Reviewerly/exHarmony huggingface dataset:
A systematic mapping study of performance analysis and modelling of cloud systems and applications

Cloud computing is a paradigm that uses utility-driven models in providing dynamic services to clients at all levels. Performance analysis and modelling is essential because of service level agreement guarantees. Studies on performance analysis and modelling are increasing in a productive manner on the cloud landscape on issues like virtual machines and data storage. The objective of this study is to conduct a systematic mapping study of performance analysis and modelling of cloud systems and applications. A systematic mapping study is useful in visualization and summarizing the research carried in an area of interest. The systematic study provided an overview of studies on this subject by using a structure, based on categorization. The results are presented in terms of research such as evaluation and solution, and contribution such as tools and method utilized. The results showed that there were more discussions on optimization in relation to tool, method and process with 6.42%, 14.29% and 7.62% respectively. In addition, analysis based on designs in terms of model had 14.29% and publication relating to optimization in terms of evaluation research had 9.77%, validation 7.52%, experience 3.01%, and solution 10.51%. Research gaps were identified and should motivate researchers in pursuing further research directions
Artemis: A Novel Annotation Methodology for Indicative Single Document Summarization

We describe ARTEMIS (Annotation methodology for Rich, Tractable, Extractive, Multidomain, Indicative Summarization), a novel hierarchical annotation process that produces indicative summaries for documents from multiple domains.Current summarization evaluation datasets are single-domain and focused on a few domains for which naturally occurring summaries can be easily found, such as news and scientific articles.These are not sufficient for training and evaluation of summarization models for use in document management and information retrieval systems, which need to deal with documents from multiple domains.Compared to other annotation methods such as Relative Utility and Pyramid, ARTEMIS is more tractable because judges don't need to look at all the sentences in a document when making an importance judgment for one of the sentences, while providing similarly rich sentence importance annotations.We describe the annotation process in detail and compare it with other similar evaluation systems.We also present analysis and experimental results over a sample set of 532 annotated documents.
Aircraft Troubleshooting Optimization Using Case-based Reasoning and Decision Analysis

&#x0D; &#x0D; &#x0D; The Fault Isolation Manual (FIM) can be seen as a specialist system that carries the expectations and expertise of engineers and technical team concerning the aircraft components and systems operation. It is basically a manual that supports the maintainers regarding the actions to perform in determined situations to properly isolate a fault. Although the FIM is the most common tool that assists maintainer on the troubleshooting process today, it does not adequately consider field experience and it does not explore situations where the maintenance operator has limited resources, such as a lack of tools and equipment. These drawbacks are essentially caused by the lack of flexibility or adaptability of this method since it is a static manual. There are several dynamic methods studied in the field of system troubleshooting and aircraft maintenance such as Artificial Neural Networks, Support Vector Machine, K Nearest Neighbor and many other machine learning algorithms. These techniques are considered very powerful and useful; however, the training process of the data-driven strategies requires a large amount of data to provide a reliable result.&#x0D; In this context, the present work proposes a combination of data-driven with legacy knowledge-based approaches. The following techniques are employed to integrate the concepts mentioned: decision trees that explore the legacy knowledge with its topology based on the FIM, truth tables and decision analysis that explores Bayes’ rule to assist the decision- making process and case-based reasoning, technique that enables the learning from the field experience.&#x0D; &#x0D; &#x0D;
A Two-stage Model for Slot Filling in Low-resource Settings: Domain-agnostic Non-slot Reduction and Pretrained Contextual Embeddings

Learning-based slot filling -a key component of spoken language understanding systemstypically requires a large amount of in-domain hand-labeled data for training.In this paper, we propose a novel two-stage model architecture that can be trained with only a few indomain hand-labeled examples.The first step is designed to remove non-slot tokens (i.e., O labeled tokens), as they introduce noise in the input of slot filling models.This step is domain-agnostic and therefore, can be trained by exploiting out-of-domain data.The second step identifies slot names only for slot tokens by using state-of-the-art pretrained contextual embeddings such as ELMO and BERT.We show that our approach outperforms other state-of-art systems on the SNIPS benchmark dataset.
E-Book as a Learning Resource for Indonesian Language in the New Normal Era

Outbreaks of Covid-19 have a major influence on aspects of human life, not least in terms of education, especially in Indonesian.This is evident from the policies taken by the government to continue to run the education process in the new normal era.In this regard, this article aims as an alternative for the world of education to continue to run an effective and efficient learning process in accordance with the health protocol in the new normal era, namely by using e-books as a learning resource for learning Indonesian.The research used is the study of literature by collecting data on research results from various articles, documents and sources of literature in accordance with the theme of the e-book as a source of learning in the new normal era then the data is reviewed, analyzed and concluded
Implementasi Teknologi Energi Surya Sebagai Wujud Mandiri Energi Listrik Di Masjid Al-Falah Serua Ciputat Tangerang Selatan

The existence of street lighting and or public facilities is one of the things that is very important for the smooth running of residents' activities, especially at night. However, not all of them had lighting installed as in the Al-Falah Serua Ciputat Mosque in South Tangerang. This village is a village that is electrified but electricity supply for public facilities and roads is still minimal, due also to the large cost of using electricity. Therefore it is necessary to install public and street lighting facilities in this case in the form of solar lights as an effective alternative to solutions in lighting and to reduce the cost of using electricity from PLN. The solar lights are very suitable to be installed in this location, considering that the lights do not require electricity from residents' houses and PLN, and geographically the location is very potential for utilizing solar power because it is still very green. Solar lights are installed at strategic points on the road to Al Falah Mosque and the front porch of Al Falah Mosque itself. The lamp used is a HILIOS SL-40W lamp with 40Watt power. In addition to street lighting and public facilities, an economic analysis is also carried out on this activity by comparing the total costs required between incandescent lighting with solar power. Where the nominal difference obtained in rupiah is Rp 311,055.2 lighter than PJU incandescent lamps, and solar lights are not affected by TDL (basic electricity tariff).
DORB: Dynamically Optimizing Multiple Rewards with Bandits

Policy gradients-based reinforcement learning has proven to be a promising approach for directly optimizing non-differentiable evaluation metrics for language generation tasks.However, optimizing for a specific metric reward leads to improvements in mostly that metric only, suggesting that the model is gaming the formulation of that metric in a particular way without often achieving real qualitative improvements.Hence, it is more beneficial to make the model optimize multiple diverse metric rewards jointly.While appealing, this is challenging because one needs to manually decide the importance and scaling weights of these metric rewards.Further, it is important to consider using a dynamic combination and curriculum of metric rewards that flexibly changes over time.Considering the above aspects, in our work, we automate the optimization of multiple metric rewards simultaneously via a multi-armed bandit approach (DORB), where at each round, the bandit chooses which metric reward to optimize next, based on expected arm gains.We use the Exp3 algorithm for bandits and formulate two approaches for bandit rewards: (1) Single Multi-reward Bandit (SM-Bandit); (2) Hierarchical Multi-reward Bandit (HM-Bandit).We empirically show the effectiveness of our approaches via various automatic metrics and human evaluation on two important NLG tasks: question generation and data-to-text generation.Finally, we present interpretable analyses of the learned bandit curriculum over the optimized rewards.
Steane-enlargement of quantum codes from the Hermitian function field

In this paper, we study the construction of quantum codes by applying Steane-enlargement to codes from the Hermitian function field. We cover Steane-enlargement of both usual one-point Hermitian codes and of order bound improved Hermitian codes. In particular, the paper contains two constructions of quantum codes whose parameters are described by explicit formulae, and we show that these codes compare favourably to existing, comparable constructions in the literature. Furthermore, a number of the new codes meet or even exceed the quantum Gilbert–Varshamov bound.
TED-MDB Lexicons: Tr-EnConnLex, Pt-EnConnLex

In this work, we present two new bilingual discourse connective lexicons, namely, for Turkish-English and European Portuguese-English created automatically using the existing discourse relation-aligned TED-MDB corpus.In their current form, the Pt-En lexicon includes 95 entries, whereas the Tr-En lexicon contains 133 entries.The lexicons constitute the first step of a larger project of developing a multilingual discourse connective lexicon.
Variational Multiscale Nonparametric Regression: Algorithms and Implementation

Many modern statistically efficient methods come with tremendous computational challenges, often leading to large-scale optimisation problems. In this work, we examine such computational issues for recently developed estimation methods in nonparametric regression with a specific view on image denoising. We consider in particular certain variational multiscale estimators which are statistically optimal in minimax sense, yet computationally intensive. Such an estimator is computed as the minimiser of a smoothness functional (e.g., TV norm) over the class of all estimators such that none of its coefficients with respect to a given multiscale dictionary is statistically significant. The so obtained multiscale Nemirowski-Dantzig estimator (MIND) can incorporate any convex smoothness functional and combine it with a proper dictionary including wavelets, curvelets and shearlets. The computation of MIND in general requires to solve a high-dimensional constrained convex optimisation problem with a specific structure of the constraints induced by the statistical multiscale testing criterion. To solve this explicitly, we discuss three different algorithmic approaches: the Chambolle-Pock, ADMM and semismooth Newton algorithms. Algorithmic details and an explicit implementation is presented and the solutions are then compared numerically in a simulation study and on various test images. We thereby recommend the Chambolle-Pock algorithm in most cases for its fast convergence. We stress that our analysis can also be transferred to signal recovery and other denoising problems to recover more general objects whenever it is possible to borrow statistical strength from data patches of similar object structure.
Dialogue Games for Minimal Logic

In this paper, we define a class of dialogue games for Johansson's minimal logic and prove that it corresponds to the validity of minimal logic. Many authors have stated similar results for intuitionistic and classical logic either with or without actually proving the correspondence. Rahman, Clerbout and Keiff [17] have already specified dialogues for minimal logic; however, they transformed it into Fitch-style natural deduction only. We propose a different specification for minimal logic with the proof of correspondence between the existence of winning strategies for the Proponent in this class of games and the sequent calculus for minimal logic.
The Effects of Input Modality, Word Difficulty and Reading Experience on Word Recognition Accuracy

Language users encounter words in at least two different modalities. Arguably, the most frequent encounters are in spoken or written form. Previous research has shown that – compared to the spoken modality – written language features more difficult words. An important question is whether input modality has effects on word recognition accuracy. In the present study, we investigated whether input modality (spoken, written, or bimodal) affected word recognition accuracy and whether such a modality effect interacted with word difficulty. Moreover, we tested whether the participants’ reading experience interacted with word difficulty and whether this interaction was influenced by modality. We re-analyzed data from 48 Dutch university students that were collected in the context of a vocabulary test development to assess in which modality test words should be presented. Participants carried out a word recognition task, where non-words and words of varying difficulty were presented in auditory, visual and audio-visual modalities. In addition, they completed a receptive vocabulary and an author recognition test to measure their exposure to literary texts. Our re-analyses showed that word difficulty interacted with reading experience in that frequent readers (i.e., with more exposure to written texts) were more accurate in recognizing difficult words than individuals who read less frequently. However, there was no evidence for an effect of input modality on word recognition accuracy, nor for interactions with word difficulty or reading experience. Thus, in our study, input modality did not influence word recognition accuracy. We discuss the implications of this finding and describe possibilities for future research involving other groups of participants and/or different languages.
PENGARUH PENGGUNAAN QUIZIZZ SEBAGAI LATIHAN SOAL TERHADAP HASIL BELAJAR SISWA KELAS V SD

The learning process during the Covid-19 pandemic has the potential to cause saturation in students. Variations in learning activities during the Covid-19 pandemic are a necessity for learning in elementary schools. Quizizz is a technology-based platform that has an interactive display and is attractive to the millennial generation. The use of quizzes in the learning process is an innovative effort made by the teacher. This study aims to determine the effect of using quizizz as a question practice platform on the learning outcomes of fifth grade elementary school students. This study used a quasi-experimental method with a pretest-posttest control group design. The sample was selected by random sampling method with a population of class V SDN Kepanjen II Jombang. Research data in the form of student learning outcomes obtained by test techniques. Data analysis using t test. The results showed that t count was greater than t table, so the research hypothesis was accepted. The use of quizizz as a question practice platform has a positive effect on learning outcomes of fifth grade elementary school students. The implication of this research is the need to use technology-based platforms in learning to create pleasant learning conditions in elementary schools.
The Practicality of Problem Solving E-Book in Plant Growth and Development Material to Train Critical Thinking Skills

The ability to solve problems or problem-solving steps include identify, define, enumerate, analyze, list, and self-correct. Each of them can be used to practice critical thinking skills. The purpose of this research was to produce a practical problem-solving based e-book on plant growth and development topic in order to facilitate students' critical thinking skills. This study used a 4-D development model namely Define, Design, Develop, and Disseminate, but the disseminate stage was not conducted. Limited trials were conducted on 20 students of class XII IPA 2 of SMA Negeri 16 Surabaya. The research instruments were readability sheet, test sheet, questionnaire sheet for student and teacher respons. The data were analyzed quantitative descriptively. The practicality of the e-book was reviewed from the results of the readability test, critical thinking skills, student and teacher responses. The results of the study indicated that the readability test results categorrized as level 12 which was suitable for use by XII grade high school students. E-Book was stated to be very good for practicing critical thinking skills of 85,26% completeness indicators. Student and teacher responses showed a very positive results with a score of 97.4% and 100% respectively.&#x0D; Keywords: practicality of e-books, critical thinking skills, plant growth and development.
Grounding Awareness on Belief Bases

We introduce a multi-agent logic of explicit, implicit belief and awareness with a semantics using belief bases. The novelty of our approach is that an agent’s awareness is not a primitive but is directly computed from the agent’s belief base. We prove soundness and completeness of the logic relative to the belief base semantics. Furthermore, we provide a polynomial embedding of the logic of propositional awareness into it.
Early Detection of Breast Cancer Using Ensemble Machine Learning Algorithm

As showed by World Health Organization (WHO), Breast cancer growth is the most incessant disease among ladies, 627,000 ladies died due to breast cancer in the year 2018, which implies about 15 present of all cancer deaths among ladies. In order to improve breast cancer results and endurance, early recognition is significant. For detecting breast cancer growth early for the most part AI techniques are used. Right now proposed flexible outfit utilizing ensemble procedure for examining breast cancer using Wisconsin Breast Cancer (WBC) dataset. The purpose of the introduced paper is to consider and explain how different classification algorithms works on our dataset and to identify best algorithm for outfit models, for example, Random Forest Voting Ensemble and XGBoost work and how ensemble systems make the performance of the predictive models better by improving their precision and accuracy.
Enhanced Imperialist Competitive Algorithm for Optimal Structural Design

Solution of complex engineering problems by meta-heuristics, requires powerful operators to maintain sufficient diversification as well as proper intensification during the search. Standard Imperialist Competitive Algorithm, ICA, delays the search intensification by propagating it via a number of artificial empires that compete each other until one concurs the others. An Enhanced Imperialist Competitive Algorithm is developed here by adding an evolutionary operator to the standard ICA followed by greedy replacement; in order to improve its effectiveness. The new operator introduces a walk step directed from the less fit to the fitter individual in each pair of the search agents together with a random scaling and pick up scheme. EICA performance is then compared with ICA as well as GA, PSO, DE, CBO, TLBO, SOS; first in a set of fifteen test functions. Second, a variety of continuous and discrete engineering benchmarks and structural sizing problems are solved to evaluate EICA in constrained optimization. In this regard, a diversity index is traced as well as the other convergence metrics. The results exhibit considerable improvement of the algorithm by the proposed features of EICA and its competitive performance with respect to the other treated methods.
3D Reconstruction Using SfM with Sequence Images in Hadoop

Reconstructing a 3D structure from 2D images is a hot research topic in computer vision, and many approaches have been proposed to solve this problem in recent years.The structure from motion (SfM) algorithm has the potential to successfully reconstruct geometry from an image set, but the execution of the SfM algorithm on a single computer is only possible with small image sets.If an image set is large, the reconstruction speed decreases and it may even be too slow to complete 3D reconstruction.With the advent of cloud computing platforms, increasingly complex algorithms are being applied via cloud computing.Thus, the algorithm calculation speed for 3D reconstruction may be improved by taking advantage of the computational power of computer clusters.In this paper, we propose an algorithm for realizing 3D reconstruction from sequence images on the Hadoop cluster.Our test results reveal that, using Hadoop, we can achieve fast 3D reconstruction.
Kelayakan LKS Berbasis Auditory Intellectually Repetition sebagai Bahan Ajar Alternatif Siswa

Tujuan dari penelitian ini yaitu untuk mengetahui validitas dan keterbacaan LKS berbasis Auditory Intellectually Repetition (AIR). Penelitian ini merupakan penelitian deskriptif kuantitatif dengan tiga tahap define, desain dan develop. Data penelitian dikumpulkan menggunakan lembar validasi dan angket keterbacaan LKS. Lembar kerja siswa yang telah disusun kemudian divalidasi oleh 3 validator ahli dan dilakukan uji keterbacaan kepada 21 siswa kelas VII. Adapun hasil penelitian pengembangan LKS berbasis Auditory Intellectually Repetition valid digunakan (skor: 3,28 dengan kategori valid), keterbacaan LKS dinyatakan baik (skor: 3,41 dengan kategori baik). Sehingga dapat disimpulkan bahwa LKS berbasis Auditory Intellectually Repetition layak dan dapat digunakan sebagai bahan ajar alternatif siswa.The Eligibility of Auditory Intellectually Repetition-Based Worksheets as Alternative Teaching Materials for StudentsAbstractThe purpose of this study was to determine the validity and legibility of Auditory Intellectually Repetition (AIR) based worksheets. This research is a descriptive quantitative research with three stages i.e define, design and develop. The research data were collected using a validation sheet and a student worksheet readability questionnaire. The student worksheets that had been compiled were then validated by 3 expert validators and readability tests were carried out on 21 class VII students. The results of research on the development of Auditory Intellectually Repetition-based worksheets were valid (score: 3.28 in the valid category), the readability of the worksheets was declared good (score: 3.41 in the good category). So it can be concluded that Auditory Intellectually Repetition-based worksheets was eligible and can be used as alternative teaching materials for students.
Designing Software Application Decision With Multi Criteria Android-based Analytic Network Process Algorithm

Multi-criteria decision-making is one of the techniques in determining a choice of several alternative options. There are several methods in making decisions, one of which is ANP (Analytic Network Process). ANP is a development of AHP (Analytic Hierarchy Process) method. AHP is a hierarchical method in which any existing criteria will be compared first which will produce an ordinary matrix. While ANP is a network method that compares all things including criteria and alternatives that exist so as to obtain a super matrix is very complex. Currently ANP method is not only calculated but already there is software support one of them is Super Decisions. The software can be used to generate a decision by the ANP method. Every software must have advantages and disadvantages. That's why there is a need for software development that has been there before to appear new software better.