Reviewerly/exHarmony huggingface dataset:
Integrative approach to designing the content of secondary specialized vocational education

espanolEl articulo analiza los problemas de los enfoques integrados para el diseno de contenido de software para la educacion profesional. El estudio se realizo utilizando el metodo de abstraccion, ascenso de lo abstracto a lo concreto; analisis logico de literatura cientifica sobre el tema del articulo; descriptivo y diacronico. Como resultado, la integracion del proceso de aprendizaje contribuye al aprendizaje integral, nos permite desarrollar el pensamiento logico y figurado en partes iguales, apoya areas de actividades racionales y emocionales y asegura su unidad. En conclusion, la necesidad de integracion es cerrar los limites entre los campos de la ciencia. EnglishThe article discusses the issues of integrated approaches to the design of software content for vocational education. The study was conducted using the method of abstraction, ascent from the abstract to the concrete; logical analysis of scientific literature on the topic of the article; descriptive and diachronic. As a result, the integration of the learning process contributes to comprehensive learning, allows us to develop logical and figurative thinking in equal parts, supports rational and emotional areas of activity and ensures their unity. In conclusion, the need for integration is to bridge the boundaries between fields of science.
EigenGame: PCA as a Nash Equilibrium

We present a novel view on principal components analysis as a competitive game in which each approximate eigenvector is controlled by a player whose goal is to maximize their own utility function. We analyze the properties of this PCA game and the behavior of its gradient based updates. The resulting algorithm---which combines elements from Oja's rule with a generalized Gram-Schmidt orthogonalization---is naturally decentralized and hence parallelizable through message passing. We demonstrate the scalability of the algorithm with experiments on large image datasets and neural network activations. We discuss how this new view of PCA as a differentiable game can lead to further algorithmic developments and insights.
QoS-Based Collaborative Filtering for Web Service Mining

Stored information in the databases is heterogeneous, from various sources, and of large volumes. The web service selection becomes nontrivial, as the users are easily overloaded by vast amount candidates. Using the keyword-based search method, users are struggling to choose the best web services among those having similar features. In the traditional methods, the users set different constraints and QoS parameters of a web service from what's claimed by the provider. Moreover, different researches challenge this problem, introducing semantic discovery process to enable relevant and desired search results. These approaches don't give importance to users' opinions and the selection history. The classical development of the ontology is typically entirely based on high human participation. In this paper, the authors use ontology-based querying, user profile to know the history, new collaborative filtering to calculate user, and query similarity and QoS as the final step for web service selection. The approach combines the syntactic and semantic methods to increase the selection precision.
Electronic Educational Environment Moodle in English Language Training

The article deals with the causes, methods and functions of using E-learning in the process of training students English, as well as changing teacher's and learner's roles in the educational process.The introduction of information and communication technologies in educational and research processes is a compulsory requirement to implement the concept of modernization in Russian education.Recently, interest in English as a means of international communication has increased significantly, it has already been recognized as the language of professional communication in various fields of activity.The relevance of this work is undoubted.Modern higher school teachers are constantly looking for more effective approaches in training English and in the process of creating an electronic educational environment that would allow students access to various sources of information and help satisfy the interests of the modern student.The goal of the research is to increase the efficiency of training English in the system of higher professional education through the development and use of educational software products that apply modern information technology.Methods to achieve this goal include the method of using computer technology, methods of network planning and project management.The research showed that the organization of the information-instrumental educational environment uses a student centered approach, the development of an electronic educational and methodological complex (EEMC) is a search for an algorithm, most adapted to the student's cognitive needs, and EEMC in English creates the best conditions for the principle of interactivity.The practical significance of the study is determined by the fact that its materials related to the method of creating multimedia tasks and virtual environments can be used in the practical work of universities, as well as in the system of further training for specialists.
Is Privacy and Personal Data Set to Become the New Intellectual Property?

A pressing concern today is whether the rationale underlying the protection of personal data is itself a meaningful foundation for according intellectual property (IP) rights in personal data to data subjects. In particular, are there particular technological attributes about the collection, use and processing of personal data on the Internet, and global access to that data, that provide a strong justification to extend IP rights to data subjects? A central issue in so determining is whether data subjects need the protection of such rights in a technological revolution in which they are increasingly exposed to the use and abuse of their personal data. A further question is how IP law can provide them with the requisite protection of their private space, or whether other means of protecting personal data, such as through general contract rights, render IP protections redundant, or at least, less necessary. This paper maintains that lawmakers often fail to distinguish between general property and IP protection of personal data; that IP protection encompasses important attributes of both property and contract law; and that laws that implement IP protection in light of its sui generis attributes are more fitting means of protecting personal data than the alternatives. The paper demonstrates that one of the benefits of providing IP rights in personal data goes some way to strengthening data subjects’ control and protection over their personal data and strengthening data protection law more generally. It also argues for greater harmonization of IP law across jurisdictions to ensure that the protection of personal data becomes more coherent and internationally sustainable.
Task-Driven Self-Supervised BI-Channel Networks Learning for Diagnosis of Breast Cancers with Mammography

Deep learning can promote mammography-based computer-aided diagnosis (CAD) for breast cancers, but it generally suffers from the small size sample problem. In this work, a task-driven self-supervised bi-channel networks learning (TSBNL) framework is proposed to improve the network performance with limited mammograms. In particular, a new gray-scale image mapping (GSIM) task for image restoration is designed as the pretext task to improve discriminative feature representation with label information of mammograms. TSBNL then innovatively integrates this image restoration network and the downstream classification network into a unified SSL framework, and transfers the knowledge from the pretext network to the classification network with improved diagnostic accuracy. The proposed algorithm is evaluated on a public INbreast mammogram dataset. The experimental results indicate that it outperforms the conventional SSL algorithms for the diagnosis of breast cancers with limited samples.
Graph Convolution with Low-rank Learnable Local Filters

Geometric variations like rotation, scaling, and viewpoint changes pose a significant challenge to visual understanding. One common solution is to directly model certain intrinsic structures, e.g., using landmarks. However, it then becomes non-trivial to build effective deep models, especially when the underlying non-Euclidean grid is irregular and coarse. Recent deep models using graph convolutions provide an appropriate framework to handle such non-Euclidean data, but many of them, particularly those based on global graph Laplacians, lack expressiveness to capture local features required for representation of signals lying on the non-Euclidean grid. The current paper introduces a new type of graph convolution with learnable low-rank local filters, which is provably more expressive than previous spectral graph convolution methods. The model also provides a unified framework for both spectral and spatial graph convolutions. To improve model robustness, regularization by local graph Laplacians is introduced. The representation stability against input graph data perturbation is theoretically proved, making use of the graph filter locality and the local graph regularization. Experiments on spherical mesh data, real-world facial expression recognition/skeleton-based action recognition data, and data with simulated graph noise show the empirical advantage of the proposed model.
RETRACTED: Fast 3D image reconstruction algorithm based on artificial intelligence technology

In order to improve the 3D reconstruction capability of high-resolution fine-grained 3D images, a fast 3D image reconstruction algorithm based on artificial intelligence technology is proposed. The cross-gradient sharpening detection method is used to collect features and extract information from high-resolution fine-grained three-dimensional images, and establish an edge contour feature detection model for high-resolution fine-grained three-dimensional images. Combining the salient feature analysis method and the subspace feature analysis method to cluster and analyze the high-resolution fine-grained three-dimensional image. In the artificial intelligence environment, the saliency of the three-dimensional image is detected and analyzed, and the multi-dimensional segmentation and gray histogram of the high-resolution fine-grained three-dimensional image are reconstructed through the subspace segmentation method. According to the reconstruction results of the gray histogram, fast 3D image reconstruction and image fusion processing are performed. Finally, the accurate detection and recognition of the reconstructed image is realized. The simulation results show that this method has a good effect on 3D image reconstruction, and the time cost of image reconstruction is relatively short. It improves the recognition and feature analysis capabilities of high-resolution fine-grained 3D images, and has good application value in the reconstruction, detection and recognition of high-resolution fine-grained 3D images.
Computational Staining of Pathology Images to Study Tumor Microenvironment in Lung Cancer

The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). In order to facilitate the study of cellular spatial organization and interactions, we developed a comprehensive nuclei segmentation and classification tool to characterize the TME from standard Hematoxylin and Eosin (H&E)-stained pathology images. This tool can computationally "stain" different types of cell nuclei in H&E pathology images to facilitate pathologists in analyzing the TME.A Mask Regional-Convolutional Neural Network (Mask-RCNN) model was developed to segment the nuclei of tumor, stromal, lymphocyte, macrophage, karyorrhexis and red blood cells in lung adenocarcinoma (ADC). Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas (TCGA) lung ADC dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (pv= 0.001), with a hazard ratio of 2.23 [1.37-3.65] after adjusting for clinical variables. Furthermore, the image-derived TME features were significantly correlated with the gene expression of biological pathways. For example, transcription activation of both the T-cell receptor (TCR) and Programmed cell death protein 1 (PD1) pathways was positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway was positively correlated with the density of stromal cells.This study developed a deep learning-based analysis tool to dissect the TME from tumor tissue images. Using this tool, we demonstrated that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways. Although developed from the pathology images of lung ADC, this model can be adapted into other types of cancers.Funding Statement: This work was partially supported by the National Institutes of Health [5R01CA152301, P50CA70907, 1R01GM115473, and 1R01CA172211], and the Cancer Prevention and Research Institute of Texas [RP190107 and RP180805].Declaration of Interests: The authors declare that they have no competing interests.Ethics Approval Statement:Not required; date is available online.
Taking Notes on the Fly Helps Language Pre-Training

How to make unsupervised language pre-training more efficient and less resource-intensive is an important research direction in NLP. In this paper, we focus on improving the efficiency of language pre-training methods through providing better data utilization. It is well-known that in language data corpus, words follow a heavy-tail distribution. A large proportion of words appear only very few times and the embeddings of rare words are usually poorly optimized. We argue that such embeddings carry inadequate semantic signals, which could make the data utilization inefficient and slow down the pre-training of the entire model. To mitigate this problem, we propose Taking Notes on the Fly (TNF), which takes notes for rare words on the fly during pre-training to help the model understand them when they occur next time. Specifically, TNF maintains a note dictionary and saves a rare word's contextual information in it as notes when the rare word occurs in a sentence. When the same rare word occurs again during training, the note information saved beforehand can be employed to enhance the semantics of the current sentence. By doing so, TNF provides a better data utilization since cross-sentence information is employed to cover the inadequate semantics caused by rare words in the sentences. We implement TNF on both BERT and ELECTRA to check its efficiency and effectiveness. Experimental results show that TNF's training time is 60% less than its backbone pre-training models when reaching the same performance. When trained with same number of iterations, TNF outperforms its backbone methods on most of downstream tasks and the average GLUE score. Code is attached in the supplementary material.
Flex Sensor Based Glove to Control Wheel Chair and Sign Language Translator for Speech Impaired People

The idea of this paper is proposed for rehabilitation of people who have mobility issues particularly for the paralytic Stroke Patients and mute person with difficulty to communicate in a society. To overcome these complications, this system is developed to control the wheelchair based on finger movement and it also provides the voice output. It consists of flex sensors to control the wheelchair and in another mode it uses flex sensor and voice module to provide audio output. Person can move the wheelchair in any direction by gesture. Gesture is a good medium to give command because it can be used in a noisy condition and so he/she can communicate in public places. The patients can control the wheelchair through hand gesture, so they can survey themselves for their basic needs and need not to depend on others to guide those patients. Thus, it is multipurpose hand gloves to control motion of wheelchair and also to convert hand gestures to speech.
AI and Copyright

This chapter examines the most pertinent issues facing copyright law as it encounters increasingly sophisticated artificial intelligence (AI). It begins with a few introductory examples to illuminate the potential interactions of AI and copyright law. Section 2 then tackles the question of whether AI-generated works are copyrightable in Canada and who, if anyone, might own that copyright. This involves a doctrinal discussion of “originality” (the threshold for copyrightability) as well as reflections on the meaning of “authorship,” and concludes with the suggestion that autonomously generated AI outputs presently (and rightly) belong in the public domain. Section 3 turns to consider issues of copyright infringement. First, it addresses the law in respect of AI inputs (the texts and data used to train AI systems, which may themselves be copyrightable works) and highlights the need for greater limits and exceptions to ensure that copyright law does not obstruct best practices in the development and implementation of AI technologies. It then examines the matter of potentially infringing AI outputs (which may, of course, resemble copyright-protected, human-created works), identifying current uncertainties around independent creation, agency, and the allocation of liability. Section 4 addresses the deployment of AI in automated copyright-enforcement, emphasizing its increasingly critical role in shaping our online environment and citizens’ everyday encounters with copyright enclosures. The chapter concludes with reflections on the risks and opportunities presented by AI in the copyright context, and identifies key gaps and questions that remain to be answered as copyright law and policy adjust to evolving AI technologies.
Low-light image enhancement via layer decomposition and optimization

When capturing images in low-light conditions, the images are often degraded with low visibility and severe noise. To improve the visual quality and repress the noise simultaneously, a kind of low-light image enhancement method via layer decomposition and optimization was proposed. Firstly, the low-light image was smoothed via iterative least squares thus we could get the noise-free basic layer. Secondly, by means of subtraction of the original image and the basic layer we could get the detailed layer. Then we enhance the basic layer via variational Retinex-based method. At the meantime, we weaken the noise of the detailed layer by non-subsampled shearlet transform. Finally, we could obtain the enhanced image by fusion of the optimized basic layer and detailed layer. Experimental results of a number of low-light images reveal the efficiency of the proposed method and show its superiority over several state-of-the-arts.
The Legislative Process Leading to the Hate Speech Elimination Act

The author of this chapter is a member of the Upper House of Japan who proposed and established the Hate Speech Elimination Act: a philosophical law that includes no penalty provisions because if punishments were imposed on the grounds of the content of expression, then the government would have the authority to decide which expressions were to be subject to punishment – an obvious violation of the ‘freedom of expression’ provided for under the Constitution of Japan. In this chapter, the author discusses the importance of empowering a society in which everyone spontaneously raises their own voice and acts to eliminate hate speech, rather than prescribing a society in which people think, ‘Hate speech will not disappear unless it is punished.’ Through the effects of the Act, local governments and judiciaries were finally able to define hate speech as ‘unacceptable’ and use their own discretion to implement measures accordingly. Even though it is a philosophical law, the Act has therefore raised public awareness and inspired a mass movement seeking to eliminate discrimination voluntarily.
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning

Graph Representation Learning (GRL) methods have impacted fields from chemistry to social science. However, their algorithmic implementations are specialized to specific use-cases e.g. message passing methods are run differently from node embedding ones. Despite their apparent differences, all these methods utilize the graph structure, and therefore, their learning can be approximated with stochastic graph traversals. We propose Graph Traversal via Tensor Functionals (GTTF), a unifying meta-algorithm framework for easing the implementation of diverse graph algorithms and enabling transparent and efficient scaling to large graphs. GTTF is founded upon a data structure (stored as a sparse tensor) and a stochastic graph traversal algorithm (described using tensor operations). The algorithm is a functional that accept two functions, and can be specialized to obtain a variety of GRL models and objectives, simply by changing those two functions. We show for a wide class of methods, our algorithm learns in an unbiased fashion and, in expectation, approximates the learning as if the specialized implementations were run directly. With these capabilities, we scale otherwise non-scalable methods to set state-of-the-art on large graph datasets while being more efficient than existing GRL libraries -- with only a handful of lines of code for each method specialization.
Of Neural Network Model Robustness Through Generating Invariant to Attributes Embeddings

Model robustness to minor deviations in the distribution of input data is an important criterion in many tasks. Neural networks show high accuracy on training samples, but the quality on test samples can be dropped dramatically due to different data distributions, a situation that is exacerbated at the subgroup level within each category. In this article we show how the robustness of the model at the subgroup level can be significantly improved with the help of the domain adaptation approach to image embeddings. We have found that application of a competitive approach to embeddings limitation gives a significant increase of accuracy metrics in a complex subgroup in comparison with the previous models. The method was tested on two independent datasets, the accuracy in a complex subgroup on the Waterbirds dataset is 90.3 {y : waterbirds;a : landbackground}, on the CelebA dataset is 92.22 {y : blondhair;a : male}.
Automatic generation of documents and reports for educational process

Abstract Management of educational process implies a large number of documents and reports that are interconnected and have high impact upon students and teachers. Coordinators of educational processes struggle with time management, correlation between implied parts and specific requirement. Present paper is focused on distance learning educational process that has specific requirement regarding students, teachers timetables and discipline management documents. Authors developed an informatic application that allows users to obtain: students’ and teachers’ timetables; reports regarding teachers from all study program; calendars of disciplines. Using this application, time invested in the process is reducing with almost 55%, proving the opportunity and efficiency of application.