Reviewerly/exHarmony huggingface dataset:
Determining Formulas Related to Point Compression on Alternative Models of Elliptic Curves

Let E be an elliptic curve given by any model over a field K. A rational function f : E → K of degree 2 such that f(P) = f(Q) ⇔ Q = ±P can be used as a point compression on E. Then there exists induced from E multiplication of values of f by integers given by [n]f(P) := f([n]P), which can be comput ed using the Montgomery ladder algorithm. For this algorithm one needs the generalized Montgomery formulas for differential addition and doubling that is rational functions A(X1, X2, X3) ∈ K(X1, X2, X3) and [2] ∈ K(X) such that f(P + Q) = A(f(P), f(Q), f(Q − P)) and [2]f(P) = f([2]P) for generic P,Q ∈ E. For most standard models of elliptic curves generalized Montgomery formulas are known. To use compression for scalar multiplication [n]P for P ∈ E, one can compute after compression [n]f(P), which is followed by [n + 1]f(P) in the Montgomery ladder algorithm, then one can recover [n]P on E, since there exists a rational map B such that [n]P = B(P, [n]f(P), [n + 1]f(P)) for generic P ∈ E and n ∈ Z. Such a map B is known for Weierstrass and Edwards curves, but to our knowledge it seems that it was not given for other models of elliptic curves. In this paper for an elliptic curve E and the above compression function f we give an algorithm to search for generalized Montgomery formulas, functions on K induced after compression by endomorphisms of E, and the above map B for point recovering. All these tasks require searching for solutions of similar type problems for which we describe an algorithm based on Gröbner bases. As applications we give formulas for differential addition, doubling and the above map B for Jacobi quartic, Huff curves, and twisted Hessian curves.
Recursive parameter estimation algorithm of the Dirichlet hidden Markov model

The recursive (online, incremental) estimation of the hidden Markov model (HMM) parameters has become a more popular research subject. The complexity of the recursive methods is linear, and this complexity allows the estimation of parameters in real time. Most of the recursive parameter estimation methods use Gaussian mixtures and do not explore other distributions. However, the underlying structure of the data might be non-Gaussian. Thus, we propose a novel recursive method for estimating the parameters of the Dirichlet HMM. The Dirichlet distribution is popular because of its flexibility in modelling data. The proposed estimation is based on the maximum likelihood method, which is known to give close to optimal results. The performance of our algorithm is tested using a computer simulation and the clustering of several data-sets. Several experiments were conducted in order to compare the performance of the Gaussian HMM and Dirichlet HMM in the classification of several data-sets.
“Unknown Symbols”: Online Legal Research in the Age of Emoji

Over the last decade, emoji and emoticons have made the leap from text messaging and social media to legal filings, court opinions, and law review articles. However, emoji and emoticons’ growth in popularity has tested the capability of online legal research systems to properly display and retrieve them in search results, posing challenges for future researchers of primary and secondary sources. This article examines current display practices on several of the most popular online legal research services (including Westlaw Edge, Lexis Advance, Bloomberg Law, Fastcase, HeinOnline, and Gale OneFile LegalTrac) and suggests effective workarounds for researchers.
Collective Shape-changing Interfaces

In this paper, I introduce collective shape-changing interfaces, a class of shape-changing interfaces that consist of a set of discrete collective elements. Through massively parallel transformation, locomotion, and connection of individual building blocks, the overall physical structure can be dynamically changed. Given this parallel change of individual elements, I propose three approaches for user interaction: dynamic, improvised, and actuated collective shape transformation. I exemplify each approach through my own work and possible future work.
AutoML and Meta-learning for Multimedia

AutoML and meta-learning are exciting and fast-growing research directions to the research community in both academia and industry. This tutorial is to disseminate and promote the recent research achievements on AutoML and meta-learning as well as their potential applications for multimedia. Specifically, we will first advocate novel, high-quality research findings and innovative solutions to the challenging problems in AutoML and meta-learning. Then we will discuss scenarios of multimedia where AutoML and meta-learning serve as candidates for solutions. Finally, we will point out future research directions on AutoML and meta-learning as well as their potential new applications for multimedia.
Quantum Digital Signature Scheme Based on Finite Automata

A quantum digital signature is the quantum cryptographic primitives that combine classical digital signature and quantum mechanics to provide unconditional security for the identity of the sender and the integrity of the classical messages. Currently, lots of quantum digital signature schemes have been investigated. In this paper, we propose a new quantum digital signature scheme based on finite automata and show its security against existing attacks. Finally, the noteworthy features like no entangled states are indicated.
Nitelik Çıkarımı Yöntemlerinin Türkçe Metinlerin Sınıflandırılmasına Etkisi

Feature extraction is the most important preprocessing step of text classification task. Effects of preprocessing techniques on text mining for English have been extensively studied. However, studies for Turkish are limited and generally belong to a specific problem domain. In this study, we investigate the effects of feature extraction techniques on four different Turkish text classification problems including news classification, spam e-mail detection, sentiment analysis, and author detection to show the differences and similarities among the problems. We also propose a new feature selection method to reduce feature space. The experimental analysis has showed that, stopword removal improves classification performance. However, stemming does not make any positive effect on classification accuracy. The most successful term weighting methods are tf and tf*idf. The proposed feature selection method improves classification performance and has higher accuracy than the well-known methods.&amp;nbsp;
The Positional Effect in Soft Classification Accuracy Assessment

Recent research has included the rapid development of soft classification algorithms and soft classification accuracy assessment beyond the traditional hard approaches.However, less consideration has been given to whether conditions and assumptions generated for the hard classification accuracy assessment are appropriate for the soft one.Positional error is one of the most significant uncertainties that need to be considered.This research examined the impacts of positional errors on the accuracy measures derived from the soft error matrix using NLCD 2011 as reference data and several coarser maps generated from NLCD 2011 as classification maps at the spatial resolutions of 150m, 300m, 600m, and 900m.Eight study sites, with a spatial extent of 180km×180km, of different landscape characteristics were investigated using a two-level classification scheme.Results showed that with existing registration accuracies achieved by current global land cover mapping, the errors in overall accuracy (OA-error) were 2.13% -39.98% and 2.53%-48.82%for the 8 and 15 classes, respectively and the errors in Kappa (Kappa-error) were 6.64%-57.09% and 7.08%-58.81%for the 8 and 15 classes, respectively if soft classifications were implemented based on images where spatial resolutions varied from 150m to 900m.More complex landscape characteristics and classes in the classification scheme produced a greater impact of the positional error on the accuracy measures.To keep both OA-error and Kappa-error under 10 percent, the average required registration accuracy should achieve 0.1 pixels.This paper strongly recommends the addition of uncertainty analysis due to positional error in future global land cover mapping.
An Impact Analysis of Information Security Professional’s Job Stress and Job Satisfaction to Turnover Intention: Moderation of Organizational Justice

The purpose of this study is to empirically verify the relationship of how job stress and job satisfaction of information security professionals affect turnover intention, a precursor of actual turnover. The moderation effect of organizational justice is also explored within these causal relationships. This empirical analysis used 150 responses from information security professionals within 4 different industries. The analysis result from survey responses shows that job stress increases turnover intention, and job satisfaction decreases turnover intention, and that interactional justice, a subordinate concept of organizational justice, has a negative moderating effect at the relationship between job stress and turnover intention. The moderating effect of interactional justice, which can reduce turnover intention with warm words from managers or colleagues even when information security professionals who respond to emergencies such massive incidents are with high job stress, is a piece of important knowledge for information security managers. To reduce voluntary turnover of information security professionals from the organizational perspective, making efforts to lower job stress and raise job satisfaction and interactional justice is necessary.
Least-Squares Optimal Contrast Limited Histogram Equalisation

Contrast Limited Histogram Equalisation moves the input image histogram gently towards one which has a more uniform distribution. Viewed as a tone mapping operation, CLHE generates a tone curve with bounded max and min slopes. It is this boundedness which ensures that the processed images have more detail but few artefacts. Outside of limiting contrast, recent improvements to histogram equalisation include constraining the tone curve to make good whites and blacks and constraining the tone curve to be smooth. This paper makes three contributions. First, we show that the CLHE formalism is not least-squares optimal but optimality can be achieved by reformulating the problem in a quadratic programming framework. Second, we incorporate the additional constraints of tone curve smoothness and good whites and blacks in our quadratic programming CLHE framework. Third, experiments demonstrate the utility of our method.
Object Segmentation Using Pixel-Wise Adversarial Loss

Recent deep learning based approaches have shown remarkable success on object segmentation tasks. However, there is still room for further improvement. Inspired by generative adversarial networks, we present a generic end-to-end adversarial approach, which can be combined with a wide range of existing semantic segmentation networks to improve their segmentation performance. The key element of our method is to replace the commonly used binary adversarial loss with a high resolution pixel-wise loss. In addition, we train our generator employing stochastic weight averaging fashion, which further enhances the predicted output label maps leading to state-of-the-art results. We show, that this combination of pixel-wise adversarial training and weight averaging leads to significant and consistent gains in segmentation performance, compared to the baseline models.
Haze Effects on Satellite Remote Sensing Imagery and their Corrections

Imagery recorded using satellite sensors operating at visible wavelengths can be contaminated by atmospheric haze that originates from large scale biomass burning.Such issue can reduce the reliability of the imagery and therefore having an effective method for removing such contamination is crucial.The principal aim of this study is to investigate the effects of haze on remote sensing imagery and develop a method for removing them.In order to get a better understanding on the behaviour of haze, the effects of haze on satellite imagery were initially studied.A methodology of removing haze based on haze subtraction and filtering was then developed.The developed haze removal method was then evaluated by means of signal-to-noise ratio (SNR) and classification accuracy.The results show that the haze removal method is able to improve the haze-affected imagery qualitatively and quantitatively.
Investigation of Earthquake Warning for the Threshold of P-Wave, Using an Amplitude Ratio of S-Wave to P-Wave

地震動の規定値に対するオンサイト警報は波群の識別を行っておらず, 一般的にはS波の振幅を利用しているのが現状である．そこで, 本研究では, より迅速に規定値に対する地震警報を出力することを目的に, 観測点のS波/P波の振幅比を利用したP波規定値に対する地震警報の検討を行った．S波/P波の振幅比は観測点直下のP波とS波の速度構造の寄与が大きいことを理論から導出し, 観測点で取得した実測データより, 安定してS波/P波の振幅比を抽出できることを示した．観測点のS波/P波の振幅比を利用してP波から予測されたS波は観測と良く整合しており, 本手法の有効性が実証的に示された．本研究では, 観測されたP波の振幅とS波/P波の振幅比を利用することで, 従来のS波の規定値に対する地震警報よりTs-pの時間分早く警報を出力できることを示した．
Ship trajectory anomaly detection

This paper compares a number of state-of-the-art anomaly detection methods on real ship trajectories obtained by an Automatic Identification System (AIS) in the Baltic sea. Because most methods need fixed length trajectory representations, the paper also gives some solutions for reducing variable l ength trajectories to a fixed size.
Pruning Convolutional Neural Networks via Stochastic Gradient Hard Thresholding

In this paper, we introduce an iterative filter pruning method to compress deep convolutional neural networks. Different from many existing network compression methods which fall into the training-pruning-fine-tuning framework, we propose a new hybrid stochastic gradient hard thresholding algorithm (AHSG-HT) for adaptive structured pruning. Our approach trains the original network from scratch and continually prunes the “unimportant” filters by setting their values to zero in order to let algorithm automatically select filters. After training, we adopt physically pruning method to discard the filters with zero weights. Finally, we reconstruct the network and obtain a compact network without fine-tuning which also has a comparable performance. We conduct several experiments using ResNet on CIFAR-10 and CIFAR-100. Our approach achieves 92.40 $$\%$$ accuracy on ResNet-56 with 63 $$\%$$ reduction in parameters and FLOPs. The accuracy of CIFAR-10 on Resnet-110 with 20 $$\%$$ pruning rate can reach up to 94.09 $$\%$$ , which exceeds the baseline accuracy of the full network without pruning.
A Foresight on E-Healthcare Trailblazers

With the start of Internet of Things (IoT) network connectivity, endless manufacturer opportunities arise for medical devices and gadgets. The transfer and analysis of medical-related data using IoT sensory devices have revolutionized healthcare organizations. Medical practitioners will be able to guide and provide prescriptions for their patients in a more convenient manner and more accurately with the instant sharing of their medical reports and information. This chapter provides a detailed overview of the future of ehealth using IoT networks and provides details about several ongoing project in this area of research.
Analysis of Information Security for a Voting Process for Sectional Governments in Ecuador

Knowing about different events in several Latin American countries, as well as in Ecuador, in relation to data alteration, in the different sectional voting processes, it has been necessary to carry out a security analysis provided by the systems of choice in the different sectional governments of the country.The objective of this study is to analyze information security policies, their processes, standards and encryption methods, for the voting processes of the National Electoral Council of Ecuador (CNE).In this study a qualitative, deductive and exploratory research approach was applied, which allowed an analysis of the articles in reference.The articles of law that the Constitution has stipulated were reviewed, since the country manages a traditional system, which is carried out through several stages or phases, but which in turn uses technological tools.During these stages there are risks that can harm the sectional candidates, in such a way that a review of the Ecuadorian reality is essential, which implies the commitment of their actors, working as a team with specialists in the different areas, as well as compliance with public policies.It is concluded that the prevention, processes and policies proposed by the entity are based on the pillars of information security of the ISO 27001 standard, it is also pertinent that the cryptography implemented by the CNE with the hash code is in constant integration with new more robust and secure cryptographic methods providing greater security, integrity and confidentiality avoiding alterations in the data entered in the electoral process.
Super Resolution via Residual Restructured Dense Network

Recently, Convolutional Neural Networks (CNN) have made an impressive breakthrough in single image super-resolution (SISR). By utilizing the advantages of densely connected neural network and classical residual network, we propose a deep convolutional network named residual restructured dense network (RRDN). By combining dense concatenation and residual skip connection, we design residual restructured dense block (RRDBlock), which extracts long and short memory features with different receptive fields and helps the network reuse effective features. Moreover, direct paths from the previous layers to the subsequent layers helps RRDN mitigate the problems of gradient vanishing and instability during training. We evaluate the proposed method using four standard benchmark datasets and the results show that RRDN achieves high reconstruction performance.
Poleward Moving Aurora Recognition with Deep Convolutional Networks

Deep learning has become the most powerful tool for action recognition in recent years. The aim of this paper is to investigate the proper convolutional network architecture to classify poleward moving auroras (PMAs). The first challenge is that the auroral images have complex morphological and motion characteristics, so it is difficult to get the discriminative motion information to recognize the PMA events. Second, the imbalanced dataset will cause the serious problems, such as the network tends to identify a given sequence as the majority of events (non-PMAs). To address these issues, we use 3D ResNet-18 to get the motion information of auroral image sequence based on optical flow and also use the spatial attention mechanism to make the network pay more attention to PMA events. The empirical results demonstrate that our method achieves significant performance improvement compared with the previous method.
Improving Neural Machine Translation by Achieving Knowledge Transfer with Sentence Alignment Learning

Neural Machine Translation (NMT) optimized by Maximum Likelihood Estimation (MLE) lacks the guarantee of translation adequacy.To alleviate this problem, we propose an NMT approach that heightens the adequacy in machine translation by transferring the semantic knowledge learned from bilingual sentence alignment.Specifically, we first design a discriminator that learns to estimate sentence aligning score over translation candidates, and then the learned semantic knowledge is transfered to the NMT model under an adversarial learning framework.We also propose a gated self-attention based encoder for sentence embedding.Furthermore, an N -pair training loss is introduced in our framework to aid the discriminator in better capturing lexical evidence in translation candidates.Experimental results show that our proposed method outperforms baseline NMT models on Chinese-to-English and English-to-German translation tasks.Further analysis also indicates the detailed semantic knowledge transfered from the discriminator to the NMT model.
Open Domain Conversational Chatbot

The main medium for communication between human beings is natural language in oral or textual form. Artificial Intelligent systems are supposed to be of use to humans such that they help in solving queries related to health, education, social and various other domains. Chatbots are faster and always available compared to traditional techniques of communication, ensuring quick and easy answers related to problems in different domains. The proposed solution implements a conversational chatbot using the seq2seq encoder-decoder model. The model is trained on a Twitter corpus containing conversational exchanges. The recurrent encoder-decoder performs the encoding of text conversations. RNN deals with sequential data which ideally captures the semantic summary of the input sequence and then based on context, the decoder generates output one word at a time step.
Modified Method of Structural Identification of Interval Discrete Models of Atmospheric Pollution by Harmful Emissions from Motor Vehicles

The paper deals with a problem of modeling of dynamics of harmful emissions from motor vehicles using a mathematical model in the form of a difference equation. To build such models, a method of structural identification based on the bee colony behavioral models is widely used. It is shown that in order to reduce the time complexity of this method and simultaneously ensure the possibility of finding of a unified model that would be applicable for different points in the city, it is important to ensure the completeness of a set of structural elements. It is shown that in order to increase its efficiency, it is expedient to pre-process the input data obtained in an interval form. It is proposed to use the subtractive clustering method for this purpose. The example of building of model of atmospheric pollution by harmful emissions from motor vehicles using cluster analysis of experimental data to form the initial set of structural elements is considered.
The Perception Of Primary School Students About The Earthquake In Sabah Malaysia

The Ranau earthquake that struck on June 5, 2015 was the strongest ever experienced in Sabah causing locals, especially children at the epicenter of the earthquake to panic. Consequently, it was decided to undertake a qualitative research study to determine the perceptions and knowledge of primary school students in Ranau on earthquakes, and to identify factors influencing their knowledge and perceptions. Purposive sampling was used to select the school that had suffered the worst damage due to the earthquake. Six of the level two students from this school were randomly selected to be interviewed: Three male and three female. Qualitative data was collected using a semi-structured interview method. The findings show that primary school students in Ranau have no knowledge about the concept of ‘earthquake’. The respondents perceptions of the earthquake evoked phrases and comments such as ‘scary disaster’, ‘swirling houses’, ‘houses collapsed’, ‘damaged roads’ and ‘quakes will cause death’. The Damage to the school buildings made them frightened to go to school amid fears that the buildings would collapse. The findings also show that the perceptions of these school children were influenced by their experience in the face of the earthquake, as well as factors related to their cultural and religious backgrounds. The results of this study can be used to devise educational programs to educate school children and provide them with a clearer picture of an earthquake disaster so that they will be better prepared to face this kind of disaster in the future.