Reviewerly/exHarmony huggingface dataset:
PERBANDINGAN FUZZY TIME SERIES DENGAN METODE CHEN DAN METODE S. R. SINGH (Studi Kasus : Nilai Impor di Jawa Tengah Periode Januari 2014 – Desember 2019)

Import is one of the efforts of an area to meet the needs of its population in order to stabilize prices and maintain stock availability. The value of imports in Central Java throughout 2016 amounted to 8811.05 Million US Dollars. The value of imports in Central Java is the top 10 in all provinces in Indonesia with a percentage of 6.50%. Import data in Central Java is included in the time series data category. To maintain the stability of imports in Central Java, it is deemed necessary to make a plan based on a statistical model. One of the time series models that can be applied is the fuzzy time series model with the Chen method approach and the S. R. Singh method because the method is suitable for cyclical patterned data with monthly time periods such as Import data in Central Java. Important concepts in the preparation of the model are fuzzy sets, membership functions, set basic operators, fuzzy variables, universe sets and domains. The fuzzy time series modeling procedure is carried out through several stages, namely the determination of universe discourse which is divided into several intervals, then defines the fuzzy set so that it can be performed fuzzification. After that the fuzzy logical relations and fuzzy logical group relations are determined. The accuracy calculation in both methods uses symmetric Mean Absolute Percentage Error (sMAPE). In this study the sMAPE value obtained in the Fuzzy Time Series Chen method of 10.95% means that it shows good forecasting ability. While the sMAPE value on the Fuzzy Time Series method of S. R. Singh method by 5.50% shows very good forecasting ability. It can be concluded that the sMAPE value in the S. R. Singh fuzzy time series method is better than the Chen method.Keywords: Import value, fuzzy time series , Chen, S. R. Singh, sMAPE
Contextual Word Representation and Deep Neural Networks-based Method for Arabic Question Classification

Contextual continuous word representation showed promising performances in different natural language processing tasks.It stems from the fact that these word representations consider the context in which a word appears.But until recently, very little attention was paid to the contextual representations in Arabic question classification task.In the present study, we employed a contextual representation called Embeddings from Language Models (ELMo) to extract semantic and syntactic relations between words.Then, we build different deep neural models according to three types: Simple models, CNN and RNN mergers models, and Ensemble models.These models are trained on Arabic questions corpus to optimize the cross entropy loss given questions representations and their expected labels.The dataset consists of 3173 questions labeled according the Arabic taxonomy and an updated version of the Li & Roth taxonomy.We performed various comparisons with models based on the widely known contextfree word2vec word representation.These evaluations confirm that ELMo representation achieves top performances.The best model scores up to 94.17%, 94.07%, 94.17% in accuracy, macro F1 score, and weighted F1 score, respectively.
Quantum Weighted Model Counting

In Weighted Model Counting (WMC) we assign weights to Boolean literals and we want to compute the sum of the weights of the models of a Boolean function where the weight of a model is the product of the weights of its literals. WMC was shown to be particularly effective for performing inference in graphical models, with a complexity of $O(n2^w)$ where $n$ is the number of variables and $w$ is the treewidth. In this paper, we propose a quantum algorithm for performing WMC, Quantum WMC (QWMC), that modifies the quantum model counting algorithm to take into account the weights. In turn, the model counting algorithm uses the algorithms of quantum search, phase estimation and Fourier transform. In the black box model of computation, where we can only query an oracle for evaluating the Boolean function given an assignment, QWMC solves the problem approximately with a complexity of $\Theta(2^{\frac{n}{2}})$ oracle calls while classically the best complexity is $\Theta(2^n)$, thus achieving a quadratic speedup.
Building a Multi-Domain Neural Machine Translation Model Using Knowledge Distillation.

Lack of specialized data makes building a multi-domain neural machine translation tool challenging. Although emerging literature dealing with low resource languages starts to show promising results, most state-of-the-art models used millions of sentences. Today, the majority of multi-domain adaptation techniques are based on complex and sophisticated architectures that are not adapted for real-world applications. So far, no scalable method is performing better than the simple yet effective mixed-finetuning, i.e finetuning a generic model with a mix of all specialized data and generic data. In this paper, we propose a new training pipeline where knowledge distillation and multiple specialized teachers allow us to efficiently finetune a model without adding new costs at inference time. Our experiments demonstrated that our training pipeline allows improving the performance of multi-domain translation over finetuning in configurations with 2, 3, and 4 domains by up to 2 points in BLEU.
Automatic Aircraft Collisions Algorithm Development for Civil Aircraft

This study provides significant contributions to solve a problem of worldwide utmost interest: Collision Detection and Resolution CDR, due to the constant increase in air traffic. Based on the geometry equations exposed in this paper, numerical simulations for UAV's within 3D space have been carried on and a detailed analysis of TCAS operating around another UAV. On this basis, a specific algorithm for collision avoidance in case of multiple aircraft has been developed. An important contribution to the study is the collision detection in random velocity multiple objects environment. The simulations have been conducted for two significant UAV group configurations subjected to potential risk of collision.
Haze Removal with Fusion of Local and Non-Local Statistics

Most of the outdoor images suffer from contrast degradation caused by fog and haze. Two statistical frameworks have been proposed in recent years that exploit local (dark channel prior) and non-local (haze-lines) characteristics of hazy images for the estimation of scene configurations and the restoration of scene albedo. Both frameworks show intrinsic limitations due to the basic assumptions they rely on. In this paper we propose a novel dehazing method that combines the advantages of local and non-local dehazing methods. Exploiting their complementary statistical properties, we use the local features to regulate the estimation of non-local haze-lines for a better final restoration at challenging regions. Both quantitative and qualitative results validate the effectiveness of our proposed method over state-of-the-art frameworks.
PERBANDINGAN METODE SMOTE RANDOM FOREST DAN SMOTE XGBOOST UNTUK KLASIFIKASI TINGKAT PENYAKIT HEPATITIS C PADA IMBALANCE CLASS DATA

Hepatitis causes around 1.4 million people die every year. This number makes hepatitis to be the largest contagious disease in the number of deaths after tuberculosis. Liver biopsy is still the best method for diagnosing the stage of hepatitis C, but this method is an invasive, painful, expensive, and can cause complications. Non-invasively method needs to be developed, one of non-invasif method is machine learning. Random Forest and XGboost are classification methods that are often used, since they have many advantages over classical classification methods. The SMOTE algorithm can be used to improve the accuracy of predictions from imbalanced data. the data in this study have 24 independent variables in the form of patients self-data, hepatitis C symptoms, and laboratory test results. The dependent variable in this study is a binary category, namely the level of hepatitis C disease (fibrosis and cirrhosis). The results showed that the random forest and XGboost had an accuracy of around 74% but the recall value was less than 2%. SMOTE random forest dan SMOTE XGboost have an accuracy &amp; recall value more than 75%. SMOTE random forest has a higher accuracy for predicting fibrosis class while SMOTE XGboost is better in cirrhosis class. Variables that are more influental in determining hepatitis C stage are variables from laboratory test. Keyword : Fibrosis, Cirrhosis, Random Forest, SMOTE, XGboost
Domain-Invariant Prior Knowledge Guided Attention Networks for Robust Skull Stripping of Developing Macaque Brains

Non-human primates, especially macaque monkeys, with close phylogenetic relationship to humans, are highly valuable and widely used animal models for human neuroscience studies. In neuroimaging analysis of macaques, brain extraction or skull stripping of magnetic resonance imaging (MRI) is a crucial step for following processing. However, the current skull stripping methods largely focus on human brains, and thus often lead to unsatisfactory results when applying to macaque brains, especially for macaque brains during early development. In fact, the macaque brain during infancy undergoes regionally-heterogeneous dynamic development, leading to poor and age-variable contrasts between different anatomical structures, posing great challenges for accurate skull stripping. In this study, we propose a novel framework to effectively combine intensity information and domain-invariant prior knowledge, which are important guidance information for accurate brain extraction of developing macaques from 0 to 36 months of age. Specifically, we introduce signed distance map (SDM) and center of gravity distance map (CGDM) based on the intermediate segmentation results and fuse their information by Dual Self-Attention Module (DSAM) instead of local convolution. To evaluate the performance, we adopt two large-scale and challenging MRI datasets from rhesus macaques and cynomolgus macaques, respectively, with totally 361 scans from two different scanners with different imaging protocols. We perform cross-validation by using one dataset for training and the other one for testing. Experimental results show the robustness of our plug-and-play method on cross-source MRI datasets without any transfer learning.
Incremental Learning of Latent Forests

In the analysis of real-world data, it is useful to learn a latent variable model that represents the data generation process. In this setting, latent tree models are useful because they are able to capture complex relationships while being easily interpretable. In this paper, we propose two incremental algorithms for learning forests of latent trees. Unlike current methods, the proposed algorithms are based on the variational Bayesian framework, which allows them to introduce uncertainty into the learning process and work with mixed data. The first algorithm, incremental learner, determines the forest structure and the cardinality of its latent variables in an iterative search process. The second algorithm, constrained incremental learner, modifies the previous method by considering only a subset of the most prominent structures in each step of the search. Although restricting each iteration to a fixed number of candidate models limits the search space, we demonstrate that the second algorithm returns almost identical results for a small fraction of the computational cost. We compare our algorithms with existing methods by conducting a comparative study using both discrete and continuous real-world data. In addition, we demonstrate the effectiveness of the proposed algorithms by applying them to data from the 2018 Spanish Living Conditions Survey. All code, data, and results are available at https://github.com/ferjorosa/incremental-latent-forests.
Impact of OVL Variation on AUC Bias Estimated by Non-parametric Methods

The area under the ROC curve (AUC) is the most commonly used index in the ROC methodology to evaluate the performance of a classifier that discriminates between two mutually exclusive conditions. The AUC can admit values between 0.5 and 1, where values close to 1 indicate that the model of classification has a high discriminative power. The overlap coefficient (OVL) between two density functions is defined as the common area between both functions. This coefficient is used as a measure of agreement between two distributions presenting values between 0 and 1, where values close to 1 reveal total overlapping densities. These two measures were used to construct the arrow plot to select differential expressed genes. A simulation study using the bootstrap method is presented in order to estimate AUC bias and standard error using empirical and kernel methods. In order to assess the impact of the OVL variation on the AUC bias, samples from various continuous distributions were simulated considering different values for its parameters and for fixed OVL values between 0 and 1. Samples of dimensions 15, 30, 50 and 100 and 1000 bootstrap replicates for each scenario were considered.
Polish Python: A Short Report from a Short Experiment

Using a programming language based on English can pose an obstacle for learning programming, especially at its early stage, for students who do not understand English. In this paper, however, we report on an experiment in which higher-education students who have some knowledge of both Python and English were asked to solve programming exercises in a Polish-language-based version of Python. The results of the survey performed after the experiment indicate that even among the students who both know English and learned the original Python language, there is a group of students who appreciate the advantages of the translated version.
PERBANDINGAN METODE K-NEAREST NEIGHBOR DAN ADAPTIVE BOOSTING PADA KASUS KLASIFIKASI MULTI KELAS

The company's financial health provides an indication of company’s performance that is useful for knowing the company's position in industrial area. The company's performance needs to be predicted to knowing the company's progress. K-Nearest Neighbor (KNN) and Adaptive Boosting (AdaBoost) are classification methods that can be used to predict company's performance. KNN classifies data based on the proximity of the data distance while AdaBoost works with the concept of giving more weight to observations that include weak learners. The purpose of this study is to compare the KNN and AdaBoost methods to find out better methods for predicting company’s performance in Indonesia. The dependent variable used in this study is the company's performance which is classified into four classes, namely unhealthy, less healthy, healthy, and very healthy. The independent variables used consist of seven financial ratios namely ROA, ROE, WCTA, TATO, DER, LDAR, and ROI. The data used are financial ratio data from 575 companies listed on the Indonesia Stock Exchange in 2019. The results of this study indicate that the prediction of company’s performance in Indonesia should use the AdaBoost method because it has a classification accuracy of 0,84522 which is greater than the KNN method’s accuracy of 0,82087. Keywords: company’s performance, classification, KNN and AdaBoost, classification accuracy.
Thermal entanglement in a spin-1/2 Ising–Heisenberg butterfly-shaped chain with impurities*

We investigate the effect of impurities on the thermal entanglement in a spin-1/2 Ising–Heisenberg butterfly-shaped chain, where four interstitial Heisenberg spins are localized on the vertices of a rectangular plaquette in a unit block. By using the transfer-matrix approach, we numerically calculate the partition function and the reduced density matrix of this model. The bipartite thermal entanglement between different Heisenberg spin pairs is quantified by the concurrence. We also discuss the fluctuations caused by the impurities through the uniform distribution and the Gaussian distribution. Considering the effects of the external magnetic field, temperature, Heisenberg and Ising interactions as well as the parameter of anisotropy on the thermal entanglement, our results show that comparing with the case of the clean model, in both the two-impurity model and the impurity fluctuation model the entanglement is more robust within a certain range of anisotropic parameters and the region of the magnetic field where the entanglement occurred is also larger.
Enlightenment and Influence of Water Control Elements in Traditional Literary Works on Modern River Overflow Control

Song, J. and Liu, H.Z., 2020. Enlightenment and influence of water control elements in traditional literary works on modern river overflow control. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 849–853. Coconut Creek (Florida), ISSN 0749-0208.The modern river overflow treatment method violates the natural law, seriously damages the water environment such as the river course, and causes the ecological environment to be destroyed. There are many elements about water control in our traditional literature works, and a lot of experience has been accumulated in river overflow treatment. Based on this, this paper puts forward the enlightenment and influence of water control elements in traditional literary works on modern river overflow control. This paper analyzes the relevant historical stages of water control in traditional literary works, so as to fully understand the content of ancient water control elements in China, and introduce them into the modern river overflow treatment, so as to obtain the Enlightenment of modern river overflow control.
Revisiting Multipath Congestion Control for Virtualized Cloud Environments

Virtualized datacenters are often designed from scratch with multiple, redundant paths. Yet the majority of the existing congestion control schemes for virtual machines or containers are variants based on single-path TCP design. Lacking the flexibility of leveraging underlying paths, these schemes cannot further improve the utilization of datacenter networks, or mitigate hotspot links. In this paper, we examine the performance of multipath congestion control design on typical hypervisor and container virtualization platforms. We observe that, the involvement of virtual switch, together with the multi-tenancy nature on these platforms, poses new challenges when handling multipath traffic. Through realworld experiments with production-grade applications, we further reveal that, while multipath congestion control increases per-connection throughput and achieves better traffic balancing, it experiences performance degradation when the number of connections is abruptly increased or there exist path-sharing subflows. These issues are due to the enforced QoS policies and interface mapping schemes applied by virtual switch. To this end, we present vMCC, a practical solution which incorporates explicit congestion notification (ECN) support on virtual switches and ECN-aware multipath congestion control algorithms. We show by comprehensive evaluations that vMC-C improves throughput, round trip time, fairness, and energy efficiency for cloud datacenter traffic, and subsequently benefits typical cloud workloads.
Revisiting Vangelis: Sonic Citation and Narration in the Score for Blade Runner 2049

The popular narrative that developed to explicate this seemingly sudden shift was that Jóhannsson's music didn't adequately cite Vangelis' original score for the 1982 Blade Runner.This article explores the extratextual musical citation present in Zimmer and Wallfisch's score for Blade Runner 2049.While only one cue from the original Blade Runner is directly cited in the film, I propose that the composers adoptVangelis' 1982 soundtrack as a primary compositional referent for their whole score and that his source material permeates the entire film: melodically, harmonically, and sonorously.By separately addressing the film's (i) sonorous and (ii) motivic citation of Vangelis' original material for Blade Runner, I hope to highlight the diverse array of narrational, structural and musical functions that musical citation can facilitate in scoring practice, even without audience identification of the source context.
Use of digital tools by PHC professionals as instrument for health decision-making

Abstract Background Investments have been made in precision public health strategies, as a way to better assess needs and evaluate the health services. Experiences point out that the computerization of health systems reduces costs and improves the quality of information. Primary Health Care (PHC) workers are key elements of this transformation, which can be enhanced with the use of digital health strategies. This paper aims to know the perception of the use of digital tools provided by the Ministry of Health by PHC workers in municipalities in the state of Ceará/Brazil. Methods Focus groups and field monitoring were carried out with different professional profiles. The inclusion criterion was the use of digital tools in their daily work. Questionnaires were built to conduct semi-structured interviews. The analyzes were made using content analysis method. Results Among the 21 participants (32-49 years old, mostly female),3 reported not having digital knowledge prior to the use of digital tools in their daily work. Mostly associated the use of digital tools with greater agility and ease in the work routine and greater accuracy of information. The challenges were mainly related to the use of the equipment(tablet),such as fear of breakage, theft and problems in the synchronization of the information system. There was a consensus that the data collected are useful, however they are underutilized for the work process in PHC. Conclusions The use of digital tools in the health work process is able to foster the critical view of professionals for analysis and decision making, pointing out strategies to face health issues in the territories. Pointed out as a facilitator in daily work, the use of digital tools does not exclude the use of paper sheets. Despite the greater supply of data, these are not used to their full potential, mainly due to insufficient time, given the numerous tasks for professionals. Key messages Carefully planned, developed and deployed digital interventions are powerful tools to improve health care system performance. The use of digital tools in the health work process is able to foster the critical view for analysis and decision making, pointing out strategies to face health issues in the territories.
Driving Mobile Robots using a Deep LSTM Architecture: An Experimental Approach

Driving a mobile robot consists in moving the robot to its goal position quickly, without colliding with obstacles, and with no deviations from the planned path. This process requires a skilled pilot, with expertise and well trained. Among the various techniques available to help pilots to drive mobile robots, many of them are based on automatic learning approaches, as deep learning. This work presents the proposal of a new approaching for applications using a deep Long-Short Term Memory (LSTM) architecture, to assist the driving activities in mobile robots, using data extracted from an expert pilot as the main source of learning data. The main contributions of this proposal are i) a new application of the architecture for deep LSTM; ii) a new information data fusion strategy in the guidance command stage; and iii) a large number of tests in scenarios using a real mobile robot.