Reviewerly/exHarmony huggingface dataset:
Graph-based cumulative score using statistical features for multilingual automatic text summarisation

Multilingual summarisation began to receive more attention these late years. Many approaches can be used to achieving this, among them: statistical and graph-based approaches. Our idea is to combine these two approaches into a new extractive text summarisation method. Surface statistical features are used to calculate a primary score for each sentence. The graph is used to selecting some candidate sentences and calculating a final score for each sentence based on its primary score and those of its neighbours in the graph. We propose four variants to calculating the cumulative score of a sentence. Also, the order of sentences is an important aspect of summary readability. We propose some other algorithms to generating the summary not just based on final scores but on sentences connections in the graph. The method is tested using MultiLing'15 workshop's MSS corpus and ROUGE metric. It is evaluated against some well known methods and it gives promising results.
Syntactically Enriched Multilingual Sentiment Analysis

Sentiment analysis of natural language texts needs to deal with linguis- tic phenomena like negation, intensification or adversative clauses. In this talk, I present an approach to tackle such phenomena by means of syntactic information. Our approach combines machine learning and symbolic processing: the former is used to obtain dependency trees for input sentences, and the latter to obtain the sentiment polarity for each sentence using handwritten rules that traverse the tree. Thanks to uni- versal guidelines for syntactic annotation, our approach is applicable to multiple languages without rewriting the rules. Additionally, very accurate parsing is not needed for our approach to be helpful: fast and simple parsers will do, even if they lag behind state-of-the-art accuracy.
Determinants of Inflation in Africa: A Systematic Review

Inflation refers to increase in general level of price of a basket of goods and services that is representative of an economy over a period of time.When there is a general rise in price level the entire economy will be affected.Rising price known as inflation impact the cost of living, the cost of doing business, borrowing money and every other facet of the economy.Several factors are responsible for rise in price level in a given country/nation.The main aim of this study is to assess the determinants of inflation in Africa based on empirical studies conducted in the past and give insights for other researchers.For this purpose, concentrated and careful literature review was done on 13 papers conducted in 13 African countries.Seven variables were considered for analysis such as broad money supply, GDP, price of imported goods and services, exchange rate, interest rate, price expectation and population growth.The result shows that output/national income, broad money supply, price of imported goods and services and exchange rate are the critical variables affecting the performance of inflation.The others variables: interest rate, price expectation and population growth are also slightly important in explaining inflation.In the literature selected for review it is observed that almost all are used macro variables without considering other factors such as political, social institutional and others.The study finally recommends that countries should seriously work in creating moderate inflation to grow their economy by increasing their national income in addition to stable fiscal and monetary policy and also focusing on the aforementioned factors is crucial.I declare that this is my own work and it is original.
DABGEO: A Reusable and Usable Global Energy Ontology for the Energy Domain

The heterogeneity of energy ontologies hinders the interoperability between ontology-based energy management applications to perform a large-scale energy management. Thus, there is the need for a global ontology that provides common vocabularies to represent the energy subdomains. A global energy ontology must provide a balance of reusability-usability to moderate the effort required to reuse it in different applications. This paper presents DABGEO: a reusable and usable global ontology for the energy domain that provides a common representation of energy domains represented by existing energy ontologies. DABGEO can be reused by ontology engineers to develop ontologies for specific energy management applications. In contrast to previous global energy ontologies, it follows a layered structure to provide a balance of reusability-usability. In this work, we provide an overview of the structure of DABGEO and we explain how to reuse it in a particular application case. In addition, the paper includes an evaluation of DABGEO to demonstrate that it provides a balance of reusability-usability.
Structure and composition of picritic gabbro-dolerites of the Оctyabrskoye deposit central part

Issues relating to the genesis and deposit formation conditions in the Norilsk region are still debatable. At present, ore formation model in an open magmatic system is most popular. To study the actual process, the structure and composition of sulfide minerals in the Oktyabrskoye deposit central part were examined in two boreholes, RT-30 and RT-107, that reveal orebodies C-3 and C-4, respectively. Particular attention was paid to the structural patterns of disseminated ores in picritic gabbro-dolerites horizon. It has been established that sulfide inclusions are missing in the upper part of the horizon and their size and quantity gradually increase in depth, so that these inclusions grade into sulfide streaks in the lower part of the horizon. This indicates gravitational sulfide deposition in a closed magmatic system.
Building Chinese Request Pattern Graphs for Chinese-Korean Translation

This paper aims to describe diverse request patterns in Chinese for the automatic classification and the Chinese-Korean translation of music-request sentences in AI assistant platforms.These patterns are formalized by Local-Grammar Graphs (LGGs), a finite-state grammar, and represented under directed acyclic graphs based on the main function categories.The UNITEX graph editor has been used to construct Chinese LGGs and the request pattern tags that are proposed for Korean sentences based on DECO Korean Electronic dictionaries was applied.The result of this study has been examined and evaluated by being compared with Google Translate results at the end of the paper.Experimental results prove that the LGG grammar constructed in this paper can effectively cover and annotate various request patterns.This will provide the possibility to generate a large-scale bilingual annotation corpus in the field of music listening.
Infancias cubanas y acceso al conocimiento bajo el signo de la COVID-19

COVID-19 in Cuba has forced a restructuring of the traditional way in which children access knowledge. Although schools were kept closed, teleclasses broadcast on national television allowed the school year to continue and placed families at the heart of the process. In this sense, this article analyzes how the teleclasses have been received by a group of children from different territories and how the media have influenced their health and hygiene education in this context. To this end, a qualitative approach was used and work was done with a non-probabilistic sample of 47 children (between 5 and 11 years of age), from six Cuban provinces.
Adaptive Mutual Feedback Teaching Mode of Synchronous Online + Asynchronous Online Under COVID-19 Epidemic Situation

The epidemic situation has posed a severe threat to teaching development.Under this background, the use of online teaching encountered various problems, including the lack of information construction hardware conditions, uneven teachers' information teaching level, needing psychological adjustment for teachers and students, insufficient or too much teaching supervision, etc.On the basis of analyzing the problem, the adaptive mutual feedback teaching mode of synchronous online + asynchronous online was put forward to solve the problem.This thesis explained the connotation of the teaching model, the relationship between dynamic adjustment and auxiliary, and the teaching attention points of adopting the model.Finally, it drew the conclusion that the teaching model is an effective way to adapt to the current online teaching situation.
Using Bootstrap to Increase Data in Predictive Analytics with Extreme Value Distribution

The bootstrap is one of the method of studying statistical math which this article uses it but is a major tool for studying and evaluating the values of parameters in probability distribution. Overview of the theory of infinite distribution functions. The tool to deal with the problems raised in the paper is the mathematical methods of random analysis by theory of random process and multivariate statistics. Observations (realisations of a stationary process) are not independent, but dependence in time series is relatively simple example of dependent data. Through a simulation study we found that the pseudo data generated from the bootstrap method always showed a weaker dependence among the observations than the time series they were sampled from, hence we can draw the conclusion that even by re-sampling blocks instead of single observations we will lose some of structural from of the original sample. A potential difficulty by the using of likelihood methods for the GEV concerns the regularity conditions that are required for the usual asymptotic properties associated with the maximum likelihood estimator to be valid. To estimate the value of a parameter in GEV we can use classical methods of mathematical statistics such as the maximum likelihood method or the least squares method, but they all require a certain number samples for verification. For the bootstrap method, this is obviously not needed; here we use the limit theorems of probability theory and multivariate statistics to solve the problem even if there is only one sample data. That is the important practical significance that our paper wants to convey. In predictive analysis problems, in case the actual data is incomplete, not long enough, we can use bootstrap to add data.
Do FX Interventions Lead to Higher FX Debt? Evidence from Firm-Level Data

Central banks often buy or sell reserves---so called FX interventions (FXIs)---to dampen sharp exchange rate movements caused by volatile capital flows. At the same time, these interventions may entail unintended side effects. In this paper, we investigate whether FXIs incentivize firms to take on more unhedged FX debt, thereby increasing medium-term corporate vulnerabilities. Using a novel dataset with close to 5,000 nonfinancial firms across 19 emerging markets covering 2002--2017, we find that the firm-level share of FX debt rises following intensive use of FXIs, particularly for non-exporting firms in shallow financial markets with no FX debt to begin with. The magnitude of this effect is economically significant, with one standard deviation increase in FXI leading to an average 2 percentage points increase in the FX debt share. For reference, the median share of FX debt in the sample is zero.
Implementation of Real-Time Object Detection System using Machine Learning Algorithm

Object detection is generally important capability required by a variety of computer vision robots and systems. Object detection is one of these fields which are observing enormous success in domain of computer vision. In Object detection system images have diverse types of object this want to be accurately identified by computer vision system. This is very elementary problems of computer vision system. Classical object detection techniques built on simple architectures and handcrafted features. This type of method is very slow and less accurate. However, due to rise of machine learning methods, performance and accuracy of such types of problems is significantly improved. New machine learning method able to greatly increase effectiveness of such systems. Compared with traditional object detection techniques, the deep learning based on objects detection method is incredibly fast and also more accurate. Due to lots of development in high computing power and improved powerful tools, therefore developed system can learn semantic segmentation, high-level understanding and complex deeper features of image to address existing problems in traditional architectures. Real-time object detection and object tracking systems encompass range of applications such as autonomous vehicle, surveillance systems and recognition of license plates. This paper introduces the implementation of the real-time object detection and objects counting system using YOLO algorithm and realtime object counting with their class-names. YOLO algorithm works in real-time. This algorithm is extremely fast and tremendously accurate. The convolutional neural networks (CNN) are basis for these types of algorithms. In this paper, the case study of the YOLO algorithm and how this algorithm can be applied to detection of daily objects for various task and test accuracy of the object detection system.
DSRNet: A Novel Feature Extraction Network Achieving Trade off between Accuracy and Speed

It is important to reduce the computation complexity while maintaining the accuracy of convolution neural networks. We deem it is possible to further reduce the network complexity while ensuring the accuracy. In this paper, we propose a novel feature extraction network called DSRNet which is lightweight but effective. DSRNet follows the basic ideas of stacking modules and short connection, introduces Depthwise Separable convolution and utilizes the Dilated convolution. We conducted comprehensive experiments on CIFAR10, CIFAR100 and STL10 datasets, and the results showed the DSRNet has great performance improvement in terms of accuracy and speed.
Comparing the risk management approaches and practices at two European academic libraries

The globalization and digitalization of the service provision has meant also for the libraries that the risk management has become a vital part of the library work and management. At the same time, the libraries use more and more outsourced services in the digital world with technology that develops fast and to users that require more than maybe ever before in the human history. These all set high standards for the risk management and prevention. The adoption of a quality management system is a strategic decision for an organization that can help to improve its overall performance and provide a sound basis for sustainable development initiatives. Our paper compares two risk analysis approaches implemented at the Universitat Autonoma Barcelona and at the University of Eastern Finland.
Library Guides: CJS 190: Introduction to Crime and Justice Studies: High Quality News and Research Websites

This guide is for students in Professor Baboolal's Introduction to Criminal Justice Studies class and any CJS student new to the discipline. Use it to introduce yourself to the discipline and to locate high quality news and research resources.
Neural Network for Handwriting Recognition

Today, in the digital age, the problem of pattern recognition is very relevant. In particular, the task of text recognition is important in banking, for the automatic reading of documents and their control; in video control systems, for example, to identify the license plate of a car that violated traffic rules; in security systems, for example, to check banknotes at an ATM and in many other areas. A large number of methods are known for solving the problem of pattern recognition, but the main advantage of neural networks over other methods is their learning ability. It is this feature that makes neural networks attractive to study. The article proposes a basic neural network model. The main algorithms are considered and a programming model is implemented in the Python programming language. In the course of research, the following shortcomings of the basic model were revealed: low learning rate (the number of correctly recognized digits in the first epochs of learning); retraining - the network has not learned to generalize the knowledge gained; low probability of recognition - 95.13%.To solve the above disadvantages, various techniques were used that increase the accuracy and speed of work, as well as reduce the effect of network retraining.
The Booking Agent's Book of Secrets for Touring Musicians

The way the author walks the reader through, dividing one calendar year into four quarters, Q1, Q2, Q3, Q4, and then filling each with single drops, album releases, show dates, etc , lends itself to be an excellent, hands-on assignment for students on the mechanics of building tours Considering the number of music business books dedicated to the live and touring segment, of which there are not many, this is a welcome addition, especially because it is written from the perspective of an agent Though the touring market took a big hit with COVID-19 globally, when artists get back on the road, this will be a useful book for those who want to build their touring career Since 2005, he has produced and booked hundreds of concerts nationwide including collaborations with Lincoln Center, Central Park SummerStage in New York, and the Kennedy Center in Washington, D C He currently serves as Director of Programming at Drom, a 300 people-capacity club in downtown Manhattan and is the curator of the New York Gypsy Festival
ARTIFICIAL INTELLIGENCE CHATBOTS FOR LIBRARY REFERENCE SERVICES

The digital based world is very competent in the use of cutting edge, innovative technologies in various fields. It covers every area, from higher education to information resources. The aim of this study is to identify the potential of applying artificial intelligence chatbot applications to library reference services. This paper systematically reviews and explores the integrated applications of artificial intelligence chatbots into academic library reference services in order to cater to the demands of millennial users. The researchers discuss the benefits and the potential impact of artificial intelligence chatbots on the reference services of academic libraries. The artificial intelligence chatbot features in the reference services to enhance the library commitment of the millennial users with library services. This study discussed the benefits of artificial intelligence chatbots in reference services of academic libraries in general and especially in Bahrain.
The Diversified Style of Erhu Music and the Reform and Innovation of Educational Model

different performing artists are known because they have their own artistic styles. Style is the characteristic of an artist, representing the artistic expression of an artist. But only after grasping the artistic style well can they further show the unique charm of art to the audience. Based on the study of the artistic style of the North and South School of Erhu, this paper takes the method of mastering artistic style as the research object, hoping to put forward feasible suggestions for the reform of erhu education mode.
A Benchmark for End-User Structured Data User Interfaces

During the years, it has been possible to assess significant improvements in the computational efficiency of Semantic Web search and exploration systems. However, it has been much harder to assess how well different semantic systems' user interfaces help their users. One of the key factors facilitating the advancement of research in a particular field is the ability to compare the performance of different approaches. Though there are many such benchmarks in Semantic Web fields that have experienced significant improvements, this is not the case for Semantic Web user interfaces for data exploration. We propose and demonstrate the use of a benchmark for evaluating such user interfaces, which includes a set of typical user tasks and a well-defined procedure for assigning a measure of performance on those tasks to a semantic system. We have applied the benchmark to four such systems. Moreover, all the required resources to apply the benchmark are openly available online. We intend to initiate a community conversation that will lead to a generally accepted framework for comparing systems and for measuring, and thus encouraging, progress towards better semantic search and exploration tools.
Technology of Creation of New Generation Electronic Textbooks to Improve the Quality of the Educational Process in the Higher Education System

Among the various traditional textbooks in the education system, the textbook occupies a special place due to its importance. Because it is the most effective and most common didactic resource, it serves as a learning tool for learners. One of the important issues in the system of higher education is the creation of a new generation of textbooks based on information technology that meet modern pedagogical requirements. Quality textbooks increase the interest of students and play a special role in the quality and effective organization of the teaching process, especially in the quality organization of distance education, good reading, learning and effective mastery of the new generation of e-textbooks. Today's society is a society in which science is rapidly developing, information flows are increasing rapidly, modern information and communication media are widespread, in which the rapid updating of knowledge in various fields of science, as well as their rapid acquisition by students puts the task of learning. In particular, raising the rating of higher education institutions, the formation of sectoral educational resources through the introduction of mechanisms for the integration of education, innovation and production activities based on the effective use of domestic innovative potential, improving the competitiveness of the education system, attracting investments, innovations and IT technologies issues such as.
Virtual Connections and Virtual Futures: A Commentary on Attending RDAP2020 Remotely and a Look to the Future of Online Conferences

This commentary describes the experience of attending RDAP 2020 remotely after the author’s trip cancellation due to COVID-19 travel restrictions. The author describes the highs and lows of the remote viewing experience, and the potential future landscape of virtual conferences and remote attendance. Maintaining networking and casual conversation during a virtual conference is an area that needs improvement but has potential. Takeaways from several conference sessions, including the keynote speaker, are also included along with discussion of how the author learned valuable information or could apply the topics to her own work.