Reviewerly/exHarmony huggingface dataset:
Marketing Policy of the State Public Scientific Technological Library of the SB RAS in Relation to Library and Information Resources and Services

An important factor for not only development, but existence of the library, is its work to attract the audience, for which varieties of marketing technologies are used. However, the larger and more diverse the library audience becomes, the more difficult it is to monitor and regulate the completeness of the use of marketing techniques aimed at different segments of users. The purpose of this article is to provide an example of an analysis of a library’s outreach activities through the prism of a marketing policy, allowing the library the streamline of its marketing tools and resources, monitoring of their effectiveness in a comprehensive way, and the identification of possible gaps in the implementation of such activities. The analysis revealed two main gaps in the marketing activities of the SPSTL SB RAS, related to the completeness of the marketing technologies used and the analysis of the audience outside the scientific community. In addition, the grounds were noted, worked out as a part of the development strategy of the library and allow to combine the achievements of the projects implemented by the library in the marketing policy in the future, after the closure of the gaps found. This approach to analyzing the formation of marketing policy is available for replication by other types of libraries, and its use will improve the level of their marketing work.
Artificial Intelligence in Security Technologies: Levels of Intelligent Autonomy and Risk

The development and use of Artificial Intelligence (AI) within security technologies presents a number of opportunities and risks for security professionals. The study was undertaken to investigate the use of AI in security technologies within the functional categories of Observing, Detecting, Controlling and Responding technologies, exploring the risks associated with enhanced AI across these technologies. In exploring these risks, the study developed a novel scale to define the level of intelligent autonomy a technology may possess during operation, in addition the degree of human involvement retained at each level across the various stages of the AI operational cycle. The Security Technology Intelligent Autonomy Scale may be used to consider the risks which may emerge at each level, with a weighting towards consequences of outcome.
Blockchain Technology in Health Care Sector: An Experimental Approach

Due to the innovation in the technology field, many researches are happening in the field of blockchain technology, and its scope in different industries are increasing. Blockchain technology is a chain of blocks which provides a secure environment between the users. To address the security, trust issues, many research studies are attracted toward blockchain in customizing and applying for their working process. In this paper, we propose how blockchain technology can be adopted in healthcare sector. Blockchain could provide secure storage of patient health information’s. The security and privacy for patient’s information are provided using proof of work (PoW). The blockchain network has been validated by miners, and cryptocurrency is used for transactions with security and transparency.
Application of Cloud Computing Modes and Methods in Big Data of Power System

This paper discusses the urgency and practicability of applying cloud computing to big data in power systems, combining the characteristics of complex power flow calculations and big data applications in power systems, and studying the relationship between power system power flow calculation methods and cloud computing. The important role of cloud computing in power system dispatching, planning, and scientific research is analyzed, and three visualization methods are realized, namely: basic chart visualization, SVG visualization, and map visualization. Among them, for SVG visualization, this paper proposes an SVG visualization display method based on layered thinking.
Pay Attention to Histogram: Spatial Weight Histogram Encoding for LBP

Texture feature is a global feature that describes the surface properties of the scene to which the image. Local Binary Pattern (LBP) is a widely used local texture feature extraction operator in computer vision, and the histogram encoding in the local binary pattern has proven to be a very powerful texture feature expression mode. In this paper, we drive an effective weight histogram encoding (WHE) which takes into account the defect that non-local information is ignored in classical histogram encoding. We propose two WHE methods for LBP, which utilize Gaussian function and Gabor function as weight function respectively, to integrate non-local information of images. The proposed methods are prominent in supplementing non-local information. Experimental results on three classic hyperspectral image classification datasets show that the introduction of non-local spatial configuration LBP operator (WHE-LBP) leads to better discrimination due to the good performance of the weight histogram coding model at the joint expression level of local-non-local spatial configurations.
Metropolis-style random sampling of quantum gates for the estimation of low-energy observables

We propose a quantum algorithm to compute low-energy expectation values of a quantum Hamiltonian by sampling a partition function associated with the average energy of that Hamiltonian. For any given quantum circuit-Hamiltonian pair, there is an associated average energy. The sampling is done through an accept/reject Metropolis-style algorithm on the quantum gates of the circuit itself. Observables calculated under the canonical ensemble from these samples of circuits are extrapolated from higher-energies to the ground state.
FF-PRec: A Feature Fusion-Based Paper Recommendation Method on Academic Big Data of Heterogeneous Network

With the development of information technology, academic data has increased dramatically. Nowadays, recommendation algorithm is widely used for scholars to find useful information from massive data. However, due to the discrete data and complex semantic relationship in the academic network, existing recommendation algorithms have limitations in feature extraction and data sparsity, which bring negative impacts on recommendation accuracy and personalization. In order to solve the above problems, we propose FF-PRec, a paper recommendation method based on feature fusion in the academic network. First, FF-PRec implements graph representation learning and a natural language processing tool to extract network features and text features respectively. Two types of features are combined as the representation vectors of scholars and papers. Second, a meta-path is designed based on prior knowledge to guide semantic information extraction. To validate our proposed method, we conducted experiments on the AMiner dataset. The experimental results indicate that FF-PRec performs better than traditional methods in paper recommendation tasks and shows high accuracy and correlation.
Parallel Capsule Network: A Novel Method for Image Denoising

Image denoising is a traditional and hot research topic. During the past years, tremendous advances in image denoising tasks have been achieved using Convolutional Neural Networks (CNN), filtering, sparse coding, classical external priors, or deep learning methods. Although these methods can gain good performance in image denoising field. However, in real life, due to different sources of noise, noise will be generated in several stages such as image acquisition, transmission, and compression. So, we need more about image denoising methods. In this work, we present a novel denoising image method that is based on image classification. To achieve denoising images, the result of image classification can be regarded as prior information for image denoising. We call it the relationship between global and local, such relationship between global and local finds it is a foothold in gestalt psychology, where they believe that the global is not a simple sum or addition of locals, the global is not determined by the locals, and the various locals of the global are determined by the internal structure and nature of the global, so the gestalt organization law means that people always perceive Will organize the empirical materials into a meaningful whole according to a certain form. We evaluated our model on the MNIST dataset and FASHION MNIST dataset. To the best of our knowledge, this is the first such an effort on image denoising in image classification.
Fine-Grained Joint Chinese Word Embedding*

Word vector research is one of the basic research in the field of natural language processing. The existing Chinese word embedding model does not make full use of the relationship between subcharacters that make up Chinese characters, nor does it fully consider the different contribution of subcharacters to semantics. In this paper, we propose a Fine-grained Joint Chinese word embedding model(FPWE). Firstly, Chinese characters are decomposed into fine-grained sub-characters with semantic information. Secondly, Bi-LSTM is used to establish the correlation between sub-characters of Chinese characters. At the same time, the attention mechanism is introduced to deal with the different semantic contributions between sub-characters, word information and sub-character information. Finally, we evaluate our model on the word similarity calculations and word reasoning tasks, the results showed that our model performance of this paper is better than that of the baseline model.
Effectful improvement theory

Optimizing programs is hard. Not only one must preserve semantics, but one also needs to ensure that an optimization truly makes the program better. The first part, preserving program semantics, has been, and still is, the subject of much research. We follow a line of work that starts with Morris' observational equivalence, continues with Abramsky's applicative bisimilarity and Howe's method, and concludes in a recent abstract formalization of applicative bisimilarity in the presence of algebraic effects by Dal Lago, Gavazzo and Levy. The second part, ensuring that an optimization truly makes the program better, is a path less traveled, with the improvement theory of Sands being the most prominent example. In this work, we connect these two parts by obtaining an abstract theory of improvements based on effectful applicative bisimilarity that extends Sands' notion of improvement to effectful languages.
Degree Histogram Publishing Method of Dynamic Graph Data Based on Node Differential Privacy

Dynamic graph data can not only reveal the rules of network evolution, but also contain a large amount of personal privacy information. The degree of nodes is an important indicator to analyze the relationship between nodes in the network and to quantify the status of nodes. From the node degree histogram of the dynamic network, we can obtain information such as key nodes and network structure, but there is also the risk of leaking user privacy information. This paper studies the degree histogram publishing method of dynamic graph data based on node differential privacy. And abstracts dynamic graph data into incremental graph data. Aiming at the problem of high sensitivity of query functions, a pretreatment method based on graph projection technology is proposed.On this basis, we use a hierarchical clustering algorithm to group the difference sequences of the histogram sequence. Finally, combining the above two methods, we give a method for publishing the degree histogram of dynamic graph data based on node difference privacy. Simulation experiments show that the algorithm proposed in this paper not only guarantees the privacy of the data, but also improves the utility of the released data.
Bayesian Networks: Theory and Philosophy

This chapter explores the theory of Bayesian networks with particular reference to Maximum Entropy Formalism. A discussion of objective Bayesianism is given together with some brief remarks on applications.
A Comparative Study on the Use of Augmented Reality in Indoor Positioning Systems and Navigation

In today’s world, when smartphones are readily available, printed maps are outdated and replaced by mobile apps. These apps rely on technologies like GPS, Wi-Fi, etc. Unfortunately, in closed places like shopping malls, colleges, etc., these apps do not provide the best results. To tackle this problem of indoor navigation, multiple solutions using augmented reality (AR) have emerged over the last decade. In this paper, we discussed augmented reality and its application in indoor positioning and navigation. Furthermore, we have done a comparative study of these methods on the basis of attributes like compatibility, requirements, availability, and usability. The comparison helps state the advantages and limitations of the systems clearly.
A Multimodal Entity Linking Approach Incorporating Topic Concepts

The characteristics of social media posts are ambiguous, noisy, and not rich in context and semantics, which poses a significant challenge to the task of entity linking. To this end, we propose a multimodal entity linking method TCMEL that 1) integrates topic concepts by Clustering entity concepts to clarify text topics, 2) extracts context features from both text and image using Bi-LSTM and CNN respectively, and 3) aggregates contextual information extracted from subject concepts, text, images and lexical symbols and calculates the similarity between knowledge base entities and text mentions. We demonstrate the effectiveness of the proposed TCMEL method by conducting comparative experiments on the MELBench dataset containing multimodal data from multiple domains.
Working of Jaya and Rao Optimization Algorithms and Their Variants

This chapter presents the details of the working of the Jaya algorithmJaya algorithm, Rao algorithmsRao algorithm and their modified versions, namely multi-team perturbation-guiding Jaya algorithm, adaptive multi-team perturbation-guiding Jaya algorithm, elitist Rao algorithms and self-adaptive population Rao algorithmSelf-adaptive population algorithm. Also, it presents the implementation of these algorithms to multi-objective optimizationMulti-objective optimization problems. In addition, multi-objective optimizationMulti-objective optimization performance indicators such as hypervolumeHypervolume, spacingSpacing, coverageCoverage and inverted generational distanceInverted generational distance are described. Furthermore, the results of application of the Jaya and Rao algorithms along with their modified versions to 53 unconstrained single-objective optimizationSingle-objective optimization benchmark problems and five multi-objective optimizationMulti-objective optimization benchmark problems are presented.
Evaluation of Various Cryptographic Techniques Based on File Size on Cloud Storage Security

Cloud storage and Internet applications are growing rapidly in the recent years. These applications are used by millions of users and controlled by authorized administrators. With the rapid growth of the Internet, there should be a need for protecting confidential data. The Internet provides the services to make use of the application with minimum security. Along these issues, providing the information security, it utilizes the various encryption strategies to protect the information. Though many cryptographic algorithms are developed by researchers to provide better security, the encryption and decryption techniques have some limitation based on performance metrics. This paper presents the comparison of various cryptographic algorithms based on performance metrics such as block size, key, storage, computational cost and execution time. This work will be useful for further research to implement a new encryption algorithm which provides a higher security with less computational time.
Modeling User Portrait Using Psycholinguistic Analysis of Social Media Data

This paper presents an approach to determining the psychological characteristics of a user of social networks by analyzing text messages on social networks. This approach is to classify the user’s copyright texts using machine learning. Parsing the extracted text data suggests using the vectorizer—transformer—classifier sequence. The training dataset included the results of a Big Five analysis of user surveys and a set of copyright text data from social media pages. The effectiveness of the proposed approach was confirmed by the results of experiments with open data of more than 1000 users of the social network Facebook.
Semantic-guided Image Feature Matching for Agricultural Robots

Image feature matching is one of the most important basic techniques for agricultural robots. This task has been tried, but because of the complexity of the image, especially the non-rigid transformations, the performance is still far from satisfactory. This paper proposed a semantic-guided feature matching method for both agricultural mobile robot and UAV via semantic segmentation and probabilistic inference. Given an original image pair, a learning-based method, which is very active to the challenging variation of outdoor luminosity, is introduced firstly to produce a sematic image pair. This step shifts the robot's focus to the task itself, while improving matching efficiency at the same time. Then, based on the characteristic similarity of the ROI region of the semantic image pair, a series of putative correspondences are generated. Next, the focuses on eliminating outliers from that series while deducting the transformation, where this procedure was developed on a maximum likelihood Bayesian model. The paper has also applied three effective regularization techniques to the correspondence, which helped to come up with the best solution. Finally, as the problem was solved by EM algorithm and extensive experiments on those images taken from the real farmland, it is safe to say that the accurate results of this method are superior to other state-of-the-art methods.
Performance evaluation and model quantization of object detection algorithm for infrared image

Object detection technology has always been one of the important research directions in the field of computer vision. It has important application value and prospects in both civil fields and military fields. With the emergence of artificial intelligence technology, deep learning has gradually replaced the traditional algorithm with its higher accuracy. Considering that most of the current algorithms are for color image, compared with color image, infrared image contains less feature information, and it is more difficult in object detection. In this paper, different algorithms are used for object detection in infrared images, and the detection results are compared. This paper chooses YOLO V5 and combines it with MobileNet to lighten the model. After lightening, the parameters are reduced by 30%, but the accuracy is only reduced by 5%. Finally, this paper quantify YOLO V5 based on the model quantization method of PyTorch. After quantization, the accuracy of the model decreases by 2%.
Research on Investment and Operation Mode of Multi-station Integration Data Center Station

As an extension of the main business of the grid company, multi-station integration can give full play to the value of power grid resources and drive the development of the industry. It is in line with the strategic objectives of the grid company and the requirements of the development of emerging industries. It has a good development prospect and urgently needs to study its investment and operation mode. Firstly, this paper analyzes investment and operation subject of multi-station integration of the grid company;On this basis, the research method of Multi-station integration investment and operation strategy based on SWOT analysis model is proposed;Finally, the investment and operation strategy of multi-station integration data center station in a northwest province is clarified. The results show that: in terms of investment and operation subject, it is recommended to use provincial and municipal company investment (main business investment), and provincial communication company as the operation subject to carry out multi-station integration commercial operation;In the aspect of operation strategy, under the premise of leading investment and operation, it is suggested to adopt diversification strategy to deal with external competition.
Learning from Peer Mistakes: Collaborative UML-Based ITS with Peer Feedback Evaluation

Collaborative Intelligent Tutoring Systems (ITSs) use peer tutor assessment to give feedback to students in solving problems. Through this feedback, the students reflect on their thinking and try to improve it when they get similar questions. The accuracy of the feedback given by the peers is important because this helps students to improve their learning skills. If the student acting as a peer tutor is unclear about the topic, then they will probably provide incorrect feedback. There have been very few attempts in the literature that provide limited support to improve the accuracy and relevancy of peer feedback. This paper presents a collaborative ITS to teach Unified Modeling Language (UML), which is designed in such a way that it can detect erroneous feedback before it is delivered to the student. The evaluations conducted in this study indicate that receiving and sending incorrect feedback have negative impact on students’ learning skills. Furthermore, the results also show that the experimental group with peer feedback evaluation has significant learning gains compared to the control group.
A Subtyping Scheme for Nominal and Structural Types Based on Class Graph Equivalence

Subtyping and multiple inheritance are the essential features of oo and component-based programming languages, in particular with the presence of interfaces and contracts. More general, the composability of these constructs admitting the subtype relation powers the reusability, modular, flexibility and reliability of oo-based systems. While nominal classes allow annotation of user intention to the types, operations on interfaces and contracts naturally result structural constructs. Structural types are also necessary if we need the types to have value-semantics, so that they can be transferred around in distributed systems. Building a type system that allows the coexistence of nominal and structural classes, while maintaining the usual subtype relation is critical and challenging. We present a subtyping scheme that encodes a class to a directed and edge-labeled graph, which has the convenience to handle recursive types. The names of a class and its superclasses are encoded as tags to label the edges of the graph, turning the nominal construct into a structural one. This encoding allows us to unify the handling of class relations into graph relations. We define the class representation, canonical form, value-identity and subtype relation in the notion of graphs, and justify our subtyping scheme in the cases of multiple inheritance, class intersection and union. Our scheme is general, easy to implement and compatible with most of the existing oo type systems, providing a solid base for further oo language and tool development.