Reviewerly/exHarmony huggingface dataset:
История земского самоуправления: опыт компьютеризированного историографического исследования

The paper presents the possibilities of using computer technologies for the zemstvo institutions’ historiography research. Today, when the number of publications about Zemstvo is extremely increasing, it is impossible to analyze data by using only qualitative methods. The author describes the importance of bibloimetrics and gives the algorithm of its usage in historiography study. In support of that, some examples of conformable academic works are presented in the research.  The research goal is based on quantitative analysis of the three known Zemstvo historiography periods during 1864 – 2014. The bibliographic information was organized according to special attributes in MS Access database: title, author’s name, year of publication, place of publication, object of study, chronology, geography, etc. It gives an opportunity to create queries in order to calculate data for further historiographical interpretation. The quantitative method gives the new statistical evidence to already known facts. In addition, it helps to structure all publications. Besides that, the author also examines modern state of the Zemstvo historiography, which is a brand new knowledge. According to regional specificity, the text reveals the regions that are more or less explored by scholars. It also examines the objects of contemporary studies and show their percentage. In conclusion, the author looks back at Kovalchenko’s words that quantitative and qualitative methods reach the maximal scholarly importance only when they are dialectically connected.
Perancangan Dan Implementasi Jaringan Komputer Smk Negeri 1 Tahuna

SMK Negeri 1 Tahuna is one of the Educational Institutions that aims to develop the potential of the nation's children so that they can compete at the world level. To meet these objectives Educational Institutions need to be equipped with adequate computer networks. This study aims to manage the computer network system in SMK Negeri 1 Tahuna so that it can be optimally utilized by teachers and students. The research method used in this study is the Network Development Life Cycle (NDLC). Steps being taken are: Analysis, Design, Prototype Simulation, Implementation, Observation, and Arrangement. There has been an increase in network quality after this study. Such as the delay value on the TKJ LAB SSID decreased by 47%, the value of packet lost by 67%, the difference in the value of downloads rose by 1.17 Mbps, the difference in the value of uploads rose by 0.99 Mbps and the delay value on the SSID SMK 1 year decreased by 47%, the value of the packet lost 33%, the difference in the value of downloads rose 0.74 Mbps, the difference in the value of uploads rose 1.75 Mbps. Keywords— design, network, NDLC
SRQ: Self-Reference quantization scheme for lightweight neural network

Lightweight neural network (LNN) nowadays plays a vital role in embedded applications with limited resources. Quantized LNN with a low bit precision is an effective solution, which further reduces the computational and memory resource requirements. However, it is still challenging to avoid the significant accuracy degradation compared with the heavy neural network due to its numerical approximation and lower redundancy. In this paper, we propose a novel robustness-aware self-reference quantization scheme for LNN (SRQ), as Fig. 1 shows, which improves the performance by efficiently distillation of the structural information and takes the robustness of the quantized LNN into consideration. Specifically, SRQ considers a structural loss between the original LNN and quantized LNN, witch enable the scheme not only improve accuracy performance, but also can further fine tuning of the quantization network by applying the Lipschitz constraint to the structural loss. In addition, we also consider the robustness of quantized LNN for the first time, and propose a non-sensitive perturbation loss function by introducing an extraneous term of spectral norm. The experimental results show that the SRQ can effectively improve the accuracy and robustness of the state-of-the-art quantization methods, such as DoReFa and PACT.
Panoptic Feature Pyramid Network Applications In Intelligent Traffic

Intelligenta transportation is an important part of urban development. The core of realizing intelligent transportation is to master the urban road condition. This system processes the video of dashcam based on the Panoptic Segmentation network and adds a tracking module based on the comparison of front and rear frames and KM algorithm. The system mainly includes the following parts: embedded device, Panoptic Feature Pyramid Network, cloud server and Web site.
Virtual Health Assistant

Developed system would be a comprehensive, integrated data system designed to manage the executive and clinical aspects of a hospital.People usually don't recognize the that department they must choose their specific medical complaints. thus associate degree intelligent system is needed to help them. They can evoke a meeting, have a facilitate larva to help them for knowing what issues they're facing.A developing variety of hospitals, nursing homes, and even personal centers, presently utilize on-line Chatbots for human services on their sites. These bots connect with potential patients visiting the positioning, serving to them discover specialists, booking their appointments, and obtaining them access to the right treatment.In our project, the chatbot would collect patient symptoms and predict the malady from the dataset and afterward designation it'll assist patients for appointment programming.
METHODOLOGY AND ASSESSMENT CRITERIA FOR MATHEMATICS ASSESSMENT TASKS

The article presents the results of a study on the assessment of students' knowledge and skills in mathematics, the development of assignments and assessment criteria - on the example of the chapter Simple fractions in grade 5.
Формирование индивидуальных образовательных траекторий в традиционных LMS

Personalized learning is one of the most significant trends in education. The article discusses the individualization of learning to use traditional LMS based on adaptive learning and artificial intelligence technologies. Several of the approaches have been considered, a method based on the construction of a software package connecting an adaptive engine, content source, LMS using the LTI protocol was selected for implementation. The developed system for constructing personalized learning paths includes: a competency model, student models, a multi-level content, adaptive engines and a web application that connecting all components. The system is based on the model of competencies, which is a composition of indicators of achievement of competencies. Educational content is developed on the basis of a competency model, tasks are responsible for certain indicator of achievement of competencies and have different complexity. The course is built from micro-modules based on a student's model and a competency model, the content of which is formed in the learning process using adaptive technologies. The current version of the application is developed on the basis of LMS Moodle using IRT and tested on first-year students.
Object Detection In Surveillance Video

Surveillance is a process to gather an information from the analysis. The surveillance can be applied in all applications like video, communication, data monitoring and in hospital. In all these field, the surveillance is performed to monitor the abnormal conditions in it. In this, the ship detection is performed in the marine surveillance videos. Because, the ships are easily crossed the other country border without knowing or with any intentional. In such a state, there is a risk of safety. To avoid this, the marine officers will routine check their area to find out any invasion of ships. It is possible during normal weather conditions. But, in abnormal conditions, it becomes a difficult process and time consuming is high for monitoring. In order to overcome such difficulties, the image processing technique were used to determine the ships in the sea. Most of the techniques were based on the YOLO processing which requires perfect threshold for detection. In order to eliminate the thresholding process, in this an optimized regional convolutional neural network is proposed. Here, the frames in the video will be subjected to firefly optimization based clustering process to separate the shore and sea area. Then, the sea area is subjected to regional convolutional neural network for different type of ship detection. Due to this optimized approach, it able to detect the ship effectively and in minimal time consumption of regional convolutional neural network by applying on the sea area alone. The ship detection process is implemented using MATLAB R 2018a version and evaluated in terms of accuracy, precision and Recall. The proposed optimized RCNN able to outperform saliency based YOLO function for ship detection.
Digital Watermarking Technique using DWT, SVD, and AES

As we are competing in advanced era, where the advancement and upgradation in technology takes place at every parameter (i.e. digitization, education) on daily basis. Such kind of use of internet involved in business environment, banking, hospitals etc seeking security, protection, concern towards illegal use of digital data. Internet is termed as the collection of various data in terms of audio, videos, images, text etc. Basically, while considering internet, the ownership identity needs to be protected. The suggested work will help in protecting the data from illegal use. In this manuscript we will present a technique named Water marking which will based on different algorithms like Singular Value Decomposition, Discrete Wavelet Transform and Advanced Encryption Standard. The term Watermarking is the way of securing data from copying, considering unauthorized access. The data availability can be either online or offline.
Filtered Belief Revision and Generalized Choice Structures

In an earlier paper [Rational choice and AGM belief revision, Artificial Intelligence, 2009] a correspondence was established between the choice structures of revealed-preference theory (developed in economics) and the syntactic belief revision functions of the AGM theory (developed in philosophy and computer science). In this paper we extend the re-interpretation of (a generalized notion of) choice structure in terms of belief revision by adding: (1) the possibility that an item of information'' might be discarded as not credible (thus dropping the AGM success axiom) and (2) the possibility that an item of information, while not accepted as fully credible, may still be taken seriously'' (we call such items of information allowable''). We establish a correspondence between generalized choice structures (GCS) and AGM belief revision; furthermore, we provide a syntactic characterization of the proposed notion of belief revision, which we call filtered belief revision.
Cognitive Approach to Literature Education: Memory, Thinking and Cognition

The article examines the development of literature education, creativity in students, thinking in the development of visual potential, the formation of scientific knowledge and views on the perception of the artistic text.
CHATBOT FOR COLLEGE INFORMATION SYSTEM

Chatbot is a software application that is responsible for making interaction possible between man and machine. People use language for communicating with each other, likewise Chat-bot use favorable language to communicate with its user. Chatbot for college information system is a web application that aims on providing the required information to the user regarding college related queries. As the curriculum of a college varies periodically, as a result, the database has to be altered and upgraded from time to time. The project focuses on developing a Chat-bot which will be used by college students to get their queries solved. Students can ask any College-related query on the chat section of the chatbot and the bot will respond to the query with an appropriate answer. Students need not follow any certain format for asking queries. This system will help students to stay updated about college-related activities. With the help of this Chatbot, the work of college administration is reduced to a certain extent, giving them relief from responding to each and every student separately. The Chatbot is capable of making friendly conversations; answer the queries; give the link for the academic calendar; display faculty information; and give the timings, address, contact details, and events information of a respected college.
On the Application of Background Music in University Library

University library is one of the main places for college students to study.The integration of background music and library services can achieve unexpected results.The correct embedding of music elements can reduce the fatigue of teachers and students in their work and study, relieve the bad emotions caused by the pressure of life, improve the efficiency of study and work, and improve their musical quality and artistic attainments.
Artificial intelligence and trade mark assessment

Artificial intelligence has an unparalleled potential for facilitating IP administration processes, in particular in the context of examining trade mark applications (e.g. descriptiveness or immorality of terms) and assessing prior marks in opposition and infringement proceedings (e.g. assessing the likelihood of confusion with another sign). Several stakeholders have developed AI-based algorithms that are claimed to enhance the productivity of trade mark professionals by carrying out, without human input, (parts of) the legal tests required to register a trade mark, oppose it or claim an infringement thereof. The goal of this chapter is to assess the functionality of the AI tools currently used and to highlight the possible limitations of AI tools to carry out autonomously the legal tests enshrined in trade mark law. In fact, many of these tests are rather subjective and highly depend on the facts of the case, such as an assessment of the distinctive character of a mark, whether the relevant public is likely to be confused or whether a third party has taken unfair advantage of a mark. We use doctrinal research methods and interview data with 14 stakeholders in the field, including algorithm developers, trade mark examiners, judges, in-house trade mark counsels and attorneys from different jurisdictions. We find that so far, AI tools are unable to reflect the nuances of the subjective legal tests in trade mark law. Even in the near future, we argue that AI tools are likely to carry out merely parts of the legal tests and present information that a human will have to assess, taking prior doctrine and the circumstances of the case into account.
DATA CLASSIFICATION ON USER LOCATION WITH SPATIAL DATA

The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. K-nearest neighbors (KNN) algorithm may be a sort of supervised machine learning algorithm which may be used for both classifications also as regression predictive problems. However, it is mainly used for classification predictive problems in many industries. It is widely used for large datasets to achieve the best result. The main objective of KNN algorithm is to classify the dataset and categorize the nearest neighbors. KNN is a classification algorithm which is used in this paper to classify the dataset and cluster the nearest location. Classifying the spatial dataset of Coimbatore using k-nearest neighbors algorithm and cluster the closest common location into a cluster. The clustered result will used to find the Euclidean distance to find the nearest group query.
Leveraging Randomized Compiling for the QITE Algorithm

Author(s): Ville, Jean-Loup; Morvan, Alexis; Hashim, Akel; Naik, Ravi K; Lu, Marie; Mitchell, Bradley; Kreikebaum, John-Mark; O'Brien, Kevin P; Wallman, Joel J; Hincks, Ian; Emerson, Joseph; Smith, Ethan; Younis, Ed; Iancu, Costin; Santiago, David I; Siddiqi, Irfan | Abstract: The success of the current generation of Noisy Intermediate-Scale Quantum (NISQ) hardware shows that quantum hardware may be able to tackle complex problems even without error correction. One outstanding issue is that of coherent errors arising from the increased complexity of these devices. These errors can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like Quantum Imaginary Time Evolution are susceptible to these errors. This article presents the combination of both noise tailoring using Randomized Compiling and error mitigation with a purification. We also show that Cycle Benchmarking gives an estimate of the reliability of the purification. We apply this method to the Quantum Imaginary Time Evolution of a Transverse Field Ising Model and report an energy estimation and a ground state infidelity both below 1\%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices.
Research on the Optimization Strategies of Higher Vocational English Stratification Teaching Mode Based on the Promotion of Students’ Professional Competence and Quality

Under the background of diversified enrollment structure in higher vocational colleges, the traditional level-based English teaching system cannot meet the needs of language ability development and professional quality improvement.In the past two years, we have built a dynamic assessment system based on ITEST platform.Based on a survey of the English proficiency needs of students with different majors, we have reconstructed the overall design of college English curriculum from the following key aspects: the refinement of the graduates' qualification standards, the reconstruction of the whole college English curriculum system, the refinement of the English teaching and mentoring modes for different level college students, the optimization and adjustment of the level-by-level assessment system, the reconstruction of the teaching and learning quality evaluation and diagnosis system etc.This paper sums up a set of learner centered strategies to optimize the reform of stratification English teaching mode by taking Qingdao Technical College as an example.The reform aims at the improvement of students' professional ability and quality by optimizing the stratification teaching mode of English in higher vocational colleges.It helps students with different enrollment backgrounds achieve individuation development and comprehensive accomplishment not only in English language skills but also professional qualities.
IPO Artificial Intelligence and Intellectual Property: Call for Views Copyright

In response to the UK Intellectual Property Office’s call for views on Artificial Intelligence and Intellectual Property, this paper by the Oxford Intellectual Property Research Centre addresses the impact of artificial intelligence (AI) on exceptions in copyright law. Response focus on balancing the protection of the rights of copyright-holders as against the need for a variety of large data inputs from international sources for ethical, robust and innovative AI development. This paper argues that AI developers may benefit from the quotation exception as a form of ‘global mandatory fair use’, whilst calls for enhanced collective licensing arrangements to ensure proper remuneration for copyright-holders.
Prometheus string

Prometheus' string series attempt various artistic experiments applied to 'data refraction', a concept that breaks frame of data with modern concept of accurate delivery of information and induces more creative results. The resulting installation work includes various processes such as data extraction from living creature into 3D shape generation and printing, as well as robotic sculpture and data visual performance.
Discourse Based Opinion Mining on Roman Urdu Data

The use of Roman Urdu (in the form of web and user-generated content) is a common mode of communication on social media. Content like comments, reviews, feedbacks and social networking posts have been generated in Roman Urdu in large volumes. But this area is not much worked on in terms of sentiment and opinion analysis. Roman Urdu (the scripting style for Urdu language) is one of the limited resource languages that brings forward the challenges and problems for performing Opinion Mining. Adequate opinion mining is not just about understanding the overall sentiment of a document or a single paragraph, but it is also important to be able to extract sentiments on a very granular level and relate each sentiment to the aspect it corresponds to. On the more advanced level, the analysis can go beyond only positive or negative attitude and identify complex attitude types. We, therefore, developed a model for performing discourse-based opinion mining, so we could also consider the impact that various discourse elements have on the overall sentiment of the text. Our work differs from the existing body of knowledge in that not much work has been carried out on processing of Roman Urdu data for opinion mining considering discourse elements. Since our work focuses on performing discourse-based opinion mining it can be considered as first attempt in this direction as none of the literature surveyed revealed discourse[1]based analysis of Roman Urdu text. The overall gist of this research work is to have insights of the nature of user-generated content in Roman Urdu and to build necessary resources and devise algorithms to make an advancement in Sentiment Analysis and Classification for Roman Urdu
Enhanced Group Query optimization with Machine Learning Algorithm on spatial data

In Machine learning algorithm, here we are using proMiSH algorithm to find the Euclidean distance. Grouping the points to find the shortest distance. Machine learning algorithm shows a result by removing unwanted data and shows the best result. Machine learning algorithm much more time and space efficient but returns results whose diameters are within a small approximation ratio of the diameters of the true results. By Group Nearest query, each nearest point matches at least one of the query keywords of the User. Using the dataset, we will be finding out the NEAREST GROUP QUERY it means we are going to fetch the location that the user satisfies all the multiple queries and finds the result by using Euclidean formula.