Reviewerly/exHarmony huggingface dataset:
Holistic Process Models: A Bayesian Predictive Ensemble Method for Single and Coupled Unit Operation Models

The coupling of individual models in terms of end-to-end calculations for unit operations in manufacturing processes is a challenging task. We present a probability distribution-based approach for the combined outcomes of parametric and non-parametric models. With this so-called Bayesian predictive ensemble, the statistical moments such as mean value and standard deviation can be accurately computed without any further approximation. It is shown that the ensemble of different model predictions leads to an uninformed prior distribution, which can be transformed into a predictive posterior distribution using Bayesian inference and numerical Markov Chain Monte Carlo calculations. We demonstrate the advantages of our method using several numerical examples. Our approach is not restricted to certain unit operations, and can also be used for the more robust interpretation and assessment of model predictions in general.
Development of Speaker-Independent Automatic Speech Recognition System for Kannada Language

Objectives:The primary goal is to address attempts to establish a Continuous Speech Recognition (CSR) framework for recognising continuous speech in Kannada.It is a difficult challenge to deal with a local language such as Kannada, which lacks the resources of a single language database.Methods: Modelling techniques such as monophone, triphone, deep neural network (DNN)-hidden Markov model (HMM) and Gaussian Mixture Model (GMM)-HMM-based models were implemented in Kaldi toolkit and used for continuous Kannada speech recognition (CKSR).To extract feature vectors from speech data, the Mel frequency Cepstral (MFCC) coefficient technique is used.The continuous Kannada speech database consists of 2800 speakers (1680 males and 1120 females) belong to the age group 8 years to 80 years.The training and testing data are in the ratio 80:20.In this paper the hybrid modelling techniques are implemented to recognize the spoken words.Findings: The model efficiency is determined based on the word error rate (WER) and the obtained results are assessed with the well-known datasets such as TIMIT and Aurora-4.This study found that using Kaldi-based features ex-traction recipes for monophone, triphone, DNN-HMM and GMM-HMM acoustic models had a word error rate (WER) of 8.23%, 5.23%, 4.05% and 4.64% respectively.The experimental results suggest that the rate of recognition of Kannada speech data has increased higher than that of state-of-the-art databases.Novelty : We propose a novel automatic speech recognition system for Kannada language.The main reason for developing the automatic speech recognition system for Kannada language is that there are only limited sources of standard continuous Kannada speech are available.We created large vocabulary Kannada database.We implemented monophone, triphone, Subspace Gaussian mixture model (SGMM) and hybrid modelling techniques to develop the automatic speech recognition system for Kannada language.
A Neural Algorithm for the Detection and Correction of Anomalies: Application to the Landing of an Airplane

The location of the plane is key during the landing operation. A set of sensors provides data to get the best estimation of plane localization. However, data can contain anomalies. To guarantee correct behavior of the sensors, anomalies must be detected. Then, either the faulty sensor is isolated or the detected anomaly is filtered. This article presents a new neural algorithm for the detection and correction of anomalies named NADCA. This algorithm uses a compact deep learning prediction model and has been evaluated using real and simulated anomalies in real landing signals. NADCA detects and corrects both fast-changing and slow-moving anomalies; it is robust regardless of the degree of oscillation of the signals and sensors with abnormal behavior do not need to be isolated. NADCA can detect and correct anomalies in real time regardless of sensor accuracy. Likewise, NADCA can deal with simultaneous anomalies in different sensors and avoid possible problems of coupling between signals. From a technical point of view, NADCA uses a new prediction method and a new approach to obtain a smoothed signal in real time. NADCA has been developed to detect and correct anomalies during the landing of an airplane, hence improving the information presented to the pilot. Nevertheless, NADCA is a general-purpose algorithm that could be useful in other contexts. NADCA evaluation has given an average F-score value of 0.97 for anomaly detection and an average root mean square error (RMSE) value of 2.10 for anomaly correction.
COIN: Counterfactual Image Generation for Visual Question Answering Interpretation

Due to the significant advancement of Natural Language Processing and Computer Vision-based models, Visual Question Answering (VQA) systems are becoming more intelligent and advanced. However, they are still error-prone when dealing with relatively complex questions. Therefore, it is important to understand the behaviour of the VQA models before adopting their results. In this paper, we introduce an interpretability approach for VQA models by generating counterfactual images. Specifically, the generated image is supposed to have the minimal possible change to the original image and leads the VQA model to give a different answer. In addition, our approach ensures that the generated image is realistic. Since quantitative metrics cannot be employed to evaluate the interpretability of the model, we carried out a user study to assess different aspects of our approach. In addition to interpreting the result of VQA models on single images, the obtained results and the discussion provides an extensive explanation of VQA models' behaviour.
Patient Readiness for Remote Healthcare Services in the Context of the COVID-19 Pandemic: Evidence From European Countries

Despite the fact that remote services were successfully implemented in most European social and health systems before 2020, the COVID-19 pandemic has led to an unprecedented development of health and social care services provided in this form. This paper compares the readiness of patients to use the digital solutions in healthcare systems implemented in EU countries, in response to the current pandemic situation. In the study, technological, health insurance, and demographic variables were selected on the basis of substantive criteria. Next, the linear ordering method was applied to make a ranking of the analyzed countries according to the level of patients' readiness to use digital healthcare services. The main findings show that the Netherlands and Ireland are characterized by the highest level of patients' readiness for using remote healthcare services. On the other hand, Romania and Bulgaria are among the countries with the lowest readiness. The study also made it possible to group European countries according to the level of patients' preparedness.
Verification of Distributed Quantum Programs

Distributed quantum systems and especially the Quantum Internet have the ever-increasing potential to fully demonstrate the power of quantum computation. This is particularly true given that developing a general-purpose quantum computer is much more difficult than connecting many small quantum devices. One major challenge of implementing distributed quantum systems is programming them and verifying their correctness. In this paper, we propose a CSP-like distributed programming language to facilitate the specification and verification of such systems. After presenting its operational and denotational semantics, we develop a Hoare-style logic for distributed quantum programs and establish its soundness and (relative) completeness with respect to both partial and total correctness. The effectiveness of the logic is demonstrated by its applications in the verification of quantum teleportation and local implementation of non-local CNOT gates, two important algorithms widely used in distributed quantum systems.
E-learning Media for the Ability to Recognize and Count Numbers in Kindergarten Students

The Covid-19 pandemic causes learning activities to be carried out online, causing teachers to be able to utilize technology-based learning media. This study aims to determine whether there is a significant difference between the ability to work on kindergarten students' numeracy questions before and after being given learning through YouTube media. The method that will be used is descriptive quantitative to 19 kindergarten students. The results show that the data processing of the Wilcoxon test results is 0.449&gt;α=0.05. From these results, we can conclude that the YouTube learning media has no significant effect on the ability to recognize numbers and do arithmetic problems. This is because (i) the research time is short, (ii) the instructional video is only given once, (iii) some parents do not provide learning videos to students, and (iv) there are still many students who have difficulty writing numbers correctly. But of course, the experience of students learning to use YouTube can still be useful to help students continue their education to the next level.
Incentive-Based Delay Minimization for 6G-Enabled Wireless Federated Learning

Federated Learning (FL) is a promising decentralized machine learning technique, which can be efficiently used to reduce the latency and deal with the data privacy in the next 6th generation (6G) of wireless networks. However, the finite computation and communication resources of the wireless devices, is a limiting factor for their very low latency requirements, while users need incentives for spending their constrained resources. In this direction, we propose an incentive mechanism for Wireless FL (WFL), which motivates users to utilize their available radio and computation resources, in order to achieve a fast global convergence of the WFL process. More specifically, we model the interaction among users and the server as a Stackelberg game, where users (followers) aim to maximize their utility/pay-off, while the server (leader) focuses on minimizing the global convergence time of the FL task. We analytically solve the Stackelberg game and derive the optimal strategies for both the server and the user set, corresponding to the Stackelberg equilibrium. Following that, we consider the presence of malicious users, who may attempt to mislead the server with false information throughout the game, aiming to further increase their utility. To alleviate this burden, we propose a deep learning-aided secure mechanism at the servers’ side, which detects malicious users and prevents them from participating into the WFL process. Simulations verify the effectiveness of the proposed method, which result in increased users’ utility and reduced global convergence time, compared with various baseline schemes. Finally, the proposed mechanism for detecting the users’ behavior seems to be very promising in increasing the security of WFL-based networks.
Interactive and iterative visual exploration of knowledge graphs based on shareable and reusable visual configurations

Knowledge graphs denote structured data which represent entities and relationships between them in a form of a graph, often expressed in the RDF data model. It may be hard for lay users to explore existing knowledge graphs, especially when graphs from different data sources need to be integrated. In this paper, we present an approach to knowledge graph visual exploration based on the concept of shareable and reusable visual configurations. A visual configuration comprises domain specific views on a knowledge graph which define operations such as node detail or expansion. These operations are easy to understand for lay users who can use them to explore a graph while complexities unnecessary in a given application context remain hidden. We introduce an ontology which enables to express and publish visual configurations and reuse their components in other configurations. We also provide an experimental implementation called KGBrowser. We evaluate the proposed approach with real users. We also compare our implementation KGBrowser with other existing tools for knowledge graph visualization and exploration.
Media Competence of Modern Students: Problems and Possibilities of Its Formation in the System of Higher Education

The purpose of the article is to analyze the level of students' media competence, to study the possibilities of its formation in the higher education system.The results of the study showed that the respondents highly appreciate the level of their own media competence.Students consider themselves competent in creating presentations, however, more complex activities are rated in the middle range.It can be assumed that those aspects of activities that are in demand when studying at a university show higher grades.At the same time, the skills associated with in-depth analysis of information and the creation of media products, are not included in the focus of attention when organizing educational activities.These tendencies illustrate the presence of risk zones in the process of forming students' media competence: insufficient attention to evaluative/interpretive and practical-operational skills of working with information.Only less than half of the surveyed respondents believe that conditions have been created in the modern education system for the development of a person's media competence.The analysis of the problems that limit this process is centered, according to students, in the field of organizing training sessions, a knowledge assessment system, and setting assignments for independent work.
GAN-based matrix factorization for recommender systems

Proposed in 2014, Generative Adversarial Networks (GAN) initiated a fresh interest in generative modelling. They immediately achieved state-of-the-art in image synthesis, image-to-image translation, text-to-image generation, image inpainting and have been used in sciences ranging from medicine to high-energy particle physics. Despite their popularity and ability to learn arbitrary distributions, GAN have not been widely applied in recommender systems (RS). Moreover, only few of the techniques that have introduced GAN in RS have employed them directly as a collaborative filtering (CF) model.
Graph-theoretic approach to quantum error correction

We investigate a class of quantum error-correcting codes to correct errors on both qubits and higher-state quantum systems represented as qudits. These codes arise from an original graph-theoretic representation of sets of quantum errors. In this framework, we represent the algebraic conditions for error correction in terms of edge avoidance between graphs providing a visual representation of the interplay between errors and error-correcting codes. Most importantly, this framework supports the development of quantum codes that correct against a predetermined set of errors, in contrast to current methods. A heuristic algorithm is presented, providing steps to develop codes that correct against an arbitrary noisy channel. We benchmark the correction capability of reflexive stabilizer codes for the case of single-qubit errors by comparison to existing stabilizer codes that are widely used. In addition, we present two instances of optimal encodings: an optimal encoding for fully correlated noise, which achieves a higher encoding rate than previously known, and a minimal encoding for single-qudit errors on a four-state system..
Ortaokul Türkçe Ders Kitapları Millî Mücadele ve Atatürk Teması Metinlerinin Okunabilirlik Düzeyleri Üzerine Bir İnceleme

The readability level of the texts in the textbooks is a very important factor in terms of the effectiveness of the related texts.As a matter of fact, the readability level of a text is an important indicator of its suitability for the class level to be used.In this study, an examination was carried out on the readability levels of the texts on the National Struggle and Ataturk theme of the secondary school Turkish textbooks used in the 2020-2021 academic year.Within the scope of the study, three different readability formulas were used to determine the readability levels of the reading texts in the theme.The research data were obtained from the texts on the theme of National Struggle and Ataturk in a total of 6 Turkish textbooks used in the 5th, 6th, 7th and 8th grades.Description method was used in the research.The data of the study were obtained by document review and analyzed using three different readability formulas presented in the literature.As a result of the research, it was determined that the readability levels of the texts on the theme of the National Struggle and Ataturk accumulated in the middle, educational and high school levels according to the formulas used.Again, it was determined that the readability difficulty levels of the texts examined in the study did not show an increase from lower class levels to upper class levels.According to these results, it was concluded that the readability levels of the texts used in the Turkish textbooks on the theme of National Struggle and Ataturk were not suitable for grade levels.
ENGINEERING PEDAGOGY COURSE MAPPING

Graduate students of technical universities have practical difficulties with learning and successful instructional implementation of the fundamentals of engineering didactics. The paper is focused on the formulation of a thought-provoking curriculum with computational assignments for the course of “Technical University Pedagogic and Methodological Foundations of Engineering Education” (TUPMFEE) for graduate and Ph.D. students. The paper uses computational modelling of behavioral processes in socio-educational systems. The TUPMFEE-curriculum teaches future engineers to apply computational techniques to modeling of socio-technical phenomena. The author-formulated and a computer modeling-supported metaphor for the psycho-educational effects of high social pressure impact on student learning dynamics was allegorically visualized using mechanical rolling stress distribution for the nonlinear social process of student knowledge acquisition during instructor-enhanced education with description of some successive forgetting of the previously acquired instructional material upon the studied course completion. The author-proposed TUPMFEE-course successfully triggers graduate students’ interest in both social, mechanical and computer sciences.
The Impact of Covid-19 on Cybersecurity

The world as of now is encountering a dreadful pandemic. Coronavirus has affected the world differently. Economies are disintegrating searching for solutions for the crisis brought about by this pandemic. Education in certain nations has been stopped while others are contriving new ways for teachers and students to interact observing the necessary protocols to guarantee no loss happens on these difficult occasions. Health sectors are the major heroes at these difficult times trying their possible best to fight the virus and get many people immunized against the virus. The internet has seen a lot more compromises because of the new ways adopted by many sectors to still get their businesses running. One of the ways employed mostly is the use of the internet to perform tasks. With the increased use of the internet, it has become even more important now to ensure cybersecurity or information technology protection. Protection of information from unauthorized disclosure, theft, or damage to hardware, software, or electronic data, as well as from disruption or misdirection of services information technologies provide. This paper delves deep into the impact of COVID-19 on cybersecurity, the various cybersecurity trends that are attacking various institutions as this pandemic came about, the various effects of COVID-19 on society, from the perspective of cybersecurity threats and outcomes, and a detailed deliberation on why it is still of great relevance in the teaching and learning of cybersecurity.
Improved Artificial Bee Colony Algorithm Based on Harris Hawks Optimization

&lt;p&gt;Artificial bee colony algorithm, as a kind of bio-like intelligent algorithm, used by various optimization problems because of its few parameters and simple structure. However, there are also shortcomings such as low convergence accuracy, slow convergence speed, and not easy to jump out of the local optimum. Aiming at this shortcoming, this paper proposes an evolutionary algorithm of improved artificial bee colony algorithm based on reverse learning Harris Hawk (HABC). The basic inspiration of HABC comes from the good convergence of Harris Hawk algorithm in the process of finding the best point of the function. First, introduce the Harris Hawks optimization progressive rapid dives stage in the onlooker bee phase to speed up the algorithm convergence; Secondly, Cauchy reverse learning is added in the scout phase to make the algorithm development more promising areas in order to find a better solution; Finally, 13 standard test functions and CEC-C06 2019 benchmark test results are used to test the proposed HABC algorithm and compare with ABC, Markov Chain based artificial bee colony algorithm (MABC), dragonfly algorithm (DA), particle swarm optimization (PSO), learner performance based behavior algorithm (LPB), and fitness dependent optimizer (FDO). Compared with other algorithms, the convergence speed, optimization accuracy and algorithm success rate of the HABC algorithm are relatively excellent.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm

Offering incentives (e.g., coupons at Amazon, discounts at Uber and video bonuses at Tiktok) to user is a common strategy used by online platforms to increase user engagement and platform revenue. Despite its proven effectiveness, these marketing incentives incur an inevitable cost and might result in a low ROI (Return on Investment) if not used properly. On the other hand, different users respond differently to these incentives, for instance, some users never buy certain products without coupons, while others do anyway. Thus, how to select the right amount of incentives (i.e. treatment) to each user under budget constraints is an important research problem with great practical implications. In this paper, we call such problem as a budget-constrained treatment selection (BTS) problem. The challenge is how to efficiently solve BTS problem on a Large-Scale dataset and achieve improved results over the existing techniques. We propose a novel tree-based treatment selection technique under budget constraints, called Large-Scale Budget-Constrained Causal Forest (LBCF) algorithm, which is also an efficient treatment selection algorithm suitable for modern distributed computing systems. A novel offline evaluation method is also proposed to overcome an intrinsic challenge in assessing solutions' performance for BTS problem in randomized control trials (RCT) data. We deploy our approach in a real-world scenario on a large-scale video platform, where the platform gives away bonuses in order to increase users' campaign engagement duration. The simulation analysis, offline and online experiments all show that our method outperforms various tree-based state-of-the-art baselines. The proposed approach is currently serving over hundreds of millions of users on the platform and achieves one of the most tremendous improvements over these months.