Reviewerly/exHarmony huggingface dataset:
Query Intent Understanding

Search engines aim at helping users find relevant results from the Web. Understanding the underlying intent of queries issued to search engines is a critical step toward this goal. Till now, it is still a challenge to have a scientific definition of query intent. Existing approaches attempting to understand query intents can be classified into two categories: (1) query intent classification: mapping queries into categories and (2) query intent mining: finding subtopics covered by the queries. For the first group of work, the mapping between queries and categories can be conducted in various ways, including classifying based on navigational, informational, or transactional intent, based on geographic locality, temporal intent, topical categories, or available vertical services. For query intent mining, the output can be a list of explicit subqueries, or some implicit representation of subintent, such as a list of document clusters, a list of entities, etc. In this chapter, we will introduce these query intent prediction approaches in detail.
PROSPECTS FOR USING THE EXPERT SYSTEM TO DEFINE THE LEVEL OF KNOWLEDGE OF RUSSIAN AS A FOREIGN LANGUAGE

Modern information society requires a change in the basis on which familiar systems, such as education, have been built. To bridge the gap between the competencies formed by traditional teaching methods and the demands of modern society, which requires an accelerated rate of acquiring skills and abilities of a higher quality than before, there is a need for digitalization of the educational system. Increasing the quality of education through digitalization is possible by using expert systems. The article describes the ongoing projects of the Russian Federation to introduce digital technologies in education, the principle of expert systems, Russian and international experience in implementing these projects. The advantages and disadvantages of expert systems in relation to traditional methods and techniques of education are considered, as well as the possibility of their application in teaching Russian as a foreign language.
Research on the missile-borne image dehazing based on dark channel with gradient transmittance

In land and sea operations, foggy weather will greatly reduce the accuracy of target detection, which will have a greater impact on the tracking and guidance and control of intelligent ammunition. Therefore, the research on missile-borne images dehazing is particularly important. This article first briefly introduces the physical model of atmospheric scattering, and then analyzes the characteristics of several missile-borne image dehazing methods. Finally, based on the dehazing principle of image restoration, for the specific application environment of the missile-borne camera, a dehazing model Based on Dark Channel with Gradient Transmittance is established. The experiment shows that a dehazing image is suitable for human visual characteristics, which greatly improves the target recognition rate in foggy weather, and is of great significance for improving the accuracy of ammunition hits.
A Study on the Effect of Emotional Intelligence on the Resilience of Duty Free Shop Workers

This study selected duty-free shop workers for research considering recent fast growth of domestic duty-free industry, and aimed to verify the causal relationship between emotional intelligence and resilience in duty-free shop workers. The survey was conducted on employees working at local duty-free shops in South Korea, and 302 responses from them were used to withdraw possible effect of emotional intelligence on resilience. The results are as follows. First, the four factors of emotional intelligence were verified from factor analysis. Among them, 'other's emotion appraisal,' 'regulation of emotion,' and "use of emotion' appeared to have a positive effect on their 'resilience,' while 'self-emotion application' has no significant effect on it. Second, the 'emotional intelligence' of duty-free shop workers does not have a significant effect on 'optimism', a sub-factor of 'resilience'. Third, among the 'emotional intelligence' of duty-free shop workers, 'regulation of emotion' does not have a noticeable impact on 'resilience.' The results of this study would give a helpful implication to duty-free shop managements for the efficient management of human resources.
Multicriteria Decision Analysis on Information Security Policy: A Prioritization Approach

Security is the most serious concern in the digital environment. To provide a sound and firm security policy, a multi-holistic approach must be considered when making strategic decisions. Thus, the objective of this study was to evaluate the information security (IS) and decision making of Davao Oriental State University (DORSU) using the analytic hierarchy process (AHP) approach. The four aspects of IS, namely, the technology, management, economy, and culture were used with the three IS components consisting of confidentiality, integrity, and availability to implement the AHP. The results showed that the technology and management have higher significant values than the economic and cultural aspects. Meanwhile, for the IS components, the integrity signifies the highest priority followed by confidentiality, lastly, and availability. These results emphasize an imbalance in implementing IS policy, which must be addressed to ensure that the data integrity, confidentiality, and availability are balanced, particularly during the information exchange transactions.
Strengthen the Construction of Cultural Education Projects in Universities, and Build the Foundation of Undergraduate Students’ Cultural Confidence—Take the Chinese Excellent Culture Inheritance and Development Project of Faculty of Chinese Language and Culture, Guangdong University of Foreign Studies as an Example

Aiming at the problems of college students' lack of in-depth understanding of the connotation of Chinese excellent traditional culture and the urgent need for improvement, this paper takes the construction of the Chinese Excellent Cultural Inheritance and Development Project of Faculty of Chinese Language and Culture of Guangdong University of Foreign Studies as an example, proposes the construction path of the cultural education project in universities, and summarizes the actual effect and experience.
Existence of Certain Finite Relation Algebras Implies Failure of Omitting Types for L n

Fix 2 < n < ω . Let CA n denote the class of cylindric algebras of dimension n , and let RCA n denote the variety of representable CA n ’s. Let L n denote first-order logic restricted to the first n variables. Roughly, CA n , an instance of Boolean algebras with operators, is the algebraic counterpart of the syntax of L n , namely, its proof theory, while RCA n algebraically and geometrically represents the Tarskian semantics of L n . Unlike Boolean algebras having a Stone representation theorem, RCA n ⊊ CA n . Using combinatorial game theory, we show that the existence of certain finite relation algebras RA , which are algebras whose domain consists of binary relations, implies that the celebrated Henkin omitting types theorem fails in a very strong sense for L n . Using special cases of such finite RA ’s, we recover the classical nonfinite axiomatizability results of Monk, Maddux, and Biro on RCA n and we re-prove Hirsch and Hodkinson’s result that the class of completely representable CA n ’s is not first-order definable. We show that if T is an L n countable theory that admits elimination of quantifiers, λ is a cardinal < 2 ℵ 0 , and F = 〈 Γ i : i < λ 〉 is a family of complete nonprincipal types, then F can be omitted in an ordinary countable model of T .
Comparison of four visual tracking algorithms based on deep learning

To evaluate the visual tracking algorithm proposed by our research team, we compare the algorithm with other three visual tracking algorithms. Firstly, the four visual tracking algorithms are introduced. There are SiamFC, SiamRPN++, ATOM and TDLD, which are all based on deep learning. The first three algorithms are the state-of-the-art trackers of different periods. The last algorithm is proposed by ourselves. And then we do some experiments in seven video sequences from OTB-100 dataset. We qualitatively compare the robustness of the four algorithms on the five tracking challenging factors. The average centre location error (ACLE) and average overlap score (AOC) of the four algorithms are calculated to make a quantitative analysis. The SiamRPN++ algorithm gets the best result of ACLE three times, and the TDLD gets twice. Both the SiamRPN++ and the TDLD get the best result of AOC three times respectively. The analysis results show that performance of the TDLD is very close to the state-of-the-art trackers.
A MULTI-STAGE GAN BASED FRAMEWORK FOR THE GENERATION OF HIGH-RESOLUTION MAMMOGRAPHY IMAGES

Generative adversarial networks (GANs) have garnered a lot of attention because of their ability to generate data.This synthetic data generation is crucial for training machine learning models for domains where data is scarcely available, such as medical imaging, where the generation of data is not only expensive but also has privacy concerns.In these cases, such synthetic data can be used for data augmentation, for detecting out-ofdistribution samples or for domain transfer tasks.Generating high-resolution medical images, such as mammograms are challenging because of their detailed features as well as their tissue-specific properties.In this work, we have implemented a multi-stage GAN based framework that can be used to synthesize highresolution mammography images.In the first stage, a low-resolution image is generated from latent noise, while in the second stage, an Enhanced Super Resolution GAN (ESRGAN) is used to upscale the image.
Construction of Intelligent Management Platform for Scientific Research Instruments and Equipment Based on the Internet of Things

With the rapid development of scientific research capacity building and the increasing investment in scientific research infrastructure by the state, the fixed assets management mode based on financial management is far from suitable and can not meet the reality in the face of the huge instrument and equipment asset information, function information, use record, and other data and the lack of a unified and timely instrument information sharing platform. This paper proposes an intelligent management platform for scientific research instruments and equipment based on Internet of things technology to improve the sharing degree and resource utilization efficiency of instruments and equipment in the company internal, to effectively improve the fine management level of instruments and equipment and better serve the scientific research management.
Research on Traffic Information Monitoring System to CVIS

In order to realize intelligent event management of freeways and improve its safety and efficiency, this paper presents research on traffic information monitoring system to cooperative vehicle instructure system (CVIS). The current traffic information monitoring system monitoring data content and its characteristics are described first. Then, the principle of traffic information data collection and exchange is analyzed to guide the routine collection and exchange of data. Meanwhile, the traffic information monitoring scheme and traffic information monitoring system architecture are established according to the content of traffic information monitoring and the principle of collection and exchange. After that, the existing problems and technical requirements of traffic information monitoring system to CVIS in China are analyzed. Finally, according to the technical requirements, the paper puts forward the improvement of the traffic information monitoring system.
Impression-Based Fabrication Support for Shadow Box Expressions

This paper focuses on a shadow box, a type of papercraft, and implements an impression-based fabrication support system that automatically generates shadow box expressions suitable for the user’s desired impression. The system consists of a design generation unit creating design candidates and an impression change evaluation model assessing the candidates in terms of their appropriateness to the desired impression. In developing the system, an impression evaluation experiment is first conducted to clarify the relationship between various shadow box expressions and their impression. By training neural networks using the experimental result, a set of impression change evaluation models is implemented. The design generation unit is built based on the approach of an evolutionary computation in which the implemented model set is used as an evaluation function. An impression evaluation experiment for the shadow boxes based on the system output demonstrates the validity of this system.
Efektivitas Pelatihan Literasi Kitab untuk Mendukung Studi dan Penelitian Mahasiswa Fakultas Ushuluddin,Adab dan Humaniora di Perpustakaan IAIN Salatiga dan IAIN Purwokerto

AbstractThis research purpose to explore the level of effectiveness of Kitab literacy training program at IAIN Salatiga and IAIN Purwokerto. This research is a descriptive research with a quantitative approach. The sampling technique in this research was using non-probability sampling-purposive sampling technique. The research instrument used a questionnaire while the data collection technique was by filling out a questionnaire. Data were analyzed using descriptive techniques with percentages. The results showed that the effectiveness level of the kitab literacy training at IAIN Salatiga was 82.15% in the very effective category, as well as IAIN Purwokerto in the very effective category at 93.8%. And has contributed to the achievement of the objectives of kitab literacy training at both IAIN Salatiga and IAIN Purwokerto. However, the training still needs some improvement, mainly in the development of training materials, improving the model of trainers in delivering the material, extending the time for training publication / socialization, increasing practice time, good training time management, providing representative training media technology and an adequate internet networks, and development of digital kitab information systems. AbstrakPenelitian ini bertujuan untuk mengetahui tingkat efektivitas pelatihan literasi kitab di IAIN Salatiga dan IAIN Purwokerto. Penelitian ini merupakan penelitian deskriptif dengan pendekatan kuantitatif. Teknik pengambilan sample pada penelitian ini menggunakan teknik non-probability sampling-purposive sampling. Instrumen penelitian menggunakan angket sedangkan teknik pengumpulan data dengan pengisian angket. Data dianalisis menggunakan teknik deskriptif dengan persentase. Hasil Penelitian menunjukkan tingkat efektivitas pelatihan literasi kitab di IAIN Salatiga sebesar 82.15% pada kategori sangat efektif, begitu juga IAIN Purwokerto pada kategori sangat efektif dan sebesar 93.8%. Dan telah berkontribusi bagi pencapaian tujuan pelatihan literasi kitab baik di IAIN Salatiga maupun IAIN Purwokerto. Namun, tingkat pelatihan yang sudah dicapai masih perlu ditingkatkan, khususnya pada pengembangan materi pelatihan, memperbaiki model pelatih dalam menyampaikan materi, memperpanjang waktu publikasi/sosialisasi pelatihan, penambahan waktu praktek, manajemen waktu pelatihan yang baik, menyediakan teknologi media pelatihan yang representatif dan jaringan internet yang memadai, dan pengembangan sistem informasi kitab digital.
3D Face Capture for Rehabilitation Progress Assessment After Brain Surgery

The aim of this article is to show how modern sensors can be used in rehabilitation monitoring after a specific brain surgery that affects the facial muscles. Rehabilitation takes several months, it is important for both doctors and patients to be able to monitor progress quantitatively. To evaluate a patient’s facial disorders, the stereo-vision camera is used to detect and capture precise facial movements, which the subject is asked to perform. From these videos, specific patterns can be extracted for the rehabilitation process description. In this paper, we focused on a 3D analysis app for mobile phones.
Risk Assessment of Internet Credit Based on Big Data Analysis

In recent years, network technology has continued to develop, and Internet finance has rapidly developed into a new business area. Internet credit is one of the important ways for banks to conduct business, and the scale of online credit has continued to expand. Due to the existence of various unpredictable factors, frequent emergencies, and online financial fraud, the overall market risk in the field of online credit has increased, and the rate of non-performing loans has continued to increase. Online financial fraud cases show that online credit risk has become one of the most prominent risks in the operation of commercial banks, which has a direct impact on the stability and development of commercial banks. We can build a bank database system based on big data, introduce professional big data analysis technical personnel, and constantly improve the big data sharing analysis platform, so that commercial banks can use system data more fully and effectively, and facilitate relevant business personnel to use big data technology for analysis and calculation. Big data is constantly produced, which provides basic materials for online credit risk assessment. Big data analysis technology is gradually mature, and it has the necessary conditions for online credit risk assessment. Based on the theories and technologies related to big data analysis, this paper comprehensively evaluates the online credit risk in the form of example data analysis, thereby effectively reducing the online credit risk coefficient.
CLOUD SECURITY PRE-ASSESSMENT MODEL FOR CLOUD SERVICE PROVIDER BASED ON ISO/IEC 27017:2015 ADDITIONAL CONTROL

Cloud computing technology has succeeded in attracting the interest of both academics and industries because of its ability to provide flexible, cost-effective, and adaptable services in IT solution deployment. The services offered to Cloud Service Subscriber (CSS) are based on the concept of on-demand self-service, scalability, and rapid elasticity, which allows fast deployment of IT solutions, whilst leads to possible misconfiguration, un-patched system, etc. which, allows security threats to compromise the cloud services operations. From the viewpoint of Cloud Service Provider (CSP), incidents such as data loss and information breach, will tarnish their reputations, whilst allow them to conserve the issues internally, in which there is no transparency between CSP and CSS. In the aspects of information security, CSP is encouraged to practice cybersecurity in their cloud services by adopting ISO/IEC27017:2015 inclusive of all additional security controls as mandatory requirements. This study was conducted to identify factors that are influencing the CSP readiness level in the cybersecurity implementation of their cloud services by leveraging the developed pre-assessment model to determine the level of cloud security readiness. Approached the study is based on the combination of qualitative and quantitative assessment method in validating the proposed model through interview and prototype testing. The findings of this study had shown that factors that influence the CSP level of cloud security readiness are based on these domains; technology, organisation, policy, stakeholders, culture, knowledge, and environment. The contribution of the study as a Pre-Assessment Model for CSP which is suitable to be used as a guideline to provide a safer cloud computing environment.
Documentary Publications on the History of the Second World War and the Great Patriotic War on the Website of the Boris Yeltsin Presidential Library

The article analyses publication of a large set of historical and archival documents on the history of the Second World War and the Great Patriotic War on the website of the Boris Yeltsin Presidential Library. Since 2009 it functions as the national electronic repository of digital copies of the most important documents on the history of Russian statehood and Russian language, as well as multimedia, multifunctional, cultural, scientific, educational and information-analytical centre with the status of the national library of Russia. In the “Collections” section, the libraryʼs website places online publications of various forms and subjects. The author considers the methods of publishing digitized copies of archival documents. Within the frames of the first part of the Internet project “The Second World War in archival documents (set of digitized archival documents, footage and photo materials)” there are published 1767 electronic copies of documents, then promised to continue. There is placed the full list of published documents, it provides the ability to sort them, search by date and place of storage. Virtual multimedia exhibition “The Great Patriotic War, which determined the outcome of the Second World War. For the 75th anniversary of the victory in the Great Patriotic War of 1941—1945” presents more than 500 official documents, documentary photographs, periodical materials and leaflets. The authors of the exhibition apparently consider the online publication “Combat actions of the air defence forces of the navy in the Great Patriotic War of 1941—1945” as a kind of rare publication, therefore they decided to publish the facsimile reproduction of it. The article concludes on the great significance of the project “The Second World War in archival documents (set of digitized archival documents, footage and photo materials)” that placed a large set of documents from Federal and departmental archives, many of which were first declassified. The author reveals shortcomings of Internet publications of archival documents in the field of placement methods related to inaccurate determination of their readership.
Privacy-preserving Decentralized Federated Learning

Federated learning is a promising framework for learning over decentralized data spanning multiple regions. This approach avoids expensive central training data aggregation cost and can improve privacy because distributed sites do not have to reveal privacy-sensitive data. In this paper, we develop a privacy-preserving decentralized aggregation protocol for federated learning. We formulate the distributed aggregation protocol with the Alternating Direction Method of Multiplier (ADMM) and examine its privacy weakness. Unlike prior work that use Differential Privacy or homomorphic encryption for privacy, we develop a protocol that controls communication among participants in each round of aggregation to minimize privacy leakage. We establish its privacy guarantee against an honest-but-curious adversary. We also propose an efficient algorithm to construct such a communication pattern, inspired by combinatorial block design theory. Our secure aggregation protocol based on this novel group communication pattern design leads to an efficient algorithm for federated training with privacy guarantees. We evaluate our federated training algorithm on image classification and next-word prediction applications over benchmark datasets with 9 and 15 distributed sites. Evaluation results show that our algorithm performs comparably to the standard centralized federated learning method while preserving privacy; the degradation in test accuracy is only up to 0.73%.