Reviewerly/exHarmony huggingface dataset:
INSTAGRAM AS A MEANS OF MASTERING INDONESIAN VOCABULARY

The Language Development and Fostering Agency make a breakthrough so that the development of Indonesian language can be disseminated quickly and right on target. One of them is by disseminating information using Instagram. Instagram is a social media favored by teenagers. So it is very suitable to be used to disseminate information. By following badanbahasakemendikbud account, users will automatically be able to see information about the uploaded language. The objective of this study is to determine the use of Instagram as a means of mastering Indonesian vocabulary. The research method used is descriptive qualitative. The result obtained from this study is that by participating in the Kemendikbud Language Agency account, it can improve the mastery of users, namely students. So, it can be concluded that the Kemendikbud language agency Instagram account can be used by students and educators as a means of learning the Indonesian language especially in aspects of vocabulary mastery. Keywords: Instagram, vocabulary, Indonesian Language
EFFECTIVE IMPLEMENTATION OF LARGE-SCALE DATABASES AND CLOUD TECHNOLOGIES IN INDUSTRY

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data. Moreover, the recent advances in Web technology has made it easy for any user to provide and consume content of any form. This has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. Cloud computing is associated with a new paradigm for the provision of computing infrastructure. This paradigm shifts the location of this infrastructure to the network to reduce the costs associated with the management of hardware and software resources. This paper gives a comprehensive survey of numerous approaches and mechanisms of deploying data-intensive applications in the cloud which are gaining a lot of momentum in both research and industrial communities. We analyze the various design decisions of each approach and its suitability to support certain classes of applications and end-users. A discussion of some open issues and future challenges pertaining to scalability, consistency, economical processing of large scale data on the cloud is provided. We highlight the characteristics of the best candidate classes of applications that can be deployed in the cloud. Index Terms-Cloud Compu.
Ensemble Classifier for Praise or Complaint Classification and Visualization from Big Data

With the advent in Big Data Analytics, IoT and Machine Learning newer opportunities are created for Business organizations to analyze, monitor and mine user-generated contents in real time for business intelligence using cognitive IoT. Customers share their opinions online through social media platforms like review sites, Twitter and Facebook, etc. Sentiment analysis combined with real-time reporting can provide precise valuable contextual insights enabling more improved decision making. The existing sentiment analysis techniques identify only positive, negative or neutral sentiments and do not consider informativeness of reviews while analyzing the sentiments. The extreme opinions like praise and complaint sentences are informative subsets of positive and negative sentences and are very difficult to find. This chapter proposes the Ensemble classifier using linguistic features for praise or complaint classification from big customer review datasets and visualization of it. The Praise and Complaint sentences are further classified based on aspect and analysis at aspect level is presented from business intelligence point of view. The performance of the four different supervised machine learning classifiers, namely Random forest, SVC, KNeighbours, MLP with linguistic hybrid features and Ensemble of above algorithms is evaluated on Hotel and Amazon product reviews dataset using parameters Accuracy, Precision, Recall, and F1-score. The proposed approach has given excellent 99.7% Accuracy and 99.6% F1-Measure and outperforms existing approaches.
Exceptionally Au-rich mid-crustal fluids from the Kola super deep borehole

In the drillcore of the Kola super-deep borehole(SG-3, 12262m depth) gold bearing rocks of Archaean age have been located at depths of 9500 to 11000 m. Quartz veins, between 9052 and 10744 m, within this gold zone, contain fluid inclusions with exceptional concentrations of gold, which are present as nanoparticles. There are 4 types of fluid inclusions (1) gas inclusions of dense CO2, (2) liquid-vapour two-phase aqueous inclusions, (3) three-phase inclusions with NaCl daughter crystals, and(4)CO2-aqueous inclusions. In all inclusion types, there are extremely high concentrations of Au. In the high salinity FIs the average concentration isc. 750 ppm and may be as high as 6000 ppm. We suggest these fluids could be a precursor of “Orogenic gold fluids” which, at the Au concentrations determined, would reduce the requirements for large volumes of metamorphic fluids to form orogenic ore deposits. The gold is currently present as colloids and indicates that in this form it is possible to transport greater amounts of gold than in true solution
Implementasi Model Technique For Order Preference By Similarity To Ideal Solution (TOPSIS) dalam Pemilihan Geuchik

The election of the village head is a democratic election which is held every five years. The existence of the village head election (geuchik) can produce a geuchik that is in accordance with the wishes of the gampong community and is of good quality in advancing the village area. However, the number of geuchik candidates who followed made the community confused, because not much is known from his experience during his tenure and the public trust that is not yet widely known. Therefore we need a model in the geuchik election decision support sistem in the village. It is hoped that this sistem can be used in every village head election in Aceh specifically in North Aceh District. The purpose of this study is to facilitate the village community in seeing the results of the election taking place and the public can see the candidates along with recommendations from the displayed sistem. Research methods: conducting a survey on each candidate for data collection, in the form of nationalism, integrity, nationalism, dignity, decisiveness, age, work loyalty and lifestyle that will be included in the topsis model. Then there is the implementation phase of the design of decision support sistem applications into the programming language programming. Finally, do the test by trying to detail the application displayed by the interface. The results of this sistem can see the ranking of geuchik candidates in the election using the topsis method and can be a solution for the village community to be able to fulfill the trust that has been given which can then fulfill the trust that has been given and can provide the best service for the village community.
Combined Vehicle Tracking and Obstacle Detection Based on D-S Evidential Reasoning

Intelligent vehicle tracking can form vehicle formation or have more applications in AGV navigation vehicle. In the case of obstacle detection, when we use line-of-sight positioning and half-line-of-sight positioning, the difference of data obtained by these two methods is different, which can effectively identify obstacles. The predicted distance is divided into X direction and Y direction. The interval probability statistics of the difference between the two directions are carried out. The probability of obstacles is predicted separately by the difference between the two directions, and the D-S evidence reasoning algorithm is applied to the detection of obstacles. Experiments show that the combined positioning can not only control the positioning error within 1m to about 4cm, but also achieve 91.5% accuracy of obstacle recognition.
A Comparative Study of Various Techniques and Challenges for Hand Written Document Processing of Indian Script

Handwritten text recognition has been the most focused research area in the research community since last fifty years, due to many challenges in terms of recognition and accuracy parameter. India is a multi-linguistic country where people use different languages in different areas, and the way of writing style of people is also different in different areas. Various techniques have been proposed for recognizing and improving the result of hand written documents of Indian languages. This research work presents a review and an analysis of relevant papers in Indian script recognition techniques and its challenges. This survey describes the strength, weaknesses, technical aspects and datasets being used to verify the proposed technique and obtain the result. This paper also suggests a list of technology gaps with respect to the diverse Indian script handwritten document recognition and also challenges faced in the document recognition process.
TLCS-Anchor: a new anchor strategy for detecting small-scale unmanned aerial vehicle

Faster R-CNN is a general-purpose detection algorithm that performs well in most cases. However, Faster R-CNN performs poorly on detecting small-scale UAVs. In order to improve the detection performance for small-scale UAVs, a new anchor strategy (TLCS-Anchor) which could be adopted by Faster R-CNN is proposed in this paper. Firstly, the anchor templates are designed to be suitable for the UAV dataset by using the clustering method so that the aspect ratios and scales for anchors are more targeted to UAVs. Then, a new compensation strategy of anchors is proposed to help detect small-scale UAVs in this paper, which could not only improve the number of anchors matched with the UAVs, but also alleviate the problem that small-scale UAVs can’t match with enough anchors to some extent. Experimental results show that TLCS-Anchor can help improve the detection performance for UAVs, especially for small-scale UAVs. In theory, TLCS-Anchor can also be used to detect other small-scale targets.
Essence-based Method Chunk Metamodel

This paper proposes an essence-based method chunk metamodel. Method chunk is one of the terms used for method part in Situational Method Engineering (SME). SME is an engineering process to construct a new situational software development method which is composed from method parts. The method part can be extracted from any existing software development method which is described in various terms and notations. Applying SME in a software project will improve the quality of the software product, since it uses a project specific (or situational) method. The proposed metamodel is described using Object Management Group (OMG)'s standard for method modeling, called Essence Framework, which defines Essence Kernel and Language. Later, the proposed metamodel can be used by various method chunk providers as a guidance in method chunk modeling. Using OMG's standard in the method chunk modeling will increase interoperability in applying SME since method chunks from various provider will use the same notations. The elements of the proposed metamodel are defined based on existing method part metamodels. This metamodel also includes the context model as additional attributes of a situational method chunk. Essence kernel elements such as alpha, activity space, and competency are used to describe elements in the proposed metamodel. Evaluation of the proposed metamodel is conducted by checking the completeness of method chunk elements, and by instantiate essence-based method chunks using the proposed metamodel.
Literature-Based Character Education

Knowledge lights, The more knowledgeable a student becomes, the more enlightened he will be.The teaching of literature must be able to instill good character values in students.For example, students can be encouraged to explore and identify values of proverbs.These values, once they learn, can be applied in their daily lives.Literature comes in various forms such as fairy tales, proverbs, poetry, prose fiction, and drama.These literature forms, when used for educational purposes, can help students develop their potentials, self-control, personality, intelligence, good character, and other necessary life skills.
Coin Detection based Mobile Charging System

In today’s generation, the need for mobile phones is growing exponentially. Mobile phones are an irreplaceable component of present society, not only are they a source of entertainment but also a big boon for those who are in dire need of help. However, the major challenge for mobile phone manufacturers is their woeful battery problems. So, to combat with this problem, the presented paper outlines an efficient, economical and ready-to-go solution for the benefit of people. The aim of the project is to install these vending machine type installations to charge the mobile phones at a very economical cost. This solution can be utilized at Airports, Exhibition Centers, Markets, Conference Halls, Rest Stops, Stadiums, Bus stops etc. where people are present in a large number. An android application has also been developed to easily find a nearby installation of the proposed system.
A Study on Redundant Computation of Matrix-Vector Product for Fault-Tolerant Neural Networks

Dependable computation of matrix-vector product will become important in large-scale neural networks (NNs) used in safety critical electronics systems, e.g., self-driving cars and robot control systems. This paper examines a fault-tolerant (FT) computation of matrix-vector product using error control codes defined over the set of real numbers. Simulation results show that, under a faulty computation model with random bit errors, NN with the FT computation can suppress effects of errors, in comparison to NN without the FT computation.
Search Based Risk Reduction Supporting the Intelligent Components Selection Process

In Component-Based Software Engineering, the problem of selecting software components involve several risk factors. Traditionally, these have been identified and mitigated using software project management techniques. However, the new demand for intelligent components has added complexity to the process. Despite the success and technological advances, its development in an environment ready for production faces many challenges. There are numbers of technical issues that limit their adoption and selection, due to the introduction of new risk factors, different from traditional ones. Thus, our goal is to formulate a technique to minimize the risk in the intelligent component selection process. To achieve this goal, first, we review the literature to Figure out how software project management takes care of the risk and to identify and classify the risks factor associated with intelligent software components. Second, we formulate the component selection problem as a search based optimization problem. And Third, we illustrate our proposal by presenting an example in the context of an air pollution forecasting component. As a result, we were able to: (1) identify a lack of useful tools to manage the risk factor effectively in a software project; (2) we classify intelligent component associated risk; and (3) we introduce a risk management technique that supports the component selection process by maximizing requirement accomplishment, that is, minimizing the risk of the provision of the functionalities that satisfy the requirements. Overall, our work is an initial step in using search-based optimization in risk management to the component selection process.
Use of the Tec Digital Platform in Courses of Oral Communication and Written Communication, Technological of Costa Rica (TEC)

This paper describes the class experience with respect to the use of the official platform of the Technological of Costa Rica in the courses of Oral Communication and Written Communication (first semester-2019). The independence of the student is shown through the use of this technological tool and the strengthening and usefulness that it represents in the courses for the student as well as for the educator. The technological tool is described in its structure, mode of use, associated functions and the criteria of the students about their frequency of use, degree of utility and mode of access to this tool. The results are based on an analysis of data provided by teachers and students in the communication area of said Costa Rican University. This analysis gathers criteria of students from different careers, mostly engineering, with respect to the use of this tool in the aforementioned courses. In addition, the experience of teachers who teach these subjects and use the Tec Digital platform. The information was collected through two digital surveys in Google Forms, one for teachers and another for students.
Quality Enhancement of DDBTC Decoded Image

This paper presents two methods for improving the quality of Dot Diffused Block Truncation Coding (DDBTC) decoded image. The first method exploits the usability and effectiveness of decimated Discrete Wavelet Transform and stationary Wavelet Transform for reducing the half-toning artifact. While, the second method employs the Vector Quantization (VQ) approach for performing the image patch replacement and suppressing the unpleasant artifact of DDBTC decoded image. As documented in Experimental Section, these two methods performs well for improving the quality of DDBTC decoded image.
Set approximation of ontologies and properties

Ontology is considered as a kind of agreement on a domain representation. It is very essential in achieving semantics information processing as well as information sharing between agents or systems processing same or related data. Despite the importance of ontologies in information system, several ontological operations remain without formal definition as well as a formal description on how they should be achieved or proved. In this paper, the concept of soft set initiated by Molodtsov is used to define Description logics ontologies and to formalize its basic operations. We show that, by approximating ontologies using soft set, basic ontological operations and their properties are preserved and can be formally defined as well as proved algebraically as sets operations.Keywords: Ontologies, Description Logics, Soft set, ontology Algebra
Sistem Pakar Diagnosa Penyakit Pencernaan dan Solusi Penanganan dengan Metode Forward Chaining Berbasis Web

Indigestion is a disorder caused by abnormalities in the digestive system that come from food or a personâ€™s physical condition. Indigestion in children is usually a functional disorder caused by immaturity or immaturity of the digestive system, where most of the bodyâ€™s immune system formation comes from the the digestive tract. The design of this expert system uses forward chaining as a method in which the user must provide data or facts before the infrenceengine works or processes. So that the inference engine traces the knowledge base according to the data or facts provided to produce a final conclusion. This expert system is more focused on one object which is called indigestion experienced by children, so that is expected to produce an expert system that is effective and accurate in handling indigestion in children early. And can provide information to users whether or not further treatment requires treatment by a doctor. DOI : 10.29408/jit.v3i1.1816
Mining Underlying Interface Semantics Based Web Services Discovery Approach

Web services are one of the most important resources on the Internet. Faced with the increasing numbers of Web services and service users, researchers in the services computing field have attempted to address a challenging issue, i.e., how to quickly find the suitable ones according to user queries. Many previous studies have been reported towards this direction. In this work we proposing a new web service discovery approach which is consist of semantics based mining. This approach helps to mine underlying semantics and user to employ web services. First we creating web database which contains dataset which is extracted from well-known registry sites. Clustering is performed to cluster the data which contains similarity i.e. synonyms, abbreviations and disordered fragments using k-means clustering. using clusters, similarity index libraries are created for semantic mining. Semantic mining is performed before receiving request from the user. Finally web service discovery approach is designed which accepts the request from the users and produces related services based on semantic mining. In this we performing data redundancy to avoid time complexity and it lead to faster response time. The experimental results will show better precision and recall rate than existing approaches.
Real-time Object Detection for Visually Impaired with Optimal Combination of Scores

In this paper, we propose a computer vision based object detection mechanism for visually impaired individuals by optimally combining the detection scores to aid their indoor navigation. Proposed framework comprises of three stages, viz. image acquisition, object detection and class estimation followed by score combination to generate the final classes of objects. The detection is performed on the image frames captured through wireless webcam and by applying two powerful deep learning algorithms, SSD and YOLO, separately. After generating the scores for each class, they are optimally combined using Proportional Conflict Resolving principle to generate fused set of scores. Based on these score values, object category is chosen with the category having the highest probabilistic score. The particular audio file narrating the object name is selected and the audio signal is sent to the blind user through the attached wearable headphone. The proposed framework achieved a high Mean Average Precision score of 86.15. Model performs well in an indoor environment and successfully aids a blind individual in mobility and mapping surrounding environment effectively.
A Study on Security Mitigation Models in Cloud Computing

In the present cloud computing scenario, with varied complication levels among SaaS, PaaS, IaaS, many types of security challenges exist. In order to overcome the above, there also exist many models to mitigate them. With this two way growth, it becomes essential to choose the right mitigation model for a particular class of security threats. This paper aims at analyzing the different mitigation models for various security threats possible across different cloud computing scenarios so that the cloud users, researchers and service providers would get benefited. The cloud computing concept is integrated for the current study and its implications and risks are identified in terms of security.
Supply chains and identity

Distributed ledgers record and make available information through time. Unsurprisingly, their use for managing records of goods (and services) as they travel through supply chains has been the second most prominent application of distributed ledger technology after cryptocurrencies. This chapter details the economics of supply chain management on blockchains as the creation and accumulation of identity attributes in historical time. It first details how distributed ledger supply chain management reduces opportunism, and consequently where value is distributed on the chain. It then outlines an identity theory of supply chains, and relates that theory to the ledger-centric view of the economy.