Reviewerly/exHarmony huggingface dataset:
Privacy Preserving Adjacency Spectral Embedding on Stochastic Blockmodels

For graphs generated from stochastic blockmodels, adjacency spectral embedding is asymptotically consistent. Further, adjacency spectral embedding composed with universally consistent classifiers is universally consistent to achieve the Bayes error. However when the graph contains private or sensitive information, treating the data as non-private can potentially leak privacy and incur disclosure risks. In this paper, we propose a differentially private adjacency spectral embedding algorithm for stochastic blockmodels. We demonstrate that our proposed methodology can estimate the latent positions close to, in Frobenius norm, the latent positions by adjacency spectral embedding and achieve comparable accuracy at desired privacy parameters in simulated and real world networks.
Learning Robust Options by Conditional Value at Risk Optimization

Options are generally learned by using an inaccurate environment model (or simulator), which contains uncertain model parameters. While there are several methods to learn options that are robust against the uncertainty of model parameters, these methods only consider either the worst case or the average (ordinary) case for learning options. This limited consideration of the cases often produces options that do not work well in the unconsidered case. In this paper, we propose a conditional value at risk (CVaR)-based method to learn options that work well in both the average and worst cases. We extend the CVaR-based policy gradient method proposed by Chow and Ghavamzadeh (2014) to deal with robust Markov decision processes and then apply the extended method to learning robust options. We conduct experiments to evaluate our method in multi-joint robot control tasks (HopperIceBlock, Half-Cheetah, and Walker2D). Experimental results show that our method produces options that 1) give better worst-case performance than the options learned only to minimize the average-case loss, and 2) give better average-case performance than the options learned only to minimize the worst-case loss.
A Single SMC Sampler on MPI that Outperforms a Single MCMC Sampler

Markov Chain Monte Carlo (MCMC) is a well-established family of algorithms which are primarily used in Bayesian statistics to sample from a target distribution when direct sampling is challenging. Single instances of MCMC methods are widely considered hard to parallelise in a problem-agnostic fashion and hence, unsuitable to meet both constraints of high accuracy and high throughput. Sequential Monte Carlo (SMC) Samplers can address the same problem, but are parallelisable: they share with Particle Filters the same key tasks and bottleneck. Although a rich literature already exists on MCMC methods, SMC Samplers are relatively underexplored, such that no parallel implementation is currently available. In this paper, we first propose a parallel MPI version of the SMC Sampler, including an optimised implementation of the bottleneck, and then compare it with single-core Metropolis-Hastings. The goal is to show that SMC Samplers may be a promising alternative to MCMC methods with high potential for future improvements. We demonstrate that a basic SMC Sampler with 512 cores is up to 85 times faster or up to 8 times more accurate than Metropolis-Hastings.
Entangled States as Robust and Re-usable Carriers of Information

Entangled states can be used as secure carriers of information much in the same way as carriers are used in classical communications. In such protocols, quantum states are uploaded to the carrier at one end and are downloaded from it in safe form at the other end, leaving the carrier intact and ready for reuse. Furthermore, protocols have been designed for performing quantum state sharing in this way. In this work, we study the robustness of these protocols against noise and show that multiple uses of these carriers do not lead to accumulative errors, rather the error remains constant and under control.
DISCOURSE MARKERS IN ESSAY WRITING OF SENIOR HIGH SCHOOL STUDENTS

The objectives of the study were to find out the discourse markers naturally that used by students in the essay writing, the most frequent inappropriateness in terms of the use of discourse markers, and to describe the reasons why they used the discourse markers in the way they do. The study was conducted by applying a descriptive qualitative research. The subjects of the research were the students of senior high school at SMA Budisatrya Medan. The data were the students’ writing that were analyzed by using Fraser Taxonomy (1999). It was fond that the students used 7 out 8 types of discourse markers. Furthermore, the data were also analyzed by using Kao &amp; Chen Taxonomy (2011). It was also fond that the students used 3 out 6 types of misuse patterns of discourse markers. There were seven reasons the students used discourse markers: Limitation of knowledge, lack of ability in writing, lack of interest against english, lack of interest against writing, reading resources, teachers’ explanation and internet resources. Keywords: Discourse Markers, Misuse Patterns, Students’ essay writing.
ROBUST AND SECURE DATA DISSEMINATION BASED ON CRYPTOGRAPHIC SCHEME FOR INTERNET OF THINGS

Io T integrated smart applications are growing rapidly to make everything smart.Researchers all over the world contribute enormously for the realm of smartness with the maxim of anytime anywhere paradigm. This growing technology is at the risk of security requirements such as authentication, confidentiality, integrity and non-repudiation.This paper describes in detail about the various security methods that can be applied to internet of things .Elliptic Curve Cryptography is one of the methods in access booting that can efficiently encrypt/decrypt the data by the use of digital signatures. Key generation serves as an important part in Elliptic Curve Cryptography, as both public and private key needs to be generated. This method ensures to provide an efficient privacy and security when compared with the other algorithms used in cryptography.
RANCANG BANGUN ALAT IRIGASI TETES BERBASIS WAKTU MENGGUNAKAN ALAT MIKROKONTROLER UNTUK MENINGKATKAN PERTUMBUHAN TANAMAN SAWI

This study aims to design a time-based automatic control irrigation system that can minimize the use of water in plant cultivation, and can meet the needs (supply) of plants according to the water needed. This research uses research and development methods (R &amp; D), the results are analyzed using quantitative data analysis and descriptive statistics, the The design of time-based drip irrigation equipment uses a microcontroller through several mechanisms including frame making the installation of material components and the manufacture of microcontroller systems, the installation components and inputting language programs on microcontroller systems can be used effectively and efficiently if used by manual watering by humans.
Compressive Sensing Berbasis Dct-dwt Untuk Kompresi Watermark Pada Watermarking Citra Digital Dengan Domain Swt-svd

Abstrak Image watermarking adalah proses perlindungan hak cipta dari suatu objek (gambar) dengan memberikan tanda di dalam objek tersebut. Teknik image watermarking, dikatakan baik apabila tanda yang disisipkan tidak tampak oleh kasat mata dan objek pembawanya tidak mengalami penurunan kualitas serta tanda yang disisipkan harus tahan terhadap berbagai pengolahan sinyal. Pada tugas akhir ini di implementasikan sistem image watermarking pada watermark dan host dalam bentuk citra hitam putih dan Red Green Blue (RGB). Pada sistem image watermarking menggunakan metode Compressive Sensing (CS) berbasis Discrete Cosine Transform-Discrete Wavelet Transform (DCT-DWT), Stationary Wavelet Transform-Singular Value Decomposition (SWT-SVD) dan proses rekontruksi menggunakan algoritme Orthogonal Matching Pursuit (OMP). Pengujian dilakukan terhadap sistem yang di bangun dan telah dilakukan beberapa skenario pengujian dalam memperoleh analisis terkait dengan performansi sistem yang mampu dihasilkan. Pengaruh CS pada pengujian menghasilkan nilai Mean Square Error(MSE) 4,396208 , Peak Signal to Noise Ratio (PSNR) 41,70416 dan Bit Error Rate (BER) 0,185565. Dan pengaruh tanpa CS pada pengujian menghasilkan nilai Mean SquareError(MSE) 3,53102, Peak Signal to Noise Ratio (PSNR) 42,6518 dan Bit Error Rate (BER) 0,19686 Kata kunci: Image watermarking, CS, DCT-DWT, SWT-SVD, OMP, MSE, PSNR, BER Abstract Image watermarking is the process of protecting the copyright of an object (image) by giving a sign inside the object. Image watermarking technique is said to be good if the inserted sign does not appear to be visible and the carrying object does not experience a decrease in quality and the inserted sign must be resistant to various signal processing. This final project implements an image watermarking system on watermarks and hosts in the form of black and white and Red Green Blue (RGB) images. The image watermarking system uses the Compressive Sensing (CS) method based on Discrete Cosine Transform-Discrete Wavelet Transform (DCT-DWT), Stationary Wavelet Transform-Singular Value Decomposition (SWT-SVD) and the reconstruction process using the Orthogonal Matching Pursuit (OMP) algorithm. Tests are carried out on systems that are built and several test scenarios have been carried out in ISSN : 2355-9365 e-Proceeding of Engineering : Vol.6, No.1 April 2019 | Page 492 obtaining analysis related to the system performance that can be produced. The effect of CS on the test results in the value of Mean Square Error (MSE) 4,396208, Peak Signal to Noise Ratio (PSNR) 41,70416 and Bit Error Rate (BER) 0,185565. And the effect without CS on the test resulted in the value of MSE 3.53102, PSNR 42.6518 and BER 0.19686 Keywords:Image watermarking, CS, DCT-DWT, SWT-SVD, OMP, MSE, PSNR, BER
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to 'mine' variables of interest from available data, followed by the inclusion of those variables into an econometric framework, with the objective of estimating causal effects. Recent work highlights that, because the predictions from machine learning models are inevitably imperfect, econometric analyses based on the predicted variables are likely to suffer from bias due to measurement error. We propose a novel approach to mitigate these biases, leveraging the ensemble learning technique known as the random forest. We propose employing random forest not just for prediction, but also for generating instrumental variables to address the measurement error embedded in the prediction. The random forest algorithm performs best when comprised of a set of trees that are individually accurate in their predictions, yet which also make 'different' mistakes, i.e., have weakly correlated prediction errors. A key observation is that these properties are closely related to the relevance and exclusion requirements of valid instrumental variables. We design a data-driven procedure to select tuples of individual trees from a random forest, in which one tree serves as the endogenous covariate and the other trees serve as its instruments. Simulation experiments demonstrate the efficacy of the proposed approach in mitigating estimation biases and its superior performance over three alternative methods for bias correction.
Genre Classification Problem: in Pursuit of Systematics on a Big Webcorpus

This article is devoted to the problem of defining a genre in computer linguistics and searching for parameters that could formalize the concept of a genre. All kinds of existing typologies of genres rely on different types of features, whereas in the practice of NLP, any modern applications are adapted to learning on big data, and therefore - on text features that do not require additional non-automatic markup. Based on such text-internal features, in this article we focus on differentiation of various genres and their grouping on the basis of a similar distribution of features. The description of the contribution of various types of features to the final result and their interpretation are given, and also an analysis of how such features can be used to further adaptation of NLP models is provided. The materials of the "Taiga" corpus with genre annotation are used as experimental data.
Reconstruct the Back of 3D Face Model Using 2D Gradient Based Interpolation

The recent development of 3D sensing technology enables a number of consumer facing 3D cameras, such as Kinect, TrueDepth camera on IPhoneX etc., emerge. These cameras are much cheaper than conventional and professional 3D scanning devices, and thus they can be acquired by consumers easily. However, consumer 3D scanning applications bring a new set of challenges. One of the challenges is that it is difficult for consumers to obtain the full head model by self-scanning. The proposed algorithm in this paper aims at reconstructing 3D human back head model based on gradient filling method. Due to the lack of related researches, to be more specific, repairing large holes without extra information in the 3D scale, the problem is migrated to 2D scale by projecting 3D model to a spherical space. Then the depth value at each position in back head is calculated via gradient interpolation. The algorithm is simple and effective and it can reconstruct a model within seconds.
Identification and ranking of effective factors on the use of information technology in subscribers of Golestan gas company using the AHP method

The purpose of this study was to identify and rank the effective factors on the use of information technology in subscribers of Golestan gas company using the AHP method.Methodology:The research method used is descriptive and survey. The research data were collected using library and field method and the tools used were researcher-made questionnaire. Reliability and validity of the questionnaires have been approved. Also, for analyzing the data, the AHP model was used using Expert Choice and Excel software.Results:The results of the calculated analysis show that the results of the calculated analysis show that the organizational factors with the relative weight of 0.513 were ranked first and then human factors with a relative weight of 0.298 in the second rank and environmental factors with a relative weight of 0.186 in the third place Is important.Conclusion:Finally, in service organizations, due to the emergence of the phenomenon of information technology and attention to its knowledge, all of the critical factors of these organizations have been overshadowed, as the use of technology has been added as an existing requirement Has transformed the organization's function in the transfer of knowledge and information, and the concept of learning has evolved, and even managers in these organizations are familiar with how they are used, in order to make use of learning tools such as information technology and the Internet in their organizations. Provide. Information Technology in Golestan Gas Company as a Tool for Knowledge and Skills for Students' Readiness to Achieve Economic Development Goals.
Stochastic Inverse Reinforcement Learning

The goal of the inverse reinforcement learning (IRL) problem is to recover the reward functions from expert demonstrations. However, the IRL problem like any ill-posed inverse problem suffers the congenital defect that the policy may be optimal for many reward functions, and expert demonstrations may be optimal for many policies. In this work, we generalize the IRL problem to a well-posed expectation optimization problem stochastic inverse reinforcement learning (SIRL) to recover the probability distribution over reward functions. We adopt the Monte Carlo expectation-maximization (MCEM) method to estimate the parameter of the probability distribution as the first solution to the SIRL problem. The solution is succinct, robust, and transferable for a learning task and can generate alternative solutions to the IRL problem. Through our formulation, it is possible to observe the intrinsic property of the IRL problem from a global viewpoint, and our approach achieves a considerable performance on the objectworld.
MOBA: A multi-objective bounded-abstention model for two-class cost-sensitive problems

Abstaining classifiers have been widely used in cost-sensitive applications to avoid ambiguous classification and reduce the cost of misclassification. Previous abstaining classification models rely on cost information, such as a cost matrix or cost ratio. However, it is difficult to obtain or estimate costs in practical applications. Furthermore, these abstention models are typically restricted to a single optimization metric, which may not be the expected indicator when evaluating classification performance. To overcome such problems, a multi-objective bounded-abstention (MOBA) model is proposed to optimize essential metrics. Specifically, the MOBA model minimizes the error rate of each class under class-dependent abstention constraints. The MOBA model is then solved using the non-dominated sorting genetic algorithm II, which is a popular evolutionary multi-objective optimization algorithm. A set of Pareto-optimal solutions will be generated and the best one can be selected according to provided conditions (whether costs are known) or performance demands (e.g., obtaining a high accuracy, F-measure, and etc). Hence, the MOBA model is robust towards variations in the conditions and requirements. Compared to state-of-the-art abstention models, MOBA achieves lower expected costs when cost information is considered, and better performance-abstention trade-offs when it is not.
DEVELOPING VISUAL ART IN LEARNING SPEAKING SKILL FOR STUDENTS OF JUNIOR HIGH SCHOOL

The objective of this study was to develop English learning media based on students’ needs. This research was conducted in SMP N 37 Medan for students in grade VII which consists of 30 students. This study followed the steps of research and development proposed by Borg and Gall (1983), which are 1) need analysis; 2) planning (develop preliminary form of product); 3) Collecting data; 4) designing media; 4) media evaluation; 5) revising (designing final product). It was found that 96.66% students agreed that the existing an interactive learning medium currently used by the students of Junior High School. The more relevant media were developed based on student needs, which can provide opportunities for students to participate in the classroom. The learning media were developed into 4 media based on 4 units of speaking materials. Then the learning media were validated by two experts. The average score of the validation was 90. It showed that the developed learning media were very good, relevant and feasible.Keywords: English learning media, speaking, visual art media.
Manuscript Exchange Common Approach: Supporting Better Publication Flows

The Manuscript Exchange Common Approach (MECA) project is new to the National Information Standards Organization (NISO), creating a recommended practice to support the transfer of scholarly manuscripts and associated materials between publishers and manuscript submission systems. This column describes the project and maps out its plan of work for 2019.
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration

In this paper, we consider the nonparametric least square regression in a Reproducing Kernel Hilbert Space (RKHS). We propose a new randomized algorithm that has optimal generalization error bounds with respect to the square loss, closing a long-standing gap between upper and lower bounds. Moreover, we show that our algorithm has faster finite-time and asymptotic rates on problems where the Bayes risk with respect to the square loss is small. We state our results using standard tools from the theory of least square regression in RKHSs, namely, the decay of the eigenvalues of the associated integral operator and the complexity of the optimal predictor measured through the integral operator.
PREFERENSI KONSUMEN TERHADAP MEDIA SOSIAL DALAM MENCARI DAN MEMBELI PRODUK SECARA ONLINE

When consumers buy goods, they certainly go through the information search stages. Through the internet, it would be easier for consumers to find references. As a media that is frequently visited, social media can be one of the consumer's references. Several social media give information the products what consumer is going to buy. The purpose of this study is to get the best combination of attributes chosen by consumers in finding and buying goods using social media. This study uses Conjoint Analysis, which is one of the techniques in multivariate analysis that is used to understand how consumers prefer the value of a product by combining separate benefits that exist from each attribute of the product. From the research, it was found that the combination of chat feature, social media accounts have more than 1000 follower, safe shopping and easy search for the desired items were ranked first as the most considered combination by respondents. Keywords : Social Media, Consumers Preferences, Conjoint Analysis
Adapted Active Contours for Catadioptric Images using a Non-euclidean Metrics

This paper contributes to adapt classical active contour methods in a new framework to handle real time free space extraction in a sequence of omnidirectional images. In order to resolve the classical processing errors related to the difference in the resolution of the omnidirectional images. We propose a solution, based on a model of parametric active contour in Riemannian metric, which is adapted for the catadioptric images. This model of active contour is designed for the extraction of the free space, which will allow establishing a system of autonomous navigation of a mobile robot in real time. The approach adopted here is compared with methods of classic active contours.
Multi-Scale Dual-Branch Fully Convolutional Network for Hand Parsing

Recently, fully convolutional neural networks (FCNs) have shown significant performance in image parsing, including scene parsing and object parsing. Different from generic object parsing tasks, hand parsing is more challenging due to small size, complex structure, heavy self-occlusion and ambiguous texture problems. In this paper, we propose a novel parsing framework, Multi-Scale Dual-Branch Fully Convolutional Network (MSDB-FCN), for hand parsing tasks. Our network employs a Dual-Branch architecture to extract features of hand area, paying attention on the hand itself. These features are used to generate multi-scale features with pyramid pooling strategy. In order to better encode multi-scale features, we design a Deconvolution and Bilinear Interpolation Block (DB-Block) for upsampling and merging the features of different scales. To address data imbalance, which is a common problem in many computer vision tasks as well as hand parsing tasks, we propose a generalization of Focal Loss, namely Multi-Class Balanced Focal Loss, to tackle data imbalance in multi-class classification. Extensive experiments on RHD-PARSING dataset demonstrate that our MSDB-FCN has achieved the state-of-the-art performance for hand parsing.
Liars, Bullies and Thugs Target Resisting Hate Founders

It is rare I bother addressing any of the threats I receive. Being a co founder of an anti hate organisation it is sadly part of the job to expect death ...