Reviewerly/exHarmony huggingface dataset:
IDEOLOGY OF JOKOWI AS INDONESIAN PRESIDENT IN CRITICAL DISCOURSE ANALYSIS STUDY

People as social human beings need a language for communication in daily activities. Communication can be found in many forms, one of them is speech. Thus, related to speech and the function of the speech to society the writer is interested in analysing the language, power and ideology in the speech of President Jokowi’s. This study uses critical discourse analysis theory, where it can be found that ideology can be revealed with the use of vocabulary, grammar and text structure of the speaker. The writer uses qualitative method by analysing Jokowi’s speech transcript. The writer also uses identity method by applying sociolinguistics method and pragmatic method. For presenting the data, the writer uses informal method. The writer found eight ideologies in analysing Jokowi’s speech such as the ideology of equality, the ideology in unity, the ideology to honour the law, the ideology of social justice, the ideology in prioritizing citizen, the ideology of national prosperity, the ideology of hard working, and the ideology in assertiveness. The result of this study shows a proof about the relationship between language, power, and ideology. As an Indonesian president, Jokowi has the power to choose the utterance in his speech. Through power over language, Jokowi can reveal the ideology that he holds so that the citizens can understand and follow his ideology.
role and status of drama in spiritual and material culture

The relevance of the work is determined by the fact that the full development of society is possible only on the condition that dramatic changes occur in it. The basis for changes in any society is both culture and the motives that flow into culture in order to realise the factors of social formation as much as possible. We believe that culture and its development are possible if there are phenomena in society when an individual rethinks each aspect of his being. Consequently, the role of drama not only as an artistic device in literature, but also as a social phenomenon, has its own direction and significance for a number of projects for the development of both the individual and the social environment as a whole. The novelty of the study is the assertion that each aspect of the implementation of the drama forms, first of all, a response phenomenon that fully manifests itself both in the course of personal experience and in the formation of individual forms of material culture. The authors of the article show the way to form attitude to spiritual and material cultures on the basis of personal dramatic experience.
Penjadwalan Produksi Job shop Mesin Majemuk Menggunakan Algoritma Non Delay untuk Meminimalkan Makespan

Scheduling is an important factor in both manufacturing and service industry environments. Scheduling is a resource allocation arrangement for completing tasks that involve work, resources and time. With the scheduling, all work can be completed according to priority and can minimize processing time, so that makespan is minimal. In addition, it can reduce idle machines and reduce the inventory of semi-finished goods. Maryati Small Micro and Medium Enterprises (IKM) is a business that is engaged in the manufacture of clothing that produces various types of products such as baby clothes, teenage clothes to adults. So far, IKM Maryati is in the process of machine scheduling by determining the order of Job execution based on the longest to shortest total Job processing time. Scheduling with this method creates problems for the company, as evidenced by the accumulation of semi-finished goods at several work-stations. Another problem is when orders arrive at a certain period with a large variety and number of products, causing Job completion that exceeds the target time (due-date). The size of the makespan causes the production time to increase, so the company is late to start production of orders in the following month. In this study, the Non-delay algorithm is used to solve the problems that arise in IKM Maryati. The result obtained is the scheduling using the existing method by IKM Maryati which produces 44 days makespan value. Meanwhile, by applying the Non-delay compound engine algorithm, it produces a makespan of 42 days. This shows that the compound machine Non-delay Algorithm method can minimize the makespan value in IKM Maryati. There is an efficiency of 4.55% in both time and cost variables.
Many-Body Quantum Lock-In Amplifier

Achieving high-precision detection of time-dependent signals within noise is a ubiquitous issue in physics and a critical task in metrology. Lock-in amplifiers are detectors that can extract alternating signals within extreme noise via a known carrier frequency. Here we present a protocol for achieving an entanglement-enhanced lock-in amplifier via use of many-body multipulse quantum interferometry. The many-body quantum lock-in amplifier is implemented by application of a periodic multi-π-pulse sequence during the interrogation. Our analytic results show that, by our choosing suitable input states and readout operations, the frequency and amplitude of an unknown alternating field can be simultaneously extracted via population measurements. The lock-in point can be determined via the symmetry of the signal during a single interrogation time or the time-averaged signals for multiple interrogation times. We find that the measurement signal at the lock-in point is independent of the interrogation time. In particular, if we input spin cat states and apply interaction-based readout operations, the measurement precisions for frequency and amplitude can both approach the Heisenberg limit. Moreover, our many-body quantum amplifier is also robust with regard to extreme stochastic noise. Our study paves a new way for measuring time-dependent signals with many-body quantum systems, and provides a feasible method for achieving Heisenberg-limited detection of alternating signals.7 MoreReceived 9 October 2020Revised 3 April 2021Accepted 7 September 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040317Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAtom interferometryQuantum metrologyQuantum parameter estimationQuantum sensingTechniquesPrecision measurementsQuantum InformationAtomic, Molecular & OpticalGeneral Physics
Block Chain Based Authentication Technique

The Block-chain technology contain multiple blocks are interconnected to each other with help of previous hash and current hash. The Block-chain is technology which is used to enable for moving some coin, data and assets from one user to another user. Where using hash algorithm, cryptographic algorithm and block-chain maintenance/updating. Block-chain technology contain the previous hash of first block is always zero that is called genesis block and current block will generate according to the data. In block-chain technology after complete the first block system automatically generated new block. Second block contain the previous hash will be always current hash of first block for interconnect the blocks and chain will formed ahead. According to this chain automatically detect there some transparency and this transparency says that block chain is very secured technology. This block-chain technology with transaction is very safe for companies, colleges and business. It is layered framework technology. Where perception layer, transmission layer, storage layer and application layer are present. In block-chain technology transaction contain there need not any third trusted party. Previous concept of block-chain with IOT that is not secured where some disadvantages of limited storage present and reduce that limitation we are using distributed ledger of block-chain technology. Where system have occurred peer to peer transaction. Further, block-chain contain well organized their weaknesses, strengths, opportunities, and threats of block-chain based transaction application. In block-chain contain using with OTP this block-chain will be more secured and easily transfer the money. Their future scope in business, education and companies.
VALIDITY OF DISASTER E-BOOK TO IMPROVE DISASTER LITERACY SKILLS AT JUNIOR HIGH SCHOOL

Natural disasters in Indonesia constantly occur every single year and have adverse effects. In order to minimize those negative effects, it is crucially needed to increase disaster literacy in the society, which starts from the students. To improve disaster literacy, students must be assisted with teaching materials that focus on disaster material, such as disaster education e-books. Disaster education e-books are teaching materials that contain the vibration, waves, and disaster materials combined with a webbed integration model. This research aimed to measure the validity level of the developed disaster education e-book. The research method applied was research and development with the 4D development model proposed by Thiagarajan et al. (1974). However, this research did not reach the disseminating stage, so the development model used became a 3D model consisting of defining, designing, and developing stages. Validation questionnaires were used to measure product validity, and these questionnaires were assessed by two expert lecturers and three science teachers. The development of teaching materials in the disaster education e-books form scored 81%, and it was categorized in the valid category. As a result, the disaster education e-book developed was valid for use at the junior high school level.
Secure Document Search in Cloud Computing using MapReduce

Nowadays, cloud computing is an important technology, which is part of our daily lives. Moving to cloud brings some benefits: create new applications, store large sets of data, process large amount of data. Individual users or companies can store own data on cloud (e.g. maritime, environmental protection, physics analysis etc.). An important thing before storing in cloud is that data needs to be encrypted, in order to keep its confidentiality. Among these, users can store encrypted documents on cloud. However, when owner needs a specific document, they should retrieve all documents from cloud, decrypt them, chose the desired document, encrypt again and finally store back encrypted documents on cloud. To avoid these entire steps, a user can choose to work with searchable encryption. This is an encryption technique, where key words (or indexes) are associated to encrypted documents, and when the owner needs a document, he/she only needs to search throw key words and then retrieve the documents that have associated the desired keywords. An important programming paradigm for cloud computing is MapReduce, which allows high scalability on a large number of servers in a cluster. Basically, MapReduce works with (key, value) pairs. In the current study paper, we describe a new technique through which a user can extract encrypted documents stored on cloud servers based on key words, using searchable encryption and MapReduce.
Head Position and Pose Model and Method for Head Pose Angle Estimation based on Convolution Neural Network

Head position and pose model is created.Also, a method for head poses angle estimation based on Convolution Neural Network (CNN) is proposed.3D head position model is created from these locations and obtain 3D coordinate of head position.The method proposed here uses CNN.As for the head pose detection, OpenCV and Dlib of the open-source software tools are used with Python program.The images used were RGB images, RGB images + thermography, grayscale images, and RGB images assuming images obtained by near infrared rays, with only the red channel elements extracted.As a result, the RGB image model was the most accurate, but considering the criteria set, the RGB image model was used for morning and daytime detection, and the near-infrared image was used for nighttime and rainy weather scenes.It turned out that it is better to use the model obtained by the training in.The experimental results show almost perfect head pose detection performance when the head pose angle ranges from 0 to 180 degrees with 45 degrees steps.
Photometric Calibration for Stereo Camera with Gamma-like Response Function in Direct Visual Odometry

Direct visual odometry algorithms assume that every frame from the camera has the same photometric characteristics. However, the cameras with auto exposure are widely used outdoors as the environment often changes. The vignetting also affects the pixel's brightness on different frames, even if the exposure time is fixed. We propose an online vignetting correction and exposure time estimation method for stereo direct visual odometry algorithms. Our method works on a camera that has a gamma-like response function. The inverse vignetting function and exposure time ratio between neighboring frames are estimated. Stereo matching is used to select correspondences between the left image and right image in the same frame at the initialization step. Feature points are used to pick the correspondences between different frames. Our method provides static correction results during the experiments on datasets and a stereo camera.
Penentuan Standar Spesifikasi Kerja di Café Berdasarkan Big Data dengan Metode LDA dan AHP

A good human resources management practice will produce quality and quantity output for sustainable business. The employee recruitment process is a crucial initial stage to fulfil the needs of a quality employee. In this process, a company usually opens an advertisement for a job vacancy and selects based on job qualification requirements. This research contributes to determining job specification standards in a café or coffee shop based on Big Data analysis. Analysis has been done by Latent Dirichlet Allocation (LDA), and Analytical Hierarchy Process (AHP) method is implemented to select the type of works and work qualifications available in the food and beverages sector. Questionnaires and interviews have been conducted to collect primary data, and three leading websites of job seeker online platforms (Jobstreet.com, Gawean.id, and Lokerindonesia.com) are used as references in data collection. Finding gives information regarding four types of works in a cafe: manager, barista, chef, and waiter; eleven points for manager specifications, twelve points for barista specifications, eleven points for chef specifications, and fifteenth points for barista waiter specifications.
Visual Scan-Path based Data-Augmentation for CNN-based 360-degree Image Quality Assessment

360-degree Image quality assessment (IQA) is facing the major challenge of lack of ground-truth databases.This problem is accentuated for deep learning based approaches where the performances are as good as the available data.In this context, only two databases are used to train and validate deep learning-based IQA models.To compensate this lack, a dataaugmentation technique is investigated in this paper.We use visual scan-path to increase the learning examples from existing training data.Multiple scan-paths are predicted to account for the diversity of human observers.These scan-paths are then used to select viewports from the spherical representation.The results of the data-augmentation training scheme showed an improvement over not using it.We also try to answer the question of using the MOS obtained for the 360-degree image as the quality anchor for the whole set of extracted viewports in comparison to 2D blind quality metrics.The comparison showed the superiority of using the MOS when adopting a patch-based learning.
Achieving Adversarial Robustness Requires An Active Teacher

A new understanding of adversarial examples and adversarial robustness is proposed by decoupling the data generator and the label generator (which we call the teacher).In our framework, adversarial robustness is a conditional concept-the student model is not absolutely robust, but robust with respect to the teacher.Based on the new understanding, we claim that adversarial examples exist because the student cannot obtain sufficient information of the teacher from the training data.Various ways of achieving robustness is compared.Theoretical and numerical evidence shows that to efficiently attain robustness, a teacher that actively provides its information to the student may be necessary.
DssP: Efficient Dual-Server Secret Sharing Protocol Based on Password Authentication for Cloud Storage Services

Ensuring the security of cloud storage services is an ongoing challenge, due to the increasingly complex and dynamic threat landscape. One particular challenge is how to design security and efficient data encryption approaches. For example, the key used to encrypt data is generally a string of long random number, and this key is uploaded to the cloud server for security. The threshold password authentication secret sharing (TPASS) protocol is commonly used to save encryption keys. However, existing TPASS protocols may not fully achieve the desired security and efficiency levels due to the complexity of the protocol design. Hence, in this article, we propose an efficient dual-server secret sharing protocol (DssP) based on password authentication for cloud storage services. In our approach, users only need to remember a simple password to achieve random key access through dual server cooperation. We then mathematically prove that DssP meets the security requirements, and also evaluate its performance to show that it has less computational and communication overheads than other similar protocols.
A Need Analysis for English Speaking and Listening Assessment Media with Web Responsive in Computer Science Faculty

This study aimed at finding and analyzing the information of the ideal speaking and listening online assessment media needed in Computer Science Faculty, University of Putra Indonesia YPTK. The study used descriptive and qualitative approaches. The data instrument was a questionnaire using Likert-scale and open-questions responded by 431 students and 17 English lecturers. The result of the study showed that the students need interactive and interesting assessment web responsive while the lecturers need web responsive media which enable them to assess speaking and listening online including scoring and sending feedback. The result of the study will also be used as a matrix for developing online assessment media.
Dual Parallel Reverse Attention Edge Network : DPRA-EdgeNet

In this paper, we propose Dual Parallel Reverse Attention Edge Network (DPRA-EdgeNet), an architecture that jointly learns to segment an object and its edge. Specifically, the model uses two cascaded partial decoders to form two initial estimates of the object segmentation map and its corresponding edge map. This is followed by a series of object decoders and edge decoders which work in conjunction with dual parallel reverse attention modules. The dual parallel reverse attention (DPRA) modules repeatedly prunes the features at multiple scales to put emphasis on the object segmentation and the edge segmentation respectively. Furthermore, we propose a novel decoder block that uses spatial and channel attention to combine features from the preceding decoder block and reverse attention (RA) modules for object and edge segmentation. We compare our model against popular segmentation models such as U-Net, SegNet and PraNet and demonstrate through a five fold cross validation experiment that our model improves the segmentation accuracy significantly on the Kvasir-SEG dataset and Kvasir-Instrument dataset.
Approaching explanatory adequacy in phonology using Minimum Description Length

A linguistic theory reaches explanatory adequacy if it arrives at a linguistically-appropriate grammar based on the kind of input available to children. In phonology, we assume that children can succeed even when the input consists of surface evidence alone, with no corrections or explicit paradigmatic information – that is, in learning from distributional evidence. We take the grammar to include both a lexicon of underlying representations and a mapping from the lexicon to surface forms. Moreover, this mapping should be able to express optionality and opacity, among other textbook patterns. This learning challenge has not yet been addressed in the literature. We argue that the principle of Minimum Description Length (MDL) offers the right kind of guidance to the learner – favoring generalizations that are neither overly general nor overly specific – and can help the learner overcome the learning challenge. We illustrate with an implemented MDL learner that succeeds in learning various linguistically-relevant patterns from small corpora.
An Efficient Ad Click Prediction System using Machine Learning Techniques

Ad-click prediction is a learning problem that is highly related to the multi-billion-dollar ad- promoting the online advertising industry. As the number of internet users in India reached 525 million in 2019, online advertising companies are trying to influence internet usage users for promoting their business. Machine learning is a technique in which systems getting to act without any explicit programming. Currently, machine learning is pervasive today and we can use machine learning models in every research field. The accuracy of the ad-click prediction system impacts business revenue, so it is very important to build a prediction system with fewer false positives and false negatives.in this paper, we proposed an ad-click prediction system based on machine learning techniques. The dataset is taken from Kaggle. The dataset contains nine features. The goal of the model is to evaluate the probability of an online user to click on a given ad. We built a machine learning model based on these features. We applied a voting classifier on the dataset and achieved an accuracy of 98%.We used python language for implementation.
The Comparison of Tree-Based Ensemble Machine Learning for Classifying Public Datasets

This study aims to determine the best methods of tree-based ensemble machine learning to classify the datasets used, a total of 34 datasets. This study also wants to know the relationship between the number of records and columns of the test dataset with the number of estimators (trees) for each ensemble model, namely Random Forest, Extra Tree Classifier, AdaBoost, and Gradient Bosting. The four methods will be compared to the maximum accuracy and the number of estimators when tested to classify the test dataset. Based on the results of the experiments above, tree-based ensemble machine learning methods have been obtained and the best number of estimators for the classification of each dataset used in the study. The Extra Tree method is the best classifier method for binary-class and multi-class. Random Forest is good for multi-classes, and AdaBoost is a pretty good method for binary-classes. The number of rows, columns and data classes is positively correlated with the number of estimators. This means that to process a dataset with a large row, column or class size requires more estimators than processing a dataset with a small row, column or class size. However, the relationship between the number of classes and accuracy is negatively correlated, meaning that the accuracy will decrease if there are more classes for classification.