Reviewerly/exHarmony huggingface dataset:
Unpublished Scientific Documents as Object of Library and Information Services in the Digital Environment

The author considers the problem of including unpublished digital scientific documents published on the websites of scientific institutions of the Russian Academy of Sciences (RAS) in the system of library and information services. The paper describes the study of this class of information objects conducted in May — June 2020 on 200 websites of academic institutions of social and humanitarian profile. The author discovered the unpublished scientific materials of socio-humanitarian profile on the websites of 158 institutions; five institutions had no websites or did not work; and 37 websites didn’t have the materials the author was looking for. The research is based on the data included in the Navigator of information resources on social Sciences (NIRON), developed at the Institute of Scientific Information on Social Sciences of the Russian Academy of Sciences (INION RAS). The author proposes classification of unpublished documents, which includes both well-known types of scientific documents, such as dissertations and reports on research work, which processing is regulated, and new types, specific for modern scientific communication, such as overviews on activities of institutions and scientific departments. In total, the described class of unpublished scientific documents consists of 10 types, some of them are divided into subtypes. The paper discloses the quantitative data of the conducted accounting, including distribution of documents by the retrospective depth. For scientific reports, the author made the comparison with the data on reports in the Unified state information system for recording the results of research, development and technological works for civil purposes. The article analyses the specific features of presenting different types of unpublished scientific materials on the websites. The author proposes to include these documents in the service system using the information-reference system. When designing such a system, many controversial issues arise, in particular: the problems of metadata, the choice of subject classification, determination of the exact composition of unpublished digital scientific documents, unification of the retrospective depth, the feasibility of their reworking and editing for unification, inclusion of unpublished documents in bibliographic databases and lists, and the possibility of using them in the anti-plagiarism system. These and other problems require further research and solutions.
I Wanna Rotate 5427.89 Miles

I Wanna Rotate 5427.89 Miles is an interactive installation that uses projection, sensor-based interactivity, and creative computation to realize a complex multifaceted experience linked to the spatio-temporal context of a visitor interacting with the installation. This paper describes the intention behind the installation, and its implementation and interaction. The original intention relates to the Anthropocene and the human impact on the world: this work is to (1) express, in the context of art, the duality between an individual's micro-level actions in the world and the macro-level impact of humanity on the world, and (2) render this duality in experiential interactive artwork to let visitors reflect on their being in the world. Interaction is central in this work and allows visitors to get a subtle sense of connection and disconnection in micro and macro perspectives. The development process of I Wanna Rotate 5427.89 Miles is an experiment with multimodal interactions and performative technologies. The design of interaction allows for a connection between the artistic expression of intention and visitors’ participative experiences, extending the work beyond the artist's idiosyncratic bubble and reaching for a sense of objectivity.
Depth image based on rehabilitation human behavior recognition (Withdrawal Notice)

Publisher’s Note: This paper, originally published on 5 November 2020 was withdrawn on 13 November 2020 per author request.
Towards Privacy-Preserving Aggregated Prediction from SPDZ

Machine learning models trained on data collected from multiple parties can offer prediction services to clients. However, it raises privacy concerns for both model owners and clients. The models may disclose the details of the training data inadvertently by querying and the clients’ private input may be obtained by service providers. In this work, a privacy-preserving aggregated prediction framework is proposed, which combines two privacy-preserving techniques, i.e., Differential Privacy and Secret Sharing, to ensure privacy. Specifically, individual parties first train local models that meet differential privacy. Then two non-colluding servers collect the shares of multi-party trained models and clients’ inputs independently and provide online prediction. Finally, the clients reconstruct prediction from servers and aggregate them into the final prediction. It is worth mentioning that during prediction phase, no one can obtain the private information of others. We evaluate the performance of our framework on MNIST dataset. The experimental results show that the framework can strike a balance between utility and privacy.
THE ROLE AND SIGNIFICANCE OF USING THE VISUAL-COGNITIVE APPROACH IN HIGHER EDUCATION

This article discusses visual programs and their capabilities aimed at organizing higher education today. Based on innovative foreign experience, it is possible to fully understand the goals and objectives of science by further developing its methodological aspects. The development of visualized methodological support makes it possible to make fundamental aspects of science widely unknown. KEYWORDS: visual, 3D model, video, Delphi, Flash program, cognitive-visual, content.
The archetypal origins of the «second reality» (based on the plays «Memento», «Black Panther and White Bear» by V. Vynnychenko)

The article highlights and analyses archetypical images pictured on artists’ canvases in V. Vynnychenko’s plays. This is the new aspect of studying master’s drama, which has determined studio’s relevance and goal: to substantiate artistic nature, specific content and functional of the archetypal origins. The question is all about similar images that have archetype nature and are connected to a symbolical depiction of the matter of life. The human fantasy is one of the main sources of the archetypes, as well as the basis of the artistic process, showing, by the way, prototypes that are located in the collective unconscious. Images, located in collective unconscious, deep in people’s fears and hopes, can show up through art precisely, that is why they’re able to bring a resonance in the receivers’ souls. Archetypes are used to cause any type of effective infusion, therefore the artist – is the one who possesses the high level of perception to archetypical forms and is able to realize them extremely accurately, which approximates artist to prophets and other psychological types according to K. G. Jung. So the notion «archetype» as the studies tool allows us to highlight text nuances, understand the mechanism of the psychological impact of the art matter on the reader. The result of analysis is that the archetype oh the mother, father and child are mythological and irrational symbols. Their variety is changing, but the overall essence is common for them all. They are directly connected to the archetype of «self», that determines realization of the individualization process as the self-assertion in the world. Such decoding allows us to realize the philosophical subtext of the plays and denote the author’s views. The presence of the pictures with the child archetype in both texts – is the product of complicated psychological processes in the V. Vynnychenko’s consciousness. There are personal worries (marriage, son’s death), that undoubtedly, has crucially affected author, continuing and realizing in his art. Actually, it is also about the impact of products of the unconscious fantasy (forgotten or displaced), and collective memory, that is inheriting and realizing in consciousness due to some accidents.
Control Point Position Calibration for Light Pen Coordinate Measuring System

Light pen coordinate measuring system is a kind of vision-based portable coordinate measuring technique. It implements coordinate measuring by analyzing the image of light pen, which has several control points. With the relative positons of the control points determined and the pixel coordinates of their image points acquired by image processing, the posture of light pen can be obtained by solving perspective- n-point problem. Therefore, the control point positons serve as the measuring basis of the whole system. However, the accuracy of existing calibration methods is not satisfactory. In this paper, a new method of control point positon calibration, which utilizes traditional coordinate measuring machine, is proposed. The light pen is fasten to the measuring arm of a coordinate measuring machine and moves along a three dimensional grid route driven by the machine. A stationary camera is located in front of the machine to capture the image of light pen at each node of the route. For each control point, an individual coordinate system is established. Then the relative positions of control points can be transformed into the differences of a series of translation vectors. Experiments show that, with the proposed method, the accuracy of light pen coordinate measuring system can reach 30 m within 10 meters.
iSalGAN - An Improvised Saliency GAN

Human visual system (HVS) is naturally attracted to the salient regions that appear distinctly in the foreground of a scene. However, for a machine, automatically detecting the region of saliency is a challenging problem. Recently, a generative model namely Saliency GAN (SalGAN) discriminates if a pixel is salient or not by generating the saliency map given the input image. The generator is guided by a content loss and adversarial loss. However, the generated saliency maps tend to be smooth lacking finer details. We propose an improvised generator called iSalGAN (improvised saliency GAN) that integrates both low-level and high-level features to produce finer saliency maps. Our iSalGAN is guided by a combination of multiple content losses and, the adversarial loss. Our model is trained on MSRA10K dataset and tested on ECSSD and DUT-OMRON datasets. Qualitative and quantitative evaluation of our model shows the superior performance of our model over state-of-the-art methods. Codes will be made publicly available.
CORRELATION OF PERIPHERAL SMEARS FINDINGS WITH RBC INDICES AND RBC HISTOGRAM IN DIAGNOSIS OF ANAEMIA IN PREGNANT FEMALES.

Background: Red Blood Cell (RBC) histogram is a graphic representation of particle size distribution (cell frequencies verses size). RBC histogram with complete blood cell counts by automated analyser provides an idea about morphological changes of red blood cells in anaemias. Peripheral smear examination (PBS) helps in diagnosing different types of anaemias by visualizing abnormal shape and size, arrangement and immature red cells. This study was intended to analyze various types of anemia in pregnant women and to correlate RBC indices, RBC histogram with peripheral smear. Aims and Objectives: 1) To Correlate RBC indices, RBC histogram with peripheral smear in anemic pregnant women. 2) To categorize the type of anemia based on RBC indices, histogram and peripheral smear. Materials and Methods: Venous blood sample was run in SYSMEX XNL/350 six-part analyser. The haematological investigations of CBC, red blood cell indices (MCV, MCH, MCHC) and RBC histogram were obtained by analyser. Peripheral blood smear study was performed. Results: The present study included 55 cases among which in decreasing order of frequency, NNA (50.9%) was more common followed by MHA (40%), macrocytic anaemia (3.63%) and dimorphic anaemia (5.45%), as categorized by peripheral smear examination. Analysis by erythrocyte indices and histogram showed similar findings except in normocytic, microcytic and dimorphic which were 13.63%, 32.14% and 1.8% respectively. The correlation was done between the diagnosis made by the two methods which showed 76% concordance and discordance of 23.63%. Conclusion: Findings of the present study shows automated analyzer correlated well with PBS in cases of macrocytic and dimorphic anemia, compared to normocytic normochromic and microcytic hypochromic anaemia. As, RBC histogram with RBC indices and PBS are supplementary to each other, both should be used in conjunction for accurate diagnosis.
Echo and the Recovery of the Past in R. Murray Schafer’s The Princess of The Stars

Canadian composer and acoustic ecologist R. Murray Schafer is known for his outdoor works. Other scholars have considered the collaboration between musicians and the physical world in Schafer’s compositions and have linked performer-environment interaction with Canadian identity and environmentalism. Yet the role and meaning of outdoor acoustics in Schafer’s music remains understudied. This article investigates the aesthetics of echo in Schafer’s outdoor theatre work The Princess of the Stars (1981–4). Echo not only sends an original sound back from a surface but also offers insight into the role that musical sounds play in prescribing an interpretation of nature.
Data-efficient Alignment of Multimodal Sequences by Aligning Gradient Updates and Internal Feature Distributions

The task of video and text sequence alignment is a prerequisite step toward joint understanding of movie videos and screenplays. However, supervised methods face the obstacle of limited realistic training data. With this paper, we attempt to enhance data efficiency of the end-to-end alignment network NeuMATCH [15]. Recent research [56] suggests that network components dealing with different modalities may overfit and generalize at different speeds, creating difficulties for training. We propose to employ (1) layer-wise adaptive rate scaling (LARS) to align the magnitudes of gradient updates in different layers and balance the pace of learning and (2) sequence-wise batch normalization (SBN) to align the internal feature distributions from different modalities. Finally, we leverage random projection to reduce the dimensionality of input features. On the YouTube Movie Summary dataset, the combined use of these technique closes the performance gap when the pretraining on the LSMDC dataset is omitted and achieves the state-of-the-art result. Extensive empirical comparisons and analysis reveal that these techniques improve optimization and regularize the network more effectively than two different setups of layer normalization.
Liver Tumor Segmentation of CT Image by Using Deep Fully Convolutional Network

Accurate segmentation of liver tumors is an important guarantee for the success of liver cancer surgery, where convolutional network has been a type of popular method. However, the performance of the traditional convolutional network is limited by the network depth. To improve the accuracy of liver tumor segmentation, we propose a cascaded deep fully convolutional network (DFCN) which uses ResNet as the basis network followed by side output layer in the upsampling stage to fuse multi-scale image features. For better localizing the liver tumors, the segmentation result is further refined by a fully connected conditional random field. Experimental results show that the proposed method achieves higher segmentation accuracy than several state-of-the-art methods.
Ola Bratteli and His Diagrams

This article discusses the life and work of Professor Ola Bratteli (1946--2015). Family, fellow students, his advisor, colleagues and coworkers review aspects of his life and his outstanding mathematical accomplishments.
An Investigation of Attention Mechanisms in Graph Convolutional Networks Applied to Link Prediction Problems

The link prediction problem is fundamental to many application domains. Recently, deep learning-based models have been proposed to tackle this kind of problem. Graph auto-encoder (GAE) is a framework for unsupervised learning on graph-structured data. GAE achieves competitive results in link prediction tasks on citation networks. Another important problem on graph-structured data is node classification. Graph attention mechanism has been shown to have good performance in these tasks. This research investigates whether graph attention mechanisms can achieve good performance in link prediction tasks. We propose the attentive graph auto-encoder (AGAE) model, which incorporates GAE with the graph attention mechanism. The model is compared with GAE on both real-world citation networks and synthetic datasets. Investigations on how the model performs on networks with different characteristics is also included. In general, AGAE achieves competitive performance with GAE on citation networks while it outperforms GAE on certain synthetic networks.
An Efficient Lightweight Algorithm for Automatic Meters Identification and Error Management in Arabic Poetry

This paper targets the problem of automatic meter identification and error management in Arabic poetry. Many approaches use high level abstractions of poems in their prosodic forms: feet patterns, cords and pegs forms, or syllables. Our algorithm manipulates directely binary representations of meters and prosodic forms of verses, with computing distances between those representations. Our algorithm does not need to handle explicitely neither the foot patterns relaxations nor defects which makes our algorithm compared to studied works simpler (i.e. no symbolic combinatory analysis rules for exception patterns), and efficient in time and memory with dynamic programming of Levenshtein distance.
Semantic Parsing of Textual Requirements

Requirements are critical components in the industry, describing qualities that a product or a service needs to have. Most requirements are only available as natural language text embedded in a document. Working with textual requirements is getting increasingly difficult due to the growing number of requirements, and having the requirements available as structured data would be beneficial. However, the work required for the translation of natural language requirements into structured data is daunting. Thus, we need tools to aid in this process. In this Ph.D. project, we propose to use state-of-the-art knowledge extraction techniques and develop novel methods to identify the terms and relationships in a requirement and align them with an existing domain-ontology. To achieve this goal, we must overcome the difficulties in working with both domain-specific technical corpora and ontologies. Furthermore, existing tools and NLP models must be adapted to the domain.
A Novel Method to Classify Videos Based VBR Trace

Video classification research has been studied for many years. Traditional video classification methods are based on text, sound, and visual content. However, all these approaches require that the video content can be inspected. If the video content can not be investigated. For example, the video frame is encrypted or transmitted on the network device, then we can only measure the size of the video frame bitrate. In this paper, we propose two novel feature extraction methods based on variable bit rate (VBR) trace. The first one is extracting features in sliding windows. The second one is based on change points techniques to obtain more reasonable windows. We carry out empirical studied on our data sets to discriminate the action videos from the other videos. The experiment shows that we can identify the action video with $$87\%$$ g-mean.
An Occurrence Description Logic

&#x0D; &#x0D; &#x0D; Description Logics (DLs) are a family of well-known terminological knowledge representation formalisms in modern semantics-based systems. This research focuses on analysing how our developed Occurrence Logic (OccL) can conceptually and logically support the development of a description logic. OccL is integrated into the alternative theory of natural language syntax in Deviational Syntactic Structures under the label ‘EFA(X)3’ (or the third version of Epi-Formal Analysis in Syntax, EFA(X), which is a radical linguistic theory). From the logical point of view, OccL is a formal logic that mainly deals with the occurrences of symbols as well as with their priorities within linguistic descriptions, i.e. natural language syntax, semantics and phonology. In this article—based on our OccL-based definitions of the concepts of strong implication and occurrence value as well as of the logical concept Identical Occurrence Constructor (IDOC) that is the most fundamental logical concept in our formalism—we will model Occurrence Description Logic (ODL). Accordingly, we will formally-logically analyse ‘occurrence(s) of symbol(s)’ within descriptions of the world in ODL. In addition, we will analyse and assess the logical concepts of occurrence and occurrence priority in ODL. This research can make a strong logical background for our future research in the development of a Modal Occurrence Description Logic.&#x0D; &#x0D; &#x0D;
Legal Control Over Copyright Protection Using Blockchain Technology

The development of digital information technologies predetermined the need to develop technical and legal mechanisms of copyright protection. Now the share of copyright in the national economies of most countries is very significant and continues to grow, the main task of national legal systems is to find and implement technical and legal solutions to protect copyright from digital piracy. One of these solutions is block chain technology. The relevance of the research topic rests at the novelty of this technology and the lack of study of issues in the field of theory and law enforcement related to the adaptation and legitimation of relations using block chain technologies, including in the field of copyright protection. The purpose of this article is to analyze the legal qualifications of block chain technology and its application in the field of copyright protection, legitimization of relations associated with the use of block chain technologies in the field of copyright protection. The study revealed that the block chain has features that allow for classification of this technology as a type of technical means of copyright protection, which is the theoretical significance of this study. It has been established that in the field of copyright protection, the block chain performs the function of fixing and confirming the legitimacy of ownership by the author or other right holder of the corresponding work, and provides control over access to the work during authorized use. In the course of the study, the authors assessed the provisions of the current procedural legislation for the use of block chain technologies as evidence in court proceedings for copyright protection and revealed that information from the block chain, including those confirming authorship, can be recognized by the courts as evidence.