education funding and not, by comparing the results before and after applying adaboost. Validation in this study uses cross validation. While the measurement of accuracy is measured by the confusion matrix. The experimental results show that there is an increase in accuracy of 7.2%. The accuracy of the application of the C4.5 algorithm reaches 91.32%. While the accuracy of the application of the C4.5 algorithm with adaboost reached 98.55%.