Reviewerly/exHarmony huggingface dataset:
THE ROLE OF VISUALIZATION IN TEACHING AND LEARNING PROCESS

Abstract. The article is devoted to disclosing contents of visualization of educational information as a relatively new trend in the modern educational process. Examples of ways to visualize learning material and reasons why these methods should be used in the learning process are given. Particular attention is paid to such visualization tools as multimedia presentation, mind maps, infographics, scribing. The perspectives and role of visualization in the modern educational space are discussed.&#x0D; The main purpose of visualization is to support logical operations at all stages of the learning activity. Imagination, attention concentrating, associative thinking, and increasing of cognition interest are also among the functions of visualization. The variety of visualization tools creates the possibility of their rational use in education, including enhancing learning activities. Visual schemes for presenting the analyzed information have a variety of styles and form a combination of conceptual and graphic elements. Active use of visualization tools in foreign language study helps to influence several channels of information perception by the student simultaneously, providing increased attention and efficiency of acquisition and memorizing new material, since the massive amount of information is presented in a concise manner.
Evidential Group Spammers Detection

Online reviews are considered as one of the most prevalent reference indicators for people to evaluate the quality of different products or services before purchasing. Since these reviews affect the buying decision of customers and control the success of the different e-commerce websites, the activity of fake reviews posting is more and more increasing. These fraudulent reviews are posted by a large number of spammers who try to promote or demote target products or companies. The reviewers spammers generally work collaboratively under group of spammers to take control of reviews given to some products, which seriously damage the review system. To deal with this issue, we propose a novel method aim to detect group spammers while relying on various group spamming behavioral indicators. Our approach is based on the K-nearest neighbors algorithm under the belief function theory to treat the uncertainty in the used behavioral indicators. Our method succeeds in distinguishing between genuine and fraudulent group of reviewers. It was tested on two large real datasets extracted from yelp.com .
A System Framework for Personalized and Transparent Data-Driven Decisions

Decision support systems that rely on data analytics are used in numerous applications. Their advantages are indisputable, however, they also present risks, possibly having severe impact on people's lives. Consequently, the need to support ethical or responsible behavior of such systems has recently emerged, putting an emphasis on ensuring fairness, transparency, accountability, etc. This paper presents a novel system framework that offers transparent and personalized services tailored to user profiles to serve their best interest. Our framework personalizes the choice of model for individuals or groups of users based on metadata about data sets and machine learning models. Querying and processing these metadata ensures transparency by supporting various kinds of queries by different stakeholders. We discuss our framework in detail, show why existing solutions are inadequate, and highlight research questions that need to be tackled in the future. Based on a prototypical implementation, we showcase that even a baseline implementation of our framework supports the desired transparency and personalization.
Decentralized Broadcast Encryption Schemes with Constant Size Ciphertext and Fast Decryption

Broadcast encryption ( BE ) allows a sender to encrypt a message to an arbitrary target set of legitimate users and to prevent non-legitimate users from recovering the broadcast information. BE has numerous practical applications such as satellite geolocation systems, file sharing systems, pay-TV systems, e-Health, social networks, cloud storage systems, etc. This paper presents two new decentralized BE schemes. Decentralization means that there is no single authority responsible for generating secret cryptographic keys for system users. Therefore, the scheme eliminates the concern of having a single point of failure as the central authority could be attacked, become malicious, or become unavailable. Recent attacks have shown that the centralized approach could lead to system malfunctioning or to leaking sensitive information. Another achievement of the proposed BE schemes is their performance characteristics that make them suitable for environments with light-weight clients, such as in Internet-of-Things (IoT) applications. The proposed approach improves the performance over existing decentralized BE schemes by simultaneously achieving constant size ciphertext, constant size secret key and fast decryption.
UFO-BLO: Unbiased First-Order Bilevel Optimization

Bilevel optimization (BLO) is a popular approach with many applications including hyperparameter optimization, neural architecture search, adversarial robustness and model-agnostic meta-learning. However, the approach suffers from time and memory complexity proportional to the length $r$ of its inner optimization loop, which has led to several modifications being proposed. One such modification is \textit{first-order} BLO (FO-BLO) which approximates outer-level gradients by zeroing out second derivative terms, yielding significant speed gains and requiring only constant memory as $r$ varies. Despite FO-BLO's popularity, there is a lack of theoretical understanding of its convergence properties. We make progress by demonstrating a rich family of examples where FO-BLO-based stochastic optimization does not converge to a stationary point of the BLO objective. We address this concern by proposing a new FO-BLO-based unbiased estimate of outer-level gradients, enabling us to theoretically guarantee this convergence, with no harm to memory and expected time complexity. Our findings are supported by experimental results on Omniglot and Mini-ImageNet, popular few-shot meta-learning benchmarks.
When ANOVA Isn't Ideal: Analyzing Ordinal Data from Practical Work in Biology

Data collected in many biology laboratory classes are on ratio or interval scales where the size interval between adjacent units on the scale is constant, which is a critical requirement for analysis with parametric statistics such as t-tests or analysis of variance. In other cases, such as ratings of disease or behavior, data are collected on ordinal scales in which observations are placed in a sequence but the intervals between adjacent observations are not necessarily equal. These data can only be interpreted in terms of their order, not in terms of the differences between adjacent points. They are unsuitable for parametric statistical analyses and require a rank-based approach using nonparametric statistics. We describe an application of one such approach, the Kruskal-Wallis test, to biological data using online freeware suitable for classroom settings.
Image Thresholding Using the Complement Feature

A new feature (the complement feature) is proposed in an Eigen formulations for performing global image thresholding.The goal is to find an intensity or gray-level value below which is the background while above it is the foreground (object).Each pixel in the image is represented by a (2D) unit vector where the x-component is the normalized (to [0,1] or [-1,1]) intensity of the pixel, while the y-component is its complement (e.g., Euclidian L2-Norm).The correlation matrix can then constructed to find the cross-correlation, Eigen vectors (axes of inertia) and Eigen values (description of respective sizes).Several implementations for each of the three previously mentioned categories are proposed to perform image thresholding.Interestingly, some of the proposed implementations do not require exhaustive search and a direct solution can be obtained.The results are promising on a wide range of images as demonstrated by comparison with the well-known Otsu method.
Binocular stereovision omnidirectional motion handling robot

Binocular stereovision has become one of the development trends of machine vision and has been widely used in robot recognition and positioning. However, the current research on omnidirectional motion handling robots at home and abroad is too limited, and many problems cannot be solved well, such as single operating systems, complex algorithms, and low recognition rates. To make a high-efficiency handling robot with high recognition rate, this article studies the problem of robot image feature extraction and matching and proposes an improved speeded up robust features (SURF) algorithm that combines the advantages of both SURF and Binary Robust Independent Elementary Features. The algorithm greatly simplifies the complexity of the algorithm. Experiments show that the improved algorithm greatly improves the speed of matching and ensures the real-time and robustness of the algorithm. In this article, the problem of positioning the target workpiece of the robot is studied. The three-dimensional (3-D) reconstruction of the target workpiece position is performed to obtain the 3-D coordinates of the target workpiece position, thereby completing the positioning work. This article designs a software framework for real-time 3-D object reconstruction. A Bayesian-based matching algorithm combined with Delaunay triangulation is used to obtain the relationship between supported and nonsupported points, and 3-D reconstruction of target objects from sparse to dense matches is achieved.
Smoothed Geometry for Robust Attribution

Feature attributions are a popular tool for explaining the behavior of Deep Neural Networks (DNNs), but have recently been shown to be vulnerable to attacks that produce divergent explanations for nearby inputs. This lack of robustness is especially problematic in high-stakes applications where adversarially-manipulated explanations could impair safety and trustworthiness. Building on a geometric understanding of these attacks presented in recent work, we identify Lipschitz continuity conditions on models' gradient that lead to robust gradient-based attributions, and observe that smoothness may also be related to the ability of an attack to transfer across multiple attribution methods. To mitigate these attacks in practice, we propose an inexpensive regularization method that promotes these conditions in DNNs, as well as a stochastic smoothing technique that does not require re-training. Our experiments on a range of image models demonstrate that both of these mitigations consistently improve attribution robustness, and confirm the role that smooth geometry plays in these attacks on real, large-scale models.
Online Attendance using Facial Recognition

In the modern world, education system is advancing day-by-day due to the introduction of concept of "smart classroom".However, the attendance system still remains primitive, where the teacher/lecturer calls the name of students to mark their attendance.The automatic attendance management will replace the manual method, which takes a lot of time, that is, it is very time consuming and is difficult to maintain.There are many biometric processes, among which face recognition is the best method.In this project, we are going to describe the attendance without human interference.In this method the camera is fixed within the classroom and it'll capture the image, the faces are detected and then it's recognized with the database and finally the attendance is marked.It also proposes a single image-based face liveness detection method for discriminating 2-D paper masks from the live faces.Still images taken from live faces and 2-D paper masks were found with the differences in terms of shape and detailedness.In order to effectively employ such differences, we exploit frequency and texture information using various algorithms.We will be trying to improve the accuracy to a great extent and thus generate the final attendance report after updation in the database.I.
Complex UAS-Geophysical Surveys at the First Stages of Geological Prospecting: Case in the Western Sayan (Russia)

Summary One of the most real factors of geological exploration optimization is low-altitude Earth remote sensing that includes geophysical prospecting methods. This article describes a case of the efficient application of complex low-altitude UAV-based aerogeophysical surveying during greenfield prospecting for gold in greenfield localized in the Western Sayan mountains (Russia). 90 sq. km site characterized by extremely complicated landscape and terrain conditions as well as controversial views on its geological conditions. Surveys from a multirotor UAV (unmanned aircraft vehicle) were made with terrain flowing and included a simultaneous magnetic and gamma-ray radiometric surveying as well as gamma-ray spectrometry by the ‘static hovering’ method and multispectral photogrammetry. The articles sets forth main principles of the method applied as well as demonstrates the advantages of unmanned aircraft technologies vs. traditional geophysical prospecting methods, i.e. high performance and low costs even in those conditions when ground and aerial surveys are economically impractical or entirely impossible. Application of low-altitude UAV-surveys allowed for a large-scale geological and geophysical mapping involving low costs, for clearing up a controversial point of the site geological conditions and for planning further prospecting, while avoiding erroneous solutions prompted by wrong priori assumptions about a geological structure of the site.
Media Pembelajaran Berbasis Android Mata Pelajaran Desain Grafis Kelas X

This study aims to develop android-based learning media on the subjects of grafic design class X. The research method used is research development with stages of designing, production, evaluation, and analyzing data by percentage. Based on different stages the trial was being done to experts: media experts (78,30) , the design (78), and the matter (80). So the percentage of overall value on the validation experts have eligibility criteria good. To scale individuals (80), small group (80,05) and field test (80,5). So the total value of the average percentage on of respondents with criteria good.
Learning Model with Error -- Exposing the Hidden Model of BAYHENN

Privacy-preserving deep neural network (DNN) inference remains an intriguing problem even after the rapid developments of different communities. One challenge is that cryptographic techniques such as homomorphic encryption (HE) do not natively support non-linear computations (e.g., sigmoid). A recent work, BAYHENN (Xie et al., IJCAI'19), considers HE over the Bayesian neural network (BNN). The novelty lies in "meta-prediction" over a few noisy DNNs. The claim was that the clients can get intermediate outputs (to apply non-linear function) but are still prevented from learning the exact model parameters, which was justified via the widely-used learning-with-error (LWE) assumption (with Gaussian noises as the error). This paper refutes the security claim of BAYHENN via both theoretical and empirical analyses. We formally define a security game with different oracle queries capturing two realistic threat models. Our attack assuming a semi-honest adversary reveals all the parameters of single-layer BAYHENN, which generalizes to recovering the whole model that is "as good as" the BNN approximation of the original DNN, either under the malicious adversary model or with an increased number of oracle queries. This shows the need for rigorous security analysis ("the noise introduced by BNN can obfuscate the model" fails -- it is beyond what LWE guarantees) and calls for the collaboration between cryptographers and machine-learning experts to devise practical yet provably-secure solutions.
How to Solve Fair $k$-Center in Massive Data Models

Fueled by massive data, important decision making is being automated with the help of algorithms, therefore, fairness in algorithms has become an especially important research topic. In this work, we design new streaming and distributed algorithms for the fair $k$-center problem that models fair data summarization. The streaming and distributed models of computation have an attractive feature of being able to handle massive data sets that do not fit into main memory. Our main contributions are: (a) the first distributed algorithm; which has provably constant approximation ratio and is extremely parallelizable, and (b) a two-pass streaming algorithm with a provable approximation guarantee matching the best known algorithm (which is not a streaming algorithm). Our algorithms have the advantages of being easy to implement in practice, being fast with linear running times, having very small working memory and communication, and outperforming existing algorithms on several real and synthetic data sets. To complement our distributed algorithm, we also give a hardness result for natural distributed algorithms, which holds for even the special case of $k$-center.
Triple-to-Text Generation with an Anchor-to-Prototype Framework

Generating a textual description from a set of RDF triplets is a challenging task in natural language generation. Recent neural methods have become the mainstream for this task, which often generate sentences from scratch. However, due to the huge gap between the structured input and the unstructured output, the input triples alone are insufficient to decide an expressive and specific description. In this paper, we propose a novel anchor-to-prototype framework to bridge the gap between structured RDF triples and natural text. The model retrieves a set of prototype descriptions from the training data and extracts writing patterns from them to guide the generation process. Furthermore, to make a more precise use of the retrieved prototypes, we employ a triple anchor that aligns the input triples into groups so as to better match the prototypes. Experimental results on both English and Chinese datasets show that our method significantly outperforms the state-of-the-art baselines in terms of both automatic and manual evaluation, demonstrating the benefit of learning guidance from retrieved prototypes to facilitate triple-to-text generation.
ParticleMDI: particle Monte Carlo methods for the cluster analysis of multiple datasets with applications to cancer subtype identification

Abstract We present a novel nonparametric Bayesian approach for performing cluster analysis in a context where observational units have data arising from multiple sources. Our approach uses a particle Gibbs sampler for inference in which cluster allocations are jointly updated using a conditional particle filter within a Gibbs sampler, improving the mixing of the MCMC chain. We develop several approaches to improving the computational performance of our algorithm. These methods can achieve greater than an order-of-magnitude improvement in performance at no cost to accuracy and can be applied more broadly to Bayesian inference for mixture models with a single dataset. We apply our algorithm to the discovery of risk cohorts amongst 243 patients presenting with kidney renal clear cell carcinoma, using samples from the Cancer Genome Atlas, for which there are gene expression, copy number variation, DNA methylation, protein expression and microRNA data. We identify 4 distinct consensus subtypes and show they are prognostic for survival rate ( $$p &lt; 0.0001$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>&lt;</mml:mo> <mml:mn>0.0001</mml:mn> </mml:mrow> </mml:math> ).
Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers

We detect out-of-training-distribution sentences in Neural Machine Translation using the Bayesian Deep Learning equivalent of Transformer models. For this we develop a new measure of uncertainty designed specifically for long sequences of discrete random variables -- i.e. words in the output sentence. Our new measure of uncertainty solves a major intractability in the naive application of existing approaches on long sentences. We use our new measure on a Transformer model trained with dropout approximate inference. On the task of German-English translation using WMT13 and Europarl, we show that with dropout uncertainty our measure is able to identify when Dutch source sentences, sentences which use the same word types as German, are given to the model instead of German.
Comparative analysis of the Russian and Western education and scientific-training system

The article compares the Russian and Western strategies of higher education and scientific training and their implementation in the state policy. The author considers the three-level structure of scientific training which includes in the West Bachelors, Masters and PhD programs, while in Russia in the previous period - specialty, PhD program - to defend the thesis and get the degree of Candidate of Science - and Postdoc program - to get the degree of Doctor of Science. After Russia signed the Bologna Declaration at the Berlin Conference of ministers of education in September 2003, these differences were eliminated and already in 2010 Russia implemented all basic principles of the Bologna process by having transformed its system of education and scientific training on the basis of the British-American model. The author considers both advantages and losses of this reform. Thus, Russia introduced the same education standards with the West and accepted the single certification system (Bachelor, Master, PhD), which is certainly a positive side of the reform. On the other hand, by removing the specialty level, Russia lost the basis for mass scientific training without the Masters level: today we have a negative situation in the system of higher education, when thousands of young people with Bachelors degree cannot find job or continue studying and scientific career without Masters degree, but they do not have money to pay for Masters program due to financial difficulties. As a result, the production and science systems do not get enough qualified specialists and young scientists for whose training the state has already spent a lot of money. The author concludes that it is necessary to use effective foreign experience but without ignoring the Russian education and science achievements and with taking into account national traditions and specifics of the national development.