Reviewerly/exHarmony huggingface dataset:
Role of SPARQL in Leveraging Sematic Web Technology

Semantic web is not just a matter of translation from HTML to RDF/OWL languages. It is a matter of understanding the content of the web through knowledge graphs. Entities need to be related with relationships. This content is composed of resources (web pages) that contain, for example, text, images and audio. Thus, there is the need of extracting entities from these resources. Currently, most of the web content is in HTML5 format which is a W3C recommendation which enables describing the structure marginally with the help of annotations. The main challenge here is to transform unstructured data from plain HTML files to structured data (e.g RDF or OWL). The current work provides the first hand information for dealing with unstructured heterogeneous data residing on web using Twinkle, a Java tool for SPARQL query execution on FOAF (Friend Of A Friend) document.
Android Platform using e-Voting System

The foremost motive of e-voting is to choose the leader or candidate of people’s choice. Many countries face the difficulties in the procedure of voting such as ridging votes during election, insecure or inaccessible polling stations, insufficient polling materials and also not experience personally. To overcome the above drawbacks online voting or polling system is proposed. To get acquainted with the system, the users, citizens shall be trained on how to vote online prior to the election. The e-Voting system will permit the casting of electronic ballots from almost any location about the world. The first phase called ID2E is made for testing the viability for international voting by means of SMS protocol along with the tools such as web 2.0 to create a platform for discussion about election main theme. The second phase is construction of voting prototype using android Smartphone, submissions and databases to collect the vote that are obtainable on dynamic web page. Online voting system can be used to give the precise consequences of votes to reveal precise voter penchants.How to cite this article:Tote AM, Gaawande RM. Android Platform usinge-Voting System. J Adv Res Sig Proc Appl 2019;1(2): 5-8
Perception of investing in crypto in India using UTAUT model

Abstract: This research was conducted to gather data and understand the perception what the Indian population holds when it comes to investing in cryptocurrency. To do so, a survey was designed using the UTAUT model and was circulated by the means of google forms. A wide range of parameters were considered to avail the maximum possible accuracy for the data collected. Parameters like, the ease of investing crypto, short term and long term benefits, monetary benefits, social benefits were considered. All of these parameters were supposed to be answered on a scale of 5. After collecting all the data, the results were analyzed and evaluated using which the hypothesis made were proved. Keywords: Cryptocurrency, UTAUT, performance expectancy, effort expectancy, perceived monetary benefits, perceived safety, social influence, adoption intension.
Building neural network models for morphological and morpheme analysis of texts

Morphological analysis of text is one of the most important stages of natural language processing (NLP). Traditional and well-studied problems of morphological analysis include normalization (lemmatization) of a given word form, recognition of its morphological characteristics and their morphological disambiguation. The morphological analysis also involves the problem of morpheme segmentation of words (i.e., segmentation of words into constituent morphs and their classification), which is actual in some NLP applications. In recent years, several machine learning models have been developed, which increase the accuracy of traditional morphological analysis and morpheme segmentation, but performance of such models is insufficient for many applied problems. For morpheme segmentation, high-precision models have been built only for lemmas (normalized word forms). This paper describes two new high-accuracy neural network models that implement morphemic segmentation of Russian word forms with sufficiently high performance. The first model is based on convolutional neural networks and shows the state-of-the-art quality of morphemic segmentation for Russian word forms. The second model, besides morpheme segmentation of a word form, preliminarily refines its morphological characteristics, thereby performing their disambiguation. The performance of this joined morphological model is the best among the considered morpheme segmentation models, with comparable accuracy of segmentation.
Analisis Aritmia (Gangguan Irama Jantung) Menerapkan Metode Certainty Factor

The heart is one of the most important organs for humans that functions to pump oxygenated blood, the heart can experience problems, one of which is arrhythmia, arrhythmia is a heart rhythm disorder or pattern of rapid changes from a normal heart rate. Heart rhythm disturbances (arrhythmias) are patterns of rapid change from the normal heart rate. This becomes a problem when not handled properly and correctly because it can cause disruption of the heart's function, even in more severe cases it can cause sudden death. An expert system is one of the artificial intelligence of humans that studies how an expert thinks in solving a problem, making decisions or drawing conclusions from a number of facts. An expert system is a system that adopts the expertise of an expert that can be used to overcome certain problems, one of which is diagnosing arrhythmias. This study applies Certainty Factor analysis which can provide additional knowledge to the public, especially knowledge on heart disease. So that arrhythmia analysis with this certainty factor method can be applied to a system that helps the general public in preventing and overcoming heart rhythm disorders
The 2030 Agenda Through Librarians' Creativity. Ideas of Projects from a Workshop Set Up by the Italian Library Association (AIB)

Libraries can contribute to the Sustainable Development Goals of 2030 Agenda: indeed, the online training course ‘Biblioteche e Agenda 2030’, organized by AIB Emilia-Romagna in spring 2021, was made up of theoretical meetings followed by a workshop where the sixteen participant librarians worked out five project plans linked to the Goals and based on partnership among different libraries. Topics and frameworks of the projects were inspired by the contents of the lectures (from the analysis of the 2030 Agenda as a strategic framework, to its acknowledgement by the international and national library world, passing through some case studies which provided the scheme for project building). Each project was then developed inside the different institutional contexts in which its authors operate. The five works together offer an essay on how the creativity of librarians can interpret the demands of the Agenda for the benefit of library audience.
An Approach to Improve Multi objective Path Planning for Mobile Robot Navigation using the Novel Quadrant Selection Method

Currently, automated and semi-automated industries need multiple objective path planning algorithms for mobile robot applications. The multi-objective optimisation algorithm takes more computational effort to provide optimal solutions. The proposed grid-based multi-objective global path planning algorithm [Quadrant selection algorithm (QSA)] plans the path by considering the direction of movements from starting position to the target position with minimum computational effort. Primarily, in this algorithm, the direction of movements is classified into quadrants. Based on the selection of the quadrant, the optimal paths are identified. In obstacle avoidance, the generated feasible paths are evaluated by the cumulative path distance travelled, and the cumulative angle turned to attain an optimal path. Finally, to ease the robot’s navigation, the obtained optimal path is further smoothed to avoid sharp turns and reduce the distance. The proposed QSA in total reduces the unnecessary search for paths in other quadrants. The developed algorithm is tested in different environments and compared with the existing algorithms based on the number of cells examined to obtain the optimal path. Unlike other algorithms, the proposed QSA provides an optimal path by dramatically reducing the number of cells examined. The experimental verification of the proposed QSA shows that the solution is practically implementable.&#x0D;
Implementasi Sistem Penjualan Online Produk Olahan dan Buah Nanas Berbasis Website

Pineapple processed products are processed products of SMEs engaged in the production and sale of typical food kualu nenas village, mining subdistrict, Kampar regency, Riau province. The sales, marketing, and promotion system still uses the system manually, whereby the buyer must come directly to the store. Therefore, a website-based sales system will be created to develop pineapple processed products using php programming language and mySQL database online better known as E-Commerce (Electronic Commerce). With the methodology used in the design of this system is using the waterfall method. The advantages in this website-based sales system is that it can make it easier for consumers to make the process of buying transactions online which can save time and costs compared to buying come directly to the store.
SIXTH SENSE TECHNOLOGY IN HEALTHCARE

In this paper we present a real-time interactive system that uses Sixth sense technology in healthcare that will help the physicians in diagnosis and treatment of patients in an efficient way. The system will be displaying a gesture control interface that will play the endoscopy video. The physician can pause, resume, take a snapshot of the video for further diagnosis. The region of interest can be cropped and plotted in a 3D graph for a better understanding of the lesions. The hardware of the system has been realized using a projector, camera, colour markers and a computing device. OpenCV, MySQL and Tensorflow has been implemented to accomplish the desired working of the system. OpenCV captures the hand gestures of the physician and decodes the movement to interact with the application designed and MySQL stores the patient information that can be retrieved and edited anytime. Artificial Intelligence has been implemented to the system which will improve the decision making of the physicians by automatically detecting the signs of cancer during. The proposed device can bring a revolutionized change to the healthcare and pave a better way of diagnosis and treatment of patient.
An Intelligent System to Improve Vocabulary and Reading Comprehension using Eye Tracking and Artificial Intelligence

When reading, many people frequently come across words they struggle with, and so they approach an online dictionary to help them define the word and better comprehend it. However, this conventional method of defining unknown vocabulary seems to be inefficient and ineffective, particularly for individuals who easily get distracted. Therefore, we asked ourselves: “how we could develop an application such that it will simultaneously aim to help define difficult words and improve users’ vocabulary while also minimizing distraction?”. In response to that question, this paper will go in depth about an application we created, utilizing an eye-tracking device, to assist users in defining words, and enhance their vocabulary skills. Moreover, it includes supplemental materials such as an image feature, “search” button, and generation report to better support users' vocabulary.
Features of teacher’s training in Finland

Presented is the analysis of teacher’s training process in Finland. Clarified are features of building education courses with the aim of ensuring the highest quality teacher’s training programs, rigorous selection for entry to teacher education courses, practical experience structuring; multilevel assessment of graduates to ensure classroom readiness; state policy in the field of postgraduate teachers’ education. Suggested is to take into account the Finnish experience of pedagogical education in Russia.
An Hypergraph Data Model for Expert Finding in Multimedia Social Networks

Nowadays, the tremendous usage of multimedia data within Online Social Networks (OSNs) has led the born of a new generation of OSNs, called Multimedia Social Networks (MSNs). They represent particular social media networks – particularly interesting for Social Network Analysis (SNA) applications – that combine information on users, belonging to one or more social communities, together with all the multimedia contents that can be generated and used in the related environments. In this work, we present a novel expert finding technique exploiting a hypergraph-based data model for MSNs. In particular, some user ranking measures, obtained considering only particular useful hyperpaths, have been profitably used to evaluate the related expertness degree with respect to a given social topic. Several preliminary experiments on Last.fm show the effectiveness of the proposed approach, encouraging the future work in this direction.
IMPLEMENTASI METODE CERTAINTY FACTOR DALAM SISTEM PAKAR UNTUK MENGIDENTIFIKASI PENYAKIT TANAMAN LADA

Pepper is an agricultural commodity that has a high selling value and is resistant to disease. Identifying the disease in pepper requires knowledge from agricultural experts to evaluate the symptoms of the disease being experienced. Involving an agricultural expert certainly has drawbacks in terms of slow handling, less effective management of large amounts of data. So we need a system that can accommodate these problems. Expert system is one of the sub-branches of science in the field of artificial intelligence, where expert knowledge is expressed in a model with an intelligent-based system. The expert system serves as an interactive consultant media in diagnosing pepper plant diseases based on symptoms. In producing the right decision, a process model is needed in the form of tracing rules and knowledge preference values based on input values in the form of probabilistic data / uncertainty for each symptom. Certainty factor modeling method has the ability to evaluate the preference value of expert knowledge. Based on the model and the weight value of each measure and level of symptoms, it can produce a high level of accuracy decision analysis. Expert systems have advantages that can accommodate expert knowledge in diagnosing pepper plant diseases in time series. This research can help farmers, researchers, experts or agencies in the field of agriculture to identify common diseases of pepper, so that it can provide basic knowledge about various types of diseases and treatment solutions to support the growth of pepper plants.
Research on Automatic Correction of the Document Images Based on Perspective Transformation

With the rapid popularization of mobile terminals and prompt development of information technology, a large number of document images will be captured through smart phone devices. Due to the differences in hardware performance and photographing methods, the document images usually have some distortions, such as tilt, shear, trapezoidal deformation. To improve the document image utilization efficiency, this paper proposes an automatic document image correction method based on perspective transformation. Firstly, the positions of all text lines are located by image morphological transformation. Secondly, the text image region to be corrected is detected by the relative position offset of the text lines. Then, the left and right boundary pixel sets of all text lines in the text image region are extracted respectively, and the corresponding boundary lines are fitted by the least square method. Finally, the minimum circumscribed quadrilateral of the text image region is determined according to the fitted left and right boundary lines along with top and bottom text line boundary lines, and the corrected document image is obtained by image perspective transformation. The experimental results show that the proposed method can accurately perform automatic correction of the images taken from paper-based documents, which has higher computational efficiency and stronger practicability
Methodological approaches and algorithms for recognizing and counting animals in aerial photographs

Introduction: The complexity of recognition and counting of objects in a photographic image is directly related to variability of related factors: physical difference of objects from the same class, presence of images similar to objects to be recognized, non-uniform background, change of shooting conditions and position of the objects when the photo was taken. Most challenging are the problems of identifying people in crowds, animals in natural environment, cars from surveillance cameras, objects of construction and infrastructure on aerial photo images, etc. These problems have their own specific factor space, but the methodological approaches to their solution are similar. Purpose: The development of methodologies and software implementations solving the problem of recognition and counting of objects with high variability, on the example of reindeer recognition in the natural environment. Methods: Two approaches are investigated: feature-based recognition based on binary pixel classification and reference-based recognition using convolutional neural networks. Results: Methodologies and programs have been developed for pixel-by-pixel recognition with subsequent binarization, image clustering and cluster counting and image recognition using the convolutional neural network of Mask R-CNN architecture. The network is first trained to recognize animals as a class from the array of MS COCO dataset images and then trained on the array of aerial photographs of reindeer herds. Analysis of the results shows that feature-based methods with pixel-by-pixel recognition give good results on relatively simple images (recognition error 10–15%). The presence of artifacts on the image that are close to the characteristics of the reindeer images leads to a significant increase in the error. The convolutional neural network showed higher accuracy, which on the test sample was 82%, with no false positives. Practical relevance: А software prototype has been created for the recognition system based on convolutional neural networks with a web interface, and the program itself has been put into limited operation.
우즈베크인 한국어 학습자의 이중모음 /ㅢ/ 발음 오류 양상과 특성

The purpose of this study is to investigate the patterns and characteristics of /ㅢ/ pronunciation errors of Uzbek Korean learners based on an experimental phonetic methodology, and to present basic educational materials. For this study, the speech data were analyzed for patterns and characteristics of pronunciation errors for vowel /ㅢ/ according to phonological conditions and morphological conditions. As a result of the experiment, the error patterns according to phonological conditions were different for male and female, but overall the error rate of /ㅢ/ was high in the word-initial and GV structure. Under morphological conditions, grammatical morphemes had a lower error rate than lexical morphemes. Also, the error rate was high in words that could be separated depending on the structure of the word. (Jeonju University)
An Empirical Study on to Identify the Factors Which Related to the Farmer’s Risk Behavior: An Exploratory Factor Analysis

Risk denotes a situation in which the probabilities are known, whereas uncertainty denotes a situation in which the probabilities are unknown to the decision maker. Agriculture risk differs from farmer to farmer and from farming system to farming system. The study's major goal is to look at the elements that impact a farmer's risk behaviour. Principal Component Analysis was used to examine the scale's eighteen positive and negative items (PCA). These factors account for 19.288, 18.328, 16.284, 8.309, and 5.918 percent of the variation, respectively. After seeing a definite split in the Scree Plot graphical depiction Following the five components, it was agreed to keep the five components for future exploration. The five-component Each item/variable has a considerable loading on each other.
A Brief Study on Current Situation and Optimization Suggestions to the Geoscience Development in China

Geoscience is an important new branch of science, almost covering every aspect of the physic science, from geology to ocean, from atmosphere to hydrology, as well as physic science, chemical sciences, geology, space physics, information sciences, science of space times and so on, and which is closely related to human lives. It is of great era significance for our social development and the earth’s sustainable development to analyze and study the current development of geoscience. There are many problems needed to be studied and dealt with by geoscience, for example, how people get to know the nature, how people utilize and transform nature reasonably, how people coexist harmoniously and how people develop sustainably with nature. Modern geoscience research has put forward more questions on how to solve problems of resources, environment and disasters caused by economic and social development. This paper mainly discusses the current situation of geoscience development and puts forward several optimization suggestions on the development of geoscience, aiming to provide suggestions for geoscience research and help fellow researchers.
Effective Deep Learning Based Architecture for Pedestrian Detection from Digital Images

This paper is to present an efficient and fast deep learning algorithm based on neural networks for object detection and pedestrian detection. The technique, called MobileNet Single Shot Detector, is an extension to Convolution Neural Networks. This technique is based on depth-wise distinguishable convolutions in order to build a lightweighted deep convolution network. A single filter is applied to each input and outputs are combined by using pointwise convolution. Single Shot Multibox Detector is a feed forward convolution network that is combined with MobileNets to give efficient and accurate results. MobileNets combined with SSD and Multibox Technique makes it much faster than SSD alone can work. The accuracy for this technique is calculated over colored (RGB images) and also on infrared images and its results are compared with the results of shallow machine learning based feature extraction plus classification technique viz. HOG plus SVM technique. The comparison of performance between proposed deep learning and shallow learning techniques has been conducted over benchmark dataset and validation testing over own dataset in order measure efficiency of both algorithms and find an effective algorithm that can work with speed and accurately to be applied for object detection in real world pedestrian detection application.
Meta-evaluation of machine translation evaluation methods

Starting from 1950s, Machine Transla- tion (MT) was challenged from different scientific solutions which included rule- based methods, example-based and sta- tistical models (SMT), to hybrid models, and very recent years the neural mod- els (NMT). While NMT has achieved a huge quality improvement in comparison to conventional methodologies, by taking advantages of huge amount of parallel corpora available from internet and the recently developed super computational power support with an acceptable cost, it struggles to achieve real human parity in many domains and most language pairs, if not all of them. Alongside the long road of MT research and development, qual- ity evaluation metrics played very impor- tant roles in MT advancement and evo- lution. In this tutorial, we overview the traditional human judgement criteria, automatic evaluation metrics, unsupervised quality estimation models, as well as the meta-evaluation of the evaluation methods.