Reviewerly/exHarmony huggingface dataset:
Discourse Analysis of Grammatical Cohesion Devices in College Students’ Academic Writing Essay

ABSTRACTThe study was undertaken to investigate grammatical cohesion devices use in college students’ academic writing essay as a part of discourse analysis. The study also explored to find out the kinds of grammatical cohesion devices specifications in college students’ academic writing essay. The researchers conducted this study with attention to know the college students’ writing quality at tertiary education. Further, the researchers employed Halliday &amp; Hasan (1976) conceptual framework related to grammatical cohesion devices. The study was conducted to 42 college students enrolling on an online writing course. The researchers applied descriptive and analytical study by giving an assignment to assess grammatical cohesion devices use in college students’ academic writing essay. Data were collected from college students’ academic writing essay. The findings revealed that 1048 grammatical cohesion devices were used in the essays. Other findings indicated that the highest use of grammatical cohesion devices were reference with 53.53% and conjunction. The data also pointed out that the college students were more familiar with reference and conjunction use rather than substitution and ellipsis. The result also indicated that the lack of grammatical cohesion devices use in terms of comprehension, knowledge and ability in writing leads the college students use inappropriate grammatical cohesion devices.ABSTRAKPenelitian ini bertujuan menginvestigasi penggunaan perangkat grammatical cohesive device/kohesi tata bahasa pada penulisan akademik esai mahasiswa. Penelitian ini juga bermaksud untuk menggali jenis-jenis spesifikasi kohesi tata bahasa pada esai mereka. Dalam penelitian ini, para peneliti ingin mengetahui kualitas penulisan esai mahasiswa di perguruan tinggi. Untuk mencapai tujuan tersebut, para peneliti menggunakan kerangka konseptual dari Halliday dan Hasan (1976) terkait dengan kohesi tata bahasa. Penelitian ini melibatkan 42 mahasiswa yang mengikuti kelas menulis via daring. Penelitian menggunakan studi deskriptif dan analitik dengan menugaskan para mahasiswa menulis esai. Temuan dari penelitian ini menunjukkan bahwa terdapat 1048 kohesi tata bahasa yang dipergunakan dalam esai-esai tersebut. Selanjutnya, temuan lain menunjukkan bahwa penggunaan kohesi tata bahasa tertinggi adalah referensi 53,53% dan kata penghubung 45,80%. Temuan lainnya adalah mahasiswa lebih familiar terhadap dua aspek tersebut daripada substitusi dan elipsis. Hasil penelitian juga menunjukkan bahwa kurangnya tata kohesi bahasa dalam hal pemahaman, pengetahuan, dan kemampuan dalam menulis menyebabkan para mahasiswa menggunakan tata bahasa kohesi yang kurang variatif. How to Cite: Trisnaningrum, Y., Alek, Hidayat, D. N. (2019). Discourse Analysis of Grammatical Cohesion Devices in College Students’ Academic Writing Essay . IJEE (Indonesian Journal of English Education), 6(1), 79-90. doi:10.15408/ijee.v6i1.12502
Tasks of Object Detection using Deep Learning Architectures

Deep learning is a subset of the field of machine learning, which is a subfield of AI. The facts that differentiate deep learning networks in general from “canonical” feedforward multilayer networks are More neurons than previous networks, More complex ways of connecting layers, “Cambrian explosion” of computing power to train and Automatic feature extraction. Deep learning is defined as neural networks with a large number of parameters and layers in fundamental network architectures. Some of the network architectures are Convolutional Neural Networks, Recurrent Neural Networks Recursive Neural Networks, RCNN (Region Based CNN), Fast RCNN, Google Net, YOLO (You Only Look Once), Single Shot detectors, SegNet and GAN (Generative Adversarial Network). Different architectures work well with different types of Datasets. Object Detection is an important computer vision problem with a variety of applications. The tasks involved are classification, Object Localisation and instance segmentation. This paper will discuss how the different architectures are useful to detect the object.
Authentication and Authorization Mechanism for Cloud Security

Cloud Computing is a most widespread and popular form of computing, promising high reliability for customers and providers both at the same point of time for many fields, where cloud storage security is based on Authentication and Authorization in cloud computing. Data is uploaded into a cloud and stored in a datacenter, Authentication and authorization are the major concerns to access the data stored in cloud by users from the data center. Security is a major issue; these are mainly deal with identity and access management, prevention of data loss and malware attack control management. In this paper we majorly concentrated on Authentication and Authorization to cloud access, we focused on identity management mechanism as cloud security solution that provides directory services for application access management. We discussed the protocols that support authorization and allows the communication across applications with the help of tokens instead of credentials. We even concentrated on the different mechanisms which plays a major role in designing a secured cloud computing architecture from malicious intrusions and attacks, it is a step to verify the presence and functioning of the cloud customers and cloud providers through security mechanisms to protect from different risks, threats and attacks. In this paper we discussed different security Algorithms and Authentication architecture along with the proposed algorithm, where analysis is done along with the computational evaluation with output
Fuzzy Tsukamoto and ITIL for Improvement Strategy on Incident Ticket Services

Ticket Incident is a service provided to clients who work with Meraki Digital Indonesia. At present Ticket incident is a significant problem in terms of service, especially in handling correction software that has the highest number of tickets, because the software development planning is not optimal because the client needs software that is built because of the large number of ticket incidents that occur, so it is necessary to improve service to clients. So we need a way to improve incident ticket service using the ITIL method. This improvement method is following the purpose of assessment in each area service operation by using questionnaires, then to correct this problem another approach is needed with Fuzzy Tsukamoto to measure how many tickets need to be completed to improve the services provided. The results of this study based on the assessment using ITIL found Service Operation Processes and Organizing Service Operations as a weakness, and from the results of Fuzzy Tsukamoto with the calculation of 162 tickets that need to be appropriately resolved so that it becomes a reference for developing and improving services provided to clients.
Teacher’s Perception on SQ4R in English Reading Comprehension Learning Using Zoom Application

Learning strategy has an important role in the English reading comprehension learning process. To comprehend the text, reading strategy focuses on students’ critical learning. The aims of the study to get teachers’ perception of SQ4R strategy application in English reading comprehension class through zoom application. The method of study used descriptive qualitative. The process of collecting data used observation and questionnaire. The data is taken from the teacher that taught English Reading Comprehension subject in semester two at English Department UIN Mataram. The data analysis used data reduction, data display, and taking a conclusion. The result of the finding concluded that 80% showed a positive attitude towards the SQ4R strategy in the English reading comprehension class. It can be comprehended from the increase in students' reading comprehension skills. The study results have implications for the suitability of the SQ4R strategy in the reading class through online learning such as zoom.
Optical Character Recognition using CRNN

Optical Character Recognition (OCR) is a computer vision technique which recognizes text present in any form of images, such as scanned documents and photos. In recent years, OCR has improved significantly in the precise recognition of text from images. Though there are many existing applications, we plan on exploring the domain of deep learning and build an optical character recognition system using deep learning architectures. In the later stage, this OCR system is developed to form a web application which provides the functionalities. The approach applied to achieve this is to implement a hybrid model containing three components namely, the Convolutional Neural Network component, the Recurrent Neural Network component and the Transcription component which decodes the output from RNN into the corresponding label sequence. The process of solving problems involving text recognition required CNN to extract feature maps from images. These sequence of feature vectors undergo sequence modeling through the RNN component predicting label distributions which are later translated using the Connectionist Temporal Classification technique in the transcription layer. The model implemented acts as the backend of the web application developed using the Flask web framework. The complete application is later containerized into an image using Docker. This helps in easy deployment on the application along with its environment across any system.
Determining the Number of Batik Motif Object based on Hierarchical Symmetry Detection Approach

In certain conditions, symmetry can be used to describe objects in the batik motif efficiently. Symmetry can be defined based on three linear transformations of dimension n in Euclidian space in the form of translation and rotation. This concept is useful for detecting objects and recognising batik motifs. In this study, we conducted a study of the symmetry effect to determine the number of batik motif objects in an image using symmetry algorithm through a hierarchical approach. The process focuses on determining the intersection line of the batik motif object. Furthermore, by utilising intersection line information for bilateral and rotational symmetry, the number of objects carried out recursively is determined. The results obtained are numbers of batik motif objects through symmetry detection. This information will be used as a reference for batik motif detection. Based on the experimental results, there are some errors caused by the axis of the symmetry line that is not appropriate due to the characteristics of batik motifs. The problem is solved by adding several rules to detect symmetry line and to determine the number of objects. The additional rules increase the average accuracy of the number of object detection from 66.21% to 86.19% (19.99% increase).
Deloc: a delegation-based privacy-preserving mechanism for location-based services

Location-based services (LBSs) are everywhere; we found them in several fields such as healthcare, entertainment, transportation, and many other daily activities. Besides, along with their presence in almost every daily task, their usefulness cannot be ignored. Moreover, with smartphone ownership growth, getting one's location became easier, and the privacy-related issues became almost inescapable. Hence, more severe solutions are strongly required to handle privacy issues and to keep LBS utility. We present in this paper Deloc, a novel location privacy protection mechanism founded on the idea of preserving location privacy without altering geographical coordinates. We introduce a new set of concepts and definitions that allow us to meet high privacy guarantees and data accuracy by delegating one's request to other LBS users. Similarly, we investigate the privacy issues related to LBSs along with state-of-art protection approaches and techniques. We evaluate Deloc on both synthetic and real-world data in a finely simulated environment with tunable parameters, and demonstrate its efficiency and usefulness in today's LBS applications, along with its higher privacy guarantees in respect to differential privacy standards.
An efficient clustering approach for fair semantic web content retrieval via tri-level ontology construction model with hybrid dragonfly algorithm

Web pages are heterogeneous and complex and there exists complicated associations within one web pages and linking to the others. The high interactions between terms in pages demonstrate vague and ambiguous meanings. Efficient and effective clustering methods are needed to discover latent and coherent meanings in context are necessary. This paper proposes an efficient clustering approach for fair semantic web content retrieval based on tri-level ontology construction model with hybrid dragonfly algorithm. Initially the query processing phase, by making use of systematic adaptive hierarchy method (SAHM) efficient ontology selection process is carried out by means of matching keywords retrieved form user query. Secondly, fuzzy sensitive near-neighbour influence (FSNI) based clustering approach relied on the ontology driven fuzzy linguistic measure, applied to estimate the uncertainty that may be relevant to the semantic content which belongs to the user quires. The proposed FSNI clustering approach with HDA algorithm performance is be evaluated and compared with existing clustering approaches in terms of retrieval accuracy and surfing time.
Laplace Particle Filter on Lie Groups Applied to Angles-Only Navigation

Particle Filters are an active field of study to address highly nonlinear estimation problems, especially when coupled with optimisation methods (e.g. with the Laplace Particle Filter). Moreover, recent studies revealed the advantage of Kalman filtering on Lie groups. This paper introduces an improved version of the Laplace Particle Filter by deriving a formulation on Lie Groups. The core idea is to harness probability density functions on Lie groups to cope with the state intrinsic geometric constraints and nonlinear nature. In addition, the Lie Group Laplace Particle Filter (LG-LPF) leverages an optimisation algorithm fully defined on Lie Groups. Numerical results on a nonlinear and angles-only navigation scenario demonstrate a significant enhancement in terms of estimation accuracy and robustness.
Digital Identity

With the penetration and spread of the Internet in our daily life, issues related to our identity on the Internet and their potential impacts on our lives, whether positive or negative, have become very important. We, as individuals, interact with a number of websites and services on the Internet that require an account (username and password) to access. These sites range from simple news sites that limit the number of articles that you can read anonymously in a certain period of time, to financial services or healthcare sites or others. Similar services on the Internet require that you know with certainty that you are definitely "you" before granting you access to any private information. Of course, we want them to confirm our identity before allowing access to this type of very sensitive information, which can be used to inflict harm. Take us in different ways if you fall into the wrong hands. The presence in the virtual world has formed the digital identity, and this digital identity has different effects on the morals and habits of the users in this virtual world. Because of the existence of a digital identity, you may also be exposed to some type of privacy crime. In this case, we must study and adapt to create patterns of coexistence and resilience in the digital environment.
Load forecasting for cloud computing based on wavelet support vector machine

Because the tasks submitted by users have random and nonlinear characteristics in the cloud computing environment, it is very difficult to forecast the load in the cloud data centre. In this paper, we combine the wavelet transform and support vector machine (SVM) to propose a wavelet support vector machine load forecast (WSVMLF) model for the cloud computing. The model uses the wavelet transform to analyse the cycle and frequency of the input data while combining with the characteristics of the nonlinear regression of the SVM, so that the task load can be modelled more accurately. Then a WSVMLF algorithm is proposed, which can improve the accuracy of the cloud load prediction. Finally, the Google cloud computing centre data set was selected to test the WSVMLF model we proposed. The comparative experimental results show that the algorithm we proposed has a better performance and accuracy than the similar forecasting algorithms.
Blockchain Usage in the Electronic Health Record System using Attribute-Based Signature

Block-chain is a list of records which are stored in its blocks that are linked through cryptography. It is used previously for bitcoin transactions only. Now the government and also other organizations are going to use this block-chain in different fields. Electronic Health Records (EHRs) are used for storing the information about the patients. In EHR the information is stored in the paper through web which has some disadvantages. Here we use block-chain and Attribute- Based Signatures (ABS) to store the information about the patient’s in the blocks of block-chain which is stored in cloud. By this we can provide security to the patient data and also there are no storage problems and also through ABS we provide some attributes to the users who are going to access the data of patient.
Local Path Planning for Intelligent Vehicles Based on Collision Risk Evaluation

摘要: 针对结构化道路环境下智能汽车的动态路径规划问题,提出了一种基于碰撞风险评估的局部路径规划算法。利用三次样条曲线参数化表达全局路径,获取道路基准线;综合考虑车辆行驶安全性、规划路径平滑性等性能指标生成候选路径,通过归一化加权代价函数评估生成的候选路径;在全局路径信息引导下,获得当前状态下的局部最优路径。针对性能代价函数,引入风险场的概念,建立了符合行车风险客观规律的静态与移动障碍物风险模型,并结合高斯卷积建立了安全性代价函数;考虑候选路径曲率变化及全局路径追踪能力,设计了路径平滑性及偏移代价函数。仿真结果表明,提出的局部路径规划算法能够实时生成平滑、无碰撞路径,且在多种道路场景中实现对静态和移动障碍物的有效规避,满足实时性的要求。
A Unique Way to Generate Password at Random Basis and Sending it Using a New Steganography Technique

Data hiding is a technique for secure transmission of confidential data. Many data hiding techniques exist and steganography is the most important one. This paper presents a new steganography method in spatial domain. We use steganography to send confidential information from sender to receiver. Here, we generate password at random basis in a unique way based on system time and date. Then we send this confidential password using steganography by implementing a totally new embedding and extraction technique based on exact length of bits in binary representation of ASCII values. Here, confidential text information is embedded into cover image generating a stego image and sent to receiver maintaining top level secrecy.
Repossession and Recognition System: Transliteration of Antique Tamil Brahmi Typescript

Tamil is among the ancient languages in the world with a rich literature.Recognition of antique Tamil scripts is difficult and different from the present form of the language.The character recognition of Brahmi script poses a big challenge even today.In this paper, a new technique for extracting the features is proposed, and converting the ancient Tamil script into the present form.Initially, the system is implemented by performing the pre-processing steps.Then the characters are individually separated using the segmentation process.The processed image undergoes a new feature extraction technique, where the system applies a chi-square test to check whether all the zoning feature values of the image are independent or dependent.The characters are recognized from the extracted features using neural networks.NNTool is employed to train the featured image and the data are compared with the database to recognize the Brahmi characters.The feature extraction technique along with the neural network achieved recognition rate accuracy of 91.3% and error rate of 8.7% using the confusion matrix.Our experiment has been simulated using MATLAB.
PELATIHAN MICROSOFT OFFICE EXCEL SEBAGAI UPAYA PENINGKATAN KEMAMPUAN MAHASISWA DALAM MENGOLAH DATA

The difficulty of students in operating Microsoft Excel programs as one of the data processing programs as a major factor in the low ability of students to process data. The training activities aim to make participants comprehensively understand the material presented, so that it can be implemented applicatively in the world of work and have the professional ability to operate Microsoft Excel and deliver participants to the International Microsoft Office Specialist Excel Certification. The method used in the implementation of this activity is teaching in a computer laboratory, tutorials (mentoring) using Microsoft Excel 2010 modules, discussions and questions and answers. The activity was carried out at the STKIP PGRI West Sumatra Laboratory. The population is the 2017 STKIP PGRI West Sumatra Mathematics Education Study Program students who will graduate. Based on the activities carried out, it was obtained that out of 44 participants who were registered to take part in the training and international certification exam, only 29 participants took part in the training. Of the 29 participants who could take the international certification exam only 22 participants. There were 45% of participants who passed the certification exam so that they received the MOS Certified certificate from Microsoft. While 32% of participants received a Certificate of Completion certificate and as many as 23% of participants received a Certificate of Attendance certificate. So it can be concluded that the training provided is able to improve the ability of students to operate Microsoft Excel.
A Highly Secure and Robust Copyright Protection Method for Grayscale Images using DWT-SVD

Digital watermarking has emerged as a potential solution to copyright-related issues of digital data. A novel, highly secure robust digital watermarking method using DWT-SVD is presented in this paper. Instead of using the conventional watermark embedding process where the watermark gets embedded over the entire host image, the coefficients for embedding the watermark inside the host image has been identified in this proposed method. Such selection of pixels used for insertion of watermark makes this algorithm highly secure, robust, and imperceptible. The experimental results demonstrate the efficiency of the proposed method in aspects of high robustness and good imperceptibility.
Face Tracking using Convolutional Neural Networks

Face detection and recognition is very active re-search area in the field of computer vision.It is one of the crucial and prepossessing steps for most of the Computer vision based application especially where human interaction is necessary.Face detection and recognition is done in many approaches like feature extraction and classification, Statistics based and image based etc.However, it is still complex problem because, of several reasons.In this work, we try to solve above mentioned problem using feature extraction and classification methods.The experiments are conducted and performance is evaluated over the various databases like Yale and AR.The Face detection and recognition problem is also solved using convolution neural networks.Results are validated on videos related to Indian movies.
Statistical Analysis of Target Detection using TRT-SGRFT

This paper proposes novel methodology for tar-get detection and motion parameter estimation for a maneuvering target. Noise like interference may inculcate few difficulties such as Range Migration (RM) and Doppler Frequency Migration (DFM). This paper proposes a novel method to overcome the issue. The method is to use Time Reversing Transform (TRT) and special generalized Radon Fourier transform (SGRFT), i.e., TRT-SGRFT. TRT separates the motion parameters and reduces the clutter. Then the SGRFT operation is employed to estimate part of the motion parameters. This method ensures a good tradeoff between the computational cost and estimation perfor-mance. Finally, simulations results are discussed to demonstrate the numerical results.