Reviewerly/exHarmony huggingface dataset:
Pengenalan Pola Tulisan Tangan Suku Kata Aksara Sasak Menggunakan Metode Moment Invariant dan Support Vector Machine

The research of Javanese and Balinese ancient script have been done by some researches. However, the researches still have problems, such as image scaling, noise reduction and image transformation. This research implemented moment invariant and support vector machine to solve these problems especially on Sasak ancient script. The input data used in this research was 2700 handwritten Sasak ancient script. The testing was done to know the effect of thinning and the number of feature by using zoning on the classification performance. Accuracy is used as performance indicator. The highest average accuracy is 89.76%, on the second scenario, the average accuracy obtained is 92.52%.
Size Constrained Clustering With MILP Formulation

Clustering is one of the essential tools for data mining since it reveals the natural structures of the unlabeled data. Many clustering algorithms have been proposed in the last decades. However, few of them are designed to adapt prior knowledge that is available in many real applications, such as the sizes of clusters. In this paper, we propose a novel iterative clustering algorithm that can impose the constraints on the sizes of clusters. Given an unordered set of cluster size constraints, the proposed method minimizes the mean squared error (MSE) while simultaneously considers the size constraints. Each iteration of the proposed method consists of two steps, namely an assignment step and an update step. In the assignment step, the observations are assigned into clusters under the size constraints. The assignment task is modeled as an integer linear programming (ILP) problem. We prove that part of the constraint matrix of this ILP problem is total unimodular. Therefore, the integer constraints on most of the variables can be omitted so that the problem would become a mixed integer programming (MILP) problem which is much easier to solve. In the update step, new cluster centroids will be updated as the centers of the observations in the corresponding clusters. Experiments on UCI data sets indicate that (1) imposing the size constraints as proposed could improve the clustering performance; (2) compared with the state-of-the-art size constrained clustering methods, the proposed method could efficiently derive better clustering results.
Camera calibration for anamorphic lenses with three-dimensional targets

Anamorphic lenses, with different optical powers along the tangential plane and the sagittal plane, are calibrated in this paper. The imaging model for anamorphic lenses is introduced. Compared with the pinhole model, it has two more intrinsic parameters: the anamorphic distance and the anamorphic angle. The anamorphic lens has two optical centers: one is in the tangential plane and the other is in the sagittal plane. The distance between the two optical centers is the anamorphic distance. The anamorphic angle refers to the angle between the camera coordinates and the pixel coordinates in the CCD plane. Formulas determining the initial value of the anamorphic distance are provided. Two experiments are conducted for the anamorphic lens calibration. As a comparison, the anamorphic lens is calibrated using the anamorphic imaging model and the pinhole model, respectively. The calibration accuracy can be improved remarkably if the anamorphic imaging model is applied, and calibrated results for the anamorphic distance and the anamorphic angle are very stable for different positions of the calibration target, which shows the validity and effectiveness of the anamorphic imaging model for anamorphic lens calibration.
Reinforcement Learning When All Actions Are Not Always Available

The Markov decision process (MDP) formulation used to model many real-world sequential decision making problems does not efficiently capture the setting where the set of available decisions (actions) at each time step is stochastic. Recently, the stochastic action set Markov decision process (SAS-MDP) formulation has been proposed, which better captures the concept of a stochastic action set. In this paper we argue that existing RL algorithms for SAS-MDPs can suffer from potential divergence issues, and present new policy gradient algorithms for SAS-MDPs that incorporate variance reduction techniques unique to this setting, and provide conditions for their convergence. We conclude with experiments that demonstrate the practicality of our approaches on tasks inspired by real-life use cases wherein the action set is stochastic.
Fact-Aware Sentence Split and Rephrase with Permutation Invariant Training

Sentence Split and Rephrase aims to break down a complex sentence into several simple sentences with its meaning preserved. Previous studies tend to address the issue by seq2seq learning from parallel sentence pairs, which takes a complex sentence as input and sequentially generates a series of simple sentences. However, the conventional seq2seq learning has two limitations for this task: (1) it does not take into account the facts stated in the long sentence; As a result, the generated simple sentences may miss or inaccurately state the facts in the original sentence. (2) The order variance of the simple sentences to be generated may confuse the seq2seq model during training because the simple sentences derived from the long source sentence could be in any order.To overcome the challenges, we first propose the Fact-aware Sentence Encoding, which enables the model to learn facts from the long sentence and thus improves the precision of sentence split; then we introduce Permutation Invariant Training to alleviate the effects of order variance in seq2seq learning for this task. Experiments on the WebSplit-v1.0 benchmark dataset show that our approaches can largely improve the performance over the previous seq2seq learning approaches. Moreover, an extrinsic evaluation on oie-benchmark verifies the effectiveness of our approaches by an observation that splitting long sentences with our state-of-the-art model as preprocessing is helpful for improving OpenIE performance.
Radial-shearing interferometry for phase LC-SLM calibration with a pair of conjugated vortex beams

Spatial light modulators are widely used in various fields such as light field control and beam shaping. It is necessary to calibrate the modulation curve of phase spatial light modulator (SLM) accurately so as to load the pattern correctly. Different from the calibration methods with interference fringes, a group of dynamic petal-like spots with different rotation angles can also be applied to calibrate the modulation curve. The number of petals is generated by a pair of conjugated vortex beams and increases as the increase of vortex topological charge. The calibration accuracy was then greatly improved by weighted algorithm based on the multiple rotated petals. The effectiveness of the proposed SLM calibration method was verified in experiments by four dynamic petals. Compared with the traditional fringe pattern calibration, this method has merit of simple structure, convenient operation and high precision.
Standards of Competency of Head of School Beginners as Leaders in Learning Innovation

This study aims to describe the personal, social, and professional competencies that a novice must possess in order to effectively begin his performance as an innovative learning leader.This research was conducted with a quantitative.The targets of this study were 10 principals in metropolitan cities, central cities and suburbs for elementary schools in East Java.The data of this study were collected by a closed questionnaire and in-depth interviews.Quantitative data collected was analysed by quantitative data analysis techniques using descriptive statistics of middle size trends.Based on data analysis, the results of the study can be concluded as follows, novice principals need competencies related to openness, responsibility, honesty, talent and work interest, integrity, and self-confidence.Social competencies needed by beginner school principals are competencies related to social sensitivity, providing assistance to others, communicating, cooperating, and participating in social activities.Beginner school principals most need competence regarding vision of learning, then proceed with a culture of learning, learning environment, and school community relations.
Gaining Motivation on English Learning for Special Need Students Using Flashcards, Foldable Books and Posters in EFL Context

Teaching special needs students becomes more challenging and frustrating. Moreover, some SLB in Kendari nowadays have not been facilitated by suitable learning media, particularly in EFL context. This study describes the research that sought to understand if the teaching media electronic or not, using flashcards, foldable books, and posters, is under the necessities of deaf and hard hearing students. This question becomes the primary goal of the investigation, and its enlighten is divided into three specific goals. They are, finding out the effects of the media used in assisting special need students and EFL teacher in the class; helping them in communication and comprehending the teaching materials especially in vocabulary English class; and gaining EFL teacher’s and special need students’ motivation in teaching and learning process utilizing the media. This study used qualitative by single case study design, which was conducted about six months in 2018. Five students by deaf and hard hearing impairment of SLB-F Mandara (special private junior high school) are as the sample of this study. The observation list and depth-interview were utilized in data collection. After doing some interventions, the data analysis used data reduction, data display, and conclusion. The results showed that by intervention and utilizing diverse media by flashcards, foldable books and posters could ultimately gain teacher’s and students’ motivation in the EFL context.
Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness

Recently smoothing deep neural network based classifiers via isotropic Gaussian perturbation is shown to be an effective and scalable way to provide state-of-the-art probabilistic robustness guarantee against ℓ2 norm bounded adversarial perturbations. However, how to train a good base classifier that is accurate and robust when smoothed has not been fully investigated. In this work, we derive a new regularized risk, in which the regularizer can adaptively encourage the accuracy and robustness of the smoothed counterpart when training the base classifier. It is computationally efficient and can be implemented in parallel with other empirical defense methods. We discuss how to implement it under both standard (non-adversarial) and adversarial training scheme. At the same time, we also design a new certification algorithm, which can leverage the regularization effect to provide tighter robustness lower bound that holds with high probability. Our extensive experimentation demonstrates the effectiveness of the proposed training and certification approaches on CIFAR-10 and ImageNet datasets.
DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets

We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games. Given a partially observed state, DefogGAN generates defogged images of a game as predictive information. Such information can lead to create a strategic agent for the game. DefogGAN is a conditional GAN variant featuring pyramidal reconstruction loss to optimize on multiple feature resolution scales. We have validated DefogGAN empirically using a large dataset of professional StarCraft replays. Our results indicate that DefogGAN can predict the enemy buildings and combat units as accurately as professional players do and achieves a superior performance among state-of-the-art defoggers.
A Fast Quantum Particle Swarm Optimization Algorithm for Image Denoising Problem

Noise can affect images while acquired, transmitted, stored or compressed.One of the best methods for noise removal is the sparse representation algorithm (SR).The Quantum Particle Swarm Optimization (QPSO) is one of the meta-heuristic algorithms.This paper shows excellent results in noise reduction in the quick version of QPSO, which uses benefit of the SRs and meta-heuristic algorithms.This approach is known as FQPSO-MP, depending on the matching pursuit algorithm (MP).A proposed Dynamic-Multi-Swarm (DMS) and a pre-learned dictionary (FQPSO-MP) method saves the time of calculating the learning dictionary.These modifications contribute to important benefits of computing efficiency (productivity improvements of approximately 90% are achieved) without sacred image quality in comparison with the initial QPSO-MP technique (the bigger reduction relative to the PSNR indexes is lower than 0.58 dB and 0.019).The proposed FQPSO-MP method compared to the original QPSO-MP method after modification.The scientific results show that the FQPSO-MP algorithm is more effective and quicker without sacrificing image quality than the FQPSO-MP algorithm.The experimental results show, in comparison to state-of-the-art denoising algorithms, that both quantitative and image quality results are achieved with the suggested FQPSO-MP method.
Incorporating dynamic factors for improving a GIS‐based solar radiation model

Abstract Solar radiation has been a major input to agricultural, hydrological, and ecological modeling. However, solar radiation is usually influenced by three groups of dynamic factors: sun–earth position, terrain, and atmospheric effects. Therefore, an integrated approach to accurately consider the impacts of those dynamic factors on solar radiation is essential to estimate solar radiation over rugged terrain. In this study, a spatial and temporal gap‐filling algorithm was proposed to obtain a seamless daily MODIS albedo dataset. A 1 km‐resolution digital elevation model was used to model the impact of local topography and shading by surrounding terrain on solar radiation. A sunshine‐based model was adopted to simulate radiation under the influence of clouds. A GIS‐based solar radiation model that incorporates albedo, shading by surrounding terrain, and variations in cloudiness was used to address the spatial variability of these factors in mountainous terrain. Compared with other independent solar radiation products, our model generated a more reliable solar radiation product over rugged terrain, with an R 2 of 0.88 and an RMSE of 2.55 MJ m −2 day −1 . The improved solar radiation products and open source app can be used further in practice or scientific research.
A Bayesian Approach for Estimating Causal Effects from Observational Data

We present a novel Bayesian method for the challenging task of estimating causal effects from passively observed data when the underlying causal DAG structure is unknown. To rigorously capture the inherent uncertainty associated with the estimate, our method builds a Bayesian posterior distribution of the linear causal effect, by integrating Bayesian linear regression and averaging over DAGs. For computing the exact posterior for all cause-effect variable pairs, we give an algorithm that runs in time O(3d d) for d variables, being feasible up to 20 variables. We also give a variant that computes the posterior probabilities of all pairwise ancestor relations within the same time complexity, significantly improving the fastest previous algorithm. In simulations, our Bayesian method outperforms previous methods in estimation accuracy, especially for small sample sizes. We further show that our method for effect estimation is well-adapted for detecting strong causal effects markedly deviating from zero, while our variant for computing posteriors of ancestor relations is the method of choice for detecting the mere existence of a causal relation. Finally, we apply our method on observational flow cytometry data, detecting several causal relations that concur with previous findings from experimental data.
Deep-Mining Backtracking Search Optimization Algorithm Guided by Collective Wisdom

The backtracking search optimization algorithm (BSA) is a recently proposed evolutionary algorithm with simple structure and well global exploration capability, which has been widely used to solve optimization problems. However, the exploitation capability of the BSA is poor. This paper proposes a deep-mining backtracking search optimization algorithm guided by collective wisdom (MBSAgC) to improve its performance. The proposed algorithm develops two learning mechanisms, i.e., a novel topological opposition-based learning operator and a linear combination strategy, by deeply mining the winner-tendency of collective wisdom. The topological opposition-based learning operator guides MBSAgC to search the vertices in a hypercube about the best individual. The linear combination strategy contains a difference vector guiding individuals learning from the best individual. In addition, in order to balance the overall performance, MBSAgC simulates the clusterity-tendency strategy of collective wisdom to develop another difference vector in the above linear combination strategy. The vector guides individuals to learn from the mean value of the current generation. The performance of MBSAgC is tested on CEC2005 benchmark functions (including 10-dimension and 30-dimension), CEC2014 benchmark functions, and a test suite composed of five engineering design problems. The experimental results of MBSAgC are very competitive compared with those of the original BSA and state-of-the-art algorithms.
Optical Flow in Deep Visual Tracking

Single-target tracking of generic objects is a difficult task since a trained tracker is given information present only in the first frame of a video. In recent years, increasingly many trackers have been based on deep neural networks that learn generic features relevant for tracking. This paper argues that deep architectures are often fit to learn implicit representations of optical flow. Optical flow is intuitively useful for tracking, but most deep trackers must learn it implicitly. This paper is among the first to study the role of optical flow in deep visual tracking. The architecture of a typical tracker is modified to reveal the presence of implicit representations of optical flow and to assess the effect of using the flow information more explicitly. The results show that the considered network learns implicitly an effective representation of optical flow. The implicit representation can be replaced by an explicit flow input without a notable effect on performance. Using the implicit and explicit representations at the same time does not improve tracking accuracy. The explicit flow input could allow constructing lighter networks for tracking.
Improving Software Development effort estimating using Support Vector Regression and Feature Selection

Accurate and reliable software development effort estimation (SDEE) is one of the main concerns for project managers. Planning and scheduling a software projects using and inaccurate estimate may cause severe risks to software project under development such as delayed delivery, poor quality software, missing features. Therefore, accurate prediction of software effort plays an important role in the minimization of these risks that can lead to projects failure. Nowadays, application of artificial intelligence techniques has grown dramatically for predicting software effort. The researchers found that these techniques are suitable tools for accurate prediction. In this study, an improved model is designed for estimating software effort using support vector regression (SVR) and two feature selection (FS) methods. Prior to building model step, a preprocessing stage is performed by random forest or Boruta feature selection methods to remove unimportant features. Next, the SVR model is tuned by a grid search approach. The performance of model is then evaluated over eight well-known datasets through 30%holdout validation method. To show the impact of feature selection on the accuracy of SVR model, the proposed model was compared with SVR model without feature selection. The results indicated that SVR with feature selection outperforms SVR without FS in terms of the three accuracy measures used in this empirical study.
Par means parallel: multiplicative linear logic proofs as concurrent functional programs

Along the lines of Abramsky’s “Proofs-as-Processes” program, we present an interpretation of multiplicative linear logic as typing system for concurrent functional programming. In particular, we study a linear multiple-conclusion natural deduction system and show it is isomorphic to a simple and natural extension of λ-calculus with parallelism and communication primitives, called λpar. We shall prove that λpar satisfies all the desirable properties for a typed programming language: subject reduction, progress, strong normalization and confluence.
When will we have a quantum computer?

• A classical computer with N bits at any given moment is in one of its possible 2 N states. • In contrast, the state of the hypothetical quantum computer with N qubits is defined by the values of 2 N continuous parameters, the quantum amplitudes. • To beat your laptop in performing some special tasks, like factoring very large numbers, one needs ∼1000 qubits. • Accordingly, the number of continuous variables that should be under our control is at least 2 1000 ∼ 10 300 . • Thus the answer to the question in the title is: as soon as physicists and engineers will learn to control thus number of degrees of freedom, which means NEVER .
A Three-Level Optimization Model for Nonlinearly Separable Clustering

Due to the complex structure of the real-world data, nonlinearly separable clustering is one of popular and widely studied clustering problems. Currently, various types of algorithms, such as kernel k-means, spectral clustering and density clustering, have been developed to solve this problem. However, it is difficult for them to balance the efficiency and effectiveness of clustering, which limits their real applications. To get rid of the deficiency, we propose a three-level optimization model for nonlinearly separable clustering which divides the clustering problem into three sub-problems: a linearly separable clustering on the object set, a nonlinearly separable clustering on the cluster set and an ensemble clustering on the partition set. An iterative algorithm is proposed to solve the optimization problem. The proposed algorithm can use low computational cost to effectively recognize nonlinearly separable clusters. The performance of this algorithm has been studied on synthetical and real data sets. Comparisons with other nonlinearly separable clustering algorithms illustrate the efficiency and effectiveness of the proposed algorithm.
Model Checking Temporal Epistemic Logic under Bounded Recall

We study the problem of verifying multi-agent systems under the assumption of bounded recall. We introduce the logic CTLKBR, a bounded-recall variant of the temporal-epistemic logic CTLK. We define and study the model checking problem against CTLK specifications under incomplete information and bounded recall and present complexity upper bounds. We present an extension of the BDD-based model checker MCMAS implementing model checking under bounded recall semantics and discuss the experimental results obtained.