Reviewerly/exHarmony huggingface dataset:
Deep and Ensemble Learning to Win the Army RCO AI Signal Classification Challenge

Automatic modulation classification is a challenging problem with multiple applications including cognitive radio and signals intelligence. Most of the existing efforts to solve this problem are only applicable when the signal to noise ratio (SNR) is high and/or long observations of the signal are available. Recent work has focused on applying shallow and deep machine learning (ML) to this problem. In this paper, we present an exploration of such deep learning and ensemble learning techniques that was used to win the Army Rapid Capability Office (RCO) 2018 Signal Classification Challenge. An expert feature extraction and shallow learning approach is discussed in a simultaneous publication. We evaluated multiple state-of-the-art deep learning network architectures and adapted them to work in the RF signal domain instead of the image/computer-vision domain. The best deep learning methods were merged with the best expert feature extraction and shallow learning methods using ensemble learning. Finally, the ensemble classifier was calibrated to obtain marginal gains. The methods discussed are capable of correctly classifying waveforms at -10 dB SNR with over 63\% accuracy and signals at +10 dB SNR with over 95\% accuracy from an Army RCO provided training set.
Attacking a high-dimensional quantum key distribution system with wavelength-dependent beam splitter*

The unconditional security of quantum key distribution (QKD) can be guaranteed by the nature of quantum physics. Compared with the traditional two-dimensional BB84 QKD protocol, high-dimensional quantum key distribution (HD-QKD) can be applied to generate much more secret key. Nonetheless, practical imperfections in realistic systems can be exploited by the third party to eavesdrop the secret key. The practical beam splitter has a correlation with wavelength, where different wavelengths have different coupling ratios. Using this property, we propose a wavelength-dependent attack towards time-bin high-dimensional QKD system. What is more, we demonstrate that this attacking protocol can be applied to arbitrary d -dimensional QKD system, and higher-dimensional QKD system is more vulnerable to this attacking strategy.
AmritaDGA: a comprehensive data set for domain generation algorithms (DGAs) based domain name detection systems and application of deep learning

In recent days, botnet plays an important role in malware distribution. This has been used as a primary approach for the proliferation of the malicious activities via the internet by attackers. To evade blacklisting, recent botnets make use of domain flux or internet protocol (IP) flux. This work focuses on domain flux. Domain flux uses domain generation algorithms (DGAs) to generate a list of domain names based on a seed and these domain names contacts command and control (C&amp;C) server till it gets access permission to the system. This work presents the fully labeled domain name data set entitled as AmritaDGA which can be used for doing research in the field of detecting domain names which are generated using DGAs. We evaluate the efficacy of deep learning architectures with Keras embedding as domain name representation method on AmritaDGA. AmritaDGA is composed of two data sets. The first data set is collected from the publicly available sources. The second data set is collected from an internal real-time network. The performance of the trained model on public data set is evaluated on unseen samples of a public data set and private corpora. Deep learning architectures performed well in most of the cases of test experiments. The baseline system has been made publicly available and the data set is distributed for Detecting Malicious Domain names (DMD 2018) shared task.
Unsupervised learning-based long-term superpixel tracking

Finding correspondences between structural entities decomposing images is of high interest for computer vision applications. In particular, we analyze how to accurately track superpixels - visual primitives generated by aggregating adjacent pixels sharing similar characteristics - over extended time periods relying on unsupervised learning and temporal integration. A two-step video processing pipeline dedicated to long-term superpixel tracking is proposed. First, unsupervised learning-based superpixel matching provides correspondences between consecutive and distant frames using new context-rich features extended from greyscale to multi-channel and forward-backward consistency constraints. Resulting elementary matches are then combined along multi-step paths running through the whole sequence with various inter-frame distances. This produces a large set of candidate long-term superpixel pairings upon which majority voting is performed. Video object tracking experiments demonstrate the accuracy of our elementary estimator against state-of-the-art methods and proves the ability of multi-step integration to provide accurate long-term superpixel matches compared to usual direct and sequential integration.
Entity Decisions in Neural Language Modelling: Approaches and Problems

We explore different approaches to explicit entity modelling in language models (LM).We independently replicate two existing models in a controlled setup, introduce a simplified variant of one of the models and analyze their performance in direct comparison.Our results suggest that today's models are limited as several stochastic variables make learning difficult.We show that the most challenging point in the systems is the decision if the next token is an entity token.The low precision and recall for this variable will lead to severe cascading errors.Our own simplified approach dispenses with the need for latent variables and improves the performance in the entity yes/no decision.A standard well-tuned baseline RNN-LM with a larger number of hidden units outperforms all entity-enabled LMs in terms of perplexity.
Suggestion Miner at SemEval-2019 Task 9: Suggestion Detection in Online Forum using Word Graph

This paper describes the suggestion miner system that participates in SemEval 2019 Task 9 -SubTask A -Suggestion Mining from Online Reviews and Forums.The system participated in the subtasks A. This paper discusses the results of our system in the development, evaluation and post evaluation.Each class in the dataset is represented as directed unweighted graphs.Then, the comparison is carried out with each class graph which results in a vector.This vector is used as features by a machine learning algorithm.The model is evaluated on, hold on strategy.The organizers randomly split (8500 instances) training set (provided to the participant in training their system) and testing set (833 instances).The test set is reserved to evaluate the performance of participants systems.During the evaluation, our system ranked 31 in the Coda Lab result of the subtask A (binary class problem).The binary class system achieves evaluation value 0.35.
Management Information System using Blockchain Technology in an E-commerce Enterprise: A Systematic Review

Information technology over the last two decades has been discerned as an emerging technology by affecting our personal, social, and public life. Thereby, the technology has made a notable impact on the quality of life. Management Information System collects data from various online systems, thus, analyzing the information, and further reporting the data to aid in management decision making. This study primarily explores and highlights the management information system using blockchain technology in an e-commerce enterprise. The paper thereby presents a systematic review based on several studies in the literature. A systematic review of management and business process journals is performed using the keywords related to the topic. Thereby, the study finds that MIS using the blockchain technology is easily acceptable in an e-commerce. Various advantages associated with the MIS are compiled from related studies in the literature. The results obtained from the assessment are gathered in an organized way and subsequently concluded in order to understand the principle functioning of the management information system using blockchain technology in an e-commerce enterprise.
The Sally Smedley Hyperpartisan News Detector at SemEval-2019 Task 4

This paper describes our system submitted to the formal run of SemEval-2019 Task 4: Hyperpartisan news detection.Our system is based on a linear classifier using several features, i.e., 1) embedding features based on the pre-trained BERT embeddings, 2) article length features, and 3) embedding features of informative phrases extracted from the by-publisher dataset.Our system achieved 80.9% accuracy on the test set for the formal run and got the 3rd place out of 42 teams.
Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference

In logic-based approaches to reasoning tasks such as Recognizing Textual Entailment (RTE), it is important for a system to have a large amount of knowledge data. However, there is a tradeoff between adding more knowledge data for improved RTE performance and maintaining an efficient RTE system, as such a big database is problematic in terms of the memory usage and computational complexity. In this work, we show the processing time of a state-of-the-art logic-based RTE system can be significantly reduced by replacing its search-based axiom injection (abduction) mechanism by that based on Knowledge Base Completion (KBC). We integrate this mechanism in a Coq plugin that provides a proof automation tactic for natural language inference. Additionally, we show empirically that adding new knowledge data contributes to better RTE performance while not harming the processing speed in this framework.
Consistency of the maximum likelihood estimator in seasonal hidden Markov models

In this paper, we introduce a variant of hidden Markov models in which the transition probabilities between the states, as well as the emission distributions, are not constant in time but vary in a periodic manner. This class of models, which we will call seasonal hidden Markov models (SHMMs), is particularly useful in practice, as many applications involve a seasonal behaviour. However, up to now, there is no theoretical result regarding this kind of model. We show that under mild assumptions, SHMMs are identifiable: we can identify the transition matrices and the emission distributions from the joint distribution of the observations on a period, up to state labelling. We also give sufficient conditions for the strong consistency of the maximum likelihood estimator (MLE). These results are applied to simulated data, using the EM algorithm to compute the MLE. Finally, we show how SHMM can be used in real-world applications by applying our model to precipitation data, with mixtures of exponential distributions as emission distributions.
Irreducible polynomials over finite fields produced by composition of quadratics

For a set $S$ of quadratic polynomials over a finite field, let $C$ be the (infinite) set of arbitrary compositions of elements in $S$. In this paper we show that there are examples with arbitrarily large $S$ such that every polynomial in $C$ is irreducible. As a second result, when $#S > 1$, we give an algorithm to determine whether all the elements in $C$ are irreducible, using only $O( #S(\log q)^3 q^{1/2} )$ operations.
Memory Stay Or Stray?: Irregular Verbs Learning Using Kahoot!

Even though gamification has been lingering for years now, its popularity has been rising ever since.In fact, across disciplines and contexts, the amplification of 21 st century learning has been giving gamification a special place in education.There are numerous studies conducted on the use of gamification among school children, university students or even trainee teachers.Coincidentally, all of these studies have shown that gamification does impact their learning.Hence, as harmless as it seems, any teachers who are not equipped with such knowledge on the benefits of gamification may hinder their learners and themselves from reaching their utmost teaching and learning experience especially when it comes to grammar learning among the younger pupils.Considering this thought in mind, this research was conducted to prove that the use of Kahoot! helps the Year 3 pupils in retaining the English Irregular verbs.This quasiexperimental designed research gathered data from a sample of 35 Year 3 pupils using the pre-test and post-test results.The findings which were analysed descriptively using sample t-test showed that pupils' performances in the post-test improved over the use of Kahoot! in classroom.In conclusion, this research provides an insight on number of ways that gamification can actually impacted the pupils' learning; especially in terms of memory retention.It also works as a stepping stone for more researches to study on the effectiveness of using Kahoot! in tackling any part of learning especially among the younger learners.
Enhancement of Retinal Image From Line-Scanning Ophthalmoscope Using Generative Adversarial Networks

A line-scanning ophthalmoscope (LSO) is a retinal imaging technique that has the characteristics of high imaging resolution, wide field of view, and high imaging speed. However, the high-speed imaging with rather short exposure time inevitably reduces the signal intensity, and many factors, such as speckle noise and intraocular scatter, further degrade the signal-to-noise ratio (SNR) of retinal images. To effectively improve the image quality without increasing the LSO system’s complexity, the post-processing method of image super-resolution (SR) is adopted. In this paper, we propose a learning-based multi-frame retinal image SR method that directly learns an end-to-end mapping from low-resolution (LR) image sequences to high-resolution (HR) images. This network was validated on down-sampled and real LSO image sequences. We evaluated the method on a down-sampled dataset with the metrics of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and perceptual distance. Moreover, the power spectra and full width at half maximum (FWHM) were used as the no-reference image quality assessment (NR-IQA) algorithms to evaluate the reconstruction results of the real LSO image sequences. The experimental results indicate that the proposed method can significantly enhance the SNR of LSO images and efficiently improve the resolution of LSO retinal images, which has great practical significance for clinical diagnosis and analysis.
Multi-Process Training GAN for Identity-Preserving Face Synthesis

Recently, the advent of generative adversarial networks (GANs) in synthesizing identity-preserving faces has aroused the considerable interest of many scholars. However, face attribute representation learning, which is explicitly disentangled from identity feature and synthesizes identity-preserving face images with high diversity and quality in other datasets, still remains challenging. To cope with that, this paper proposes multi-process training GAN, or MP-GAN for short, which significantly improves the disentangled representation, diversity, and quality. Unlike other existing single-process models that map noise to a final output resolution image in a single training process, MP-GAN divides training into multiple processes. The main idea is to first generate lower resolution images that contain lower frequency feature information through competition and then extract their disentangled facial features to generate a higher resolution image. Furthermore, an identity-preserving image with real identity feature and disentangled facial feature could be generated at the final output resolution training process. The distinct benefits are not only getting diverse facial feature generation but also achieving disentangled representation from the lower resolution training processes and rendering a photo-realistic image that contains high diversity but preserves identity at the final output resolution training process. The high performance of this method is highlighted by quantitative and qualitative comparisons. We conclude that MP-GAN can generate face images featuring high diversity and quality while efficiently preserving identity, thereby significantly outperforming most modern advanced methods.
General Sequence Teacher–Student Learning

In automatic speech recognition, performance gains can often be obtained by combining an ensemble of multiple models. However, this can be computationally expensive when performing recognition. Teacher-student learning alleviates this cost by training a single student model to emulate the combined ensemble behaviour. Only this student needs to be used for recognition. Previously investigated teacher-student criteria often limit the forms of diversity allowed in the ensemble, and only propagate information from the teachers to the student at the frame level. This paper addresses both of these issues by examining teacher-student learning within a sequence-level framework, and assessing the flexibility that these approaches offer. Various sequence-level teacher-student criteria are examined in this work, to propagate sequence posterior information. A training criterion based on the Kullback-Leibler (KL)-divergence between context-dependent state sequence posteriors is proposed that allows for a diversity of state cluster sets to be present in the ensemble. This criterion is shown to be an upper bound to a more general KL-divergence between word sequence posteriors, which places even fewer restrictions on the ensemble diversity, but whose gradient can be expensive to compute. These methods are evaluated on the augmented multi-party interaction (AMI) meeting transcription and MGB-3 television broadcast audio tasks.
Smooth, identifiable supermodels of discrete DAG models with latent variables

We provide a parameterization of the discrete nested Markov model, which is a supermodel that approximates DAG models (Bayesian network models) with latent variables.Such models are widely used in causal inference and machine learning.We explicitly evaluate their dimension, show that they are curved exponential families of distributions, and fit them to data.The parameterization avoids the irregularities and unidentifiability of latent variable models.The parameters used are all fully identifiable and causallyinterpretable quantities.
A Selectable Sloppy Heap

We study the selection problem, namely that of computing the ith order statistic of n given elements. Here we offer a data structure called selectable sloppy heap that handles a dynamic version in which upon request (i) a new element is inserted or (ii) an element of a prescribed quantile group is deleted from the data structure. Each operation is executed in constant time—and is thus independent of n (the number of elements stored in the data structure)—provided that the number of quantile groups is fixed. This is the first result of this kind accommodating both insertion and deletion in constant time. As such, our data structure outperforms the soft heap data structure of Chazelle (which only offers constant amortized complexity for a fixed error rate 0 &lt; ε ≤ 1 / 2 ) in applications such as dynamic percentile maintenance. The design demonstrates how slowing down a certain computation can speed up the data structure. The method described here is likely to have further impact in the field of data structure design in extending asymptotic amortized upper bounds to same formula asymptotic worst-case bounds.
Parameter Estimation Using the Python Package pymcmcstat

A Bayesian approach to solving inverse problems provides insight regarding model limitations as well as the underlying model and observation uncertainty.In this paper we introduce pymcmcstat, which provides a wide variety of tools for estimating unknown parameter distributions.For scientists and engineers familiar with least-squares optimization, this package provides a similar interface from which to expand their analysis to a Bayesian framework.This package has been utilized in a wide array of scientific and engineering problems, including radiation source localization and constitutive model development of smart material systems.
PENGEMBANGAN E-MODUL BERBASIS LITERASI SAINS MATERI ORGAN GERAK HEWAN DAN MANUSIA KELAS V SD

This type of research is development research with the ADDIE model. This research begins with analysis, design, development, implementation, and evaluation. The subjects in this study were media expert validators, material experts, learning experts, students, and teachers. The results of the development in the form of science-based e-modules are in the form of evaluations from expert validators and trials of students and teachers. The assessment of media experts received a score of 92.85 with the category "Very Worthy. Material experts received a rating of 92.85 in the "Very Worthy" category. The results of the assessment of learning experts get a value of 73.21 with the category "Eligible". the assessment of small-scale trials conducted by students gets a score of 95 with the category "Very Worthy" and the assessment of the teacher gets a score of 95 with the category "Very Worthy". The large-scale trials conducted by students got a score of 96.6 with the category "Very Worthy" and the assessment of the teacher received a score of 97.08 with the category "Very Worthy". Based on the results of the research obtained, science-based e-modules are very feasible to be used in the learning process at school.
The Attitudes of Second Year EFL Students at Dr Moulay Tahar University towards Learning English Pronunciation through Mobile Assisted Language

Teaching and learning pronunciation has become a neglected issue because it is regarded as an abstract science, although it is a peripheral ingredient in English oral production.With the dawn of technology and its various tools, exploring pronunciation and its effectiveness in successful communication attracts a bunch of scholarly attention.The use of Computer Assisted Language Learning opens the gateway for the possibility to integrate other tools in language learning as mobiles and gives birth to mobile learning or free education.In this vein, the incentive behind the current research work is in twofold: to investigate the attitudes of both teachers and students towards the inclusion of mobiles in language learning, and to prove the effectiveness of this new approach in improving the listening and speaking skills of foreign language learners.To fulfill these aims, the researcher held an interview with 15 teachers teaching English at Dr Moulay Tahar University of Saida.The researcher also conducted a pretest and a posttest with 95 students of the second year from the same university.To this end, the findings revealed that both teachers and students have positive attitudes towards the introduction of mobile technology in language learning, even though some teachers still favor the traditional approaches of language teaching and learning.The results also demonstrated that the applications, used in the experiment, showed the usability of Mobile Assisted Language in learning English pronunciation.