Reviewerly/exHarmony huggingface dataset:
Augmenting Paraphrase Generation with Syntax Information Using Graph Convolutional Networks

Paraphrase generation is an important yet challenging task in natural language processing. Neural network-based approaches have achieved remarkable success in sequence-to-sequence learning. Previous paraphrase generation work generally ignores syntactic information regardless of its availability, with the assumption that neural nets could learn such linguistic knowledge implicitly. In this work, we make an endeavor to probe into the efficacy of explicit syntactic information for the task of paraphrase generation. Syntactic information can appear in the form of dependency trees, which could be easily acquired from off-the-shelf syntactic parsers. Such tree structures could be conveniently encoded via graph convolutional networks to obtain more meaningful sentence representations, which could improve generated paraphrases. Through extensive experiments on four paraphrase datasets with different sizes and genres, we demonstrate the utility of syntactic information in neural paraphrase generation under the framework of sequence-to-sequence modeling. Specifically, our graph convolutional network-enhanced models consistently outperform their syntax-agnostic counterparts using multiple evaluation metrics.
An Effective Collaborative User Model Using Hybrid Clustering Recommendation Methods

Collaborative Filtering (CF) has been known as the most successful recommendation technique in which recommendations are made based on the past rating records from like-minded users. Significant growth of users and items have negatively affected the efficiency of CF and pose key issues related to computational aspects and the quality of recommendation such as high dimensionality and data sparsity. In this study, a hybrid method was proposed and was capable to solve the mentioned problems using a neighborhood selection process for each user through two clustering algorithms which were item-based k-means clustering and user-based Fuzzy Clustering. Item-based k-means clustering was applied because of its advantages in computational time and hence it is able to address the high dimensionality issues. To create user groups and find the correlation between users, we employed the user-based Fuzzy Clustering and it has not yet been used in user-based CF clustering. This clustering can calculate the degree of membership among users into set of clustered items. Furthermore, a new similarity metric was designed to compute the similarity value among users with affecting the output of user-based Fuzzy Clustering. This metric is an alternative to the basic similarity metrics in CF and it has been proven to provide high-quality recommendations and a noticeable improvement on the accuracy of recommendations to the users. The proposed method has been evaluated using two benchmark datasets, MovieLens and LastFM in order to make a comparison with the existing recommendation methods.
De-identification of Emergency Medical Records in French: Survey and Comparison of State-of-the-Art Automated Systems

In France, structured data from emergency room (ER) visits are aggregated at the national level to build a syndromic surveillance system for several health events. For visits motivated by a traumatic event, information on the causes are stored in free-text clinical notes. To exploit these data, an automated de-identification system guaranteeing protection of privacy is required.In this study we review available de-identification tools to de-identify free-text clinical documents in French. A key point is how to overcome the resource barrier that hampers NLP applications in languages other than English. We compare rule-based, named entity recognition, new Transformer-based deep learning and hybrid systems using, when required, a fine-tuning set of 30,000 unlabeled clinical notes. The evaluation is performed on a test set of 3,000 manually annotated notes.Hybrid systems, combining capabilities in complementary tasks, show the best performance. This work is a first step in the foundation of a national surveillance system based on the exhaustive collection of ER visits reports for automated trauma monitoring.
Towards Blockchain-Based Internet of Things Systems for Energy Smart Contracts With Constrained Hardware Devices and Cloud Infrastructure

Hardware solutions based on blockchain used in peer-to-peer electricity trading operations have been on rise during the last years. It is expected that due to their usage, it will become easier for prosumers to participate in the power grid on more equal terms when compared to the traditional players that have been settled in this market for the last decades. However, devices used to fully integrate prosumers are scarce and often offer minimal functionalities to perform the task of becoming integrated in those markets. This manuscript puts forward a Constrained Hardware Device enhanced with several software elements related to blockchain and cloud infrastructures, which make possible for any electricity generator or storage system to perform major actions like executing smart contracts, requesting energy prices to a Transmission System Operator and replicating the interchanged data in a cloud computing environment in case there are blockchain node failures. In this way, Renewable Energy Sources can be integrated by means of inexpensive, reliable devices with all the required software components preinstalled, with prosumers being able to further intervene in energy markets.
Tanned and Synthetic Leather Classification Based on Images Texture with Convolutional Neural Network

Tanned leather is an output from complex processes called tanning. Leather tanning is an important step that used to protect the fiber or protein structure of animal’s skin. Another reason of tanning process is to prevent the animal’s skin from any defect or rot. After the tanning is complete, the leather can be applied to produce a wide variety of leather products. Thus, the leather prices usually more expensive because it takes longer time in process. Another way to get cheaper price is make non-animal leather that usually known as synthetic or imitation leather. The purpose of this paper is to classify the tanned leather and synthetic leather by using Convolutional Neural Network. The tanned leather consist of cow, goat and sheep leathers. The proposed method will classify into four class, they are cow, goat, sheep and synthetic leathers. In each class consist of 160 images with 448x448 pixels size as the input data. With CNN method, this research shows a good result for the accuracy about 92.1%.
The Dual Graph Shift Operator: Identifying the Support of the Frequency Domain

Abstract Contemporary data is often supported by an irregular structure, which can be conveniently captured by a graph. Accounting for this graph support is crucial to analyze the data, leading to an area known as graph signal processing (GSP). The two most important tools in GSP are the graph shift operator (GSO), which is a sparse matrix accounting for the topology of the graph, and the graph Fourier transform (GFT), which maps graph signals into a frequency domain spanned by a number of graph-related Fourier-like basis vectors. This alternative representation of a graph signal is denominated the graph frequency signal. Several attempts have been undertaken in order to interpret the support of this graph frequency signal, but they all resulted in a one-dimensional interpretation. However, if the support of the original signal is captured by a graph, why would the graph frequency signal have a simple one-dimensional support? Departing from existing work, we propose an irregular support for the graph frequency signal, which we coin dual graph. A dual GSO leads to a better interpretation of the graph frequency signal and its domain, helps to understand how the different graph frequencies are related and clustered, enables the development of better graph filters and filter banks, and facilitates the generalization of classical SP results to the graph domain.
Chroma Enhancement in CIELAB Color Space Using a Lookup Table

In this study, we present a method of chroma enhancement in the CIELAB color space and compare it with that in the RGB color space. Color image enhancement using the CIELAB color space has the disadvantage that the color gamut problem occurs because the conversion to the RGB color space is necessary to display the image. However, since the CIELAB color space is based on human visual perception, the quality of the resulting images is expected to be higher than that of the RGB color space. In the method using the CIELAB color space, we introduce a lookup table to reduce the calculation costs. Experiments comparing image enhancement results obtained from two color spaces are performed using several digital images.
AN ANALYSIS OF THE UTILIZING ZOOM APPLICATION TO ENGLISH LEARNERS’ SPEAKING SKILL MOTIVATION

Speaking is one of the productive skills that should be learned and mastered by English learners which gives them the ability to communicate effectively in this global world. However, most learners face difficulties in learning English, especially in mastering speaking skills. The lack of motivation in learning English is the most common problem faced by learners. With the development of technology, employing Zoom Application in learning English is recommended for fun and exciting learning. This research aims to analyze how applying Zoom Application can improve English learners’ motivation to speak in English. The descriptive qualitative method is used in this research and to gain the data, the researcher used some instruments, there are questionnaires and interviews, which were carried out to 30 learners in English Zoom Club as subjects of the research. The result of this research suggested that applying Zoom Application is very interesting since it provides such motivating and stimulating activities for English learners. Moreover, using Zoom Application make English learners have an appropriate platform and new friends to practice improving their speaking skill at the same time. Keywords: Speaking Skill, Motivation in Language Learning, Zoom Application
Predicting with Confidence: A Case-Based Reasoning Framework for Predicting Survival in Breast Cancer

There is usually a trade-off between predictive performance and transparency, where the reasoning process behind an algorithm is shielded behind a ”black-box.” In medical domains, experts being responsible for their decisions need to understand the reasons behind machine-generated recommendations. This paper presents a transparent case-based survival analysis framework that automatically retrieves an optimal number of solved survival cases and adapts them to predict the survival of a new case. With this methodology, retrieved and adapted survival cases lend an insight into which cases a prediction is based on. Our framework is capable of integrating DNA methylation, gene expression, and their combination in breast cancer. Additionally, we test our approach with and without feature selection and demonstrate the usefulness of the adaptation phase. We demonstrate that our framework performs at least as effectively as other state-of-the-art methods while affording greater explainability.
Use of Paraconsistent Feature Engineering to support the Long Term Feature choice for Speaker Verification

The state-of-art models for speech synthesis and voice conversion can generate synthetic speech perceptually indistinguishable from human speech, and speaker verification is crucial to prevent breaches. The building feature that best distinguishes genuine speech between spoof attacks is an open research subject. We used the baseline ASVSpoof2017, Transfer Learning (TL) set, and Symlet and Daubechies Discrete Wavelet Packet Transform (DWPT) for this investigation. To qualitatively assess the features, we used Paraconsistent Feature Engineering (PFE). Our experiments pointed out that for the use of more robust classifiers, the best choice would be the AlexNet method, while in terms of classification regarding the Equal Error Rate metric, the best suggestion would be Daubechies filter support 21. Finally, our findings indicate that Symlet filter support 17 as the most promising feature, which is evidence that PFE is a useful tool and contributes to feature selection.
Dependency Parsing as MRC-based Span-Span Prediction

Higher-order methods for dependency parsing can partially but not fully address the issue that edges in dependency trees should be constructed at the text span/subtree level rather than word level.In this paper, we propose a new method for dependency parsing to address this issue.The proposed method constructs dependency trees by directly modeling span-span (in other words, subtree-subtree) relations.It consists of two modules: the text span proposal module which proposes candidate text spans, each of which represents a subtree in the dependency tree denoted by (root, start, end); and the span linking module, which constructs links between proposed spans.We use the machine reading comprehension (MRC) framework as the backbone to formalize the span linking module, where one span is used as query to extract the text span/subtree it should be linked to.The proposed method has the following merits: (1) it addresses the fundamental problem that edges in a dependency tree should be constructed between subtrees;(2) the MRC framework allows the method to retrieve missing spans in the span proposal stage, which leads to higher recall for eligible spans.Extensive experiments on the PTB, CTB and Universal Dependencies (UD) benchmarks demonstrate the effectiveness of the proposed method. 1 2
Pedestrian Detection in Blind Area and Motion Classification Based on Rush-Out Risk Using Micro-Doppler Radar

Various remote sensing technologies have been applied in intelligent vehicles and robots for surrounding-environment recognition. However, these technologies experience difficulties in detecting pedestrians in blind areas and their motions, such as rush-out behaviors. To address this issue, we present a radar-based technique for the detection of pedestrians in blind areas and the classification of different risks of rush-out behaviors among detected pedestrians. We verify their ability to detect pedestrian motion in blind areas by conducting experiments in two environments with blind areas formed by outdoor cars and indoor walls. Then, the classification of motions with different risks of rush-out behaviors among pedestrians detected in the blind areas is demonstrated. We use the clustering method to accurately classify several types of behaviors with different rush-out risks in both environments.
The Readability Level of Reading Texts in Erlangga Straight Point Series: English for Eleventh Grade Students

&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; This study aims to describe the readability level of reading texts in &lt;em&gt;ESPS: English&lt;/em&gt; for the 11&lt;sup&gt;th&lt;/sup&gt; grade students. The measurement tools were Coh-Metrix RDL2, Miyazaki EFL Reading Index and teachers’ professional judgment. The formula were effective in measuring the texts readability by calculating the number of words, letters, and sentences in the text. The Score of Coh-Metrix RDL2 showed that the reading texts was easier for reading level of 11&lt;sup&gt;th&lt;/sup&gt; graders. MEFLRI claimed that reading texts in &lt;em&gt;ESPS&lt;/em&gt; textbook are match for reading level of 11&lt;sup&gt;th&lt;/sup&gt; graders. Teachers’ judgment claimed, the texts were suitable for eleventh grade students.&lt;/p&gt;&lt;strong&gt;Abstrak:&lt;em&gt; &lt;/em&gt;&lt;/strong&gt;Penelitian ini bertujuan untuk mendeskripsikan tingkat keterbacaan 19 teks bacaan dalam &lt;em&gt;ESPS: English&lt;/em&gt; untuk siswa kelas 11. Tiga alat ukur digunakan untuk mengukur tingkat keterbacaan yakni Coh-Metrix RDL2 dan Miyazaki EFL Reading Index, dan penilaian guru. Kedua formula terbukti efektif dalam mengukur keterbacaan teks dengan menghitung total huruf, kata, dan kalimat dalam teks. Coh-Metrix menunjukkan bahwa teks bacaan lebih mudah dipahami oleh siswa pada kelas 11, sedangkan MEFLRI menunjukkan bahwa teks bacaan sangat cocok diberikan pada kelas 11. Penilaian guru menyatakan teks bacaan sesuai untuk siswa kelas 11.
Mitigating Drawbacks of Logistic Map for Image Encryption Algorithms

This paper identifies and analyses some drawbacks of the logistic map which is still one of the most used chaotic maps in image encryption algorithms. As some of the disadvantages are caused by inappropriate implementations of the logistic map, this paper proposes a set of rules which should lead to enhancement of the desired chaotic behavior. Probably the most important rule introduces alternating value of parameter utilized by the logistic map. With careful choice of values and an adapted quantization technique, some of the issues should be fixed and theoretically also the values of numerical parameters should be improved. These assumptions are verified by applying the proposed set of rules on an algorithm from our prior work. Effects of the proposed rules on the used algorithm are investigated and all necessary modifications are thoroughly discussed. The paper also compares obtained values of commonly used numerical parameters and computational complexity with some other image encryption algorithms based on more complex chaotic systems.
Design and Development of an Energy Efficient Multimedia Cloud Data Center with Minimal SLA Violation

Multimedia computing (MC) is rising as a nascent computing paradigm to process multimedia applications and provide efficient multimedia cloud services with optimal Quality of Service (QoS) to the multimedia cloud users.But, the growing popularity of MC is affecting the climate.Because multimedia cloud data centers consume an enormous amount of energy to provide services, it harms the environment due to carbon dioxide emissions.Virtual machine (VM) migration can effectively address this issue; it reduces the energy consumption of multimedia cloud data centers.Due to the reduction of Energy Consumption (EC), the Service Level Agreement violation (SLAV) may increase.An efficient VM selection plays a crucial role in maintaining the stability between EC and SLAV.This work highlights a novel VM selection policy based on identifying the Maximum value among the differences of the Sum of Squares Utilization Rate (MdSSUR) parameter to reduce the EC of multimedia cloud data centers with minimal SLAV.The proposed MdSSUR VM selection policy has been evaluated using real workload traces in CloudSim.The simulation result of the proposed MdSSUR VM selection policy demonstrates the rate of improvements of the EC, the number of VM migrations, and the SLAV by 28.37%, 89.47%, and 79.14%, respectively.
Further steps on the reconstruction of convex polyominoes from orthogonal projections

Abstract A remarkable family of discrete sets which has recently attracted the attention of the discrete geometry community is the family of convex polyominoes, that are the discrete counterpart of Euclidean convex sets, and combine the constraints of convexity and connectedness. In this paper we study the problem of their reconstruction from orthogonal projections, relying on the approach defined by Barcucci et al. (Theor Comput Sci 155(2):321–347, 1996). In particular, during the reconstruction process it may be necessary to expand a convex subset of the interior part of the polyomino, say the polyomino kernel, by adding points at specific positions of its contour, without losing its convexity. To reach this goal we consider convexity in terms of certain combinatorial properties of the boundary word encoding the polyomino. So, we first show some conditions that allow us to extend the kernel maintaining the convexity. Then, we provide examples where the addition of one or two points causes a loss of convexity, which can be restored by adding other points, whose number and positions cannot be determined a priori.
Semi-Supervised Metric Learning: A Deep Resurrection

Distance Metric Learning (DML) seeks to learn a discriminative embedding where similar examples are closer, and dissimilar examples are apart. In this paper, we address the problem of Semi-Supervised DML (SSDML) that tries to learn a metric using a few labeled examples, and abundantly available unlabeled examples. SSDML is important because it is infeasible to manually annotate all the examples present in a large dataset. Surprisingly, with the exception of a few classical approaches that learn a linear Mahalanobis metric, SSDML has not been studied in the recent years, and lacks approaches in the deep SSDML scenario. In this paper, we address this challenging problem, and revamp SSDML with respect to deep learning. In particular, we propose a stochastic, graph-based approach that first propagates the affinities between the pairs of examples from labeled data, to that of the unlabeled pairs. The propagated affinities are used to mine triplet based constraints for metric learning. We impose orthogonality constraint on the metric parameters, as it leads to a better performance by avoiding a model collapse.
Dynamic Federation in Federated Identity Management

Federated Identity Management (FIM) is a way to connect the identity management systems together. In recent years many organizations have started opting FIM single-sign-on to focus on the business rather than storing the user credentials in their own database, which also offers economic advantage to the organization, as well as convenience for the users. However, the current FIM frameworks are dynamic in nature for authentication and authorization, but they are not dynamic in nature to get federated between the Service Provider (SP) and Identity Provider (IdP). In this paper, we mention the current issues and introduce broker model and fuzzy cognitive map model (FCM) that will achieve dynamic federation between the entities based on individuals trust features requirement.
3D Human Activity Classification with 3D Zernike Moment Based Convolutional, LSTM-Deep Neural Networks

In this paper, we propose a method for classification 3D human activities using the complementarity of CNNs, LSTMs, and DNNs by combining them into one unified architecture called CLDNN. Our approach is based on the prediction of 3D Zernike Moments of some relevant joints of the human body through Kinect using the Kinect Activity Recognition Dataset. KARD includes 18 activities and each activity consists of real-world point clouds that have been carried out 3 times by 10 different subjects. We introduce the potential for the 3D Zernike Moment feature extraction approach via a 3D point cloud for human activity classification, and the ability to be trained and generalized independently from datasets using the Deep Learning methods. The experimental results obtained on datasets with the proposed system has correctly classified 96.1% of the activities. CLDNN has been shown to provide a 5% relative improvement over LSTM, the strongest of the three individual models.