Reviewerly/exHarmony huggingface dataset:
Multi-task Student Teacher Based Unsupervised Domain Adaptation for Address Parsing

In an e-commerce business, the ability to parse postal addresses into sub-component entities (such as building, locality) is essential to take automated actions at scale for successful delivery of shipments. The entities can be leveraged to build applications for logistics related operations, e.g. geocoding, assessing address completeness. Training an accurate address parser requires a significant number of manually labeled examples which is very expensive to create, especially when trying to build model(s) for multiple countries with unique address structure. To tackle this problem, in this paper, we present a novel Unsupervised Domain Adaptation (UDA) framework to transfer knowledge acquired by training a parser on labeled data from one country (source domain) to another (target domain) with unlabeled data. We specifically propose a multi-task student-teacher model comprising of three components: 1) specialized teachers trained on source data to create a pseudo labeled dataset, 2) consistency regularization, that uses a new data augmentation technique for sequence tagging data, and 3) boundary detection, leveraging signals in addresses like commas and text box boundaries. Multiple experiments on diverse address datasets (In this paper, we do not reveal the name of the e-commerce countries on which we evaluate our models due to business confidentiality. We also mask finer address details with (XX) to preserve customer's privacy.) demonstrate that our approach outperforms state-of-the-art UDA baselines for Named Entity Recognition (NER) task in terms of F1-score by 2–9%.
Formation of Readiness of Future Mathematics Teachers to Teach Mathematical Logic and Theory of Algorithms

The paper is dedicated to the issue of finding ways to increase the efficiency of teaching mathematical logic and the theory of algorithms in pedagogical higher education institutions through the formation of future teachers' professional readiness for teaching the specified type of activity. The purpose of the study was scientific justification and experimental verification of the technology of training students of pedagogical institutions of higher education to study mathematical logic and the theory of algorithms. Methods. During research, a complex of general scientific and special methods aimed at achieving the purpose (theoretical (analytical, retrospective); empirical (diagnostic, observational, praxemetric); pedagogical experiment; statistical processing of empirical data) were used. The results. In the paper the purpose and content of training students of pedagogical higher education to study mathematical logic and the theory of algorithms have been defined, that involves the acquisition by students of higher education of knowledge of the basics of science, the formation of special abilities and skills of using its methods, the acquisition of experience of creative activity, the formation of emotional and valuable experience attitude to activity. The structure of the future mathematics teacher's readiness for professional activity has characterized, which is formed by motivational-axiological, content-activity, professional-personal components. It has been proven that each of the structural components of readiness has its own peculiarities in the learning process and requires the development of their scientific and technological support in order to form the readiness of future teachers of mathematics to learn mathematical logic and the theory of algorithms. The paper highlights the issue of developing scientific and technological support for appropriate training; the influence of the implementation of the proposed technological support on the level of students’ readiness to learning mathematical logic and the theory of algorithms has been revealed. Conclusions. Readiness as an integral criterion of quality assessing of the activity of a future teacher of mathematics to mathematical logic and the theory of algorithms involves the formation of positive motives, significant for the implementation of this type of activity, personal qualities, possession of the means to achieve the goal. The performed research does not cover the issue; it may be promising to further study the issues of differentiating the training of higher education students for the specified type of activity.
Watermarks for Deep Reinforcement Learning

In this chapter, we introduce a new watermarking scheme for deep reinforcement learning protection. To protect the intellectual property of deep learning models, various watermarking approaches have been proposed. However, considering the complexity and stochasticity of reinforcement learning tasks, we cannot apply existing watermarking techniques for deep learning models to the deep reinforcement learning scenario directly. Existing watermarking approaches for deep learning models adopt backdoor methods to embed special sample–label pairs into protected models and query suspicious models with these designed samples to claim and identify ownership. Challenges arise when applying existing solutions to deep reinforcement learning models. Different from conventional deep learning models, which give single output for each discrete input at one time instant, the current predicted outputs of reinforcement learning can affect subsequent states. Therefore, if we apply discrete watermark methods to deep reinforcement learning models, the temporal decision characteristics and the high randomness in deep reinforcement learning strategies may decrease the verification accuracy. Besides, existing discrete watermarking approaches may affect the performance of the target deep reinforcement learning model. In this chapter, motivated by the above limitation, we introduce a novel watermark concept, temporal watermarks, which can preserve the performance of the protected models, while achieving high fidelity ownership verification. The proposed temporal watermarking method can be applied to both deterministic and stochastic reinforcement learning algorithms.
The Robust and Harmless Model Watermarking

Obtaining well-performed deep neural networks usually requires expensive data collection and training procedures. Accordingly, they are valuable intellectual properties of their owners. However, recent literature revealed that the adversaries can easily “steal” models by acquiring their function-similar copy, even when they have no training samples and information about the victim models. In this chapter, we introduce a robust and harmless model watermark, based on which we design a model ownership verification via hypothesis test. In particular, our model watermark is persistent during complicated stealing processes and does not introduce additional security risks. Specifically, our defense consists of three main stages. First, we watermark the model by embedding external features, based on modifying some training samples via style transfer. After that, we train a meta-classifier to determine whether a suspicious model is stolen from the victim, based on model gradients. The final ownership verification is judged by hypothesis test. Extensive experiments on CIFAR-10 and ImageNet datasets verify the effectiveness of our defense under both centralized training and federated learning.
Ownership Protection for Image Captioning Models

The development of digital watermarking on machine learning models focuses solely on classification tasks, and other tasks are forgotten. In this chapter, we demonstrate that image captioning tasks cannot be adequately protected by the present digital watermarking architecture, which are generally considered as one of the most difficult AI challenges. To safeguard the image captioning model, we propose two distinct embedding strategies in the recurrent neural network’s hidden memory state. We demonstrate through empirical evidence that a forged key will result in an unusable image captioning model, negating the intent of infringement. This is the first attempt, as far as we are aware, to propose ownership protection for the image captioning model. The effectiveness of our proposed approach to withstand different attacks without compromising the original image captioning performance has been demonstrated by the experiments on the MS-COCO and Flickr30k datasets.
Model Auditing for Data Intellectual Property

Deep neural network models are built upon a tremendous amount of labeled training data, whereas the data ownership must be correctly determined because the model developer may illegally misuse or steal other party’s private data for training. To determine the data ownership from a trained deep neural network model, in this chapter, we propose a deep neural network auditing scheme that allows the auditor to trace illegal data usage from a trained model. Specifically, we propose a rigorous definition of meaningful model auditing, and we point out that any model auditing method must be robust to removal attack and ambiguity attack. We provide an empirical study for existing model auditing methods, which shows that existing methods can enable data tracing under different model modification settings, but those methods fail if the model developer adopts the training data for the use case the data owner cannot manage and thus cannot provide meaningful data ownership resolution. In this chapter, we rigorously present the model auditing problem for data ownership and open a new revenue in this area of research.
Application of artificial neural network model in studying the mechanism of disease relapse event in patients with tuberculosis

Application of artificial neural network model in studying the mechanism of disease relapse event in patients with tuberculosis
The Challenge of Understanding Blended Learning Models and related didactic conditions

The article deals with the problem of the typology of the blended learning models that require the certain didactic conditions. In this regard, the various approaches to classifying blended learning models and determining its didactic nature in American, European and Russian scientific literature are analyzed. A critical view is offered on the research experience of the blended learning modeling, interpretation of the concepts of «the didactic conditions» and «the organizational-didactic model». The purpose of this article is to analyze the scientific understanding of the blended learning models typology and related didactic conditions. This led to the use of the comparative analysis methods and generalization. The results of the study show that the idea of the didactic nature of the blended learning, which combines technological and pedagogical principles, has become permanent in the scientific literature. Blended learning requires developed electronic information environment containing high-quality and diverse content that can form an individual learning trajectory, provide a personal virtual educational space, and monitor and record student learning achievements. And the didactic role of the teacher is also changing, who cannot do without confident IT competencies. He is responsible for shaping the content of the educational program, strengthening the individual approach to learning, and managing the planning of training. Existing attempts to classify blended learning models are, as a rule, incomplete and boil down to identifying a set of modeling criteria. As an illustration, examples of the optimal solutions to the problem of modeling and differentiation of didactic conditions are given, their influence on the teacher’s role and the requirements for a digital educational environment are shown.
Deep Learning based Breast Cancer Diagnostic System using Medical Images

A common and lethal kind of cancer, breast cancer, affects women worldwide. In the year 2020, around 2.26 million breast cancer cases were reported worldwide. In 2020, breast cancer will become the most common cancer globally with a projected 11.7% of all cancer cases or 2.3 million new cases. It is ranked as 7th cancer cause globally with 685,000 deaths. Diagnosis plays an essential role in cancer, since early diagnosis of the condition can help in better planning for treatment and prevent further complications. This research develops an integrated system to aid oncologists and clinicians in the diagnosis, confirmation and follow-up analysis for breast cancer using principles of artificial intelligence and medical imaging modalities. The decision making is made by deep learning models trained on thousands of images of several medical imaging modalities. On the whole, the proposed system can help the clinicians in their medical decisions and provide better service for patients with breast cancer.
Approximated multi-agent fitted Q iteration

We formulate an efficient approximation for multi-agent batch reinforcement learning, the approximated multi-agent fitted Q iteration (AMAFQI). We present a detailed derivation of our approach. We propose an iterative policy search and show that it yields a greedy policy with respect to multiple approximations of the centralized, learned Q-function. In each iteration and policy evaluation, AMAFQI requires a number of computations that scales linearly with the number of agents whereas the analogous number of computations increase exponentially for the fitted Q iteration (FQI), a commonly used approaches in batch reinforcement learning. This property of AMAFQI is fundamental for the design of a tractable multi-agent approach. We evaluate the performance of AMAFQI and compare it to FQI in numerical simulations. The simulations illustrate the significant computation time reduction when using AMAFQI instead of FQI in multi-agent problems and corroborate the similar performance of both approaches.
METHODOLOGICAL FEATURES OF THE ORGANIZATION AND CONDUCT OF THE STUDENT OLYMPIAD IN PHYSICS IN DISTANCE FORMAT

The article proposed by the author deals with one of the most pressing issues for employees of education management bodies, methodologists and scientists who are part of the organizing committees and juries of the All-Ukrainian student olympiads of various levels - how to properly, qualitatively, comply with all standards, and at the appropriate level organize and conduct the relevant student olympiads. The article examines the issue of methodological features of the All-Ukrainian student physics olympiad at the regional level. The study of modern scientific, pedagogical, special literature and the legislative framework revealed the absence of a unified approach to solving this problem today, although some efforts have been made recently in the introduction of distance and mixed forms of organization of education process, implementation of digital transformation of the education system, digitalization of elements of the educational process, implementation of mobile learning technologies. These are developed and implemented digital and information resources, platforms, educational environments, tools and services, approaches, methods and some practical experience. Understanding the processes of organizing and conducting olympiads of various levels allowed the author to outline the possible existing approaches and options for organizing and conducting experimental and theoretical rounds of the III (regional) stage of the All-Ukrainian student physics olympiad in remote form, their advantages and disadvantages, as well as to characterize the methodological features of these processes. For each proposed variant of organizing and conducting an experimental tour in remote form, the author provides examples of possible tasks of an experimental tour in physics based on a creative experiment using improvised means, a virtual experiment with existing platforms, the use of mobile learning elements based on existing mobile applications. The author also considered methodological features of organizing and conducting a theoretical tour of the olympiad in a remote format. The article describes possible expected difficulties in organizing and conducting the physics olympiad in a remote format and suggests ways to solve them. Based on the analysis of the presented material, the author made general conclusions and proposed directions for further scientific and methodical research of this problem.
Implementation of Rubik's Cube Algorithm and Rivest-Shamir-Adleman (RSA) Algorithm on Iris Digital Image Security

Technological developments, especially smartphones, have led to an authentication process that must be developed.One of them is the use of the iris in the authentication process.The use of the iris can increase security in the authentication process because the structure of the iris is unique and differs from individual to individual.Authentication requires encryption and decryption processes to secure data.This research uses Rubik's Cube algorithm and the Rivest-Shamir-Adleman (RSA) algorithm.This study aimed to obtain the accuracy and time efficiency results used in the encryption and decryption process.In this study, the encryption process was carried out using Rubik's Cube algorithm, followed by encryption using the RSA algorithm.In the decryption process, it was obtained using the RSA algorithm, then continued with the decryption using the Rubik's Cube algorithm.The experimental results indicate that the encrypted image results differ from the initial image.With the results, the average Structural Similarity Index Metrics (SSIM) is 0,01, and the average Mean Square Error (MSE) is 37620,59.Furthermore, evaluations of the encryption and decryption process time were also carried out using the RSA public key (197,403) and RSA private key (53,403).The maximum iteration of Rubik's Cube algorithm was 1.In the encryption process, the average time was 0,796 s, and the decryption process obtained an average time of 0,652 s.This study has brought new procedures for securing digital images that can be developed in further studies.
INNOVATIVE PROCESSES IN PEDAGOGICAL SCIENCE: PRACTICAL EXPERIENCE OF FINLAND AND ESTONIA

The article summarizes the research into the essence of the “innovation” category in the educational field. The author’s vision of the systematization of categories of educational innovation activity in Ukraine is presented. Studying the features of successful Finnish and Estonian systems of education the author is searching a relevant example for Ukraine. From the materials under research it has been determined that information and communication technologies in Finnish and Estonian schools have become an integral part of all subjects from primary school to high school, i.e. integrated into all academic disciplines. Using the example of these education models the author examines the peculiarities of the teacher’s activity in secondary schools, particularly, how IT are used in the training of teaching staff and in their professional activity. It is concluded that technologies change the role of teachers from people who transmit or retransmit the acquired knowledge to creators and mentors who help to generate the knowledge and use it in practice within the rapidly changing digital world.
Usability Analysis to Measuring Effectiveness of Online Final Project System

Final Project is one of the academic stages that must be passed by students to obtain a degree.In the era of the Covid-19 pandemic, the stages to achieving this degree must be done online by students.To accommodate this, we need a system that can bridge so that the final project stages can run even online.The system is an online final project system that was built to match the flow of the final project needs of each university.In the system there are stages of guidance, scheduling, conducting assessments, and uploading revised results.From all these stages, evaluation and assessment are needed so that the online final project system can be developed easily.The evaluation and assessment method used is usability, because usability techniques can find out the shortcomings of the system directly from the user.Using the Software Usability System (SUS) questionnaire which involved 58 respondents at State Islamic University of Maulana Malik Ibrahim Malang, resulted in an average score of 78.92.Based on the results of the average score of the SUS questionnaire on the online final project system which resulted in 78.92, the online final project system has a good usability level.
cPNN: Continuous Progressive Neural Networks for Evolving Streaming Time Series

Abstract Dealing with an unbounded data stream involves overcoming the assumption that data is identically distributed and independent. A data stream can, in fact, exhibit temporal dependencies (i.e., be a time series), and data can change distribution over time (concept drift). The two problems are deeply discussed, and existing solutions address them separately: a joint solution is absent. In addition, learning multiple concepts implies remembering the past (a.k.a. avoiding catastrophic forgetting in Neural Networks’ terminology). This work proposes Continuous Progressive Neural Networks (cPNN), a solution that tames concept drifts, handles temporal dependencies, and bypasses catastrophic forgetting. cPNN is a continuous version of Progressive Neural Networks, a methodology for remembering old concepts and transferring past knowledge to fit the new concepts quickly. We base our method on Recurrent Neural Networks and exploit the Stochastic Gradient Descent applied to data streams with temporal dependencies. Results of an ablation study show a quick adaptation of cPNN to new concepts and robustness to drifts.
Thermal Fisher and Wigner–Yanase information correlations in two-qubit Heisenberg XYZ chain

Using Wigner–Yanase and Fisher information [including local quantum uncertainty (LQU) and local quantum Fisher information (LQFI)] as well as log-negativity, we investigate the thermal non-local correlations of two-qubit Heisenberg XYZ non-X states produced in the presence of the Dzyaloshinskii–Moriya (DM) interaction. The two-spin XYZ-Heisenberg non-local correlation decay can be weakened by increasing the DM interaction as well as the spin–spin XYZ-Heisenberg interactions. The LQFI correlation has greater resistance to thermal degradation. The spin–spin y-component antiferromagnetic coupling has a higher ability to protect the generated spin–spin nonlocal correlations against high temperatures. The emergence of the phenomena of the sudden death of log-negativity and the sudden change of the LQFI and LQU depend on the spin–spin XYZ-Heisenberg and DM interactions as well as on the bath temperature. The generated asymmetric correlations resulting due to the x,y-component spin interactions confirm that the antiferromagnetic coupling has a high ability to generate a maximally correlated two-qubit state. While the generated correlations, due to two-spin z-component interaction, present symmetric dynamics, and its amount depends on the x-component spin interaction.
Multi-View Stereo Network With Gaussian Distribution Iteration

Multi-view stereo estimates the depth maps of multiple perspective images in a scene and then fuses them to generate a 3D point cloud of the scene, which is an essential technology of 3D reconstruction. In this paper, we propose a deep learning method GDINet, applying probabilistic methods to the pyramid framework, which can significantly improve reconstruction quality. In detail, we first establish a Gaussian distribution for each image’s pixel and iterate it in the pyramid framework. The mean value is the estimated depth, and the variance represents the depth estimation error. In addition, we design a novel loss function with excellent convergence to train our network. Finally, we present an initialization module to generate the coarse Gaussian distribution, controlling the parameters in a reasonable range. Our results rank <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2nd$ </tex-math></inline-formula> on both DTU and Tanks & Temples datasets, showing that our network has high accuracy, completeness, and robustness. We also make a visualization comparison on the BlendedMVS dataset (containing many aerial scene images) to demonstrate the generalization ability of our model.