Reviewerly/exHarmony huggingface dataset:
Exploiting Cross-Dialectal Gold Syntax for Low-Resource Historical Languages: Towards a Generic Parser for Pre-Modern Slavic

This paper explores the possibility of improving the performance of specialized parsers for pre-modern Slavic by training them on data from different related varieties. Because of their linguistic heterogeneity, pre-modern Slavic varieties are treated as low-resource historical languages, whereby cross-dialectal treebank data may be exploited to overcome data scarcity and attempt the training of a variety-agnostic parser. Previous experiments on early Slavic dependency parsing are discussed, particularly with regard to their ability to tackle different orthographic, regional and stylistic features. A generic pre-modern Slavic parser and two specialized parsers -- one for East Slavic and one for South Slavic -- are trained using jPTDP (Nguyen & Verspoor 2018), a neural network model for joint part-of-speech (POS) tagging and dependency parsing which had shown promising results on a number of Universal Dependency (UD) treebanks, including Old Church Slavonic (OCS). With these experiments, a new state of the art is obtained for both OCS (83.79\% unlabelled attachment score (UAS) and 78.43\% labelled attachement score (LAS)) and Old East Slavic (OES) (85.7\% UAS and 80.16\% LAS).
Empirical Research in Software Engineering

Software development is a highly complex and creative process. Producing software systems which fulfill all functional and nonfunctional requirements and exhibit adequate quality is challenging. Software Engineering aims to provide systematic methods and tools to overcomethese challenges and improve the software development process. However, since the development of software is still an endeavor heavily based on creativity and human activities, it is difficult to evaluate whether new methods, practices or tools have a positive impact or not. Empirical Software Engineering applies scientific research strategies to the field of software engineering. These strategies are common in social and behavioral sciences, and support the evaluation of human-basedactivities. This paper gives an introduction into empirical strategies in software engineering, especially case studies, and how they can be used to evaluate results of research projects considering their value in practice. With an example thesis project we aim to illustrate how the theories and concepts can be applied. The paper is primarily based on the book Experimentation in Software Engineering by Wohlin et al.
Car shape clustering using sobel edge detection with divisive average linkage and single linkage algorithm (case: bus, sedan, citycar, mpv, and truck)

Abstract This study aims to make an application that able to make clustering for car type based on shape by using edge detection and clustering algorithm. The car types being used in this study are the image of the bus, sedan, city car, mpv, and truck. In this study, the method used to classify is with the divisive average linkage and single linkage method. The goal is to prove whether the divisive average linkage and single linkage can be used to classify the shape of the car image correctly. The shape obtained by using the Sobel edge detection method because the Sobel method is an edge detector that has advantages in reducing the noise before doing calculations so that the resulting edges form an image like the original.
IMPACT OF THE CRISIS ON THE DYNAMICS OF NET INTEREST INCOME OF COMMERCIAL BANKS IN SYRIA

Commercial banks occupy a significant place in the economic system of the country. They create, accu-mulate money and contribute to their redistribution between sectors of the economy. The banking sector is of particular importance for the real segment of the economy, providing continuous cash flow and lending to industrial enterprises. At the same time, as commercial organizations, banks aim to make a profit. The main source of profit is net interest income. This article presents the results of a study of the net interest income of traditional banks in Syria, for three periods: pre-crisis, crisis, the period of recovery from the crisis.
Legal Institution Mapping Information System in DKI Jakarta

Abstract Legal institutions are very important institutions for the community. These legal institutions include the Legal Aid Institute, the Police Office and also the Court Office which have their respective roles in the field of Law. The distribution of Legal Institutions in this Region covers the administrative cities of West Jakarta, South Jakarta, East Jakarta, North Jakarta and Kepulauan Seribu. This Website Application Program was created using the Rapid Application Development (RAD) method. To store data using a MySql database which is operated through a server so that it can be accessed through the internet network. The programming language used is PHP and uses the GoogleMaps API to display map distribution data and locations on website applications. The purpose of this Legal Institution mapping website application system is made to unite various Legal Institutions in one application container so as to make it easier for people living in the DKI Jakarta area to find information and dissemination as well as the Location Map regarding the Legal Institutions they need.
Scalable trellis quantization for JPEG XS

Trellis quantization as structured vector quantizer is able to improve the rate-distortion performance of traditional scalar quantizers. As such, it has found its way into the JPEG 2000 standard, and also recently as an option in HEVC. In this paper, a trellis quantization option for JPEG XS is considered and analyzed; JPEG XS is a low-complexity, low-latency high-speed “mezzanine" codec for Video over IP transmission in professional production environments and industrial applications where high compression rates are of lesser importance than visual lossless compression at high speed. A particular challenge of trellis quantization is to make it compatible with applications where sharp rate thresholds have to be satisfied, such as in JPEG 2000 and JPEG XS. While the JPEG 2000 standard originally proposes a Lagrangian “a priori" rate allocation that is based on statistical models, such methods are less suitable for JPEG XS which only has a very small prefetch window of less than 30 lines available to drive its rate allocation. In this paper, a simple trellis quantization option for JPEG XS is proposed that is compatible with the hard-bounds rate-allocation requirements of this coding standard.
OPTIMAL STABILIZATION ALGORITHM FOR PRODUCTION LINE FLOW PARAMETERS

Context. A method for constructing an algorithm for stabilizing the interoperability of a production line is considered. The object of the study was a model of a multi-operational production line.Objective. The goal of the work is to develop a method for constructing an optimal algorithm for stabilizing the flow parameters of a production line, which provides asymptotic stability of the state of flow parameters for a given quality of the process.Method. A method for constructing an algorithm for stabilizing the level of interoperative backlogs of a multi-operational production line is proposed. The stabilization algorithm is based on a two-moment PDE-model of the production line, which made it possible to represent the production line in the form of a complex dynamic distributed system. This representation made it possible to define the stabilizing control in the form of a function that depends not only on time but also on the coordinates characterizing the location of technological equipment along the production line. The use of the method of Lyapunov functions made it possible to synthesize the optimal stabilizing control of the state of interoperation backlogs at technological operations of the production line, which ensures the asymptotic stability of the given unperturbed state of the flow parameters of the production line at the lowest cost of technological resources spent on the formation of the control action. The requirement for the best quality of the transition process from a disturbed state to an unperturbed state is expressed by the quality integral, which depends both on the magnitude of the disturbances that have arisen and on the magnitude of the stabilizing controls aimed at eliminating these disturbances.Results. On the basis of the developed method for constructing an algorithm for stabilizing the state of flow parameters of a production line, an algorithm for stabilizing the value of interoperation backlogs at technological operations of a production line is synthesized.Conclusions. The use of the method of Lyapunov functions in the synthesis of optimal stabilizing control of the flow parameters of the production line makes it possible to provide asymptotic damping of the arising disturbances of the flow parameters with the least cost of technological resources spent on the formation of the control action. It is shown that in the problem of stabilizing the state of interoperative backlogs, the stabilizing value of the control is proportional to the value of the arising disturbance. The proportionality coefficient is determined through the coefficients of the quality integral and the Lyapunov function. The prospect of further research is the development of a method for constructing an algorithm for stabilizing the productivity of technological operations of a production line.
Geometric Distortion Correction Technique of Text Images

The rapid distribution of digital cameras has caused several new problems related to text recognition. Based on experimental studies, it was revealed that existing OCR systems cannot cope with a complex perspective and geometric distortions that arise during photographing text documents. Therefore, it is necessary to apply text documents pre-processing, so the text lines were straight and horizontal. This article briefly considers the methods document pre-processing, and found that it depends on the type of distortion and is not universal. We proposed information technology and a new method involving the mathematical raising of straightened text lines on the image and heterogeneous distortion correction based on a page surface transformation model. This technology is more reliable than others as it is universal and corrects any type of geometric distortion, including a combination of several types of distortion.
Online Book Reviews and the Computational Modelling of Reading Impact

In online book reviews readers often describe their reading experience and the impression that a book left. The great volume of online reviews makes these reviews a great source for investigating the impact books have on readers. Recently, a reading impact model was introduced that can be used to automatically identify expressions of reading impact in Dutch reviews and that is able categorise them according to emotional impact, aesthetic or narrative feeling, or feelings of reflection. This paper provides an analysis of the characteristics of the book review domain that affect how this computational model identifies impact. We look at features like the length of reviews, the nature of the website on which the review was published, the genre of book and the characteristics of the reviewer. The findings in this paper provide insight in how different selection criteria for reviews can be used to study various aspects of reading impact.
Application of Artificial Neural Network in Engineering

This paper mainly studies the dynamic direction of engineering valuation based on the artificial neural network methods, and seeks for a set of rapid, convenient and practical valuation models, for building construction projects.
Privacy Control of Photo Sharing in Online Social Media

Online Social Networks were famous for the data sharing etc., they have many features like chatting, photo sharing etc. A photo can be leaked without prior permission of the owner. A mechanism was developed to allow each individual in the group aware of the photo posting activity. An efficient facial recognition system that can recognize two different photos was designed. Only the owner of the photo can share the photo, this system is superior to other systems in terms of recognition ratio. This application is developed on the windows platform.
High-dimensional quantum state manipulation and tracking

Dynamical high-dimensional quantum states can be tracked and manipulated in many cases. Using a new theoretical framework approach of manipulating quantum systems, we will show how one can manipulate and introduce parameters that allow tracking and descriptive insight in the dynamics of states. Using quantum topology and other novel mathematical representations, we will show how quantum states behave in critical points when the shift of probability distribution introduces changes.
Geo-Aware Image Caption Generation

Standard image caption generation systems produce generic descriptions of images and do not utilize any contextual information or world knowledge. In particular, they are unable to generate captions that contain references to the geographic context of an image, for example, the location where a photograph is taken or relevant geographic objects around an image location. In this paper, we develop a geo-aware image caption generation system, which incorporates geographic contextual information into a standard image captioning pipeline. We propose a way to build an image-specific representation of the geographic context and adapt the caption generation network to produce appropriate geographic names in the image descriptions. We evaluate our system on a novel captioning dataset that contains contextualized captions and geographic metadata and achieve substantial improvements in BLEU, ROUGE, METEOR and CIDEr scores. We also introduce a new metric to assess generated geographic references directly and empirically demonstrate our system’s ability to produce captions with relevant and factually accurate geographic referencing.
The Evolution of Dynamic Gaussian Process Model with Applications to Malaria Vaccine Coverage Prediction

Gaussian process (GP)-based statistical surrogates are popular, inexpensive substitutes for emulating the outputs of expensive computer models that simulate real-world phenomena or complex systems. Here, we discuss the evolution of dynamic GP model—a computationally efficient statistical surrogate for a computer simulator with time-series outputs. The main idea is to use a convolution of standard GP models, where the weights are guided by a singular value decomposition (SVD) of the response matrix over the time component. The dynamic GP model also adopts a localized modelling approach for building a statistical model for large datasets. In this chapter, we use several popular test function-based computer simulators to illustrate the evolution of dynamic GP models. We also use this model for predicting the coverage of Malaria vaccine worldwide. Malaria is still affecting more than eighty countries concentrated in the tropical belt. In 2019 alone, it was the cause of more than 435,000 deaths worldwide. The malice is easy to cure if diagnosed in time, but the common symptoms make it difficult. We focus on a recently discovered reliable vaccine called Mosquirix (RTS,S) which is currently going under human trials. With the help of publicly available data on dosages, efficacy, disease incidence and communicability of other vaccines obtained from the World Health Organization, we predict vaccine coverage for 78 Malaria-prone countries.
Automated Profiling in New Media and Entertainment Markets: What to Protect, and How?

How should a legal system treat advances of new technologies and practices that may end up reshaping human identities and social interactions? In this chapter, I consider practices of firms drawing on automated decision-making technologies to construct profiles of individuals based on their personal data and using these to develop new products and services, frame individual preferences, and foster consumption in new media and entertainment markets. Still at an early stage, the practices are becoming a basic business model of the digital economy despite all the argued social costs of removing distinctions between private and public life, appropriating valuable data resources away from individuals who generate the resources, and treating human identity as subject to effective control of the machine and its masters. Should the weight of an intellectual property system geared to the protection of intellectual assets, in this context valuable data, be extended to such activities, subject to specified limits and exceptions? Or should the practices be left to operate outside the rubric of legal regulation, at least for a while, adopting a ‘sand-box’ approach, effectively leaving it to firms to regulate themselves in exploiting the available data resources and in the process accumulating intellectual assets? Or is there a way in between, offering scope for effective legal regulation of social costs associated with misuses of personal data as and when these costs emerge, but otherwise allowing practices to operate without legal constraint?
Reflections on the Study of the Effectiveness of College English Teaching in the Department of Educational Information Technology

Nowadays, education information technology has become a commonly used teaching method in English teaching classrooms. If education information technology can be effectively used, the learning effect of students can be greatly improved, and the teaching quality of teachers can be optimized. Therefore, this article will conduct a certain research on educational information technology, find the correct use of information technology, and effectively carry out English teaching.
Classification of Breast Cancer Using Support Vector Machine and Forward Selection

Breast cancer represents about 12% of all new cancer cases and 25% of all cancers in women. Early detection and classification of cancer is essential to save a person's life. The causes of breast cancer are multi-factorial and involve family history, obesity, hormones, radiation therapy, and even reproductive factors. Each year, one million new women are diagnosed with breast cancer, according to a World Health Organization report, half of them will die, because it is usually too late when doctors detect cancer. After the selected variable is then evaluated based on certain criteria. If the first selected variable meets the criteria for inclusion, the selection continues. The procedure stops, if no other variables meet the entry criteria and adds the variables one by one. The accuracy of the Support Vector Machine is influenced by several factors, including the comparison of the amount of training data and test data adjusted for k-fold validation. In the comparison of training data and test data the resulting accuracy reaches 97.68% with a total composition of 345 training data (50%) and 345 test data (50%). In the tests carried out, the accuracy of Support Vector Machine and Forward Selection was obtained at 97.68%.
Tradução automática: uma análise crítica do desempenho da tradução automática estatística e baseada em regras

The essay provides a critical assessment of the performance of two distinct machine translation systems, Systran and Google Translate. First, a brief overview of both rule-based and statistical machine translation systems is provided followed by a discussion concerning the issues involved in the automatic and human evaluation of machine translation outputs. Finally, the German translations of Mark Twain’s The Awful German Language translated by Systran and Google Translate are being critically evaluated highlighting some of the linguistic challenges faced by each translation system.
A method of positioning and recognition of electronic scale characters based on deep learning

Abstract Digital characters are widely used in fields such as industry, equipment display and detection. According to the application background of reading recognition of electronic scale in chemical experiment examination of middle school, in order to improve the application of artificial intelligence in the education field, meet the requirements of intelligent examinations, and save manpower, a character positioning and recognition method based on the combination of deep learning and traditional image processing is proposed, which can automatically and quickly identify the reading of digital tube display screen of electronic scale, and according to the test requirements and test sites can automatically determine whether the students’ weighing operation is correct. Firstly, the yolov3 target detection algorithm and deeplabv3plus semantic segmentation algorithm are used to locate the character area of the display screen, and then a single digital tube character is obtained by projection segmentation. Finally, the recognition algorithm based on support vector machine is used to recognize characters. The experimental results show that the method has a high accuracy of positioning and recognition, and can adapt to different lighting, digital tube size and character tilt angle, has certain anti-interference ability, meets the real-time requirements, and has high application value.
Multi-object tracking system using dissimilar apparatus in video sequence

A combination approach using vision and non-vision data concurrently is to perform multi-object tracking in video sequence. Before applying modified matching stage from Gale-Shapley algorithm the two types of data will be refined to pair in advance. The confirmation of object IDs and their positions is accompanied by the trajectory analysis of each object for error correction during the matching step. The proposed method indicates the consistency and the accuracy of the system in a long video sequence. While the non-visual data keeps the IDs of multiple objects regularly when the objects are undetected or mis-detected using the visual data, the visual data corrects the technical position error of non-visual data. Due to this interaction we expect that the proposed system will be strengthened for visual object tracking. Future work will aim to capitalize on the tracking ability of proposed system with other positioning devices.
Attribute Exploration in Formal Concept Analysis and Measuring of Pupils’ Computational Thinking

The exploration of structures, mappings and tools which allow us to analyze the data in various forms is a very actual and challenging task in a knowledge based approach towards digital intelligence society. The main objectives of our chapter are to describe the computational methods for generation of attribute implications in the field of formal concept analysis and to illustrate the process of attribute exploration for special types of object-attributes tables. Moreover, we proposed the own test for measuring of pupils’ computational thinking in several consecutive stages and collected data of 29,461 pupils regarding the national testing of pupils. We applied the partial attribute implications in formal concept analysis in order the analyze these real data of pupils’ computational thinking.