Reviewerly/exHarmony huggingface dataset:
Optimized CSIDH Implementation Using a 2-Torsion Point

The implementation of isogeny-based cryptography mainly use Montgomery curves, as they offer fast elliptic curve arithmetic and isogeny computation. However, although Montgomery curves have efficient 3- and 4-isogeny formula, it becomes inefficient when recovering the coefficient of the image curve for large degree isogenies. Because the Commutative Supersingular Isogeny Diffie-Hellman (CSIDH) requires odd-degree isogenies up to at least 587, this inefficiency is the main bottleneck of using a Montgomery curve for CSIDH. In this paper, we present a new optimization method for faster CSIDH protocols entirely on Montgomery curves. To this end, we present a new parameter for CSIDH, in which the three rational two-torsion points exist. By using the proposed parameters, the CSIDH moves around the surface. The curve coefficient of the image curve can be recovered by a two-torsion point. We also proved that the CSIDH while using the proposed parameter guarantees a free and transitive group action. Additionally, we present the implementation result using our method. We demonstrated that our method is 6.4% faster than the original CSIDH. Our works show that quite higher performance of CSIDH is achieved while only using Montgomery curves.
Creativity as the Act of Transcending Oneself and the World: from Creativity to Transcendence

Abstract The text aims to present how creativity and creative behaviours, understood as an act of transcending oneself and the world, can positively influence the fields of art, science, education and upbringing. A constitutional feature of creativity is its ability to transcend what is fixed, tamed and predictable, both individually and globally, at the microcosmic and macro-cosmic level; to transcend what is within us and outside us. The author of the article analyzes the following issues: is there a positive aspect to creativity understood as the act of transcendence? And, given such an understanding of creativity, are all acts of creation, whether they involve the creation of great works of art, the pursuit of science, the processes of education and upbringing, or simply the resolution of daily problems, more spiritual than intellectual?
An improved Differential Evolution Algorithm with Self Adaptive Mutation Strategies for Global Optimization

To solve real-world global optimization problem differential evolution algorithm is used as one of the best nature influenced algorithm. The use of different effective mutation strategies and proper selection of effective control parameters directly affect the performance and convergent rate of differential evolution method. Although its performance is very good but suffers from population diversity and stagnation.In this paper, new self-adaptive mutation strategies with booster vector to improve global optimization of DE/rand/1/bin and DE/best/1/bin is proposed. Elite archive strategies with dynamic adjustment of control parameter with booster vector added to afford more bandwidth for electing an effective mutant solution. The proposed algorithm is compared with five DE and six non-DE algorithms by using a set of twenty benchmark functions on COCO (comparing Continuous Optimizers) framework. The experimental result verifies that proposed self-adaptive strategies outperformed the competitors.
Towards Understanding Transfer Learning Algorithms Using Meta Transfer Features

Transfer learning, which aims to reuse knowledge in different domains, has achieved great success in many scenarios via minimizing domain discrepancy and enhancing feature discriminability. However, there are seldom practical determination methods for measuring the transferability among domains. In this paper, we bring forward a novel meta-transfer feature method (MetaTrans) for this problem. MetaTrans is used to train a model to predict performance improvement ratio from historical transfer learning experiences, and can consider both the Transferability between tasks and the Discriminability emphasized on targets. We apply this method to both shallow and deep transfer learning algorithms, providing a detail explanation for the success of specific transfer learning algorithms. From experimental studies, we find that different transfer learning algorithms have varying dominant factor deciding their success, so we propose a multi-task learning framework which can learn both common and specific experience from historical transfer learning results. The empirical investigations reveal that the knowledge obtained from historical experience can facilitate future transfer learning tasks.
Looking into the Educational Mirror: Why Computation Is Hardly Being Taught in the Social Sciences, and What to Do About It

In the current digital era, with an increasingly complex and turbulent society, demand is rising for social scientists capable of analysing behavioural dynamics. Studying behavioural dynamics is a valuable lens, both in public policy making and community planning and in scientific projects on how human behaviour affects ecosystems. Computational social science (CSS) offers a framework for this type of studies, as it connects a complex networked systems perspective with a suite of computational tools and methodologies. Despite its potential and fast growth, CSS is still hardly found in programmes at bachelor and master levels in Europe. We would like to take a closer look at this discrepancy. We discuss why there is a need to develop computational education in the social sciences and why it is a challenge. We consider two perspectives. In the students’ perspective, there is the problem of mathematical anxiety and gender inequalities in STEM education. In the academic teachers’ perspective, there is the problem of a split within social sciences, inadequacy of statistical models for analysing dynamics, and insufficient educational resources at an appropriate level of difficulty. We build a case for creating a MOOC programme addressed specifically to social sciences students to fill in the existing gaps, facilitate the organisation of learning communities, and advance computational thinking within social science. Creating such a MOOC programme is the goal of our Erasmus+ project “Action for Computational Thinking in Social Sciences” (ACTISS).
Real-Time Object Size Measurement

This paper presents the hardware architecture of our real-time object size measurement method. In the proposed method, the size of the object can be measured using the extracted Sobel edge image and Harris corner image. The distance between the camera and the object is constant. We implemented this object size measurement on the Xilinx Virtex7 XC7V2000T FPGA (field programmable gate array) hardware. The hardware architecture was designed using the Verilog hardware description language (Verilog HDL). The FPGA receives the VGA (640×480) image from the camera (STCMC33CL) and transmits the corresponding size measurement result image to the monitor using the HDMI interface in realtime.
Retrofitting Embeddings for Unsupervised User Identity Linkage

User Identity Linkage (UIL) is the problem of matching user identities across multiple online social networks (OSNs) which belong to the same person. The solutions to UIL problem facilitate cross-platform research on OSN users and enable many useful applications such as user profiling and recommendation. As the UIL labeled data are often lacking and costly to obtain, learning user embeddings for matching user identities using an unsupervised approach is therefore highly desired. In this paper, we propose a novel unsupervised UIL framework for enhancing existing user embedding-based UIL methods. Our proposed framework incorporates two key ideas, user-discriminative features and retrofitting embedding. The user-discriminative features enable us to differentiate a specific user identity from other users in its OSN. From the user-discriminative features, we derive pairs of similar user identities across OSNs for retrofitting the base user embeddings of existing UIL methods. Through extensive experiments on three real-world OSN datasets, we show that our framework can leverage user-discriminative features to improve the accuracy of different user embedding-based UIL methods significantly. The quantum of improvement can also be surprisingly good even for existing UIL methods with very poor matching accuracy.
Cloud vs On-Premise HPC: A Model for Comprehensive Cost Assessment

Cloud Computing has emerged as an interesting alternative for running business applications, but this might not be true for scientific applications. A comparison between HPC systems and cloud infrastructure not always sees the latter winning over the former, especially when only performance and economical aspects are taken into account. But if other factors, such as turnaround time and user preference, come into play, the landscape of the usage convenience changes. Choosing the right infrastructure, then, can be essentially seen as a multi-attribute decision-making problem. In this paper we introduce an evaluation model, based on a weighted geometric aggregation function, that takes into account a set of parameters, among which job geometry, cost, execution and turnaround time. The notion of user preference modulates the model, and allows to determine which platform, cloud or HPC, might be the best one. The model has then been used to evaluate the best architecture for several runs of two applications, based on two different communication models. Results show that the model is robust and there is a not negligible number of runs for which a cloud infrastructure seems to be the best place for running scientific jobs.
TLDR: Extreme Summarization of Scientific Documents

We introduce TLDR generation, a new form of extreme summarization, for scientific papers. TLDR generation involves high source compression and requires expert background knowledge and understanding of complex domain-specific language. To facilitate study on this task, we introduce SciTLDR, a new multi-target dataset of 5.4K TLDRs over 3.2K papers. SciTLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden. We propose CATTS, a simple yet effective learning strategy for generating TLDRs that exploits titles as an auxiliary training signal. CATTS improves upon strong baselines under both automated metrics and human evaluations. Data and code are publicly available at this https URL.
USE OF INFORMATION AND COMMUNICATION TECHNOLOGIES FOR ORGANIZING SELF-EDUCATION OF PERSONALITY IN THE FIELD OF CLOTHES DESIGN

The article considers the following aspects of organization of self-education using the information and communication technologies (ICTs): what information resources can be the basis for self-education; how to organize the education; how to solidify the acquired knowledge and create necessary social connections. The information resources which can be used for self-education and their classification depending on the needs of the individual – for personal enrichment or for scientific search, – are determined; the most significant among them are electronic libraries and bibliographic and abstract databases. The ways of organization of self-education are analyzed, namely: massive online courses, educational applications for PCs and mobile phones, individual classes (webinars), consistent self-development of separate topics, etc. ICTs that promote the creation of social connections and communications with like-minded people and specialists with narrow focus (social networks, scientists’ identification systems, forums and blogs) are considered. The examples of information recourses, aimed at obtaining professionally important information by fashion designers, are provided. Among them are blogs about art, fashion and design, virtual museum sites, resources for fashion industry leaders, sites of exhibitions, contests, popular and scientific journals, designers and brands. The possibilities of assessing the reliability of scientific information using the bibliographic and abstract databases, social networks and individual profiles of scientists in the systems of identification are determined. In order to identify the specifics of ICTs use, a questionnaire survey has been conducted that has outlined the main informational resources used in the search for the necessary information (encyclopedias and databases, social networks, YouTube channels and blogs in Twitter, LifeJournal, etc.), and peculiarities of perception of such information. It is found that ICTs are the main source for searching and receiving information.
Fast Likelihood-Based Change Point Detection

Change point detection plays a fundamental role in many real-world applications, where the goal is to analyze and monitor the behaviour of a data stream. In this paper, we study change detection in binary streams. To this end, we use a likelihood ratio between two models as a measure for indicating change. The first model is a single bernoulli variable while the second model divides the stored data in two segments, and models each segment with its own bernoulli variable. Finding the optimal split can be done in $$ \mathcal {O} \mathopen {}\left( n\right) $$ time, where n is the number of entries since the last change point. This is too expensive for large n. To combat this we propose an approximation scheme that yields $$(1 - \epsilon )$$ approximation in $$ \mathcal {O} \mathopen {}\left( \epsilon ^{-1} \log ^2 n\right) $$ time. The speed-up consists of several steps: First we reduce the number of possible candidates by adopting a known result from segmentation problems. We then show that for fixed bernoulli parameters we can find the optimal change point in logarithmic time. Finally, we show how to construct a candidate list of size $$ \mathcal {O} \mathopen {}\left( \epsilon ^{-1} \log n\right) $$ for model parameters. We demonstrate empirically the approximation quality and the running time of our algorithm, showing that we can gain a significant speed-up with a minimal average loss in optimality.
A Supervised Word Alignment Method based on Cross-Language Span Prediction using Multilingual BERT

We present a novel supervised word alignment method based on cross-language span prediction. We first formalize a word alignment problem as a collection of independent predictions from a token in the source sentence to a span in the target sentence. As this is equivalent to a SQuAD v2.0 style question answering task, we then solve this problem by using multilingual BERT, which is fine-tuned on a manually created gold word alignment data. We greatly improved the word alignment accuracy by adding the context of the token to the question. In the experiments using five word alignment datasets among Chinese, Japanese, German, Romanian, French, and English, we show that the proposed method significantly outperformed previous supervised and unsupervised word alignment methods without using any bitexts for pretraining. For example, we achieved an F1 score of 86.7 for the Chinese-English data, which is 13.3 points higher than the previous state-of-the-art supervised methods.
Effect of manager entrepreneurship attitude and member motivation on organizational member participation

The purpose of this study is to know the effect of entrepreneurship attitude of manager and cooperative member motivation in enhancing member participation in North Sumatera Province, Indonesia.A questionnaire survey with a 1-5 Likert Scale is executed to achieve the objective of the study.Respondents consist of 100 managers and 100 members of the cooperatives.Data is analyzed using SPSS v. 20.The results show that manager entrepreneurship attitude and member motivations correlated significantly in increasing the participation of cooperative members with r1 = 0.866 (p-value=0.00) and r2 = 0.902 (p-value=0.00),respectively.Likewise, entrepreneurship attitude of manager and member motivation have significant effects on increasing member participation.It is also found that R 2 = 0.836, and F-value was 247,430 with α<0.01.This study revealed the independent variable significantly correlated and increased participation of member in the Province of North Sumatera, Indonesia.Thus, it is very important to insert entrepreneurship attitude and motivation in providing cooperative education.
End-to-End Slot Alignment and Recognition for Cross-Lingual NLU

Natural language understanding (NLU) in the context of goal-oriented dialog systems typically includes intent classification and slot labeling tasks. Existing methods to expand an NLU system to new languages use machine translation with slot label projection from source to the translated utterances, and thus are sensitive to projection errors. In this work, we propose a novel end-to-end model that learns to align and predict target slot labels jointly for cross-lingual transfer. We introduce MultiATIS++, a new multilingual NLU corpus that extends the Multilingual ATIS corpus to nine languages across four language families, and evaluate our method using the corpus. Results show that our method outperforms a simple label projection method using fast-align on most languages, and achieves competitive performance to the more complex, state-of-the-art projection method with only half of the training time. We release our MultiATIS++ corpus to the community to continue future research on cross-lingual NLU.
Regularized Pooling.

In convolutional neural networks (CNNs), pooling operations play important roles such as dimensionality reduction and deformation compensation. In general, max pooling, which is the most widely used operation for local pooling, is performed independently for each kernel. However, the deformation may be spatially smooth over the neighboring kernels. This means that max pooling is too flexible to compensate for actual deformations. In other words, its excessive flexibility risks canceling the essential spatial differences between classes. In this paper, we propose regularized pooling, which enables the value selection direction in the pooling operation to be spatially smooth across adjacent kernels so as to compensate only for actual deformations. The results of experiments on handwritten character images and texture images showed that regularized pooling not only improves recognition accuracy but also accelerates the convergence of learning compared with conventional pooling operations.
Variable precision depth encoding for 3D range geometry compression

State-of-the-art 3D range geometry compression algorithms that utilize principles of phase shifting perform encoding with a fixed frequency; therefore, it is not possible to encode individual points within a scene at various degrees of precision. This paper presents a novel, to the best of our knowledge, method for accurately encoding 3D range geometry within the color channels of a 2D RGB image that allows the encoding frequency—and therefore the encoding precision—to be uniquely determined for each coordinate. The proposed method can thus be used to balance between encoding precision and file size by encoding geometry along a statistical distribution. For example, a normal distribution allows for more precise encoding where the density of data is high and less precise encoding where the density of data is low. Alternative distributions may be followed to produce encodings optimized for specific applications. In general, the nature of the proposed encoding method enables the precision to be freely controlled at each point or centered around identified features of interest, ideally enabling this method to be used within a wide range of applications.
Week Ahead Time Series Prediction of Sea Surface Temperature Using Nonlinear Autoregressive Network with and Without Exogenous Inputs

The seas and the oceans have existed since the origination of the Earth and play a very significant role in balancing the ecosystem. Coincidentally, they are the primary and early indicators of global imbalances that maliciously creep in the nature. Sea surface temperature is the foremost parameter that is observed to have a direct or indirect relationship to such variations. Literature surveys have revealed linkages of the sea surface temperature with various bioparameters that further affects not only the aquatic flora and fauna but the entire global biome, at times causing drastic changes. In the Pacific Ocean, a window defined by +5° N latitudes and +180° E longitudes is selected. The time series has data collected by the array of Tropical Atmosphere Ocean (TAO) across the equatorial Pacific Ocean from 1980 to 1998 using around 70 moored buoys. National Oceanic and Atmospheric Administration’s (NOAA)’s Pacific Marine Environmental Laboratory (PMEL) has developed moorings that measure air temperature, relative humidity, sea surface temperatures, and surface winds readings. A nonlinear autoregressive network is first optimized, and then using exogenous inputs, the values of sea surface temperature is forecasted for a week and compared. As many as 10 different error parameters are tabulated to provide insight into the error analysis.
A new multivariate Laplace distribution based on the mixture of normal distributions

In this paper, we propose a new multivariate Laplace distribution from normal variance mixture models, called as Type II multivariate Laplace distribution.Unlike the original multivariate Laplace distribution whose all components must have the same value for the mixing variate, the random components in the new distribution could have different value for its own mixing variate and are correlated only through the dependence structure of the normal random vector.Thus, it contains the multiplication of independently identical distribution univariate Laplace distributions as a special case if the normal covariance matrix is diagonal.A tractable stochastic representation is used to derive the probability density function and other statistical properties.The maximum likelihood estimates of parameters via an ECM (expectation/conditional maximization) algorithm and the Bayesian methods are derived.Some simulation studies are conducted to evaluate the performance of the proposed methods.Applications in two real data sets indicate that the Type II multivariate Laplace distribution could have a better performance and is distinct from the original one.