Reviewerly/exHarmony huggingface dataset:
Pengaruh Strategi Pembelajaran Aktif Tipe Giving Question and Getting Answer terhadap Hasil Belajar Bahasa Indonesia Siswa Sekolah Dasar

This study aims to look at the effect of an active learning strategy type giving questions and getting answers to Indonesian primary school students' learning outcomes. This study uses an experimental research method in which there are treatments for the control class and the experimental class. The final results of the study showed the results of the analysis of the influence of active learning strategies type giving a question and getting an answer to the results of Indonesian language learning of third-grade students at SDN 002 Sungai Pinang, Samarinda City in the affective domain had an average affective assessment in the good predicate. This can be seen from the results of the hypothesis test thitung is 1.208 with ttable is 2.006 so that it can be written tcount &lt;ttable (1.208 &lt;2.006), which can be interpreted as Ha is rejected and H0 is accepted. Based on research results in the cognitive realm have the final test results (post-test), the experimental class obtained an average value of 77.69, the highest value of 100 and the lowest value of 60. The final test results (post-test), the control class obtained an average value of 49.64, the highest value is 80, and the lowest value is 20. This can also be seen through the hypothesis test results using the t test (independent sample t test), the tcount = 10.175 and ttable = 1.67. Hypothesis test tcount&gt; ttable (10,175&gt; 1,67). Based on the description, it can be concluded that there is an influence of active learning strategy type giving a question and getting an answer to Indonesian students' learning outcomes in the cognitive realm of class III in SDN 002 Sungai Pinang Samarinda City in 2019/2020. Hence, it shows that the hypothesis is accepted.
A Prediction Approach for Video Hits in Mobile Edge Computing Environment

Smart device users spend most of the fragmentation time in the entertainment applications such as videos and films. The migration and reconstruction of video copies can improve the storage efficiency in distributed mobile edge computing, and the prediction of video hits is the premise for migrating video copies. This paper proposes a new prediction approach for video hits based on the combination of correlation analysis and wavelet neural network (WNN). This is achieved by establishing a video index quantification system and analyzing the correlation between the video to be predicted and already online videos. Then, the similar videos are selected as the influencing factors of video hits. Compared with the autoregressive integrated moving average (ARIMA) and gray prediction, the proposed approach has a higher prediction accuracy and a broader application scope.
Operational Research Literature as a Use Case for the Open Research Knowledge Graph

The Open Research Knowledge Graph (ORKG) provides machine-actionable access to scholarly literature that habitually is written in prose. Following the FAIR principles, the ORKG makes traditional, human-coded knowledge findable, accessible, interoperable, and reusable in a structured manner in accordance with the Linked Open Data paradigm. At the moment, in ORKG papers are described manually, but in the long run the semantic depth of the literature at scale needs automation. Operational Research is a suitable test case for this vision because the mathematical field and, hence, its publication habits are highly structured: A mundane problem is formulated as a mathematical model, solved or approximated numerically, and evaluated systematically. We study the existing literature with respect to the Assembly Line Balancing Problem and derive a semantic description in accordance with the ORKG. Eventually, selected papers are ingested to test the semantic description and refine it further.
ComputeOps: Container for High Performance Computing

The High Performance Computing (HPC) domain aims to optimize code in order to use the latest multicore and parallel technologies including specific processor instructions. In this computing framework, portability and reproducibility are key concepts. A way to handle these requirements is to use Linux containers. These “light virtual machines” allow to encapsulate applications within its environment in Linux processes. Containers have been recently rediscovered due to their abilities to provide both multi-infrastructure environnement for developers and system administrators and reproducibility due to image building file. Two container solutions are emerging: Docker for microservices and Singularity for computing applications. We present here the status of the ComputeOps project which has the goal to study the benefit of containers for HPC applications.
The Effectiveness Of Using Text Messages Reminder On Adherence With Tuberculosis Patients: A Systematic Review

Non-adherence is a factor that inhibits the completion of TB cases globally. Some studies suggest that the use of digital technology in the form of text message reminders can reduce the risk of non-adherence. The purpose of writing this article is to analyze the effectiveness of text message reminders on adherence to tuberculosis (TB) patients. The method in preparing this Systematic review is based on literature studies from various electronic databases, including Scopus, ScienceDirect, ProQuest, and Sage by conducting a comprehensive review using the PRISMA guidelines. The keywords used are "Text Messaging" AND "medication adherence" AND "tuberculosis". There are 11 original articles with 9 articles using the Randomized Control Trials (RCTs) research method and 2 articles using the Quasy-experimental method that fits the inclusion criteria. The use of text message reminder interventions is effective and can be used as an optional method of increasing adherence to TB patients.
Paraphrase Generation via Adversarial Penalizations

Paraphrase generation is an important problem in Natural Language Processing (NLP) that has been addressed with neural network-based approaches recently.This paper presents an adversarial framework to address the paraphrase generation problem in English.Unlike previous methods, we employ the discriminator output as penalization instead of using policy gradients, and we propose a global discriminator to avoid the Monte-Carlo search.In addition, this work use and compare different settings of input representation.We compare our methods to some baselines in the Quora question pairs dataset.The results show that our framework is competitive against the previous benchmarks.
Witness algebra and anyon braiding

Abstract Topological quantum computation employs two-dimensional quasiparticles called anyons. The generally accepted mathematical basis for the theory of anyons is the framework of modular tensor categories. That framework involves a substantial amount of category theory and is, as a result, considered rather difficult to understand. Is the complexity of the present framework necessary? The computations of associativity and braiding matrices can be based on a much simpler framework, which looks less like category theory and more like familiar algebra. We introduce that framework here.
Interactive Music and Synchronous Reactive Programming

This paper presents Skini, a programming methodology and an execution environment for interactive structured music. With this system, the composer programs his scores in the HipHop.js synchronous reactive language. They are then executed, or played, in live concerts, in interaction with the audience. The system aims at helping composers to find a good balance between the determinism of the compositions and the nondeterminism of the interactions with the public. Each execution of a Skini score yields to a different but aesthetically consistent interpretation. This work raises many questions in the musical fields. How to combine composition and interaction? How to control the musical style when the audience influences what is to play next? What are the possible connections with generative music? These are important questions for the Skini system but they are out of the scope of this paper that focuses exclusively on the computer science aspects of the system. From that perspective, the main questions are how to program the scores and in which language? General purpose languages are inappropriate because their elementary constructs (i.e., variables, functions, loops, etc.) do not match the constructions needed to express music and musical constraints. We show that synchronous programming languages are a much better fit because they rely on temporal constructs that can be directly used to represent musical scores and because their malleability enables composers to experiment easily with artistic variations of their initial scores. The paper mostly focuses on scores programming. It exposes the process a composer should follow from his very first musical intuitions up to the generation of a musical artifact. The paper presents some excerpts of the programming of a classical music composition that it then precisely relates to an actual recording. Examples of techno music and jazz are also presented, with audio artifact, to demonstrate the versatility of the system. Finally, brief presentations of past live concerts are presented as an evidence of viability of the system.
An Inference Algorithm for Multi-label MRF-MAP Problems with Clique Size 100

In this paper, we propose an algorithm for optimal solutions to submodular higher order multi-label MRF-MAP energy functions which can handle practical computer vision problems with up to 16 labels and cliques of size 100. The algorithm uses a transformation which transforms a multi-label problem to a 2-label problem on a much larger clique. Earlier algorithms based on this transformation could not handle problems larger than 16 labels on cliques of size 4. The proposed algorithm optimizes the resultant 2-label problem using the submodular polyhedron based Min Norm Point algorithm. The task is challenging because the state space of the transformed problem has a very large number of invalid states. For polyhedral based algorithms the presence of invalid states poses a challenge as apart from numerical instability, the transformation also increases the dimension of the polyhedral space making the straightforward use of known algorithms impractical. The approach reported in this paper allows us to bypass the large costs associated with invalid configurations, resulting in a stable, practical, optimal and efficient inference algorithm that, in our experiments, gives high quality outputs on problems like pixel-wise object segmentation and stereo matching.
Product Insights: Analyzing Product Intents in Web Search

Web search engines are frequently used to access information about products. This has increased in recent times with the rising popularity of e-commerce. However, there is limited understanding of what users search for and their intents when it comes to product search on the web. In this work, we study search logs from Bing web search engine to characterize user intents and study user behavior for product search. We propose a taxonomy of product intents by analyzing product search queries. This itself is a challenging task given that only 15%-17% of queries in the web refer to products. We train machine learning classifiers with query log features to classify queries based on intent with an overall F1-score of 78%. We further analyze various characteristics of product search queries in terms of search metrics like dwell time, success, popularity and session-specific information.
LITERASI INFORMASI GURU PENDIDIKAN AGAMA BUDDHA DAN IMPLIKASINYA DALAM PEMBELAJARAN

This study aims to describe the information literacy of Buddhist Education teachers in Pati Regency covering aspects of information needs, access to information, and utilization of information in teaching. This study uses a qualitative method. The data collection technique of this study uses interviews, observation, and documentation. Data analysis using the Milles Hubberman model includes the stages of data collection, data reduction, data presentation, and drawing conclusions. The results of this study include: (1)The information needs of Buddhist Education teachers can be classified into information needs to improve teacher competence and information needs for career advancement; (2) access to information sourced from various locations, the teachers have also been able to analyze, organize, and save information well; (3) the teacher utilizes the information literacy results in teaching and learning activities in the school despite experiencing obstacles such as the lack of infrastructure to support learning.
Analysis of Resource-efficient Predictive Models for Natural Language Processing

In this paper, we presented an analyses of the resource efficient predictive models, namely Bonsai, Binary Neighbor Compression(BNC), ProtoNN, Random Forest, Naive Bayes and Support vector machine(SVM), in the machine learning field for resource constraint devices.These models try to minimize resource requirements like RAM and storage without hurting the accuracy much.We utilized these models on multiple benchmark natural language processing tasks, which were sentimental analysis, spam message detection, emotion analysis and fake news classification.The experiment results shows that the tree-based algorithm, Bonsai, surpassed the rest of the machine learning algorithms by achieve higher accuracy scores while having significantly lower memory usage.
Environmental Complexity Measurement Using Shannon Entropy

This paper presents a novel approach to measure the complexity of environments for complete coverage path planning (CCPP) approaches. For this measurement, the main challenge is to take various environmental factors together to measure the complexity of a complicated environment. The size of environments and the numbers of corners on maps are commonly taken into consideration. However, the size of robots to conduct CCPP navigation, the number of obstacles and the location of obstacles on maps are factors that cannot be ignored. To overcome the aforementioned challenge, we propose a method guided by the Shannon Entropy formula. Four environmental factors, that include area size, robot size, obstacle number, and obstacle locations, are all considered as inputs in our environmental complexity measurement. The experimental results show that our quantitative metric is consistent with visual observation, and therefore provides a new algorithmic approach to measuring spatial complexity of a 2-D map.
A comparison between CNNs and WFAs for Sequence Classification

We compare a classical CNN architecture for sequence classification involving several convolutional and max-pooling layers against a simple model based on weighted finite state automata (WFA).Each model has its advantages and disadvantages and it is possible that they could be combined.However, we believe that the first research goal should be to investigate and understand how do these two apparently dissimilar models compare in the context of specific natural language processing tasks.This paper is the first step towards that goal.Our experiments with five sequence classification datasets suggest that, despite the apparent simplicity of WFA models and training algorithms, the performance of WFAs is comparable to that of the CNNs.
LSTM-Based Online Prediction Method for Building Electric Load During COVID-19

Accurate prediction of electric load is critical to optimally controlling and operating buildings. It provides the opportunities to reduce building energy consumption and to implement advanced functionalities such as demand response in the context of smart grid. However, buildings are nonstationary and it is important to consider the underlying concept changes that will affect the load pattern. In this paper we present an online learning method for predicting building electric load during concept changes such as COVID-19. The proposed methods is based on online Long Short-Term Memory (LSTM) recurrent neural network. To speed up the learning process during concept changes and improve prediction accuracy, an ensemble of multiple models with different learning rates is used. The learning rates are updated in realtime to best adapt to the new concept while maintaining the learned information for the prediction.
LGPSolver - Solving Logic Grid Puzzles Automatically

Logic grid puzzle (LGP) is a type of word problem where the task is to solve a problem in logic.Constraints for the problem are given in the form of textual clues.Once these clues are transformed into formal logic, a deductive reasoning process provides the solution.Solving logic grid puzzles in a fully automatic manner has been a challenge since a precise understanding of clues is necessary to develop the corresponding formal logic representation.To meet this challenge, we propose a solution that uses a DistilBERT-based classifier to classify a clue into one of the predefined predicate types for logic grid puzzles.Another novelty of the proposed solution is the recognition of comparison structures in clues.By collecting comparative adjectives from existing dictionaries and utilizing a semantic framework to catch comparative quantifiers, the semantics of clues concerning comparison structures are better understood, ensuring conversion to correct logic representation.Our approach solves logic grid puzzles in a fully automated manner with 100% accuracy on the given puzzle datasets and outperforms state-of-the-art solutions by a large margin.
Learning to Pronounce Chinese Without a Pronunciation Dictionary

We demonstrate a program that learns to pronounce Chinese text in Mandarin, without a pronunciation dictionary.From non-parallel streams of Chinese characters and Chinese pinyin syllables, it establishes a many-to-many mapping between characters and pronunciations.Using unsupervised methods, the program effectively deciphers writing into speech.Its token-level character-to-syllable accuracy is 89%, which significantly exceeds the 22% accuracy of prior work.
IDSOU at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets

We introduce the IDSOU 1 submission for the WNUT-2020 task 2: identification of informative COVID-19 English Tweets.Our system is an ensemble of pre-trained language models such as BERT.We ranked 16th in the F1 score.
NILAI CINTA KASIH DALAM NOVEL NO PLACE LIKE HOME KARYA ALMA ARIDATHA DAN IMPLEMENTASINYA DALAM PEMBELAJARAN TEKS NOVEL KELAS XII SMA

ABSTRACT The purpose of this research is to describe and analyze intrinsic elements, the value of love, and the implementation of the value of love in Alma Aridatha's No Place Like Home novel in novel text learning in class XII high school students. This type of research is qualitative research with descriptive methods. The data source in this research is the No Place Like Home novel by Alma Aridatha. The results of the research were found as participants. First, there are seven intrinsic elements in Alma Aridatha's No Place Like Home novel, namely themes, characterizations, background, plot, point of view, language style, and mandate. Second, there are four values of love in Alma Aridatha's No Place Like Home novel, namely (1) 12 data the values of love for God; (2) 37 data the values of love for the family; (3) 12 data the values of love for others; and (4) 11 data the values of love for oneself. Third, the implementation of the value of love in Alma Aridatha's No Place Like Home novel in novel text learning class XII high school students 2013 curriculum with basic competencies related to three aspects of assessment, namely the assessment of knowledge, attitudes, and skills. Based on the results of the research, it is expected that the teacher suggests the No Place Like Home novel by Alma Aridatha which is easily understood by students in novel text learning in high school so that learning objectives are achieved. Kata Kunci: nilai cinta kasih, pembelajaran teks novel
Domestic Language Accent Detector Using MFCC and GMM

In day today life, speech is commonly used for communication purpose.So that its recognition has an important role.There are already recognition systems for English and that has been used in different automation and assistance purposes.But in the case of local languages it seems to be difficult in recognition.In the case of Malayalam the usage of words, its pronunciation as well as the meaning vary with respect to the regions.So the same word has different meaning in different areas and hence it become complex.This paper aims to recognize different accents of Malayalam in accounts with the place of the user.Initially a set of training data is acquired from different areas and from the data Mel Frequency Cepstral Co-efficients are extracted as features.A Gaussian Mixture Model is made in accordance with the data which uses Energy Maximization Algorithm.It recognizes the spoken accents and can be used for various home automation and virtual assisting technologies.
Data Visualization: A Practical Introduction: Kieran Healy [Book Review]

This book offers ding effective data graphics using the ggplot package for the R language. Although the implied audience is researchers in the social sciences, there is a lot of practical wisdom here for anyone who works with numerical data. The book treats both the theoretical principles of effective data visualization design as well as concrete guidance on how to integrate such wisdom into a slick data-analysis workflow in the R ecosystem. This practitioner-oriented approach is a very welcome addition to the literature: by covering both the whys and the hows of data visualization, this single volume swiftly equips researchers to build compelling graphics from their numerical data. It is clear that the author is passionate about his topic: you really feel that he is fed up with seeing sloppy data graphics in published academic work. Likewise, his commitment to free researchers from laborious scutwork is evident in the many productivity tips he is eager to provide for the R environment. The book's prose is engaging and chatty, and the style of instruction is unpretentious and practical. The manuscript itself is attractively typeset: one commendable feature is the direct integration of the many graphics alongside the corresponding passage of text, by setting them as sidenotes in the wide margins. This richly graphical approach makes the lessons engaging, tangible, and enjoyable to read.
The n-th Decay Rate of Coherence for Bell-diagonal States Under Quantum Channels

We study the degree to which the coherence of quantum states is affected by noise. We give the definition of the $n$-th decay rate and investigate the coherence of Bell-diagonal states under $n$ iterations of channels. We derive explicit formulas of the $n$-th decay rates based on $l_1$ norm of coherence, relative entropy of coherence and skew information-based coherence. It is found that the larger $n$ is, the faster the $n$-th decay rate decreases as the parameter $p$ of Bell-diagonal states increases. Moreover, for any fixed $n$, with the increase of $p$, Bell-diagonal states can be completely incoherent under generalized amplitude damping (GAD) channels, depolarization (DEP) channels and phase flip (PF) channels, while this is not the case for bit flip (BF) channels and bit-phase flip (BPF) channels. We also investigate the geometry of the relative entropy of coherence and skew information-based coherence of Bell-diagonal states under different channels when the $n$-th decay rate is one, i.e., the coherence is frozen. It is shown that compared with BF and BPF channels, when $n$ is large enough, the coherence of Bell-diagonal states will not be frozen under GAD, DEP and PF channels. For skew information-based coherence, similar properties of coherence freezing are found.