Reviewerly/exHarmony huggingface dataset:
A Swarm-based Data Sanitization Algorithm in Privacy-Preserving Data Mining

In recent decades, data protection (PPDM), which not only hides information, but also provides information that is useful to make decisions, has become a critical concern. We present a sanitization algorithm with the consideration of four side effects based on multi-objective PSO and hierarchical clustering methods to find optimized solutions for PPDM. Experiments showed that compared to existing approaches, the designed sanitization algorithm based on the hierarchical clustering method achieves satisfactory performance in terms of hiding failure, missing cost, and artificial cost.
A CORRELATIONAL STUDY BETWEEN STUDENT’S ENGLISH INTEREST, VOCABULARY MASTERY, AND STUDENT’S ACHIEVEMENT

&lt;p&gt;The study is aimed to reveal the correlation between student’s English interest, vocabulary mastery toward student’s English achievement of the second-semester Students of Islamic Education Department of Teacher Training and Education Faculty of IAIN Pontianak in the academic year of 2017/2018. This study used a test, a questionnaire, and documentation to collect the data. The population of the study is all of the second-semester students while the sample is 50 students taken by cluster random sampling technique. The techniques used to analyze the data are simple and multiple correlation and regression by using SPSS 23. The results of the study show that there is a positive correlation between student’s English interest, and vocabulary mastery toward student’s English achievement. The positive correlation indicates that English interest, and vocabulary mastery line together with student’s English achievement.&lt;br /&gt;keywords: correlation, english interest, vocabulary mastery english achievement&lt;/p&gt;
An Improved Radial Basis Function Networks in Networks Weights Adjustment for Training Real-World Nonlinear Datasets

In neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and the networks weight. The gradient descent algorithm is a widely used weight adjustment algorithm in most of neural networks training algorithm. However, the method is known for its weakness for easily trap in local minima. It suffers from a random weight generated for the networks during initial stage of training at input layer to hidden layer networks. The performance of radial basis function networks (RBFN) has been improved from different perspectives, including centroid initialization problem to weight correction stage over the years. Unfortunately, the solution does not provide a good trade-off between quality and efficiency of the weight produces by the algorithm. To solve this problem, an improved gradient descent algorithm for finding initial weight and improve the overall networks weight is proposed. This improved version algorithm is incorporated into RBFN training algorithm for updating weight. Hence, this paper presented an improved RBFN in term of algorithm for improving the weight adjustment in RBFN during training process. The proposed training algorithm, which uses improved gradient descent algorithm for weight adjustment for training RBFN, obtained significant improvement in predictions compared to the standard RBFN. The proposed training algorithm was implemented in MATLAB environment. The proposed improved network called IRBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to IRBFN for root mean square error (RMSE) values with standard RBFN. The IRBFN yielded a promising result with an average improvement percentage more than 40 percent in RMSE.
A comparative study of FAQs for software development

Developers use FAQs (Frequently Asked Questions) to access and share knowledge about software libraries, APIs, and platforms. This paper studies 2,660 questions from 43 FAQ websites. We analyzed accessibility metrics such as the steps from the main documentation page, tagging or multilingualism as well as structure and readability metrics such as code-to-text ratio, number of links, and Flesch Reading-Ease. In addition, we compared these FAQs to 69,548 Stack Overflow (SO) posts, which cover the same topics and which have been posted by developers at least twice (i.e. duplicates). Our results reveal that different software vendors give different importance to their FAQs, e.g. by investing more effort or less in structuring and presenting them. We found that studied FAQs include more references (e.g. to corresponding API documentation) and are more verbose and difficult to read than corresponding SO duplicates. We also found that FAQs cover additional topics compared to corresponding duplicate posts.
State Representation Learning for Minimax Deep Deterministic Policy Gradient

Recently, the reinforcement learning of multi-agent has been developed rapidly, especially the Minimax Deep Deterministic Policy Gradient (M3DDPG) algorithm which improves agent robustness and solves the problem that agents trained by deep reinforcement learning (DRL) are often vulnerable and sensitive to the training environment. However, agents in the real environment may not be able to perceive certain important characteristics of the environment because of their limited perceptual capabilities. So Agents often fail to achieve the desired results. In this paper, we propose a novel algorithm State Representation Learning for Minimax Deep Deterministic Policy Gradient (SRL_M3DDPG) that combines M3DDPG with the state representation learning neural network model to extract the important characteristics of raw data. And we optimize the actor and critic network by using the neural network model of state representation learning. Then the actor and critic network learn from the state representation model instead of the raw observations. Simulation experiments show that the algorithm improves the final result.
Text Genre Detection Using Doc2Vec Word-embedding Language Model

According to Biber (1993) and Biber and Conrad (2009), multiple dimensional approach on several bi-polar features can classify genres founds in texts. Problem is that the process between feature extraction and statistical model building is exhaustively applicable when the machine learning system is dependent on frequency based language models. Following the machine learning approach of Kanaris and Stmatatos (2007), nearly 8,000 frequency-based models are used to induce genre distinction. If their whole process is not successful, they have to exhaustively redo the classification process. Upon such problem, we used a word embedding language model in order to deal feature extraction and model building at the same time. Among several word embedding models, our research is based on Doc2Vec model which captures paragraph feature of texts. We used the genre distinctions from the Project Gutenberg, dividing text databases into three parts in revealing distributional characteristics of linguistic features. Our method for detecting text genre is convenient in process as well as accurate in capturing feature distribution of text genre.
ICT Acceptance for Information Seeking Amongst Pre- and Postnatal Women in Urban Slums

This paper reports findings from a study aimed to understand how urban poor pre- and postnatal women in India use ICT tools like mobile phones for information seeking. The study was divided in 2 phases. In phase 1, we conducted semi-structured interviews with 66 pre- and postnatal women from urban poor slums located in 4 cities in India. In phase 2, we conducted a survey with 102 pre- and postnatal women from urban poor slum located in 9 cities in India. The aim was to understand care giving during and after pregnancy, nutrition, terminology used during pregnancy, and interpretation of visual material. This paper summarizes the findings, which include the importance of the use of appropriate terminology to design content in a multilingual country, identifying triggers for information seeking behaviour, and approaches to technology acceptance for information seeking. Based on these findings, we make suggestions for design of future ICT-based interventions in resource constraint settings.
Dynamic Doxastic Differential Dynamic Logic for Belief-Aware Cyber-Physical Systems

Cyber-physical systems (CPS), such as airplanes, operate based on sensor and communication data, i.e. on potentially noisy or erroneous beliefs about the world. Realistic CPS models must therefore incorporate the notion of beliefs if they are to provide safety guarantees in practice as well as in theory. To fundamentally address this challenge, this paper introduces a first-principles framework for reasoning about CPS models where control decisions are explicitly driven by controller beliefs arrived at through observation and reasoning. We extend the differential dynamic logic for CPS dynamics with belief modalities, and a learning operator for belief change. This new dynamic doxastic differential dynamic logic does due justice to the challenges of CPS verification by having (1) real arithmetic for describing the world and beliefs about the world; (2) continuous and discrete world change; (3) discrete belief change by means of the learning operator. We develop a sound sequent calculus for , which enables us to illustrate the applicability of by proving the safety of a simplified belief-triggered controller for an airplane.
An Efficient Hardware Design for Combined AES and AEGIS

This paper presents an integrated design of AES, the block cipher standard and AEGIS, an AES based authenticated encryption. Our design tries to exploit the common functionalities of AES and AEGIS to achieve both confidentiality as well as confidentiality and authenticity together. The proposed design provides a cost-effective implementation on various FPGA platforms, and it achieves both the goals by using a minimum amount of extra resources compared to the stand-alone AES and AEGIS design. The performance of our design implementation has been compared with the similar design work, and it has been shown that the throughput and frequency of our design outperform the best result available in the literature.
Gated Self-attentive Encoder for Neural Machine Translation

Neural Machine Translation (NMT) has become a popular technology in recent years, and the RNN-based encoder-decoder model is its actual translation framework. However, it remains a major challenge for RNNs to handle long-range dependencies. To address this limitation, we propose a gated self-attentive encoder (GSAE) for NMT that aims to directly capture the dependency relationship between any two words of source-side regardless of their distance. The proposed GSAE gains access to a wider context and ensures the better representation in the encoder. We extensively evaluate the proposed model using three language pairs. Experiments on WMT’14 English-German and WMT’14 English-French tasks show that our shallow model achieves BLEU = 26.54 and BLEU = 38.94 respectively, which are comparable with the state-of-the-art deep models. On WMT’17 English-Chinese translation task, our GSAE-NMT outperforms two strong baselines by 1.61 and 1.15 BLEU points, respectively.
PENERAPAN PEMBELAJARAN DENGAN MENGGUNAKAN ALAT PERAGA PADA PEMBELAJARAN MATEMATIKA SMP

This research was aimed to describe the effectiveness of the use of aids in mathematics instruction in SMP, with: 1) compare the mathematics learning achievement of students who were taught using real aids with that of students who were taught using half-real aids.; 2) compare the mathematics learning mastery of the students from both schools categories.This research is a quasi-experiment with pretest-postest design with non-equivalent groups. This research used two experiment groups and two control groups. Population of this research was fifth grade students of state SMP in Detusoko. Stratified random sampling technique was used to select the sample. From the population, the researcher took two schools and every school consisted of two classes as a sample. The total of the sample are 100 people. Data were collected using test. The test were tried out to two groups of students. To compare the mathematics learning achievement of students, data were analyzed by descriptive statistic and inferential statistic by analysis of covariant (α =0.05) and was continued with Tukey-Kramer test (α =0.05), while to compare the mathematics learning mastery of the students, data were analyzed by Chi-Square test.The result of this research showed that: 1) from both schools categories the mathematics learning achievement of students who were taught using real aids is higher than that of students taught using half-real aids; 2) the mathematics learning mastery of high category school students who were taught using real aids is higher than that of high category school students who were taught using half-real aids;3) the mathematics learning mastery of low category school students who were taught using real aids is not higher than that of low category school students who were taught using half-real aids; 4) from both schools categories the students who were taught using real aids, reach the mathematics learning mastery level, while the students who were taught using half-real aids, do not reach the learning mastery level.
McCann's Retinex Enhancement Method Based on YUV for Space Station Image

In the space station, the hash and uneven illumination brings a significant challenge to the vision system, which is the precondition of monitoring, unattended operation and robot operation. This challenge is caused by the high dynamic range of light intensity and the light reflection of metal facilities. Aiming at this problem, Retinex algorithm was proposed to estimate the luminosity. Firstly, the structure and mechanism of human eyes are analyzed. Secondly, based on that, McCann's Retinex algorithm is used to enhanced the images. Finally, the original image is enhanced by merging the new brightness layer and the color layer. The experimental results show the validity of the proposed algorithm.
NRSA: Neural Recommendation with Summary-Aware Attention

Reviews are widely used in recommendation systems to handle the sparsity problem of rating matrix. However, learning the representations of users and items only from reviews would be challenging since there are less meaningful words and reviews while modeling users or items. In fact, in addition to reviews there are rich off-the-shelf summaries written by users along with reviews, but existing recommendation methods ignore this useful information. The summary of a review is to describe the review with shorter sentences, and can be seen as a high-level abstraction of the review. Thus the summary can play a guidance role to indicate the important parts in a review and the informativeness of the review. Hence, we propose a neural recommendation method to learn summary-aware representations of users and items from reviews. We firstly apply a summary encoder to learn representations of text summary, which will be used as the guidance indicator. We design a summary-aware review encoder to learn representations of reviews from raw words, and another summary-aware user/item encoder to learn representations of users or items from reviews. To be specific, we propose a hierarchical attention model with summary representations as attention vectors under word- and review-level to select important words and reviews for users/items respectively. We conduct extensive experiments on real-world benchmark datasets and the results demonstrate that our approach can effectively improve the performance of neural recommendation.
Masdar: A Novel Sequence-to-Sequence Deep Learning Model for Arabic Stemming

Preprocessing the input textual data is the main starting step in any Natural Language Processing (NLP) application. Word stemming, i.e. extracting the stem or root of the input word, is a vital process within the preprocessing step. In this process, some words like “player”, “playing”, and “played” are mapped to their stem “play”. In the English language, there are several algorithms and approaches that can be applied directly to handle this process. On the other hand, there are some trials for similar algorithms in Arabic, but all have weak performance due to the complexity of the language and the approaches used for building such algorithms. In this paper, we presented a novel deep learning-based model, called Masdar, for Arabic stemming. The proposed model leverages the power of the deep learning, especially the recurrent neural networks, in building an efficient Arabic stemmer that is capable of producing very accurate stems for most of the input words. Some experiments are conducted to compare the performance of the proposed model with the latest cited Arabic stemmers on a dataset of about 6000 Arabic word/stem pairs. The experimental results show that Masder outperformed the other stemmers. It can efficiently produce the correct stems with about 95% accuracy on the whole dataset and about 82% accuracy on the unseen test words.
Stability and Scalability of Feature Subset Selection using Particle Swarm Optimization in Bioinformatics

Feature Subset Selection (FSS) is a non-convex, non-linear, multi-modal, high-dimensional, Multi-Objective Optimization problem, where finding the optimum solution is a fairly difficult task due to its ultra-large search space and its very large number of local minima. Existing FSS methods suffer from problems like stagnation in local optima and high computational cost. Evolutionary Computation (EC) techniques are well known global search algorithms. Particle Swarm Optimization (PSO) is an EC technique that is simple to implement and that converges faster than other methods such as Genetic Algorithms (GA), Differential Evolution (DE) or Ant Colony Optimization (ACO). PSO has been successfully applied to areas such as engineering, biology, image processing, job scheduling, robotics and neural networks, but its potential for FSS has not been fully investigated. The overall goal of this thesis is to investigate and improve the capability of PSO for FSS aiming to select the smallest, most compact subset of features with the best classification performance in the least amount of time. This thesis investigates the use of PSO for wrapper, filter, embedded and hybrid approaches and it implements PSO on both single and multi-objective FSS. FSS problems are characterized by an increasing number of features and instances, massively challenging classical methods. Representing efficient global search methods, Swarm Intelligence (SI) based Evolutionary Computation (EC) techniques have increasingly been applied to solve such FSS problems. Despite their popularity, PSO and other swarm-based algorithms still suffer from a variety of problems and short comings as a consequence of high-dimensional data. We start this dissertation with a comprehensive survey, we formulate the challenges of SI algorithms that tackle FSS problems, discuss algorithmic solutions and present an overview of metrics that allow the systematic and comparable assessment of algorithms performance. Then, in bioinformatics, PSO methods can select subsets of predictive genes for sample classification. However; the obtained gene subsets lack interpretability and are usually too large to be used as biomarkers. Such characteristics may be rooted in the observation that PSO algorithms usually lose the diversity of the swarm, leading to premature convergence and leaving many areas of the search space unexplored. Furthermore, high-dimensional FSS problems like those encountered in gene expression data pose an additional challenge to EC techniques since their ability to pick out the important features must remain unchanged despite the increasing dimensionality of the feature space. Our goal is to develop an algorithm capable of scaling up to high-dimensional gene expression data while still selecting the smallest subsets that capture the information to carry the desired prediction tasks. To address these drawbacks, we introduce a new PSO variant called Combinatorial PSO (COMB-PSO). The proposed method develops a new encoding mechanism, an enhanced Pareto nondominated set…
Deep Demosaicing for Edge Implementation

Most digital cameras use sensors coated with a Color Filter Array (CFA) to capture channel components at every pixel location, resulting in a mosaic image that does not contain pixel values in all channels. Current research on reconstructing these missing channels, also known as demosaicing, introduces many artifacts, such as zipper effect and false color. Many deep learning demosaicing techniques outperform other classical techniques in reducing the impact of artifacts. However, most of these models tend to be over-parametrized. Consequently, edge implementation of the state-of-the-art deep learning-based demosaicing algorithms on low-end edge devices is a major challenge. We provide an exhaustive search of deep neural network architectures and obtain a Pareto front of Color Peak Signal to Noise Ratio (CPSNR) as the performance criterion versus the number of parameters as the model complexity that outperforms the state-of-the-art. Architectures on the Pareto front can then be used to choose the best architecture for a variety of resource constraints. Simple architecture search methods such as exhaustive search and grid search requires some conditions of the loss function to converge to the optimum. We clarify these conditions in a brief theoretical study.
Mixtape: Breaking the Softmax Bottleneck Efficiently

The softmax bottleneck has been shown to limit the expressiveness of neural lan- guage models. Mixture of Softmaxes (MoS) is an effective approach to address such a theoretical limitation, but are expensive compared to softmax in terms of both memory and time. We propose Mixtape, an output layer that breaks the softmax bottleneck more efficiently with three novel techniques—logit space vector gating, sigmoid tree decomposition, and gate sharing. On four benchmarks including language modeling and machine translation, the Mixtape layer substantially improves the efficiency over the MoS layer by 3.5x to 10.5x while obtaining similar performance. A network equipped with Mixtape is only 20% to 34% slower than a softmax-based network with 10-30K vocabulary sizes, and outperforms softmax in perplexity and translation quality.
Fragmentation-Based Quantum Key Distribution (QKD) in WDM Networks

To integrate QKD with WDM networks without affecting the provisioning of classical service requests, we propose a wavelength fragmentation assignment scheme for QKD devices, which uses Tabu search to get satisfied secure key rate.
Choiceless Logarithmic Space

One of the most important open problems in finite model theory is the question whether there is a logic characterising efficient computation. While this question usually concerns Ptime, it can also be applied to other complexity classes, and in particular to Logspace which can be seen as a formalisation of efficient computation for big data. One of the strongest candidates for a logic capturing Ptime is Choiceless Polynomial Time (CPT). It is based on the idea of choiceless algorithms, a general model of symmetric computation over abstract structures (rather than their encodings by finite strings). However, there is currently neither a comparably strong candidate for a logic for Logspace, nor a logic transferring the idea of choiceless computation to Logspace.We propose here a notion of Choiceless Logarithmic Space which overcomes some of the obstacles posed by Logspace as a less robust complexity class. The resulting logic is contained in both Logspace and CPT, and is strictly more expressive than all logics for Logspace that have been known so far. Further, we address the question whether this logic can define all Logspace-queries, and prove that this is not the case.