Reviewerly/exHarmony huggingface dataset:
Improved Document Modelling with a Neural Discourse Parser

Despite the success of attention-based neural models for natural language generation and classification tasks, they are unable to capture the discourse structure of larger documents. We hypothesize that explicit discourse representations have utility for NLP tasks over longer documents or document sequences, which sequence-to-sequence models are unable to capture. For abstractive summarization, for instance, conventional neural models simply match source documents and the summary in a latent space without explicit representation of text structure or relations. In this paper, we propose to use neural discourse representations obtained from a rhetorical structure theory (RST) parser to enhance document representations. Specifically, document representations are generated for discourse spans, known as the elementary discourse units (EDUs). We empirically investigate the benefit of the proposed approach on two different tasks: abstractive summarization and popularity prediction of online petitions. We find that the proposed approach leads to substantial improvements in all cases.
THE USE OF POWERPOINT AS MEDIA OF LANGUAGE TEACHING ON STUDENTS’ SPEAKING SKILL

Purpose of the study: Powerpoint is one of the communication tools which used to presenting learning material, and this article aims to know the use of PowerPoint as media that can improve students’ speaking skill.&#x0D; Methodology: The researcher uses the Pre-experimental method one group Pretest-Posttest design by regulating pretest to measure the subordinate variable, test treatment, and regulating posttest.&#x0D; Main Findings: The result shows that posttest frequency is higher than pretest frequency by compering median 12.6&gt; 12.2. it means that PowerPoint provides video, audio, animation, slideshow, etc. can improve students’ speaking skills.&#x0D; Applications of this study: The population in this research are all of the students of MA Al-Asyhar Bungah Gresik with 20 social learners and 23 science learners class.&#x0D; Novelty/Originality of this study: Media makes the students more understand, the instructor must prepare the exciting material, by using interesting media it helps students more understand. Because the use of media will stimulate students' understanding, the teacher can use interested and straightforward media which can make them focus on the material.
An FPGA-based design for real-time super-resolution reconstruction

For several decades, the camera spatial resolution is gradually increasing with the CMOS technology evolution. The image sensors provide more and more pixels, generating new constraints for suitable optics. As an alternative, promising solutions propose super-resolution (SR) techniques to reconstruct high-resolution images or video without modifying the sensor architecture. However, most of the SR implementations are far from reaching real-time performance on a low-budget hardware platform. Moreover, convincing state-of-the-art studies reveal that artifacts can be observed in highly textured areas of the image. In this paper, we propose a local adaptive spatial super resolution (LASSR) method to fix this limitation. LASSR is a two-step SR method including a machine learning-based texture analysis and a fast interpolation method that performs a pixel-by-pixel SR. Multiple evaluations of our method are also provided using standard image metrics for quantitative evaluation and also a psycho-visual assessment for a perceptual evaluation. A first FPGA-based implementation of the proposed method is then presented. It enables high-quality 2–4 k super-resolution videos to be performed at 16 fps, using only 13% of the FPGA capacity, opening the way to reach more than 60 fps by executing several parallel instances of the LASSR code on the FPGA.
Quaternion-valued recurrent projection neural networks on unit quaternions

Hypercomplex-valued neural networks, including quaternion-valued neural networks, can treat multi-dimensional data as a single entity. In this paper, we present the quaternion-valued recurrent projection neural networks (QRPNNs). Briefly, QRPNNs are obtained by combining the non-local projection learning with the quaternion-valued recurrent correlation neural network (QRCNNs). We show that QRPNNs overcome the cross-talk problem of QRCNNs. Thus, they are appropriate to implement associative memories. Furthermore, computational experiments reveal that QRPNNs exhibit greater storage capacity and noise tolerance than their corresponding QRCNNs.
Sistem Informasi Absensi Pegawai Menggunakan Metode RAD dan Metode LBS Pada Koordinat Absensi

Attendance can be said to be a presence data collection that is part of reporting activities that exist within an institution. A company requires a policy, especially the level of employee discipline. Discipline from employees is the main benchmark in seeing employee performance based on his presence in the company. A company must have a staff attendance system that can manage employee attendance based on obligations, prohibitions, and sanctions if an employee's obligations are not obeyed or violated. The problem that often occurs at the Bandung Central Statistics Agency is when employees are assigned to take a business trip, often the employee does not attend first to the office. By applying the Rapid Application Development (RAD) method, a method of software development with an object-oriented approach to system development, this method shorten the time in planning, designing, and implementing a system compared to traditional methods. Then the Location Based Service (LBS) method is used to access geographical information services used by users with mobile phone devices through cellular network connections to map locations to determine where the location of the user is. Thus, the existence of this research can help companies and employees in performing their performance without being bothered by pressure of workload.
CoTK: An Open-Source Toolkit for Fast Development and Fair Evaluation of Text Generation

In text generation evaluation, many practical issues, such as inconsistent experimental settings and metric implementations, are often ignored but lead to unfair evaluation and untenable conclusions. We present CoTK, an open-source toolkit aiming to support fast development and fair evaluation of text generation. In model development, CoTK helps handle the cumbersome issues, such as data processing, metric implementation, and reproduction. It standardizes the development steps and reduces human errors which may lead to inconsistent experimental settings. In model evaluation, CoTK provides implementation for many commonly used metrics and benchmark models across different experimental settings. As a unique feature, CoTK can signify when and which metric cannot be fairly compared. We demonstrate that it is convenient to use CoTK for model development and evaluation, particularly across different experimental settings.
Noise Removal from Remote Sensed Images by NonLocal Means with OpenCL Algorithm

We introduce a multi-platform portable implementation of the NonLocal Means methodology aimed at noise removal from remotely sensed images. It is particularly suited for hyperspectral sensors for which real-time applications are not possible with only CPU based algorithms. In the last decades computational devices have usually been a compound of cross-vendor sets of specifications (heterogeneous system architecture) that bring together integrated central processing (CPUs) and graphics processor (GPUs) units. However, the lack of standardization resulted in most implementations being too specific to a given architecture, eliminating (or making extremely difficult) code re-usability across different platforms. In order to address this issue, we implement a multi option NonLocal Means algorithm developed using the Open Computing Language (OpenCL) applied to Hyperion hyperspectral images. Experimental results demonstrate the dramatic speed-up reached by the algorithm on GPU with respect to conventional serial algorithms on CPU and portability across different platforms. This makes accurate real time denoising of hyperspectral images feasible.
Recognition of the character on the map captured by the camera using k-nearest neighbor

Abstract Maps are one form of an image that often encountered in various interests. For example, many books on tourist attractions or other information that provide maps as a media of information. However, sometimes people with visual impairments such as presbyopia, hypermetropia, or astigmatism have difficulty reading the map which is usually given in small size, multi orientation (multi-scale and multi-direction). Therefore, this study tries to provide a solution through an application that converts image to text in image conditions that have many orientations and different things are briefly called heterogeneous text. The Optical Character Recognition (OCR) system that was built beginning with taking pictures made through a cell phone camera as the first step in obtaining a digital map file, then enters the pre-processing, text segmentation, feature extraction from each different character, then continues to the classification stage. This OCR system for recognizing text with multiple orientations will help people make digital maps easier to read, especially for people who have presbyopia, hypermetropia, or astigmatism vision problems. The proposed model achieves good average accuracy for classifying the characters in various orientation successfully.
Probability model of sensitive similarity measures in information retrieval

In today’s Internet age, a lot of data is stored and used, which is very important. In people’s daily life, if these data are sorted, information retrieval technology will be used, and in information retrieval, some information retrieval inaccuracies often appear. Information retrieval model is an important framework and method for fast, complete, and accurate user information retrieval. With the rapid development of information technology, great changes have taken place in people’s production and life. Various information network technologies are widely used in people’s lives. The resulting flow of information shows explosive growth, information retrieval. User requirements are getting higher and higher. How to complete personalized information retrieval in a large amount of mixed information, so that retrieval technology can help us obtain effective retrieval results, has become a realistic problem worth exploring. In this article, the application of probability model based on sensitive similarity measure in information retrieval model is analyzed, and a similarity measure algorithm based on spectral clustering is proposed. By improving the similarity measure, the sensitivity problem of scale parameters is overcome and the retrieval precision is improved. In order to better reflect the superiority of the proposed algorithm, this article compares with ng-jordan-weiss (NJW) and deep sparse subspace clustering (DSSC) algorithms. The experimental results show that the proposed algorithm is superior to NJW and DSSC algorithms for different data sets in different evaluation indicators (Rand and F-measure).
A Robust Color Image Watermarking Scheme using Chaos for Copyright Protection

An exponential growth in multimedia applications has led to fast adoption of digital watermarking phenomena to protect the copyright information and authentication of digital contents. A novel spatial domain symmetric color image robust watermarking scheme based on chaos is presented in this research. The watermark is generated using chaotic logistic map and optimized to improve inherent properties and to achieve robustness. The embedding is performed at 3 LSBs (Least Significant Bits) of all the three color components of the host image. The sensitivity of the chaotic watermark along with redundant embedding approach makes the entire watermarking scheme highly robust, secure and imperceptible. In this paper, various image quality analysis metrics such as homogeneity, contrast, entropy, PSNR (Peak Signal to Noise Ratio), UIQI (Universal Image Quality Index) and SSIM (Structural Similarity Index Measures) are measures to analyze proposed scheme. The proposed technique shows superior results against UIQI. Further, the watermark image with proposed scheme is tested against various image-processing attacks. The robustness of watermarked image against attacks such as cropping, filtering, adding random noises and JPEG compression, rotation, blurring, darken etc. is analyzed. The Proposed scheme shows strong results that are justified in this paper. The proposed scheme is symmetric; therefore, reversible process at extraction entails successful extraction of embedded watermark.
Deep Learning for Biometric Face Recognition: Experimental Study on Benchmark Data Sets

There are still problems in applications of Machine Learning for face recognition. Such factors as lighting conditions, head rotations, emotions, and view angles affect the recognition accuracy. A large number of recognition subjects requires complex class boundaries. Deep Neural Networks have provided efficient solutions, although their implementations require massive computations for evaluation and minimisation of error functions. Gradient algorithms provide iterative minimisation of the error function. A maximal performance is achieved if parameters of gradient algorithms and neural network structures are properly set. The use of pairwise neural network structures often improves the performance because such structures require a small set of optimisation parameters. The experiments have been conducted on some face biometric benchmark data sets, and the main findings are presented in the form of a tutorial.
A configurable and executable model of Spark Streaming on Apache YARN

Streams of data are produced today at an unprecedented scale. Efficient and stable processing of these streams requires a careful interplay between the parameters of the streaming application and of the underlying stream processing framework. Today, finding these parameters happens by trial and error on the complex, deployed framework. This paper shows that high-level models can help to determine these parameters by predicting and comparing the performance of streaming applications running on stream processing frameworks with different configurations. To demonstrate this approach, this paper considers Spark Streaming, a widely used framework to leverage data streams on the fly and provide real-time stream processing. Technically, we develop a configurable and executable model to simulate both the streaming applications and the underlying Spark stream processing framework. Furthermore, we model the deployment of Spark Streaming on Apache YARN, which is a popular open-source distributed software framework for big data processing. We show that the developed model provides a satisfactory accuracy for predicting performance by means of empirical validation.
Filtering of multidimensional stationary sequences with missing observations

The problem of mean-square optimal linear estimation of linear functionals which depend on the unknown values of a multidimensional stationary stochastic sequence is considered. Estimates are based on observations of the sequence with an additive stationary stochastic noise sequence at points which do not belong to some finite intervals of a real line. Formulas for calculating the mean-square errors and the spectral characteristics of the optimal linear estimates of the functionals are proposed under the condition of spectral certainty, where spectral densities of the sequences are exactly known. The minimax (robust) method of estimation is applied in the case where spectral densities are not known exactly while some sets of admissible spectral densities are given. Formulas that determine the least favorable spectral densities and minimax spectral characteristics are proposed for some special sets of admissible densities.
Classifying argumentative stances of opposition using Tree Kernels

The approach proposed in this study aims to classify argumentative oppositions. A major assumption of this work is that discriminating among different argumentative stances of support and opposition can facilitate the detection of Argument Schemes. While using Tree Kernels for classification problems can be useful in many Argument Mining sub-tasks, this work focuses on the classification of opposition stances. We show that Tree Kernels can be successfully used (alone or in combination with traditional textual vectorizations) to discriminate between different stances of opposition without requiring highly engineered features. Moreover, this study compare the results of Tree Kernels classifiers with the results of classifiers which use traditional features such as TFIDF and n-grams. This comparison shows that Tree Kernel classifiers can outperform TFIDF and n-grams classifiers.
Line Hypergraph Convolution Network: Applying Graph Convolution for Hypergraphs

Network representation learning and node classification in graphs got significant attention due to the invent of different types graph neural networks. Graph convolution network (GCN) is a popular semi-supervised technique which aggregates attributes within the neighborhood of each node. Conventional GCNs can be applied to simple graphs where each edge connects only two nodes. But many modern days applications need to model high order relationships in a graph. Hypergraphs are effective data types to handle such complex relationships. In this paper, we propose a novel technique to apply graph convolution on hypergraphs with variable hyperedge sizes. We use the classical concept of line graph of a hypergraph for the first time in the hypergraph learning literature. Then we propose to use graph convolution on the line graph of a hypergraph. Experimental analysis on multiple real world network datasets shows the merit of our approach compared to state-of-the-arts.
A Perceptually Inspired Variational Model for Enhancing and Restoring Remote Sensing Images

Perceptually inspired algorithms have captured the recent attention of scientists and engineers due to their inherent capability to enhance the contrast of images, especially from the remote sensing domain. In this letter, we propose a perceptually inspired retinex model relying on the variational framework for enhancing and denoising satellite images captured by various imaging devices. A variational framework incorporates priors and data fidelity aspects in the designed functional, whose optimized solution yields the desired output. The model respects the distribution of the noise while enhancing the data. The overall performance is demonstrated using the visual and quantitative measures.
Learning Stochastic Behaviour from Aggregate Data

Learning nonlinear dynamics from aggregate data is a challenging problem because the full trajectory of each individual is not available, namely, the individual observed at one time may not be observed at the next time point, or the identity of individual is unavailable. This is in sharp contrast to learning dynamics with full trajectory data, on which the majority of existing methods are based. We propose a novel method using the weak form of Fokker Planck Equation (FPE) -- a partial differential equation -- to describe the density evolution of data in a sampled form, which is then combined with Wasserstein generative adversarial network (WGAN) in the training process. In such a sample-based framework we are able to learn the nonlinear dynamics from aggregate data without explicitly solving the partial differential equation (PDE) FPE. We demonstrate our approach in the context of a series of synthetic and real-world data sets.
The examination of the effect of the criterion for neural network’s learning on the effectiveness of the qualitative analysis of multidimensional data

Abstract A variety of multidimensional visualization methods are applied for the qualitative analysis of multidimensional data. One of the multidimensional data visualization methods is a method using autoassociative neural networks. In order to perform visualizations of n -dimensional data, such a network has n inputs, n outputs and one of the interlayers consisting of two outputs whose values represent coordinates of the analyzed sample’s image on the screen. Such a criterion for the network’s learning consists in that the same value as the one at the i th input appears at each i th output. If the network is trained in this way, the whole information from n inputs was compressed to two outputs of the interlayer and then decompressed to n network outputs. The paper shows the application of different learning criteria can be more beneficial from the point of view of the results’ readability. Overall analysis was conducted on seven-dimensional real data representing three coal classes, five-dimensional data representing printed characters, 216-dimensional data representing hand-written digits and, additionally, in order to illustrate additional explanations using artificially generated seven-dimensional data. Readability of results of the qualitative analysis of these data was compared using the multidimensional visualization utilizing neural networks for different learning criteria. Also, the obtained results of applying all analyzed criteria on 20 randomly selected sets of multidimensional data obtained from one of the publicly available repositories are presented.
Pure resolutions, linear codes, and Betti numbers

We consider the minimal free resolutions of Stanley-Reisner rings associated to linear codes and give an intrinsic characterization of linear codes having a pure resolution. We use this characterization to quickly deduce the minimal free resolutions of Stanley-Reisner rings associated to MDS codes as well as constant weight codes. We also deduce that the minimal free resolutions of Stanley-Reisner rings of first order Reed-Muller codes are pure, and explicitly describe the Betti numbers. Further, we show that in the case of higher order Reed-Muller codes, the minimal free resolutions are almost always not pure. The nature of the minimal free resolution of Stanley-Reisner rings corresponding to several classes of two-weight codes, besides the first order Reed-Muller codes, is also determined.
Efficient Safety Enforcement for Maude Programs via Program Specialization in the ÁTAME System

Program specialization is mainly recognized as a powerful technique for optimizing software systems. Nonetheless, it can also be productively employed in other application areas. This paper presents an assertion-guided program specialization methodology for efficiently imposing safety properties on software systems. The program specializer takes as input a set $$\mathcal {A}$$ of logical assertions that specifies the expected system behavior plus a software system that is modeled as a Maude program $$\mathcal {R}$$ that may violate some of the assertions in $$\mathcal {A}$$ . The outcome is a safe refinement $$\mathcal {R}^\triangleright $$ of $$\mathcal {R}$$ in which every system computation is a good run of $$\mathcal {R}$$ , i.e., it satisfies the assertions in $$\mathcal {A}$$ . The specialization technique has been fully automated in the ÁTAME system and ensures that no good run of $$\mathcal {R}$$ is removed from $$\mathcal {R}^\triangleright $$ , while the number of bad runs is reduced to zero. The efficiency and scalability of our technique is empirically demonstrated by means of a thorough experimental evaluation of the ÁTAME system, which shows fast specialization times and good performance of the computed specializations, even for large assertion sets.