Reviewerly/exHarmony huggingface dataset:
On ideal $ t $-tuple distribution of orthogonal functions in filtering de bruijn generators

&lt;p style='text-indent:20px;'&gt;Uniformity in binary tuples of various lengths in a pseudorandom sequence is an important randomness property. We consider ideal &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ t $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-tuple distribution of a filtering de Bruijn generator consisting of a de Bruijn sequence of period &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ 2^n $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and a filtering function in &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ m $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; variables. We restrict ourselves to the family of orthogonal functions, that correspond to binary sequences with ideal 2-level autocorrelation, used as filtering functions. After the twenty years of discovery of Welch-Gong (WG) transformations, there are no much significant results on randomness of WG transformation sequences. In this article, we present new results on uniformity of the WG transform of orthogonal functions on de Bruijn sequences. First, we introduce a new property, called &lt;i&gt;invariant under the WG transform&lt;/i&gt;, of Boolean functions. We have found that there are only two classes of orthogonal functions whose WG transformations preserve &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ t $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-tuple uniformity in output sequences, up to &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ t = (n-m+1) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. The conjecture of Mandal &lt;i&gt;et al.&lt;/i&gt; in [&lt;xref ref-type="bibr" rid="b29"&gt;29&lt;/xref&gt;] about the ideal tuple distribution on the WG transformation is proved. It is also shown that the Gold functions and quadratic functions can guarantee &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ (n-m+1) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-tuple distributions. A connection between the ideal tuple distribution and the invariance under WG transform property is established.&lt;/p&gt;
Robustness and Transferability of Universal Attacks on Compressed Models

Neural network compression methods like pruning and quantization are very effective at efficiently deploying Deep Neural Networks (DNNs) on edge devices. However, DNNs remain vulnerable to adversarial examples-inconspicuous inputs that are specifically designed to fool these models. In particular, Universal Adversarial Perturbations (UAPs), are a powerful class of adversarial attacks which create adversarial perturbations that can generalize across a large set of inputs. In this work, we analyze the effect of various compression techniques to UAP attacks, including different forms of pruning and quantization. We test the robustness of compressed models to white-box and transfer attacks, comparing them with their uncompressed counterparts on CIFAR-10 and SVHN datasets. Our evaluations reveal clear differences between pruning methods, including Soft Filter and Post-training Pruning. We observe that UAP transfer attacks between pruned and full models are limited, suggesting that the systemic vulnerabilities across these models are different. This finding has practical implications as using different compression techniques can blunt the effectiveness of black-box transfer attacks. We show that, in some scenarios, quantization can produce gradient-masking, giving a false sense of security. Finally, our results suggest that conclusions about the robustness of compressed models to UAP attacks is application dependent, observing different phenomena in the two datasets used in our experiments.
PENINGKATAN KEMAMPUAN MENULIS TEKS PROSEDUR DENGAN MODEL ACTIVE LEARNING

Writing is not an ability that can be mastered by itself, but must go through a process of learning and habituation. Writing procedure text is a container that can be used as a habit. Especially in the 2013 curriculum, procedure text is mandatory material that must be studied in grade VII SMP students. Based on observations during online learning in the midst of the Covid 19 pandemic at SMP Mutiara 4 Bandung, students find it difficult to learn. expressing ideas and ideas in written form, because the learning models and media used by the teacher are not attractive so that learning feels boring and unpleasant. Based on the descriptions presented above, the authors are interested in conducting Classroom Action Research (CAR) with a model. Acitive Learning through gamabar media. This classroom action research was carried out in two cycles, each of which contained four components, namely planning, implementing, observing and reflecting. The results showed that the application of the active learning model through image media could improve the ability to write procedural texts. The application of the Active Learning learning model through image media is more effective, it is proven that students are more active and more creative in developing writing and are able to improve the quality of students' writing as evidenced by an increase in the results of the average score in cycle I (62,) and the acquisition of average scores cycle II (78,6), so there is a difference in the average value (16,). Based on the research results, it can be concluded that the active learning model through image media is effective in learning to write procedural texts.
MetaInfoNet: Learning Task-Guided Information for Sample Reweighting

Deep neural networks have been shown to easily overfit to biased training data with label noise or class imbalance. Meta-learning algorithms are commonly designed to alleviate this issue in the form of sample reweighting, by learning a meta weighting network that takes training losses as inputs to generate sample weights. In this paper, we advocate that choosing proper inputs for the meta weighting network is crucial for desired sample weights in a specific task, while training loss is not always the correct answer. In view of this, we propose a novel meta-learning algorithm, MetaInfoNet, which automatically learns effective representations as inputs for the meta weighting network by emphasizing task-related information with an information bottleneck strategy. Extensive experimental results on benchmark datasets with label noise or class imbalance validate that MetaInfoNet is superior to many state-of-the-art methods.
Geostatistical assessment of trace metals in urban soils, Paraná, Brazil

Trace metals are common pollutants in urban soils thus making important the assessment of these pollutants. The main purpose of this study was investigated the concentrations of trace metals in urban soils in the industrial region of Curitiba, in the State of Paraná, Brazil and to examine the applicability of geostatistical modeling for assessing the environmental impact generated. For the analyses of trace elements, soil samples were collected at a depth of 0-20 cm and subjected to an acid digestion, in accordance with the 3050B method. The identification of trace elements was through the technique Spectroscopy of Optical Emission. The soil texture was also analyzed and the data were determined by the compositional analysis method. For the geostatistical mapping, it was used the software R, with the geoR package. The spatial variability of the sampled data was analyzed previously calculating the classic statistical indicators. The results indicated an approximated normal distribution of the data samples which is desirable. Then, the Matheron's classical estimator was used to compute the experimental semivariogram for several directions. No effect of anisotropy was shown. The semivariogram was fitted to three theoretical models: spherical, exponential and Gaussian. The results of cross-validation of spherical model suggested that it is the best model among the others. Finally, the predictions of metals at unknown locations were obtained by ordinary kriging. In relation to the values obtained of metals and the standard values of the Brazilian referee legislation of management of contaminated sites, only one site had Ni levels higher than is allowed and other metals were ok. The generated map shows clearly the spatial variation of trace metals in the studied area. The results are suitable and suggest that the use of this methodology provides significant contributions to management of contaminated areas by different trace metals.
Soft models of competence assessment in professional education

The article presents the results of research on the problem of finding new models for assessing students ' competencies in the context of global trends in modern professional education – the merger of competence and project approaches, the development of e-learning, and the introduction of digital technologies to support expert systems. An original development of one of the soft models of fuzzy multiparametric assessment of competencies is proposed, and the mathematical apparatus of the model is described taking into account the requirements of fuzzy logic.
Design and Antiblast Analysis of Mechanical Plugging Devices in Coastal Defense Engineering

Liu, Y.; Zhou, J.; Wang, P.; Kong, X.; Zhao, Y.; Chen, H., and Zhou, Y., 2020. Design and antiblast analysis of mechanical plugging devices in coastal defense engineering. In: Liu, X. and Zhao, L. (eds.), Today's Modern Coastal Society: Technical and Sociological Aspects of Coastal Research. Journal of Coastal Research, Special Issue No. 111, pp. 101–107. Coconut Creek (Florida), ISSN 0749-0208.To solve the problems associated with traditional plugging methods for various kinds of through-wall pipelines in coastal defense engineering, three plugging schemes were designed and prepared. The antiexplosion test and theoretical analysis were carried out for the plugging device with the lowest resistance, and further analysis is carried out by numerical simulation. The results show that the plugging scheme can endure a nuclear blast load with a peak value of 0.15 MPa. The maximum vertical displacement of the plugging device was only 0.07 mm under loading and can be restored to its initial position eventually. The friction coefficient between the casing and rubber and the pretightening force of the bolt are the main factors affecting the resistance of plugging devices. The research results can provide a reference for the plugging design of the through-wall pipelines in applications like coastal defense engineering.
A Conceptual Data Modelling Framework for Context-Aware Text Classification

Data analytics has an interesting variant that aims to understand an entity's behavior.It is termed as diagnostic analytics, which answers "why type questions"."Why type questions" find their applications in emotion classification, brand analysis, drug review modeling, customer complaints classification etc. Labeled data form the core of any analytics' problem, leave alone diagnostic analytics; however, labeled data is not always available.In some cases, it is required to assign labels to unknown entities and understand its behavior.For such scenarios, the proposed model unites topic modeling and text classification techniques.This combined data model will help to solve diagnostic issues and obtain meaningful insights from data by treating the procedure as a classification problem.The proposed model uses Improved Latent Drichlet Allocation for topic modeling and sentiment analysis to understand an entity's behavior and represent it as an Improved Multinomial Naïve Bayesian data model to achieve automated classification.The model is tested using drug review dataset obtained from UCI repository.The health conditions with their associated drug names were extracted from the reviews and sentiment scores were assigned.The sentiment scores reflected the behavior of various drugs for a particular health condition and classified them according to their quality.The proposed model performance is compared with existing baseline models and it is proved that our model exhibited better than other models.
Digital platform as a professional education management tool

Abstract The paper considers one of the approaches to solving the problems of managing educational institutions of secondary vocational education, associated with the use of a digital platform. The capabilities of the digital platform in the implementation of the full cycle of educational process management are characterized. The features of five interrelated subsystems of the platform are presented: performance monitoring, methodological support, management of personalized educational trajectories, record of the academic achievements and employment promotion, virtual laboratory educational and research complexes. The principles of using the platform at two levels of management are described-the level of the educational organization and the level of the regional system of vocational education.
Quantum Algorithms for Solving Ordinary Differential Equations

Identifying computational tasks suitable for (future) quantum computers is an active field of research. Here we explore utilizing quantum computers for the purpose of solving differential equations. We consider two approaches: (i) basis encoding and fixed-point arithmetic on a digital quantum computer, and (ii) representing and solving high-order Runge-Kutta methods as optimization problems on quantum annealers. As realizations applied to two-dimensional linear ordinary differential equations, we devise and simulate corresponding digital quantum circuits, and implement and run a 6$^{\mathrm{th}}$ order Gauss-Legendre collocation method on a D-Wave 2000Q system, showing good agreement with the reference solution. We find that the quantum annealing approach exhibits the largest potential for high-order implicit integration methods. As promising future scenario, the digital arithmetic method could be employed as an oracle within quantum search algorithms for inverse problems.
Towards a Ring Analogue of the Leftover Hash Lemma

Abstract The leftover hash lemma (LHL) is used in the analysis of various lattice-based cryptosystems, such as the Regev and Dual-Regev encryption schemes as well as their leakage-resilient counterparts. The LHL does not hold in the ring setting, when the ring is far from a field, which is typical for efficient cryptosystems. Lyubashevsky et al . (Eurocrypt ’13) proved a “regularity lemma,” which can be used instead of the LHL, but applies only for Gaussian inputs. This is in contrast to the LHL, which applies when the input is drawn from any high min-entropy distribution. Our work presents an approach for generalizing the “regularity lemma” of Lyubashevsky et al . to certain conditional distributions. We assume the input was sampled from a discrete Gaussian distribution and consider the induced distribution, given side-channel leakage on the input. We present three instantiations of our approach, proving that the regularity lemma holds for three natural conditional distributions.
Design and Application of Bank Big Data Platform Based on Hadoop Technology

At present, the design of big data platforms for many domestic banks is still insufficient. This article builds a Hadoop-based bank big data platform based on the big data platform construction experience of a large state-owned commercial bank. By introducing cutting-edge mainstream big data open-source tools, the overall architecture of the big data platform of commercial banks is built. At the same time, this article discusses critical technical solutions such as storage engine, resource management, calculation engine, analysis engine, interactive front end, and data management, task management, and user management to build a bank’s big data platform. This article hopes to provide a reference for other banks to make big data platforms.
Variational Interaction Information Maximization for Cross-domain Disentanglement

Cross-domain disentanglement is the problem of learning representations partitioned into domain-invariant and domain-specific representations, which is a key to successful domain transfer or measuring semantic distance between two domains. Grounded in information theory, we cast the simultaneous learning of domain-invariant and domain-specific representations as a joint objective of multiple information constraints, which does not require adversarial training or gradient reversal layers. We derive a tractable bound of the objective and propose a generative model named Interaction Information Auto-Encoder (IIAE). Our approach reveals insights on the desirable representation for cross-domain disentanglement and its connection to Variational Auto-Encoder (VAE). We demonstrate the validity of our model in the image-to-image translation and the cross-domain retrieval tasks. We further show that our model achieves the state-of-the-art performance in the zero-shot sketch based image retrieval task, even without external knowledge. Our implementation is publicly available at: https://github.com/gr8joo/IIAE
An Egocentric Network Contact Tracing Experiment

Contact tracing is one of the oldest social network health interventions used to reduce the diffusion of various infectious diseases. However, some infectious diseases like COVID-19 amass at such a great scope that traditional methods of conducting contact tracing (e.g., face-to-face interviews) remain difficult to implement, pointing the need to develop reliable and valid survey approaches. The purpose of this research is to test the effectiveness of three different egocentric survey methods for extracting contact tracing data: (1) a baseline approach, (2) a retrieval cue approach, and (3) a context-based approach. A sample of 397 college students were randomized into one of each condition and were prompted to anonymously provide contacts and populated places visited from the past four days. After controlling for various demographic, social identity, psychological, and physiological variables, participants in the context-based condition were significantly more likely recall more contacts (medium effect size) and places (large effect size) than the other two conditions. Theoretically, the research supports suggestions by field theory that assume network recall can be significantly improved by activating relevant activity foci. Practically, the research contributes to developing innovative social network data collection methods for contract tracing survey instruments.
The Status Quo and Improvement Methods of College Physical Education Evaluation

In the process of college physical education, teaching evaluation is one of the very important teaching tasks. Through a scientific and complete teaching evaluation system, the physical education process can be optimized to a certain extent, and the effective realization of physical education goals can be promoted to improve the college The quality and efficiency of physical education. This article first analyzes the status quo of college physical education evaluation, and then proposes some optimization and improvement measures for the problems found, hoping to improve the college physical education evaluation system and enhance the comprehensive ability of students.
Gender equality and extreme poverty alleviation in sub-Saharan Africa

This article assesses the link between the four pillars of gender equality and extreme poverty in sub-Saharan Africa. Accordingly, the objective of the paper is to empirically examine whether the four pillars of gender equality, namely women’s health, women education, political participation of women and economic participation facilitate extreme poverty alleviation in sub-Saharan Africa. Data were collected from the World Bank development indicators and World Economic Forum Global Gender Gap Index for 25 sub-Saharan African countries whose data appear on both indexes for three years into the SDGs era. Th e paper applied a quantitative approach with secondary data on poverty gap index drawn from the World Economic Forum Poverty Gap Index for sub-Saharan Africa. Data for twenty-fi ve sub-Saharan African countries were analysed using the fi xed-eff ect panel data regression approach using the Hauseman model selection test. Findings from the analysis indicate that, ceteris paribus, an increase in the threegender equity variables namely economic participation of women, education of women and political participation and leadership of women in sub-Saharan Africa has a signifi cant potential to reduce extreme poverty in sub-Saharan Africa within the sample of study. Since the fi ndings of this study have shown that extreme poverty can be reduced through increased women participation in economic activity, education and leadership, the SDG of poverty alleviation can be improved in sub-Saharan Africa through better government provision of economic, educational and leadership opportunities for women such as providing women with free-interest small business start-up funds, free education for women and supporting women to ascend and survive in political and leadership positions in sub-Saharan Africa through a balanced quota for female leadership positions. Given that the women’s health variable did not prove to be signifi cant on extreme poverty, further research is recommended to separate the health variable into rural health and urban health variables in order to examine the possibility that either of the health clusters might contribute signifi cantly to reducing extreme poverty. Th is paper contributes to existing literature by providing an empirical evidence to show that gender equality in sub-Saharan Africa is a viable policy strategy for achieving the SDGs 2030 Agenda of extreme poverty eradication in sub-Saharan Africa; the paper also provides empirical model for future study.
A Gadget Recommendation System using Data Science

Electronic gadgets have become vital in basic need of individual. The advances in technology, makes it necessary to satisfy different functional needs of users. Therefore, it is important to suggest gadgets for users according to personal preference and various constraints of electronic gadgets. We propose a gadget recommendation system by integrating various requirements from users.
Aix-Marseille University SSH data platforms: Skills to support research in social sciences and humanities (SSH) in the Mediterranean

Digital archiving has emerged as a major issue for science at the turn of the 21st century.Within the social sciences, the more specific question of quantitative data is not exempt from this challenge.But just as much as the preservation of data, the question of access to these archives arises.It is from this perspective that we have written this article, proposing a reflection on access to data based on two experiences of support services for researchers in accessing data.The first example takes up the French initiative to archive and disseminate data from official statistics, designed within the CNRS and aimed at archiving and disseminating, and involves the deployment of university platforms.The second is a research project that aims to make data from all Mediterranean countries accessible to the widest possible audience, while respecting the scientific requirements of documentation and rigorous data exploitation.The article therefore discusses the different dimensions of "FAIR" data, with emphasis on the issue of Access.
Digital Transformation: Case of the Polytechnic University

The article presents main approaches to building a model of a digital university based on Peter the Great St. Petersburg Polytechnic University, a functional model called Digital University and a short description of its base elements: creation of a digital platform, development of a system of digital models of educational programs and a scientific product, digitalization of the life cycle of educational and scientific products and services of a university, methodological and legal support of the Digital University model.