Reviewerly/exHarmony huggingface dataset:
Analisis Kebiasaan Belajar dan Hasil Belajar Biologi Siswa kelas XI IPA SMA Negeri 1 Tinambung

Abstract: This research is descriptive correlational with a simple paradigm research design that aims to find out the correlation between study habits and student learning outcomes. The sample of this study is a saturated sample that is entirely from the population of class XI IPA SMA Negeri 1 Tinambung amounting to 85 students, saturated sample intends to make generalizations with very small errors. Measured study habits include 7 indicators, namely regular Biology learning, preparation for learning in the night, came early to attend the class, conditions / learning atmosphere, learning to understand and to the point, habit of facing exams and practice of visiting the library. The study data of study habits were obtained through a questionnaire and the learning outcomes were obtained from secondary data on the student achievement of the even semester of the 2018/2019 school year which were then processed by Product Moment correlation analysis (r) and the coefficient of determination. The results showed that (1) Study habits of students grade XI IPA SMA Negeri 1 Tinambung is at a good category with an average of 71.98. (2) Biology achievement of student grade XI IPA SMA Negeri 1 Tinambung is found out in high category with an average of 76.12. (3) There is a relationship between the study habits and biology achievement of eleventh grade students at senior high school 1 Tinambung. The correlation level obtained is 11.5% and classified as weak or low correlation. Keywords: student habits, student achievements.
No Deal with Norway

Norway does not have a deal with the publisher Elsevier anymore and follows in Sweden and Germany's footsteps. But why didn't Norway renew their deal? And how will the Norwegian institutions and libraries cope with a future without the largest publisher of academic literature? Also, what does the newly signed deal with Wiley contain? Is that a "perfect" Open Access deal? The guest of this episode is Mona Magnussen, Head of the Collection Department at the University Library at UiT The Arctic University of Norway. The host of this episode is Erik Lieungh.&#x0D; This episode was first published 15 March 2019.
PENINGKATAN LITERASI DATA MENUJU INDONESIA 4.0

The Indonesian government has set 10 national priorities to face the Industrial Revolution 4.0 which is accompanied by an integrated roadmap known as Making Indonesia 4.0. Achieving these targets requires collaboration among the stakeholders (government institutions, associations and industry players, and academics).One of the key determinants of competitiveness in the Industry 4.0 era, is human capital including data literacy capability to analyze data generated from various technological devices becomes an information and policy, especially for the government.Politeknik Statistika STIS lecturers have held a series of activities to increase data literacy and big data technology for central and local governments, industry players, research institutions and universities. The activities were carried out in the form of a series of seminars, as well as workshops attended by decision makers of central and local governments, researchers and lecturers from various universities in Indonesia. As a result of this series of activities, the participants not only had an understanding of the importance of the data but were also able to use data to improve their evidence-based policies.Keywords: data literacy, evidence-based policy, big data, industry 4.0
A Non-Intrusive, Traffic-Aware Prediction Framework for Power Consumption in Data Center Operations

Modern cloud computing relies heavily on data centers, which usually host tens of thousands of servers. Predicting the power consumption accurately in data center operations is crucial for energy optimization. In this paper, we formulate the power consumption prediction at both the fine-grained and coarse-grained level. We carefully discuss the desired properties of an applicable prediction model and propose a non-intrusive, traffic-aware prediction framework for power consumption. We design a character-level encoding strategy for URIs and employ both convolutional and recurrent neural networks to develop a unified prediction model. We use real datasets to simulate requests and analyze the characteristics of the collected power consumption series. Extensive experiments demonstrate that our proposed framework can achieve superior prediction performance compared to other popular leading prediction methods.
Designing a Persuasive Application for Behaviour Change with Children:

Persuasive technology is a technology that is designed deliberately to change user behaviour without coercion and deception. This study demonstrates the design of a persuasive mobile application aiming to encourage children below 12 years old to take care of their teeth and increase their awareness towards the importance of dental care. We applied a design thinking approach which comprised of five different phases: empathize, define, ideate, prototype, and test. A total of 13 children participated in this study. The findings show that children found that the application informs them how to brush their teeth properly and increases their awareness about the importance of dental care. This indicates that the design thinking approach is indeed useful for the designer to design a persuasive application aiming to change childrenâ€™s behaviour.
Practitioners’ Perspective on Software Project Management Education

Despite Software project management (SPM) being one of the most relevant topicsin the area of software engineering that should be addressed in computing programs, SPM skills of recent graduates are not satisfactory yet. In this context, besides being important to know there are skill deficiencies, we also need to gather specific information on how to adjust and improve the education on the corresponding topics. In this paper we attempt to identify what knowledge deficiencies in SPM can persist after a student graduates from a computing degree program. We surveyed practitioners that graduated and worked as software project managers to gather the knowledge deficiencies from the industry perspective. In general, the results indicated that there is a number of professionals who seeks postgraduate programs to fill the deficiencies of the undergrad programs.
Language Learning Strategies Used by Indonesian Vocational High School English Debate Group

This article describes language learning strategies used by vocational high school English debate group. This research used qualitative approach. The data used in this research was in the form of information about language learning strategies. The researcher used interview as the instrument to collect the data and observation checklist to complete the data of interview. In this study, the researcher only took the three students who were recommended by the English teacher. Three student already chosen were the students who got great achievement such as third winner in Asian Debate Competition. The students used metacognitive strategies by using some strategies like arranging and planning with finding out about language learning, using evaluating learning by self evaluating. The researcher suggest that if they used language learning strategies so they can be a better language learner.
A Comparability Study Between a Graphic-Prompt and a Textual-Prompt Writing Task for English Placement Testing

Over the last several decades, both textual-prompt and source-based writing tasks (linguistic- and graphic-prompt) have been widely used for performance-based assessment. A plethora of studies have been conducted to examine comparability of the textual-prompt and linguistic source-based writing tasks from various perspectives, including composing processes, task representations, and test scores. However, identifying the equivalence of the textual-prompt and the graphic-prompt writing task, particularly for English placement testing at English medium universities, has drawn less attention. The present study investigated if the two writing tasks paralleled one another in terms of holistic (class assignment made based on the overall quality of test essays) and analytic (discourse feature analysis) ratings, as well as text length. Twenty-five ESL students studying at an Intensive English Language Program at a large public state university in the Mid-west of the United States and four international teaching assistants at the university participated in the study. Data collection instruments were a retired textual-prompt writing task from the university’s writing portion of the placement test and a graphic-prompt writing task developed by the author, as well as a background questionnaire. A two-way independent-groups chi-square, a non-parametric one-way multivariate analysis of variance, and a bootstrapped independent-samples t-test were performed with the data garnered from the participants. It was found that the two writing tasks were not significantly different in both the holistic and analytic ratings. The length comparison between the two writing tasks showed no statistical significance, either. Implications, methodological limitations, and directions for future research were discussed.
LegacyPro&amp;mdash;A DNA-Inspired Method for Identifying Process Legacies in Software Development Organizations

This article presents a novel method for determining the factual adoption of new processes in software R&D organizations. We use a DNA-inspired analysis (motifs) to categorize parts and find similarities between projects using defect-inflow profiles.
Reflection-based Word Attribute Transfer

Word embeddings, which often represent such analogic relations as --→ king ---→ man+ -----→ woman ≈ ---→ queen, can be used to change a word's attribute, including its gender.For transferring king into queen in this analogy-based manner, we subtract a difference vector --→ man------→ woman based on the knowledge that king is male.However, developing such knowledge is very costly for words and attributes.In this work, we propose a novel method for word attribute transfer based on reflection mappings without such an analogy operation.Experimental results show that our proposed method can transfer the word attributes of the given words without changing the words that do not have the target attributes.
Implementation of Signcryption Scheme on E-Payment System (ePaSe)

A large number of startups have a big influence on increasing online transactions, especially e-payment. However, the increase in the use of e-payments posing a number of threats and resulting in a decrease level of consumer confidence in e-payment. Therefore in 2018 Daniel proposed a Signcryption scheme based on Conic Curve Cryptography which was stated to be able to fulfill forward secrecy services that could reassure consumers, but still based on mathematical calculations. In this study the implementation of the Signcryption scheme was carried out on the e-payment system on the Android platform. Implementation is done by DRM methodology and prototyping model approach. The e-payment system built consists of an e-payment application with merchant and customer actors and a bank application with bank admin actors. Based on the results of the integration testing and system testing, it was found that the e-payment system (ePaSe) built had fulfilled the design that had been done.
Who is Influencing the #GDPR Discussion on Twitter: Implications for Public Relations

On May 25, 2018, the European Union (EU) implemented the General Data Protection Regulation (GDPR) to protect individuals' privacy and data.This regulation has far-reaching implications as it applies to any organization that deals with data of EU residents.By studying the discussion about this regulation on Twitter, our goal is to examine public opinions and organizational public relations (PR) strategies about GDPR.The results show that the regulation is being actively discussed by a variety of stakeholders, but especially by cybersecurity and IT-related firms and consultants.At the same time, some of the stakeholders that were expected to have a more active role were less involved, including companies that store or process personal data, government and regulatory bodies, mainstream media, and academics.The results also show that the stakeholders mostly have one-way rather than two-way communication with their audiences, thus fulfilling the rhetorical than relational function of PR.
Double Deep Features for Apparel Recommendation System

This study describes a recommendation system embedded in the double features extracted by convolutional neural networks (CNNs).Several probabilistic models, such as probabilistic matrix factorization (PMF)-based approaches, have been utilized for recommendation systems based on a CNN model.Each recommendation algorithm utilizes a single CNN model to extract precise features about documents and pictures, and the systems with CNN have contributed in improving the performance in rating prediction.Meanwhile, the systems for some items should consider at least two precise features simultaneously, and the extension to embed multiple CNN models is necessary.However, methods that integrate multiple CNN-based features into existing recommendation systems, such as PMF, are not available.Thus, this study proposes a novel probabilistic model that integrates double CNNs into PMF.For apparel goods, two trained CNNs from document and image shape features are combined, and the latent variables of users and items are optimized based on the vectorized features of CNNs and rating.Extensive experiments demonstrate that our model outperforms other recommendation models.
Data Reconstruction Based on Temporal Expressions in Clinical Notes

Learning representations of clinical notes poses challenges in handling complex content that necessitates preprocessing steps to make the data more suitable for data mining. An important issue, addressed here, is that of temporal expressions, where cues indicate the time when clinical events occur. We present a three-step data reconstruction algorithm for transforming similar clinical entities (e.g., symptoms, complications) into sequential data through unsupervised annotation of temporal expressions. First, the data reconstruction algorithm detects if an expression has temporal intent. Second, it decomposes and rewrites the expression into non-temporal sub-expression and temporal constraints. Finally, it clusters similar non-temporal sub-expressions by using unsupervised sentence embedding under the modified $K$ -medoids paradigm. We experimented with our proposed algorithm on clinical notes associated with chronic obstructive pulmonary disease (COPD). Visualizing reconstruction results of cardiology reports for a longitudinal cohort of patients with COPD demonstrated that this algorithm is feasible.
Does Virtualization Capability Maturity Influence Information Systems Development Performance? Theorizing The Non-Linear Payoffs

Firms are increasingly relying on digital transformation and virtualization of physical IT assets to develop information systems projects.However, the assessment of this virtualization on the performance of information systems development (ISD) projects is still unclear.Drawing upon the theory of process virtualization, we develop and empirically test a research model describing the relationships of virtualization capability maturity (VCM) with ISD project performance.Our findings show inverted Ushaped relationships of VCM with both ISD process and product performance.Interestingly, ISD projects achieve performance improvements as they progress incrementally from VCM levels of 0 to 2, but at VCM level 3 performance declines.Also, we observe that at higher levels of VCM, ISD process performance declines more rapidly than ISD product performance and the resources spent on ISD project execution increases non-linearly with increasing levels of VCM.Our post-hoc analysis indicates that VCM and CMM exhibit a substitutive relationship for process performance.Implications for research and practice are discussed.
Hybrid fuzzy logic and gravitational search algorithm-based multiple filters for image restoration

In this paper, we present a multiple image filters for removal of impulse noises from test images. It utilises fuzzy logic (FL) approach to design a noise detector (ND) optimised by gravitational search algorithm (GSA) and utilises median filter (MF) for restoring. The proposed multiple filters used the FL approach to detect each pixels of a tests image are noise corrupted or not. If it is considered as noise-corrupted, the multiple filters restore it with the MF filter. Otherwise, it remains unchanged. We split the image into number of windows and each window apply the multiple filters. The filter output is used for the rule generation. The optimal rules are selected using GSA and given to the fuzzy logic system to detect the noise pixel. The experimental results are carried out using different noise level and different methods. The performance measured in terms of PSNR, MSE and visual quality.
Pronunciation Model Selection, or Do You Speak American?

Presented are the results of a survey about English pronunciation attitudes and preferences aimed at first-year students of English language and literature.The main aim of the survey was to shed light on the advance of General American (GA) as the preferred pronunciation model in recent years.The survey questions cover the main features that distinguish GA from SBS (Southern British Standard), such as rhoticity, t-voicing, articulation of the bath and lot vowels as well as the presence of the low back merger.The majority of respondents report that they prefer using GA, while they prefer listening to SBS.The results suggest, however, that the majority of test subjects use a mixture of sorts, with few students being consistent in their usage of specific pronunciation features.
Modeling and measurement internal threat process based on advanced stochastic model

Previous research on internal threats was mostly focused on modeling threat behaviors. These studies have paid little attention to risk measurement. This paper analyzed the internal threat scenarios, introduced the operation related protection model into the firewall-password model, constructed a series of sub models. By analyzing the illegal data out process, the analysis model of target network can be rapidly generated based on four protection sub-models. Then the risk value of an assessment point can be computed dynamically according to the Petri net computing characteristics and the effectiveness of overall network protection can be measured. This method improves the granularity of the model and simplifies the complexity of modeling complex networks and can realize dynamic and real-time risk measurement.
An Approach Based on Customized Robust Cloaked Region for Geographic Location Information Privacy Protection

Location-based services (LBS) have gained huge popularity because of the easy availability of modern mobile devices and the fast development of geographical information science (GIS). However, the lack of protection for private user positions might give rise to privacy concerns. This kind of problem is especially serious in mobile application environment because many mobile applications tend to use LBS. In this paper, we propose a new privacy preserving approach using customized robust cloaked region (RCR), depending on a peer-to-peer structure and the premise that users do not trust each other when sharing their geographical locations. Two algorithms are used to generate the RCR with high user density. The area of the RCR is controlled by the user’s demanded degree of protection. To enhance the resistance to regional background knowledge attack, we incorporate a location semantic value into each unit of the user map. According to extensive simulations, our method can effectively obfuscate a user’s geographical location into a highly indistinguishable region because of the disturbance of nearby users and different equally possible locations.
Time for second-generation artificial intelligence in medical imaging

After decades of modest successes and setbacks, it is safe to say that artificial intelligence (AI) is here to stay. Although most of the basic concepts of modern AI technology date back to the second half of the previous century, the enormous increment in data and computational power over the past decade has enabled it to demonstrate its full potential. A branch of AI known as machine learning has been responsible for most of the progress, with remarkable results: from generating hyper-realistic fake images [1] to the detection of lymph node metastases in women with breast cancer [2].
A Comparison of Listening Activities in Middle School English Textbooks and National English Listening Comprehension Tests

The purpose of this study is to analyze listening activities in five different textbooks of Middle School English 3 and the National English Listening Comprehension Tests (NELCTs). In order to accomplish these research purposes, the listening activities and the NELCTs were compared in the light of 1) four achievement criteria (understanding of the main idea, detailed information, and the flow, and task performance) and 2) 19 communicative materials presented in listening area of the 2009 Revised National Curriculum of English. The result showed that the frequency of achievement criteria between them was similar and the criteria of understanding detailed information and understanding the main idea appeared most frequently. The communicative materials related to the daily life were appeared in a similar frequency. The findings of this study indicate that English teachers should provide students with supplementary materials to reduce the gap between the listening activities in the five different textbooks and the NELCTs by making efforts to ensure that the teaching-learning activities and the evaluation are carried out consistently.
A One-step Pruning-recovery Framework for Acceleration of Convolutional Neural Networks

Acceleration of convolutional neural network has received increasing attention during the past several years. Among various acceleration techniques, filter pruning has its inherent merit by effectively reducing the number of convolution filters. However, most filter pruning methods resort to tedious and time-consuming layer-by-layer pruning-recovery strategy to avoid a significant drop of accuracy. In this paper, we present an efficient filter pruning framework to solve this problem. Our method accelerates the network in one-step pruning-recovery manner with a novel optimization objective function, which achieves higher accuracy with much less cost compared with existing pruning methods. Furthermore, our method allows network compression with global filter pruning. Given a global pruning rate, it can adaptively determine the pruning rate for each single convolutional layer, while these rates are often set as hyper-parameters in previous approaches. Evaluated on VGG- 16 and ResNet-50 using ImageNet, our approach outperforms several state-of-the-art methods with less accuracy drop under the same and even much fewer floating-point operations (FLOPs).