Reviewerly/exHarmony huggingface dataset:
An Investigative Study of Code Mixing Among the EFL University Students in Universitas Sembilanbelas November Kolaka

This research aimed to describe the types and the reason of using Code-mixing by the fifth semester students of English Language and Education Department of Sembilanbelas November Kolaka University in the classroom. The research used qualitative methods. The subject in this research was the fifth semester students of English Language and Education Department of Sembilanbelas November Kolaka University. This research was identified to determine the type and the reason of using code-mixing proposed by Suwito. The data were collected through audio recording and interview. In analyzing the data, the researcher transcribed, identified and classified. In analyzing the data, several steps were done, namely: transcribing, reading, and coding to choose and sort the sentences which were categorized as code mixing. Then, classifying and analyzing the sentence inti different types of code-mixing in the class room. The result indicated that there were 69 utterances of code-mixing used by the students of Sembilanbelas November Kolaka University at fifth semester in teaching and learning activity; 42 code mixing in the form of word insertion, 17 code mixing in the form of phrase insertion, 2 code mixing in the form of hybrid, and 8 code mixing in the form of clause. The study also showed that the reason of using code mixing in the class room which found on the conversation in the class room were; Role Identification which was determined by social condition and educational background; and Varian Identification which was determined by social prestige.
Introduction to Multi-Atlas Auto-Segmentation

Multi-atlas segmentation is a general-purpose segmentation paradigm whereby multiple existing segmentations (atlases) are mapped onto a query image, and then merged into a single segmentation. Early advances in this technique were driven by neuroscience researchers in the mid 2000s, with applications in radiotherapy being advanced in the late 2000s. Despite recent advances in deep learning segmentation methods, multi-atlas segmentation remains relevant due to its generality and flexibility. This chapter introduces multi-atlas segmentation and provides an overview of its major algorithmic components: database creation, atlas selection, image registration, label fusion, and post-processing. A brief summary of the chapters within this part of the book is also provided.
Biresponses Kernel Nonparametric Regression: Inflation and Economic Growth

The relation between inflation and economic growth is interesting to observe. To maintain the inflation rate, two factors should be taken into account, namely keeping the economic pulse at its optimal rate and keeping people's purchasing power from decreasing. Many factors influence the inflation and economic growth of a nation; one of which is the national bank interest rate. Since the data of inflation are closely related to economic growth, this study aims at modelling the data of inflation rate and economic growth of Central Java Province in Indonesia using bi-response kernel regression. Employing the data from the first trimester of 2007 up to those from the second trimester of 2019 which were processed using kernel Gauss, the best model to minimise the value of GCV was obtained with optimum h for inflation model amounting to 0.12 and 81.75 for economic growth model. The model performance was excellent because the MAPE out sample was less than 10%. The biresponses kernel model is better than the linear biresponses model in terms of GCV, MSE, R2, and MAPE values.
KONTRIBUSI MINAT BACA DAN MOTIVASI BELAJAR TERHADAP KETERAMPILAN MENULIS TEKS DESKRIPSI SISWA KELAS VII SMP NEGERI 7 PADANG

ABSTRACT The purpose of this research is three. First, describe the contribution of reading interest to the writing text description skills of class VII students of SMP Negeri 7 Padang. Second, describe the contribution of learning motivation to the writing text description skills of class VII students of SMP Negeri 7 Padang. Third, describe the contribution of reading interest and learning motivation together towards writing text description skills of class VII students of SMP Negeri 7 Padang. The type of this research is quantitative with descriptive methods. The design used in this research is correlational design. The population of this research was the seventh grade students of the SMP Negeri 7 Padang who were enrolled in the 2018/2019 school year, which was 251 students. The sample in this research was determined by simple random sampling using the Slovin formula from the population, which is 40 students. The data of this research were scores on the results of filling in the reading interest questionnaire, the scores on the results of filling in the learning motivation questionnaire, and scores on the results of the test writing skills of the description text. The instrument of this research was a questionnaire to measure reading interest and student learning motivation and performance tests to measure description text writing skills. The results of this research are three. First, there is a contribution of reading interest towards the writing text description skills of 26,13%. Second, there is a contribution of learning motivation to the writing text description skills of 3,2%. Third, reading interest and learning motivation contribute to the writing text description skills of 30,25%. Kata Kunci: Kontribusi, Minat Baca, Motivasi Belajar, Keterampilan Menulis Teks Deskripsi.
Experimental Performance Evaluation Of Encryption Algorithms For Securing User Data In Cloud Computing

As cloud computing is a new area for research and development and also a utility where services can be remotely purchased and users can store their data in the cloud to enjoy on demand high quality applications and services from a shared pool of configurable computing resources available through which service providers offers customers management functionalities as well as the expensive hardware. Data privacy is the major security determinant and a factor that needs to be given more concern since un- trusted server is dangerous and unsafe for the users. Therefore, this research work gives concern to the security elements in cloud environment and suggests techniques to mitigate the security issues of the cloud by providing flexible access through encryption using the RSA, DES, AES and Blowfish algorithms.
The Equifax Breach

In September 2017, the largest financial identity breach of US consumers at its time was announced by Equifax. At the time of the breach, Equifax was one of the top three credit reporting agencies in the United States and a custodian of credit information for 820 million consumers and 91 million businesses. Over 145 million records were stolen by four suspected and later indicted Chinese hackers who were part of the Chinese People’s Liberation Army, a part of the Chinese armed forces. Table 4-1 shows the number of names, addresses, social security numbers, and dates of birth, among other sensitive data that were stolen. The breach was due to Equifax’s Automated Consumer Interview System (ACIS), which allowed consumers to dispute potentially incorrect information in their credit files. One root cause was a software vulnerability that was not patched, the details of which we will describe in this chapter.
ANALYSIS OF STUDENT BEHAVIOR ON E-LEARNING DUE TO COVID 19 PANDEMIC

In 2020, all countries are no exception including Indonesia in the event of coronavirusoutbreak (Covid-19) starting in Wuhan City, China. Coronavirus affects activities that areusually carried out outdoors. One of the activities that became disrupted was face-to-facelearning that is usually done on campus now being online conducted at home with e-learningmedia. This research is to find out if there are behavioral changes during e-learning due tothe Covid 19 pandemic. The method used is the quantitative method. The data was collectedby handing out questionnaires to Students of the Department of Management S1 class of 2017Regular A Universitas Widyatama as many as 300 respondents. Data analysis is conductedusing validity test, reliability test, classic assumption test, hypothesis test, and determinationcoefficient test. The results showed 71.2% variation in E- learning variables can be explainedby student behavior variables while the remaining 28.8% is explained by other variables notproposed in the study. The results of this study based on simultaneous and partial testingshow that there is a significant influence between student behavior variables (X) on Elearning Learning (Y).
KINCHEV’S REVOLUTIONARY "SPIRITUAL AEROBICS": A COGNITIVE VIEW

This article presents a cognitive analysis of the inconsistency of Kinchev’s cognitive - pragmatic aims in the framework of the author’s original methodology. The destruction of the totalitarian personality suppression machine is perceived by the rock poet (the subject - source of the CPP - cognitive - pragmatic program) as the true path to freedom and spiritual revival. However the total destructiveness embodied in the irrepressible «Will - to - Power» creates a system of «constructively creative» reflective, instinctively impulse reactions «Being - to - Freedom». There occurs a paradoxical situation, in which the destructive impulses - stimuli created by the very object of confrontation (“the System”) become the key instrument of renovation of the consciousness of the rock poet. A special destructive - affective state is formed in the poet’s mind, which can be conditionally called «Will - to - Victory». It almost completely coincides in its conceptual - semantic content with the property of the «System» - «Will - to - Power» which has the following main attributes: a) manipulativeness; b) submission (suppression); c) uncompromising character; d) extreme aggressiveness. The affective impulse provokes the formation of an automated reaction, and then it itself becomes an affective stimulus and the cycle is repeated. In other words, the stimulus - reaction phase (aggressive fear, self - aggression (challenge), etc.) goes into the reaction - stimulus phase, which leads to total «anemia» of consciousness of both the source - subject (CPP generator) and the interpreter - subject (CPP carrier).
Secret sharing holographic encryption with off-axis dual-channel metasurface

Different from traditional metasurface holographic encryption with phase-only encoding technology, we proposed a kind of secret sharing encryption scheme with off-axis dual-channel metasurface. The secret information can be recovered only by decoding and stacking all the off-axis angle channels, while chaotic images could be extracted in single channel decoding process, further improving the information security.
User-Query Processing through Dynamic Tweets Status Recommender System

With increasing user information volume in online social networks, recommender systems have been an effective method to limit such information overload. The requirements of recommender systems specified, with widespread adoption in many internet social Twitter, Facebook, and Google online applications. In recent years, the micro-blogging in Twitter has brought greater importance to online users as a channel spreading knowledge and information. Through Twitter, users can find the relevant information on the search they perform, but understanding the past, present, and future information relevant to the investigation source is needed real-time information. Estimating the successful tweet status (history, ongoing, and prospective) among the huge population of Twitter members is important to satisfy the needs of Twitter online content readers. In this paper, a Dynamic Tweets Status Recommender System (DTSRS) is designed by creating a set of dynamic recommendations to a Twitter user based on usability, consisting of people who post tweets, which is exciting present and future. The proposed recommender system is implemented through two approaches: the first is to analyze the Twitter member online tweets, select and understand the content of that tweet, and the second predicts the understanding of the tweet content, suggest the dynamic status of the tweets. In this paper, the Twitter user tweets' views are expressed after examining the depth of content, different types of user interfaces, text filtering, and machine learning technique. The set of results through tweets experimentations with database operators carried out to evaluate and comparability the proposed recommender system's performance.&#x0D;
Aspect-based Sentiment Classification via Reinforcement Learning

Aspect-based sentiment classification aims to predict sentimental polarities of one or multiple aspects in texts. As texts always contain a large proportion of task-irrelevant words, accurate alignment between aspects and their sentimental descriptions is the most crucial and challenging step. State-of-the-art approaches are mainly based on word-level attention learned from recurrent neural network variants (e.g., LSTM) or graph neural networks. From another view, these methods essentially weight and aggregate all possible alignments. However, this mechanism heavily relies on large-scale supervision training: without enough labels, it could easily overfit with difficulty in generalization. To address this challenge, we propose SentRL, a reinforcement learning-based framework for aspect-based sentiment classification. In this framework, input texts are transformed into their dependency graphs. Then, an agent is deployed to walk on the graphs, explores paths from target aspect nodes to their potential sentimental regions, and differentiates the effectiveness of different paths. By limiting the agent's exploration budget, our method encourages the agent to skip task-irrelevant information and focus on the most effective paths for alignment purpose. Our method considerably reduces the impact of task-irrelevant words and improves generalization performance. Compared with competitive baseline methods, our approach achieves the highest performance on public benchmark datasets with up to 3.7% improvement.
TraDE: A Simple Self-Attention-Based Density Estimator

We present TraDE, a self-attention-based architecture for auto-regressive density estimation with continuous and discrete valued data. Our model is trained using a penalized maximum likelihood objective, which ensures that samples from the density estimate resemble the training data distribution. The use of self-attention means that the model need not retain conditional sufficient statistics during the auto-regressive process beyond what is needed for each covariate. On standard tabular and image data benchmarks, TraDE produces significantly better density estimates than existing approaches such as normalizing flow estimators and recurrent auto-regressive models. However log-likelihood on held-out data only partially reflects how useful these estimates are in real-world applications. In order to systematically evaluate density estimators, we present a suite of tasks such as regression using generated samples, out-of-distribution detection, and robustness to noise in the training data and demonstrate that TraDE works well in these scenarios.
Defeating MITM Attacks on Cryptocurrency Exchange Accounts with Individual User Keys

Presented herein is a User-SpecificKey Scheme based on Elliptic Curve Cryptography that defeats man-inthe-middle attacks on cryptocurrency exchange accounts. In this scheme, a separate public and private key pair is assigned to every account and the public key is shifted either forward or backward on the elliptic curve by a difference of the account user’s password. When a user logs into his account, the server sends the shifted public key of his account. The user computes the actual public key of his account by reverse shifting the shifted public key exactly by a difference of his password. Alternatively, shifting can be applied to the user’s generator instead of the public key. Described in detail is as to how aman-in-the-middle attack takes place and how the proposed scheme defeats the attack. Provided detailed security analysis in both the cases of publickey shifting and generator shifting. Further, compared the effectiveness of another three authentication schemes in defending passwords against MITM attacks.
The Question of Public Participation in the Procedure for Authentic Interpretation of Laws

Authentic interpretation of laws is an interpretation of legal provisions that, due to their lack of clarity or misinterpretation in their application, is provided by the parliament. Unlike the legislative procedure, which is conducted, as a rule, in two (exceptionally three) readings, a proposal for giving an authentic interpretation is discussed in one reading. Starting from the understandings of some authors that the act of authentic interpretation of laws is contrary to the principle of democratic pluralism, and that it lacks the necessary level of democratic control and citizen participation, the author examines whether the Croatian parliamentary law enables public participation in the procedure for authentic interpretation of laws and, if so, what legal instruments can be used to implement it in parliamentary practice. To this end, the paper analyzes several relevant constitutional, legal, and procedural provisions of the Croatian parliamentary law, with reference to a parliamentary practice. Given the fact that the procedure for authentic interpretation in the Republic of Croatia, the Republic of Northern Macedonia, the Republic of Slovenia and the Republic of Serbia is regulated in a similar way by the rules of procedure of their respective parliaments, the relevant regulations of the latter three countries on the possibility of public participation in this procedure are analyzed as well. It was concluded that Croatian parliamentary law enables public participation in the procedure for authentic interpretation, through the instruments of petition, information and involvement in working groups and working bodies, and the same instruments, with certain specifics, are recognized in the parliamentary law of the latter three countries.
Efficient Human-In-The-Loop Object Detection using Bi-Directional Deep SORT and Annotation-Free Segment Identification

The present study proposes a method for detecting objects with a high recall rate for human-supported video annotation. In recent years, automatic annotation techniques such as object detection and tracking have become more powerful; however, detection and tracking of occluded objects, small objects, and blurred objects are still difficult. In order to annotate such objects, manual annotation is inevitably required. For this reason, we envision a human-supported video annotation framework in which over-detected objects (i.e., false positives) are allowed to minimize oversight (i.e., false negatives) in automatic annotation and then the over-detected objects are removed manually. This study attempts to achieve human-in-the-loop object detection with an emphasis on suppressing the oversight for the former stage of processing in the aforementioned annotation framework: bi-directional deep SORT is proposed to reliably capture missed objects and annotation-free segment identification (AFSID) is proposed to identify video frames in which manual annotation is not required. These methods are reinforced each other, yielding an increase in the detection rate while reducing the burden of human intervention. Experimental comparisons using a pedestrian video dataset demonstrated that bi-directional deep SORT with AFSID was successful in capturing object candidates with a higher recall rate over the existing deep SORT while reducing the cost of manpower compared to manual annotation at regular intervals.
Fall 2019 ORCID Members Town Hall Meeting (Americas)

Fall 2019 ORCID Members Town HallMeeting (Americas) was presented on October 7, 2019 by Laurel Haak (ORCID),Veronique Kiermer (PLOS), Alison Mitchell (Springer Nature) and members of theORCID Engagement Team.
Research on the Construction of Credit Education Evaluation System for Financial and Economic Students in Higher Vocational Colleges

The educational evaluation mechanism plays a role in monitoring and regulating the educational process, and it is promotional for the growth of students. The construction of the credit education evaluation system for finance and economics vocational students is the demand for the precise supply of moral education in the Internet era, and it provides internal motivation for finance and economics vocational students to achieve self-education. In the process of constructing the credit education evaluation system, it has always adhered to the principles of scientificity, guidance and unity of knowledge and action. In practice, the organization and management of the evaluation system, the construction of the index system and the system’s incentive feedback should be further improved.
Pendampingan Pengenalan Program Sem-Pls Pada Fakultas Ekonomi Universitas Merdeka Surabaya

This activity is designed to provide training and assistance in carrying out the Partial Least Squares - PLS 03 program. In addition to providing assistance in reading and analyzing the results / outputs of the PLS 03 program, as well as reporting. There have not been many studies in the Faculty of Economics, University of Merdeka Surabaya that use PLS version 03, because it is relatively new and the program price is quite expensive. In this case, for more than 100 data you must use the PLS 03 professional, but for data up to 100 you can use the PLS 03 Student Version. On the other hand, there are more and more demands for its use, in accordance with the development of research in the field of business / management. For this reason, the introduction and training of PLS is needed in order to have the most suitable alternative SEM method in order to obtain better research results. This PLS 03 workshop is intended for teaching staff and students who program thesis at the Faculty of Economics, Universitas Merdeka who later want to use the PLS 03 SEM method in data processing.
ICT Development for Mobile Communities

Information and Communication Technologies (ICTs) have been used and developed by mobile communities. Many rural communities around the world were latecomers to ICT development. However, when they were able to access new and emerging technologies, they often leaped over older technological tools and systems. This chapter introduces these technologies, their emergence, and the international development programs that funded their expansion into the most rural locations. While many readers from settled and Western communities use the same technologies, this chapter introduces some of the novel adaptations that herding communities have made to utilize ICTs while on the move. In this chapter, I explore the development and instillation of these emergent technologies and resulting communicative networks, which have the potential to change herding communities’ relations with the state, access to banking, and use of image testimony.