Reviewerly/exHarmony huggingface dataset:
Multi-Dimensional Recommendation Scheme for Social Networks Considering a User Relationship Strength Perspective

Developing a computational method based on user relationship strength for multi-dimensional recommendation is a significant challenge. The traditional recommendation methods have relatively low accuracy because they lack considering information from the perspective of user relationship strength into the recommendation algorithm. User relationship strength reflects the degree of closeness between two users, which can make the recommendation system more efficient between users in pairs. This paper proposes a multi-dimensional comprehensive recommendation method based on user relationship strength. We take three main factors into consideration, including the strength of user relationship, the similarity of entities, and the degree of user interest. First, we introduce a novel method to generate a user candidate set and an entity candidate set by calculating the relationship strength between two users and the similarity between two entities. Then, the algorithm will calculate the user interest degree of each user in the user candidate set to each entity in the entity candidate set, if the user interest degree is larger than or equal to a threshold, this particular entity will be recommended to this user. The performance of the proposed method was verified based on the real-world social network dataset and the e-commerce website dataset, and the experimental result suggests that this method can improve the recommendation accuracy.
The influence of the time of single photon transmission of information on the reliability of its reception in a quantum cryptographic communication channel

Expression for estimating the reliability of information during its transmission through quantum-cryptographic communication channel that contains a dead time photon counter has been obtained in this research. According to the results of mathematical modeling, the dependence of the reliability of the received data on the average time of single photon transmission of information was established. Studies have shown that with an increase in the average time of single photon transmission of information, these dependences grow, reaching saturation. Moreover, with equal parameters with an increase in the average duration of the dead time of a prolonging type, saturation occurs at large values of the average time of a single photon transmission of information.
Implementing the Cognition Level for Industry 4.0 by Integrating Augmented Reality and Manufacturing Execution Systems

In the current industrial practices, the exponential growth in terms of availability and affordability of sensors, data acquisition systems, and computer networks forces factories to move toward implementing high integrating Cyber-Physical Systems (CPS) with production, logistics, and services. This transforms today’s factories into Industry 4.0 factories with significant economic potential. Industry 4.0, also known as the fourth Industrial Revolution, levers on the integration of cyber technologies, the Internet of Things, and Services. This paper proposes an Augmented Reality (AR)-based system that creates a Cognition Level that integrates existent Manufacturing Execution Systems (MES) to CPS. The idea is to highlight the opportunities offered by AR technologies to CPS by describing an application scenario. The system, analyzed in a real factory, shows its capacity to integrate physical and digital worlds strongly. Furthermore, the conducted survey (based on the Situation Awareness Global Assessment Technique method) reveals significant advantages in terms of production monitoring, progress, and workers’ Situation Awareness in general.
4-Valued Semantics Under the OWA: A Deductive Database Approach

In this paper, we introduce a novel approach for dealing with databases containing inconsistent information. Considering four-valued logics in the context of OWA (Open World Assumption), a database $$\varDelta $$ is a pair (E, R) where E is the extension and R the set of rules. In our formalism, the set E is a set of pairs of the form $$\langle \varphi , \mathtt{v}\rangle $$ where $$\varphi $$ is a fact and v is either t, or b, or f (meaning respectively true, inconsistent or false), given that unknown facts are not stored. Moreover the rules extend Datalog$$^{neg}$$ rules allowing their heads to be a negative atom. We then define the notion of model of such a database, we show how to compute one particular model called semantics, and we investigate properties of this model. We also show how our approach applies to data integration and we review examples from the literature.
Terminology aspects in standardization in the area of library and information activities

The article focuses on the terminology standard development in the library and information environment. The establishing of terms proceeds under the influence of two trends: borrowing the concepts of regular language (usus) and standardization of terminology. The need to develop linguistic standards for the innovative areas has enhanced activity in the field of standardization and provided the revision and development of some new standards in the librarianship. The pecularity of standardization of the national terminology lies in the principles of standardization, which only allow the use of sustainable lexical units. The following selection criteria for terminological units are used when developing national standards: inambiguity, productivity, linguistic accurateness (originality and use of words of the native language), frequency of term use in the subject area and related fields of knowledge. The selection of lexical units and their interpretation should be focused on the actual characteristics of the subject area. A significant feature of modern standardization in the librarianship is the use of relational terminology from the information technology.
Automatic Detection of Malignant Masses in Digital Mammograms Based on a MCET-HHO Approach

Digital image processing techniques have become an important process within medical images. These techniques allow the improvement of the images in order to facilitate their interpretation for specialists. Within these are the segmentation methods, which help to divide the images by regions based on different approaches, in order to identify details that may be complex to distinguish initially. In this work, it is proposed the implementation of a multilevel threshold segmentation technique applied to mammography images, based on the Harris Hawks Optimization (HHO) algorithm, in order to identify regions of interest (ROIs) that contain malignant masses. The method of minimum cross entropy thresholding (MCET) is used to select the optimal threshold values for the segmentation. For the development of this work, four mammography images were used (all with presence of a malignant tumor), in their two views, craniocaudal (CC) and mediolateral oblique (MLO), obtained from the Digital Database for Screening Mammography (DDSM). Finally, the ROIs calculated were compared with the original ROIs of the database through a series of metrics, to evaluate the behavior of the algorithm. According to the results obtained, where it is shown that the agreement between the original ROIs and the calculated ROIs is significantly high, it is possible to conclude that the proposal of the MCET-HHO algorithm allows the automatic identification of ROIs containing malignant tumors in mammography images with significant accuracy.
Anonymous Symmetric-Key Communication

We study anonymity of probabilistic encryption (pE) and probabilistic authenticated encryption (pAE). We start by providing concise game-based security definitions capturing anonymity for both pE and pAE, and then show that the commonly used notion of indistinguishability from random ciphertexts (IND$) indeed implies the anonymity notions for both pE and pAE. This is in contrast to a recent work of Chan and Rogaway (Asiacrypt 2019), where it is shown that IND$-secure nonce-based authenticated encryption can only achieve anonymity if a sophisticated transformation is applied. Moreover, we also show that the Encrypt-then-MAC paradigm is anonymity-preserving, in the sense that if both the underlying probabilistic MAC (pMAC) and pE schemes are anonymous, then also the resulting pAE scheme is. Finally, we provide a composable treatment of anonymity using the constructive cryptography framework of Maurer and Renner (ICS 2011). We introduce adequate abstractions modeling various kinds of anonymous communication channels for many senders and one receiver in the presence of an active man-in-the-middle adversary. Then we show that the game-based notions indeed are anonymity-preserving, in the sense that they imply constructions between such anonymous channels, thus generating authenticity and/or confidentiality as expected, but crucially retaining anonymity if present.
Improving GP-UCB Algorithm by Harnessing Decomposed Feedback

Gaussian processes (GPs) have been widely applied to machine learning and nonparametric approximation. Given existing observations, a GP allows the decision maker to update a posterior belief over the unknown underlying function. Usually, observations from a complex system come with noise and decomposed feedback from intermediate layers. For example, the decomposed feedback could be the components that constitute the final objective value, or the various feedback gotten from sensors. Previous literature has shown that GPs can successfully deal with noise, but has neglected decomposed feedback. We therefore propose a decomposed GP regression algorithm to incorporate this feedback, leading to less average root-mean-squared error with respect to the target function, especially when the samples are scarce. We also introduce a decomposed GP-UCB algorithm to solve the resulting bandit problem with decomposed feedback. We prove that our algorithm converges to the optimal solution and preserves the no-regret property. To demonstrate the wide applicability of this work, we execute our algorithm on two disparate social problems: infectious disease control and weather monitoring. The numerical results show that our method provides significant improvement against previous methods that do not utilize these feedback, showcasing the advantage of considering decomposed feedback.
Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction

With an exponential explosive growth of various digital text information, it is challenging to efficiently obtain specific knowledge from massive unstructured text information. As one basic task for natural language processing (NLP), relation extraction aims to extract the semantic relation between entity pairs based on the given text. To avoid manual labeling of datasets, distant supervision relation extraction (DSRE) has been widely used, aiming to utilize knowledge base to automatically annotate datasets. Unfortunately, this method heavily suffers from wrong labelling due to the underlying strong assumptions. To address this issue, we propose a new framework using hybrid attention-based Transformer block with multi-instance learning to perform the DSRE task. More specifically, the Transformer block is firstly used as the sentence encoder to capture syntactic information of sentences, which mainly utilizes multi-head self-attention to extract features from word level. Then, a more concise sentence-level attention mechanism is adopted to constitute the bag representation, aiming to incorporate valid information of each sentence to effectively represent the bag. Experimental results on the public dataset New York Times (NYT) demonstrate that the proposed approach can outperform the state-of-the-art algorithms on the evaluation dataset, which verifies the effectiveness of our model for the DSRE task.
DIGITALIZATION OF EDUCATIONAL AND MANAGERIAL PROCESSES IN HIGHER SCHOOL: CHALLENGES FACED BY ACADEMIC STAFF

A. Fedorova1, O. Koropets1, I. Kacane2, G. Eremenko1, A. Ovchinnikov1 1Ural Federal University (RUSSIAN FEDERATION) 2Daugavpils University (LATVIA)
ShadowTutor: Distributed Partial Distillation for Mobile Video DNN Inference

Following the recent success of deep neural networks (DNN) on video computer vision tasks, performing DNN inferences on videos that originate from mobile devices has gained practical significance. As such, previous approaches developed methods to offload DNN inference computations for images to cloud servers to manage the resource constraints of mobile devices. However, when it comes to video data, communicating information of every frame consumes excessive network bandwidth and renders the entire system susceptible to adverse network conditions such as congestion. Thus, in this work, we seek to exploit the temporal coherence between nearby frames of a video stream to mitigate network pressure. That is, we propose ShadowTutor, a distributed video DNN inference framework that reduces the number of network transmissions through intermittent knowledge distillation to a student model. Moreover, we update only a subset of the student’s parameters, which we call partial distillation, to reduce the data size of each network transmission. Specifically, the server runs a large and general teacher model, and the mobile device only runs an extremely small but specialized student model. On sparsely selected key frames, the server partially trains the student model by targeting the teacher’s response and sends the updated part to the mobile device. We investigate the effectiveness of ShadowTutor with HD video semantic segmentation. Evaluations show that network data transfer is reduced by 95% on average. Moreover, the throughput of the system is improved by over three times and shows robustness to changes in network bandwidth.
Processing RGB-D Data from a 3D Camera using Object Detection and Written Character Recognition in Convolutional Neural Networks for Virtual Finger Writing

This study presents an approach to Virtual Finger Writing by treating it as an object detection problem. A Convolutional Neural Network using the Tiny YOLOv3 Architecture was used in this study to enable real-time hand detection. A Kinect camera is used to capture the depth data within the bounding box of the hand region to detect the nearest point in that area which is assumed to be the writing finger. The goal of this study is to present an approach for real-time virtual finger-writing in uncontrolled environments as a possible input method for future hologram or virtual reality systems. After testing and validation of trained model, the study were able to achieve a vast improvement in speed and accuracy results with the application of modern object detection.
Bisimilarity of Diagrams

In this paper, we investigate diagrams, namely functors from any small category to a fixed category, and more particularly, their bisimilarity. Initially defined using the theory of open maps of Joyal et al., we prove two characterisations of this bisimilarity: it is equivalent to the existence of a bisimulation-like relation and has a logical characterisation à la Hennessy and Milner. We then prove that we capture both path bisimilarity and strong path bisimilarity of any small open maps situation. We then look at the particular case of finitary diagrams with values in real or rational vector spaces. We prove that checking bisimilarity and satisfiability of a positive formula by a diagram are both decidable by reducing to a problem of existence of invertible matrices with linear conditions, which in turn reduces to the existential theory of the reals.
A DESIGN TEACHER IN THE CONTEXT OF PEDAGOGICAL REFLECTION

The paper deals with one of the areas in the development of pedagogical skills of art teachers and the role of reflexive pedagogy in shaping creative personality in the system of training of future designers. One of the critical issues in design education is to bridge the gap between the information provided and its practical demand. During their years of study, students acquire too much and too little knowledge at the same time. Much knowledge refers to its volume, whereas little knowledge implies their adequacy to particular circumstances under which they have to work. Reflexive pedagogy can help to solve this issue. The paper considers the most common definitions of the term “reflection”. It indicates that pedagogical reflection of a design teacher is a system of linguistic and thinking processes, ensuring the realization of goals of future designers’ professional training and education in the system of artistic and pedagogical interaction. It demonstrates that cultural reflection is aimed at rethinking cultural acts and one’s cultural experience. It justifies the obtained results to reveal fundamentally essential conditions for developing professional reflection. It proves that many psycho-pedagogical studies cover the issue of professional reflection of art teachers, and there is no concept of its development for future designers. Therefore, it is essential to find out how to develop reflexion of students and university teachers. The paper concludes that one’s ability to generate new and non-standard ideas should be considered as one of the conditions for developing creative thinking of university teachers, boosting their self-confidence and promoting individual independence of vision.
Giveme5W1H: A Universal System for Extracting Main Events from News Articles

Event extraction from news articles is a commonly required prerequisite for various tasks, such as article summarization, article clustering, and news aggregation. Due to the lack of universally applicable and publicly available methods tailored to news datasets, many researchers redundantly implement event extraction methods for their own projects. The journalistic 5W1H questions are capable of describing the main event of an article, i.e., by answering who did what, when, where, why, and how. We provide an in-depth description of an improved version of Giveme5W1H, a system that uses syntactic and domain-specific rules to automatically extract the relevant phrases from English news articles to provide answers to these 5W1H questions. Given the answers to these questions, the system determines an article's main event. In an expert evaluation with three assessors and 120 articles, we determined an overall precision of p=0.73, and p=0.82 for answering the first four W questions, which alone can sufficiently summarize the main event reported on in a news article. We recently made our system publicly available, and it remains the only universal open-source 5W1H extractor capable of being applied to a wide range of use cases in news analysis.
Duet at TREC 2019 Deep Learning Track

This report discusses three submissions based on the Duet architecture to the Deep Learning track at TREC 2019. For the document retrieval task, we adapt the Duet model to ingest a "multiple field" view of documents---we refer to the new architecture as Duet with Multiple Fields (DuetMF). A second submission combines the DuetMF model with other neural and traditional relevance estimators in a learning-to-rank framework and achieves improved performance over the DuetMF baseline. For the passage retrieval task, we submit a single run based on an ensemble of eight Duet models.
Performance Evaluation of Generative Adversarial Networks for Computer Vision Applications

Generative Adversarial Networks (GAN) generates model approaches using Convolution Neural Networks (CNN) to find out learning regularities and to discover the hidden patterns held in given input data.GAN is a generative model that is trained using two models such as generator and Discriminator both competing against each other to learn the probability distribution function, networks such as CNN, RNN, ANN etc.These traditional neural networks are easily fooled in misclassifying things by adding small amount of noise to original data, whereas GAN's are more stable and easier to train due to the amalgamation of Feed Forward Neural Network and CNN.In general, GAN's are simple Neural networks be trained in adversarial way to generate the data mimicking same distribution, Generator learns new possible sample, and the Discriminator learns how to differentiate generated samples from valid facts.Generated samples are similar in the nature but different from real distribution data.The generated samples make use of computer vision techniques such as visualization designs, realistic image generation, image classifications etc.In the proposed work, to realize the probability distribution Restricted -Boltzmann machines and Deep Belief networks are used.The performance of the GAN Networks is evaluated on various standard datasets to realize the complex tasks such as image prediction, handwritten digit's generation, clothing classification, image segmentation tasks etc. From the experimental results, it is clearly evident that the performance of GAN outperforms other state of the art classifiers on all the benchmark datasets.
How to achieve students’ self-management in educational activity?

The paper is devoted to the study of types of managing the student’s educational activity. The educational discipline “Practicum of problem solving in informatics” for students of third year study, future teachers of informatics have been chosen for realising pedagogical conditions of computer-oriented management of students’ educational activity. Progressive turn from direct management through comanagement, subsidiary management to self-management was the main idea of designing the courseware. The information and communication educational environment has been based on the platform of learning management system Moodle. The Workshop elements of Moodle played the central role in management of students’ educational activity. The results of our pedagogical observation and assessment showed the efficiency of suggested approach. Additionally, there were shown the lack of students’ competency in time planning and introspection on the base of the experimental data.
Preservation of Admissibility with Rationality and Feasibility Constraints

The paper considers the problem of in what circumstances an aggregation rule guarantees an admissible output extension that represents a good compromise between several input extensions of abstract argumentation framework, each provided by a different individual. To achieve this, we introduce the concept of concrete admissibility for abstract argumentations by strengthening Dung’s admissibility. We also define a model for extension aggregation that clearly separates the constraint supposed to be satisfied by individuals and the constraint that must be met by the collective decision. Using this model, we show that the majority rule guarantees admissible sets on newly defined admissible sets.
Imbalanced Data Stream Classification Using Hybrid Data Preprocessing

Imbalanced data streams have gained significant popularity among the researchers in recent years. This area of research is not only still greatly underdeveloped, but there are also numerous inherent difficulties that need to be addressed when creating algorithms that could be utilized in such dynamic environment and achieve satisfactory results when it comes to their predictive abilities. In this paper, a novel algorithm that combines both over- and under-sampling techniques in order to create a more robust classifier dedicated to imbalanced data streams is proposed. The efficiency and high predictive quality of the proposed method have been confirmed on the basis of extensive experimental research carried out on the real and the computer-generated data streams.