Reviewerly/exHarmony huggingface dataset:
Perbandingan Model Logistic Regression dan Artificial Neural Network pada Prediksi Pembatalan Hotel

Prediction for canceled booking hotels is an important part of hotel revenue management systems in the modern era. Because the predicted result can be used for the optimization of hotel performance. The application of machine learning will be very helpful for predicting canceled booking hotels because machine learning can process complex data. In this research, the proposed methods are Artificial Neural Network (ANN) and Logistic Regression. Later it will be done five times experiments with hyperparameter tuning to see which method is the most optimal to do prediction canceled booking hotel. From five times experiments, experiments number five (logistic regression with GridSearchCV) is the most optimal for predicting canceled booking hotels, with 79.77% accuracy, 85.86% precision, and 55.07% recall.
Implementation of Data Mining Using C4.5 Algorithm on Customer Satisfaction in Tirta Lihou PDAM

This application applies the C4.5 Algorithm to decide customer satisfaction, the C4.5 algorithm is one of the algorithms used to classify or segment, or group and it is predictive. This type of research is a classification with the concept of data mining involving 150 customers of PDAM Tirta Lihou in Totap Majawa Kab. Simalungun can be categorized as: "Satisfied and Dissatisfied". The meaning of Data Mining is an interdisciplinary subfield of computer science and statistics with the overall objective of extracting information (with intelligent methods) from data sets and converting information into understandable structures for further use. There are 5 criteria that can affect customer satisfaction, among others: Service Facilities (x1), Price Rates (x2), Smooth Water (x3), Corporate Image (x4), and Location (x5). The results of processing the C4.5 method using the RapidMiner Studio 5.3 software mean that Rapid Miner is a solution for analyzing data mining, text mining, and predictive analysis. Rapid Miner uses various descriptive and predictive techniques in providing insight to users so that they can make the best decisions with the level of accuracy, namely, class recall and class precision values, it is explained that the "Satisfied" category produces a class recall of 97.80% and a class precision of 97.80%. 98.89% and the "Not Satisfied" category resulted in a class recall of 98.31% and a class of precision of 96.67%. And the above accuracy results from the calculation of the C4.5 algorithm is 98.0%. Keywords: C4.5 Algorithm, Data Mining, Customer Satisfaction, PDAM Tirta Lihou
CPU Scheduling in Data Centers Using Asynchronous Finite-Time Distributed Coordination Mechanisms.

We propose an asynchronous iterative scheme which allows a set of interconnected nodes to distributively reach an agreement within a pre-specified bound in a finite number of steps. While this scheme could be adopted in a wide variety of applications, we discuss it within the context of task scheduling for data centers. In this context, the algorithm is guaranteed to approximately converge to the optimal scheduling plan, given the available resources, in a finite number of steps. Furthermore, being asynchronous, the proposed scheme is able to take into account the uncertainty that can be introduced from straggler nodes or communication issues in the form of latency variability while still converging to the target objective. In addition, by using extensive empirical evaluation through simulations we show that the proposed method exhibits state-of-the-art performance.
Quantifying nonclassicality of multimode bosonic fields via skew information

Abstract We quantify the nonclassicality of multimode bosonic field states by adopting an information-theoretic approach involving the Wigner-Yanase skew information. The fundamental properties of the quantifier such as convexity, superadditivity, monotonicity, and conservation relations are revealed. The quantifier is illustrated by a variety of typical examples, and applications to the quantification of nonclassical correlations are discussed. Various extensions are indicated.
Improved Single Haze Removal using Weighted Filter and Gaussian-Laplacian

Generally computer applications use digital images. Digital image plays a vital role in the analysis and explanation of data, which is in the digital form. Images and videos of outside scenes are generally affected by the bad weather environment such as haze, fog, mist etc. It will result in bad visibility of the scene caused by the lack of quality. This paper exhibits a study about various image defogging techniques to eject the haze from the fog images caught in true world to recuperate a fast and enhanced nature of fog free images. In this paper, we propose a simple but effective the weighted median (WM) filter was first presented as an overview of the standard median filter, where a nonnegative integer weight is assigned to each position in the filter window image .Gaussian and laplacian pyramids are applying Gaussian and laplacian filter in an image in cascade order with different kernel sizes of gaussian and laplacian filter .The dark channel prior is a type of statistics of the haze-free outdoor images. It is based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one-color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high-quality haze-free image. Results on a variety of outdoor haze images demonstrate the power of the proposed prior. Moreover, a high-quality depth map can also be obtained as a by-product of haze removal and Calculate the PSNR and MSE of three sample images.
Subspace coverings with multiplicities

Abstract We study the problem of determining the minimum number $f(n,k,d)$ of affine subspaces of codimension $d$ that are required to cover all points of $\mathbb{F}_2^n\setminus \{\vec{0}\}$ at least $k$ times while covering the origin at most $k - 1$ times. The case $k=1$ is a classic result of Jamison, which was independently obtained by Brouwer and Schrijver for $d = 1$ . The value of $f(n,1,1)$ also follows from a well-known theorem of Alon and Füredi about coverings of finite grids in affine spaces over arbitrary fields. Here we determine the value of this function exactly in various ranges of the parameters. In particular, we prove that for $k\geq 2^{n-d-1}$ we have $f(n,k,d)=2^d k-\left\lfloor{\frac{k}{2^{n-d}}}\right\rfloor$ , while for $n \gt 2^{2^d k-k-d+1}$ we have $f(n,k,d)=n + 2^d k -d-2$ , and obtain asymptotic results between these two ranges. While previous work in this direction has primarily employed the polynomial method, we prove our results through more direct combinatorial and probabilistic arguments, and also exploit a connection to coding theory.
Auto‐calibration Kalman filters for non‐linear systems with direct feedthrough

The problem of state estimation for non-linear systems with unknown inputs is discussed. The objective is to construct a non-linear filter where the unknown input affects both the state equation and measurement equation. An auto-calibration Kalman filter is proposed with three stages, where the first and second stages are the extension of the two-stage Kalman filter in non-linear system cases, and the third stage is the extended Kalman filter with correlated noise. Compared with the two-stage extended Kalman filter, the presented algorithm conducts a more reasonable linearization of the measurement equation in the third stage. As a result, auto-calibration is realized and more accurate and more stable state estimation can be obtained. Simulative and practical examples both demonstrate the validity and superiority of the presented auto-calibration Kalman filter. Furthermore, positioning experiments illustrate that the auto-calibration Kalman filter can obtain an accurate long-term heading estimation in pedestrian navigation and is easy to apply in engineering applications.
The implementation of Container as a Service (CaaS) cloud using openSUSE kubic

Currently, software development based on Development and Operations (DevOps) generally uses containers and Cloud Computing to support portability, reliability, scalability, and security. One combination of these two technologies is Container as a Service (CaaS) Cloud. From a number of previous researches, no one has studied the technical implementation and deployment of CaaS using openSUSE Kubic based on the Linux kernel and Linux operating system, as well as testing in it using Pods from the service and replication side. The test results prove that openSUSE Kubic is very easy and reliable to use for CaaS implementation and deployment, so it is very helpful for software developers in realizing software development more scalable and faster.
Generating a Citation Summary Based on Cited Sentences and the Implied Citation Emotions

This paper aims to generate an emotional citation summary for articles on the basis of the cited sentences and their accompanying emotions. The cited sentences to the same article were divided into three corpora according to their positive, negative or neutral emotion. Summaries were extracted from the three corpora respectively, and were then combined into a comprehensive emotional citation summary for the article. In the summary generation process, the BERT pre-training model was used to vectorize the cited sentences, Fuzzy c-means was used to cluster the vectorized citing sentences, and a LexRank + MMR combination model was applied to extract summary sentences from the clusters. The experimental results show that our algorithm outperforms all the baseline algorithms in generating citation summaries. Based on the content of the summary, the emotional citation summary was found comprehensively to feedback academic peers' evaluation of the article, in such a way as to more comprehensively describe the function and contribution of the article in the scientific community.
COMPARISON OF MACHINE LEARNING METHODS IN CLASSIFYING POVERTY IN INDONESIA IN 2018

Poverty is still one of the main problems in economic development besides inequality, unemployment, and economic growth. This study aims to model poverty directly using a discrete choice model, namely the machine learning classification method. The data used are imbalanced data where one of the categories is small enough so that the resample of both sampling method is used. In this study, several machine learning methods were applied, including the Decision Tree, Naïve Bayes, K-Nearest Neighbor (KNN), and Rotation Forest. The results show that the technique of using resample both samplings provides optimal results for the four machine learning methods. If viewed from the indicators of accuracy, specificity, sensitivity, AUC, and the highest Kappa coefficient produced, the best method is the KNN method. The KNN model has an accuracy value of 0.73 percent, sensitivity of 0.68 percent, specificity of 78 percent, and AUC of 0.73.
Automatic Setting of DNN Hyper-Parameters by Mixing Bayesian Optimization and Tuning Rules

Deep learning techniques play an increasingly important role in industrial and research environments due to their outstanding results. However, the large number of hyper-parameters to be set may lead to errors if they are set manually. The state-of-the-art hyper-parameters tuning methods are grid search, random search, and Bayesian Optimization. The first two methods are expensive because they try, respectively, all possible combinations and random combinations of hyper-parameters. Bayesian Optimization, instead, builds a surrogate model of the objective function, quantifies the uncertainty in the surrogate using Gaussian Process Regression and uses an acquisition function to decide where to sample the new set of hyper-parameters. This work faces the field of Hyper-Parameters Optimization (HPO). The aim is to improve Bayesian Optimization applied to Deep Neural Networks. For this goal, we build a new algorithm for evaluating and analyzing the results of the network on the training and validation sets and use a set of tuning rules to add new hyper-parameters and/or to reduce the hyper-parameter search space to select a better combination.
Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline

Pre-trained deep language models~(LM) have advanced the state-of-the-art of text retrieval. Rerankers fine-tuned from deep LM estimates candidate relevance based on rich contextualized matching signals. Meanwhile, deep LMs can also be leveraged to improve search index, building retrievers with better recall. One would expect a straightforward combination of both in a pipeline to have additive performance gain. In this paper, we discover otherwise and that popular reranker cannot fully exploit the improved retrieval result. We, therefore, propose a Localized Contrastive Estimation (LCE) for training rerankers and demonstrate it significantly improves deep two-stage models.
Towards Natural Language Question Answering Over Earth Observation Linked Data Using Attention-Based Neural Machine Translation

With an increase in Geospatial Linked Open Data being adopted and published over the web, there is a need to develop intuitive interfaces and systems for seamless and efficient exploratory analysis of such rich heterogeneous multi-modal datasets. This work is geared towards improving the exploration process of Earth Observation (EO) Linked Data by developing a natural language interface to facilitate querying. Questions asked over Earth Observation Linked Data have an inherent spatio-temporal dimension and can be represented using GeoSPArql. This paper seeks to study and analyze the use of RNN-based neural machine translation with attention for transforming natural language questions into GeoSPArql queries. Specifically, it aims to assess the feasibility of a neural approach for identifying and mapping spatial predicates in natural language to GeoSPARQL's topology vocabulary extension including - Egenhofer and RCC8 relations. The queries can then be executed over a triple store to yield answers for the natural language questions. A dataset consisting of mappings from natural language questions to GeoSPArql queries over the Corine Land Cover(CLC) Linked Data has been created to train and validate the deep neural network. From our experiments, it is evident that neural machine translation with attention is a promising approach for the task of translating spatial predicates in natural language questions to GeoSPArql queries.
Curvit: An open-source Python package to generate light curves from UVIT data

Curvit is an open-source Python package that facilitates the creation of light curves from the data collected by the Ultra-Violet Imaging Telescope (UVIT) onboard AstroSat, India's first multi-wavelength astronomical satellite. The input to Curvit is the calibrated events list generated by the UVIT-Payload Operation Center (UVIT-POC) and made available to the principal investigators through the Indian Space Science Data Center. The features of Curvit include (i) automatically detecting sources and generating light curves for all the detected sources and (ii) custom generation of light curve for any particular source of interest. We present here the capabilities of Curvit and demonstrate its usability on the UVIT observations of the intermediate polar FO Aqr as an example. Curvit is publicly available on GitHub at https://github.com/prajwel/curvit.
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks

The detection of fraud in accounting data is a long-standing challenge in financial statement audits. Nowadays, the majority of applied techniques refer to handcrafted rules derived from known fraud scenarios. While fairly successful, these rules exhibit the drawback that they often fail to generalize beyond known fraud scenarios and fraudsters gradually find ways to circumvent them. In contrast, more advanced approaches inspired by the recent success of deep learning often lack seamless interpretability of the detected results. To overcome this challenge, we propose the application of adversarial autoencoder networks. We demonstrate that such artificial neural networks are capable of learning a semantic meaningful representation of real-world journal entries. The learned representation provides a holistic view on a given set of journal entries and significantly improves the interpretability of detected accounting anomalies. We show that such a representation combined with the networks reconstruction error can be utilized as an unsupervised and highly adaptive anomaly assessment. Experiments on two datasets and initial feedback received by forensic accountants underpinned the effectiveness of the approach.
3-D shape reconstruction of non-uniform reflectance surface based on pixel intensity, pixel color and camera exposure time adaptive adjustment

Abstract High dynamic range 3-D shape measurement is a challenge. In this work, we propose a novel method to solve the 3-D shape reconstruction of high-reflection and colored surfaces. First, we propose a method to establish a fast pixel-level mapping between the projected image and the captured image. Secondly, we propose a color texture extraction method using a black-and-white (B/W) camera and a pixel-level projection color adjustment method. Third, we give an optimal projection fringe modulation/background intensity ratio. Fourth, we propose a method for estimating the reflectivity of the object surface and ambient light interference, and a method for adjusting the projection intensity at the pixel level and a method for estimating the optimal exposure time. Experiments show that, compared with the existing methods, the proposed method not only can obtain high-quality captured images, but also has higher measurement efficiency and wider application range.
Monochronic Vs. Polychronic: A Profile of Academic Time Use Among Online Distance Education Students

The results presented are part of a doctoral thesis which is being finalized. The research is centred on the online distance learning student experience in higher education, considering the variable time management, its relationship with technologies, and the virtualization of teaching and learning. Exploratory in nature, the research takes the form of a case study using a mixed methodology. One of the main objectives of the study is to contribute to the construction of a theoretical framework on the distance learning student time variable. We present a profile of academic time use by e-students (monochronic or polychronic) and suggest an intermediate profile. The conclusions point out that the profile of academic time use varies according to the degree of the course attended. It was found that undergraduate students show a more polychronic profile than masters and doctoral students.
Information representation space in artificial and biological neural networks

Convolutional neural networks are often used as a model of the primate visual system. However, it is often overlooked how a task that the network performs and statistics of the training set affect the representation of information in the latent space of the model. This study demonstrates that the properties of artificial neurons in the first two convolutional layers represent the signal statistics (correlation coefficients R=0.63 and R=0.44), whereas the similarity between the space of the problem and information encoding in hidden layers gradually increases in the final convolutional layers (R=0.35), reaching a value of 0.73 in the fully-connected layers. At the final stages of the processing, a category is encoded using a unique set of features, characterized by no or little overlapping with other categories. Thus, in order to increase similarity between the visual system and its model, it is important to maintain a training set and a problem space of the model coherent to those of a biological organism.
Applying various satellite image processing methods on Aster and Landsat ETM+ data to identify and separate the alteration zones around gold mine of Akhtarchi, Khomein, Iran

The study area is located 100 km southeast of Arak and in two structural zones of Central Iran in the north and Sanandaj-Sirjan in the southern part. Regarding its geological structures, the area has become the source of important mines including the Akhtarchi gold mine, Aliabad iron mine, Ochestan feldspar mine, and Dali gold and copper mines. Therefore, promising areas for exploration activities are identified using the analysis of satellite images of ASTER and Landsat ETM + in the region to identify alteration areas. For this purpose, the necessary corrections were applied to the satellite images. Then, to identify the alteration parts related to the gold deposits, different satellite image processing methods of ETM + and ASTER were used. These methods include making a false-color composite, band ratio, Selective Principal Components Analysis (SPCA), Spectral Angle Mapper (SAM) method, Spectral Information Divergence Classification (SID), Endmember Collection Dialog Components (ECDC), and innovative methods such as Principal Component Analysis (PCA) and Spectral Angle Mapper, as well as unsupervised classification methods. In the end, the major alterations in the region were observed. In the obtained images, the prophylitic zone and the phyllic and argillic zones in the region were observed. To introduce the optimal method, the results of the various methods mentioned were compared with each other and with the current situation of the mines. The alteration zones were identified through band ratio and SAM methods and the combined methods with more power. Finally, SAM, 2:1 ratio, and the combined methods were identified as successful methods for more accurate separation of the alteration zones.
Interactive Epistemology I: Knowledge

Formal Interactive Epistemology deals with the logic of knowledge and belief when there is more than one agent or player. One is interested not only in each person's knowledge about substantive matters, but also in his knowledge about the others' knowledge. This paper examines two parallel approaches to the subject. The first is the semantic approach, in which knowledge is represented by a space of states of the world, together with partitions of the state space for each player i; the atom of i's partition containing a given state of the world represents i's knowledge at that state - the set of those other states that i cannot distinguish from that state. The second is the syntactic approach, in which knowledge is embodied in sentences constructed according to certain syntactic rules. This paper examines the relation between the two approaches, and shows that they are in a sense equivalent.
Derivation of the robustness from the concurrence

Adding the maximally mixed state with some weight to the entanglement system leads to disentanglement of the latter. For each predefined entangled state there exists a minimal value of this weight for which the system loses its entanglement properties. These values were proposed to be used as a quantitative measure of entanglement called robustness [G. Vidal and R. Tarrach, Phys. Rev. A 59, 141 (1999)]. Using the concurrence, we propose the derivation of this measure for the system of two-qubit. Namely, for a two-qubit pure state, an exact expression of robustness is obtained. Finally, in the same way, the robustness of special cases of mixed two-qubit states is calculated.