Reviewerly/exHarmony huggingface dataset:
Healthcare Technology with Flexible Smash Crystal Access Management System

Abstract The Internet of Things (IoT) healthcare has been suggested as a talented way of importantly increasing patient productivity also satisfaction. In IoT, medical instruments calculate the critical symptoms of the patients and add them onto medical databases that are processed and retrieved by healthcare personnel in the cloud. Encryption is usually used to maintain authentication by trusted parties to medical data while avoiding unauthorized access to them to protect patient privacy. The prompt access to patient files in emergency cases is important in healthcare. In this article we suggest a scheme that facilitates the access to protected medical data, attribute-based access and broken glass access, which is a compact blow access control (LiBAC). Under normal cases an attribute-set medical worker can decode and view the data that complies with the access policy of the medical file. The break-glass entry system circumvents the patient file access regulation, allowing emergency medical services or rescue personnel to access the records in good time. As the instruments in the IoT healthcare network conduct very few calculations and storage in addition communication overheads are minimal, LiBAC is light-weight. LiBAC in the standard model has been officially proven stable, and comprehensive studies are carried out to illustrate its efficacy.
Research and Development Circuits for Error Correction

One of the main problems to be overcome in the near future for the practical creation of quantum computer to make quantum computing resistant to interaction with the external environment and to eliminate inaccuracy of operations. In this paper we study quantum codes correcting errors, proposed as a way to protect against the occurrence errors due to the influence of the external environment. Results of work are considered software implemented model of the simplest quantum code. A number of questions related to quantum computing are also raised. In their works, Shor (factorization algorithm), Grover (search in a disordered database) and a number of other scientists showed that a quantum computer (QC) is capable of processing a small class of functions with higher performance than any of the existing “classic” computers. This work is a study of the influence of the environment on the quantum system of qubits. The article is supposed to describe the foundations of quantum information theory, as well as the place in it of the concept of quantum entanglement. A technique for correcting two main types of quantum errors has been developed, based on the implementation and execution of certain quantum circuits. The main difficulties of ensuring the protection of a quantum channel from various types of errors are analyzed and considered.
Concept Drift Detection Using Minimum Prediction Deviation

Machine learning solutions are trained on non-stationary datasets that change over time. Data distributions shift with time because data is a representation of the changing world. These changes in data are known as concept drift. In this paper, we propose a new algorithm to detect concept drift and apply it to a phishing dataset which is semi-stationary. Our algorithm “minimum prediction deviation detection” (MPD3) utilizes the minimum prediction deviation (MPD), a metric that describes a models uncertainty in order to detect drift. We compare MPD3 with a number of known concept drift algorithms such as IKS, KS, and D3 both on streaming data and on semi-stationary data. MPD3 obtains 70.10% average accuracy on the phishing dataset compared to 54.75 accuracy with no detection method.Abirami, M.G. Gressel, Gilad
HMaViz: Human-machine analytics for visual recommendation

Visualizations are context-specific. Understanding the context of visualizations before deciding to use them is a daunting task since users have various backgrounds, and there are thousands of available visual representations (and their variances). To this end, this paper proposes a visual analytics framework to achieve the following research goals: (1) to automatically generate a number of suitable representations for visualizing the input data and present it to users as a catalog of visualizations with different levels of abstractions and data characteristics on one/two/multi-dimensional spaces (2) to infer aspects of the user’s interest based on their interactions (3) to narrow down a smaller set of visualizations that suit users analysis intention. The results of this process give our analytics system the means to better understand the user’s analysis process and enable it to better provide timely recommendations.
Principal Component Analysis in the Stochastic Differential Privacy Model

In this paper, we study the differentially private Principal Component Analysis (PCA) problem in stochastic optimization settings. We first propose a new stochastic gradient perturbation PCA mechanism (DP-SPCA) for the calculation of the right singular subspace to achieve (Iµ,I´)-differential privacy. For achieving a better utility guarantee and performance, we then present a new differential privacy stochastic variance reduction mechanism (DP-VRPCA) with gradient perturbation for PCA, which has a near-optimal utility bound. To the best of our knowledge, this is the first work of stochastic gradient perturbation for (Iµ,I´)-differential privacy PCA. We also compare the proposed algorithms with existing state-of-the-art methods in the literature, and experiments on real-world datasets and on classification tasks confirm the improved theoretical guarantees of our algorithms.
A Generative Text Summarization Model Based on Document Structure Neural Network

Aiming at the low accuracy of the automatic generation of text summaries in the field of data mining, as well as the defects of the existing encoder and decoder models, this paper proposes a generative text summarization model based on the document structure neural network. The model introduces the document structure, divides the text into a word encoding layer and a sentence encoding layer, and builds a top-down hierarchical structure to avoid the back propagation error problem caused by the long input sequence in the traditional encoder and decoder model; At each level, an attention mechanism is added, and a multi-attention mechanism is proposed and introduced, which refines the granularity of the attention mechanism, thereby improving the accuracy of text summary generation. Experimental results show that, compared with the original encoder-decoder model, this model can effectively refines the granularity of the attention mechanism and significantly improve the accuracy of text summary generation.
Gibbs Sampling for Mixtures in Order of Appearance: The Ordered Allocation Sampler

Gibbs sampling methods are standard tools to perform posterior inference for mixture models. These have been broadly classified into two categories: marginal and conditional methods. While conditional samplers are more widely applicable than marginal ones, they may suffer from slow mixing in infinite mixtures, where some form of truncation, either deterministic or random, is required. In mixtures with random number of components, the exploration of parameter spaces of different dimensions can also be challenging. We tackle these issues by expressing the mixture components in the random order of appearance in an exchangeable sequence directed by the mixing distribution. We derive a sampler that is straightforward to implement for mixing distributions with tractable size-biased ordered weights, and that can be readily adapted to mixture models for which marginal samplers are not available. In infinite mixtures, no form of truncation is necessary. As for finite mixtures with random dimension, a simple updating of the number of components is obtained by a blocking argument, thus, easing challenges found in transdimensional moves via Metropolis-Hastings steps. Additionally, sampling occurs in the space of ordered partitions with blocks labeled in the least element order, which endows the sampler with good mixing properties. The performance of the proposed algorithm is evaluated in a simulation study. Supplementary materials for this article are available online.
Establishment and Development of Chinese Language and Literature Database Under the Background of Information

Today’s world is a world of information globalization. The time and space for people to communicate is getting wider and wider, and the communication between countries is getting closer and closer. Mankind has entered the era of globalization. More and more countries are beginning to attach importance to the important role of language in the game of national culture. As China’s comprehensive national strength continues to increase, China’s international influence has also increased substantially. Chinese is very popular all over the world. Of course, the unique tool charm and cultural charm of the Chinese make it an important tool for shaping Chinese national culture. The subsystem of Chinese national culture has an irreplaceable function of national identification. An ideal Chinese language and culture will enable China to seize the opportunity to compete with other countries in the world and help China gain an advantage in international competition. In the context of information technology, this article adopts the literature research method and the cross-research method, and draws the following conclusions that the number of people who can use Mandarin accurately and fluently accounts for 40% of the participants; those who can use Mandarin but have some accents account for 35%; Basically, 15% can talk; only 10% can understand but don’t speak well, which proves that Mandarin in China is developing towards a good trend.
Real VR

The articles in this special section focus on real virtual reality (VR). Since the times of the Lumiere brothers, the way we watch movies has not fundamentally changed: whether in movie theaters, on mobile devices, or on TV at home, we still experience movies as outside observers, watching the action through a proscenium defined by the size of the screen. Unfortunately, the limited size of the screen causes our peripheral vision to remind us subconsciously that we are only outside observers and not "really there". With the advent of consumer VR headsets, this outsideobserver paradigm of visual entertainment has given way to a truly immersive experience. In immersive displays, the action completely encompasses the viewer, drawing us into the action and providing an acute sense of "being there". Unsurprisingly, this paradigm shift has taken hold first in video gaming where content has always been completely computer-generated. Now, Real VR lays the foundations for the next step: How to import real-world video recordings into VR headsets? To attain this goal, two ambitious challenges must be tackled in unison. Suitable digital representations must be created from real-world footage that allow real-time rendering from arbitrary vantage points, including stereo vision and egomotion parallax. And the degree of perceptual realism of VR headsets must be enhanced to the point where depictions of real-world scenes appear truly genuine to our visual sense. Finding viable solutions to these challenges in Real VR will allow us to experience movies, concerts, or even live sports events with a sense of "being there", which is unattainable with today’s existing technologies.
Nonverbal Communication through Facial Expression in Diverse Conditions

In this chapter, we investigated computer vision technique for facial expression recognition, which increase both - the recognition rate and computational efficiency. Local and global appearance-based features are combined in order to incorporate precise local texture and global shapes. We proposed Multi-Level Haar (MLH) feature based system, which is simple and fast in computation. The driving factors behind using the Haar were its two interesting properties - signal compression and energy preservation. To depict the importance of facial geometry, we first segmented the facial components like eyebrows, eye, and mouth, and then applied feature extraction on these facial components only. Experiments are conducted on three well known publicly available expression datasets CK, JAFFE, TFEID and in-house WESFED dataset. The performance is measured against various template matching and machine learning classifiers. We achieved highest recognition rate for proposed operator with Discriminant Analysis Classifier. We studied the performance of proposed approach in several scenarios like expression recognition from low resolution, recognition from small training sample space, recognition in the presence of noise and so forth.
Concept and Preliminary Testing of the Two-Stage Technology of Terminology Extraction on the Basis of Topic Modeling and Context Analysis

The paper deals with the task of automated terminology extraction. A two-stage technology for its solution is proposed, based on topic modeling and analyzing the context of the use of lexical units. The results of experimental verification of the technology and the prospects for its further development are presented.
ALGORITHMS FOR EXAMINATION AND VERIFICATION OF WORK PROGRAMS OF DISCIPLINES FOR USE IN INFORMATION SYSTEMS

The article considers a syllabus as a core component of the educational program and an essential tool for describing an academic course for both a teacher and a student. Nowadays, there is gradual automation of document flow in educational institutions, including educa-tional and methodological documentation. It is required to develop the university's information system and the algorithm itself for the formal and content-wise review to automate the syllabus validation and verification process. The syllabus review process at St. Petersburg State University, RANEPA, and HSE was considered in the study. Therefore, a universal algorithm applicable to information systems has been designed. Since March 2021, ITMO University has been widely using “Educational Program Maker” web service to manage educational program elements. The developed system utilizes educational analytics methods which are widely used in online education for student behavior patterns evaluation and education results improvement. The proposed algorithm is implemented for syllabi development and review. Introduction of the module for the syllabi creation in a standardized and unified format allows to describe the prerequisites and post requisites of academic courses and link various educational activities with learning outcomes, making it possible to reveal the course content. Introducing the verification modules made it possible to automate the process of syllabi approvement
Touring the MetaCoq Project (Invited Paper)

Proof assistants are getting more widespread use in research and industry to provide certified and independently checkable guarantees about theories, designs, systems and implementations.However, proof assistant implementations themselves are seldom verified, although they take a major share of the trusted code base in any such certification effort.In this area, proof assistants based on Higher-Order Logic enjoy stronger guarantees, as self-certified implementations have been available for some years.One cause of this difference is the inherent complexity of dependent type theories together with their extensions with inductive types, universe polymorphism and complex sort systems, and the gap between theory on paper and practical implementations in efficient programming languages.MetaCoq is a collaborative project that aims to tackle these difficulties to provide the first fully-certified realistic implementation of a type checker for the full calculus underlying the Coq proof assistant.To achieve this, we refined the sometimes blurry, if not incorrect, specification and implementation of the system.We show how theoretical tools from this community such as bidirectional type-checking, Tait-Martin-Löf/Takahashi's confluence proof technique and monadic and dependently-typed programming can help construct the following artifacts:• a specification of Coq's syntax and type theory, the Polymorphic Cumulative Calculus of (Co)-Inductive Constructions (PCUIC);• a monad for the manipulation of raw syntax and interaction with the Coq system;• a verification of PCUIC's metatheory, whose main results are the confluence of reduction, type preservation and principality of typing;• a realistic, correct and complete type-checker for PCUIC;• a sound type and proof erasure procedure from PCUIC to untyped λ -calculus, i.e., the core of the extraction mechanism of Coq.
On Intuitionistic Fuzziness

In the present work we try to provide an overview of the main results related to intiuitionistic fuzzy sets and related concepts contributed by scientists from the Bulgarian Academy of Sciences from the very birth of the concept to present day. Of course, it is impossible not to mention briefly the contributions of other authors but we will try to keep the focus of our exposition on the results of the groups that have worked within the Academy.
Discussion of Intelligent IP Camera Application in Nuclear Power Plant Video Monitoring System

This article mainly discusses the application of intelligent IP camera in the video monitoring system of nuclear power plant. Firstly, three monitoring requirements of existing video monitoring system of nuclear power plant are analyzed, and the advantages and characteristics of intelligent IP camera are given. On this basis, the monitoring scenarios that can be realized by the video monitoring system of nuclear power plant based on IP camera are given. It is concluded that the application of intelligent IP camera can greatly improve the digital and intelligent level of nuclear power plant monitoring system.
Coreset-Based Data Compression for Logistic Regression

The coreset paradigm is a fundamental tool for analysing complex and large datasets. Although coresets are used as an acceleration technique for many learning problems, the algorithms used for constructing them may become computationally exhaustive in some settings. We show that this can easily happen when computing coresets for learning a logistic regression classifier. We overcome this issue with two methods: Accelerating Clustering via Sampling (ACvS) and Regressed Data Summarisation Framework (RDSF); the former is an acceleration procedure based on a simple theoretical observation on using Uniform Random Sampling for clustering problems, the latter is a coreset-based data-summarising framework that builds on ACvS and extends it by using a regression algorithm as part of the construction. We tested both procedures on five public datasets, and observed that computing the coreset and learning from it, is 11 times faster than learning directly from the full input data in the worst case, and 34 times faster in the best case. We further observed that the best regression algorithm for creating summaries of data using the RDSF framework is the Ordinary Least Squares (OLS).
Research on the release management technology of the mobile application for electricity dispatching

Abstract With the continuous development of the power system and the rapid growth of the scale of the power grid, the information that needs to be monitored, inquired, and maintained in the power grid dispatching system is increasing. The traditional way of understanding the realtime operation status of the power grid through dispatching workstations is increasingly unable to meet the requirements of modern power grids for monitoring, operation and maintenance in real-time, timeliness, and anywhere. In the face of such a huge business demand market, how to develop mobile applications that meet customer needs with minimal investment in resources and efficient development efficiency has become our research direction. This paper discusses a release management technology of the mobile application for electricity dispatching that meets the above requirements. This technology adopts the design mode of “shared application + personalized page”, without too much intervention by developers, Users can edit and compress their own personalized pages, then upload to the version server and update the main page on the mobile devices themselves; this technology improves the efficiency of opening graphics file and the response time of operation through caching the graphic files and only refreshing the operation area mechanisms; through the publish/subscribe message bus mechanism, the hot update effect of the main page is realized; the service is restored through one-click, which provides users with more operating space.
Aspects of Inductive Inference in Statistics and Machine Learning

We take a brief look at some connections between the old philosophical problem of inductive inference and the modern areas of statistics and machine learning. Certain aspects of inductive inference appearing in specific techniques of the latter two areas are made explicit. Our contention is that statistics and machine learning techniques do not lead to a resolution of the problem of induction. Finally, we sketch an agenda for studying reductions between various kinds of inductive inferences.
Research on Emotional Design of Sleep Aid Products Based on the Theory of Design for Sustainable Behavior

Staying up late has become an increasingly common bad habit among young people, and it can cause great harm. Based on the promotion of human sustainable development, this research introduces the theory of sustainable behavior design. Through literature review, it is known that the effectiveness of sustainable behavior design strategies is positively related to product dominance, and there is a relationship between product dominance and user acceptance contradiction. In order to improve user acceptance, this research introduces emotional design theory, and builds a product emotional design model based on sustainable behavior theory by analyzing the combination of sustainable behavior design theory and emotional design theory in the Fogg behavior model. Finally, based on this model, the design practice of desk lamps is used to improve the behavior of staying up late, while verifying the logic of the theory.
Coupling Blockchain with Emerging Real-Life Applications

This chapter discusses some real-life blockchain-based applications and describes the applicability and necessity of integrating blockchain with the existing centralized systems. Particularly, we describe some widely popular domains where blockchain can be applied, i.e., healthcare, energy, finance, agriculture, smart city, smart manufacturing, e-voting, and personal identity management. To clearly understand the applications, we present visual representations of deploying blockchain’s conceptual understanding within those real-life applications. This chapter presents in detail how blockchain can be blended with those areas to provide a secure service highlighting the potential areas or domains for blockchain technology
The Use of a WhatsApp Record for Communication With Students

The purpose of this study was to explore the impact of WhatsApp usage in disseminating information to students at the Management, Zimbabwe Open University's Mashonaland West Regional Campus. This study employed a mixed methodology wherein both the qualitative and quantitative approaches were used. Data was collected using questionnaires and interviews. The questionnaires were distributed to students through WhatsApp groups for all faculties. A total of 255 questionnaires were distributed to students through the existing WhatsApp groups. Of the distributed questionnaires, 128 were returned, and of these, 69 were usable for data analysis. The study revealed that the majority of students utilize WhatsApp for communication with the university. The other finding was that WhatsApp communication was characterized by data bundle costs and internet connectivity challenges. The study recommended that there be a WhatsApp policy and widening of the use for teaching and learning.