Reviewerly/exHarmony huggingface dataset:
The Use of the Project-Based Learning in the Study of the Course of Mathematical Analysis

The project-based learning is a means by which learning objectives are achieved through a comprehensive study of the problem, the final stage of which is the practical result. The aim of this work is to study the possibility of using the project-based learning in the study of the course of mathematical analysis, comparing this method with traditional training and evaluating the effectiveness of this method. The motivation of this study is to improve the efficiency of the process of teaching students. Therefore, the objectives of the study are: the study of the possibility of the project-based learning in the organization of the learning process of students, the study of the possibility of using this technology in the study of mathematical analysis, finding out how effectively this method allows you to master mathematical knowledge. The method of projects was used in the study of the course of mathematical analysis, as a tool to increase the conscious independent work of students in extracurricular time. For this purpose 2 types of projects were offered students at choice: general education and applied. The results of testing of students who were trained according to traditional methods of training and results of testing of students who were trained with use of project activity are analyzed. The analysis of influence of type of the project on extent of assimilation by students of the studied material is made. As a result of the study, the hypothesis was confirmed: the use of project activities as an additional tool in the organization of independent cognitive activity of students contributes to the effective assimilation of educational material and stimulates the developing the competencies and skills to independently acquire new knowledge.
Learning to Detect and Retrieve Objects From Unlabeled Videos

Learning an object detection or retrieval system requires a large data set with manual annotations. Such data sets are expensive and time consuming to create and therefore difficult to obtain on a large scale. In this work, we propose to exploit the natural correlation in narrations and the visual presence of objects in video, to learn an object detector and retrieval without any manual labeling involved. We pose the problem as weakly supervised learning with noisy labels, and propose a novel object detection paradigm under these constraints. We handle the background rejection by using contrastive samples and confront the high level of label noise with a new clustering score. Our evaluation is based on a set of 11 manually annotated objects in over 5000 frames. We show comparison to a weakly-supervised approach as baseline and provide a strongly labeled upper bound.
Styles of professional activity of a teacher of higher education of a pedagogical profile

This article discusses the different styles of professional activity of a teacher of higher education of a pedagogical profile. The purpose of the study: to develop the basic characteristics of the styles of professional activity of teachers in the humanitarian, natural sciences and physical education and sports areas of teacher education and experimentally prove the effectiveness of their implementation in the process of interaction "teacher-student". The obtained statistical data in the study prove the need for differentiation of styles of professional activity for students of various training profiles of the pedagogical institute. Based on the style of the teacher’s professional activity and the direction of the student’s pedagogical training, a reliably positive result was revealed in increasing the academic performance of students of the pedagogical institute.
Linking Learning Tools, Learning Companion and Experimental Tools in a Multitouch Learning Book

Multitouch Learning Books (short: MLB) are digital interactive E-Books that can be used in class enriched with individual tools. Due to their multifunctionality, they offer an excellent framework for integrating further didactic functions exceeding the role of a learning companion. In this study a Multitouch Learning Book was developed which contains all three didactic functions of ICT (Information and Communication Technology). The MLB provides the digital framework for the series of lessons and accompanies the entire learning process. Learning tools include isolated applications, Augmented Reality and measured data logging, which fulfills the didactic function of an experimental tool. The topic "galvanic cell" was implemented and tested in two different classes. The intervention resulted in unanimously positive feedback from teachers and pupils alike
Effects of Core Basic Nursing Skills Education Combined with Online Learning on Self-directed Learning Preparation, Academic Self-efficacy, and Self-confidence on Core Basic Nursing Skills

The purpose of this study was to examine the effects of core basic nursing skills education combined with online learning. The study was an one group pre-posttest research design. The participants were 59 senior nursing students who took the core basic nursing practicum. Online learning was provided by mobile video clips on core basic nursing skills and the participants were required to select online learning contents by themselves and repeat them. In addition, online quizzes and nursing guidance and evaluations and feedback in class were made. Data were analyzed using the SPSS 24 version with descriptive statistics, paired t-test, and Pearson correlation coefficients. After the applying online learning, there were significant increase in self-directed learning preparation (t=-4.367, p<.001), academic self-efficacy (t=-4.429, p<.001), and self-confidence on core basic nursing skills (t=-7.013, p<.001). Through this study, it is recommended to use various online learning methods to prepare self-directed learning and enhance self-efficacy in future core basic nursing practice training.
Gaussian implementation of the multi-Bernoulli mixture filter

This paper presents the Gaussian implementation of the multi-Bernoulli mixture (MBM) filter. The MBM filter provides the filtering (multi-target) density for the standard dynamic and radar measurement models when the birth model is multi-Bernoulli or multi-Bernoulli mixture. Under linear/Gaussian models, the single target densities of the MBM mixture admit Gaussian closed-form expressions. Murty's algorithm is used to select the global hypotheses with highest weights. The MBM filter is compared with other algorithms in the literature via numerical simulations.
Gender Specifics of Personality Component in the Structure of Career Potential of University Students

The article presents data of a study on gender differences of the personality component in the structure of career potential in future managers. Identifying the level of career potential components formation at the initial stage of university education allows to outline a program of harmonization of career potential and to implement it at each stage of university education, taking into account the age, personal characteristics and professional orientation of the students. The study involved 234 students aged 18—19 years, 117 males and 117 females. It was revealed that the male subjects, as compared to the female subjects, showed higher developmental levels of motivational, regulatory and emotional characteristics of the personality component in the structure of their career potential, which in the future could become the basis for their rapid career growth. The articleconcludes that there’s an obvious need for the development and implementation of programmes supporting the individual career path in university students.
Performance Comparisons Of Hybrid Fuzzy-LQR And Hybrid PID-LQR Controllers On Stabilizing Double Rotary Inverted Pendulum

Double Rotary Inverted Pendulum (DRIP) is a member of the mechanical under-actuated system which is unstable and nonlinear. The DRIP has been widely used for testing different control algorithms in both simulation and experiments. The DRIP control objectives include Stabilization control, Swing-up control and trajectory tracking control. In this research, we present the design of an intelligent controller called “hybrid Fuzzy-LQR controller” for the DRIP system. Fuzzy logic controller (FLC) is combined with a Linear Quadratic Regulator (LQR). The LQR is included to improve the performance based on full state feedback control. The FLC is used to accommodate nonlinearity based on its IF-THEN rules. The proposed controller was compared with the Hybrid PID-LQR controller. Simulation results indicate that the proposed hybrid Fuzzy-LQR controllers demonstrate a better performance compared with the hybrid PID-LQR controller especially in the presence of disturbances.&#x0D;
Learning From Strategic Agents: Accuracy, Improvement, and Causality

In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model (predicting some outcome) that accounts for agents' incentives to game their features in order to receive better decisions. Whereas the strategic classification literature generally assumes that agents' outcomes are not causally dependent on their features (and thus strategic behavior is a form of lying), we join concurrent work in modeling agents' outcomes as a function of their changeable attributes. Our formulation is the first to incorporate a crucial phenomenon: when agents act to change observable features, they may as a side effect perturb hidden features that causally affect their true outcomes. We consider three distinct desiderata for a decision-maker's model: accurately predicting agents' post-gaming outcomes (accuracy), incentivizing agents to improve these outcomes (improvement), and, in the linear setting, estimating the visible coefficients of the true causal model (causal precision). As our main contribution, we provide the first algorithms for learning accuracy-optimizing, improvement-optimizing, and causal-precision-optimizing linear regression models directly from data, without prior knowledge of agents' possible actions. These algorithms circumvent the hardness result of Miller et al. (2019) by allowing the decision maker to observe agents' responses to a sequence of decision rules, in effect inducing agents to perform causal interventions for free.
Efficient Authentication Scheme Using Blockchain in IoT Devices

Authentication of users and devices is one of the security issues on the internet of things (IoT). Traditionally, the authentication system is based on a centralized server, which makes it prone to various security threats and potential attacks; also, the stored data can tamper. In this paper, existing issues are resolved using Blockchain. IoT devices do not have much computational power. Blockchain technology, which uses the Proof-of-Work concept, consumes a lot of energy, which makes it not suitable for IoT devices. A new efficient scheme to reduce the computational energy of the devices is discussed, along with an authentication scheme that uses no centralized servers and makes IoT devices interact directly with Blockchain is designed and is implemented. The smart contract concept is used in this scheme, which provides users the flexibility to design there own rules for authentication.
An Event-B Based Approach for Formal Modelling and Verification of Smart Contracts

While smart contracts are becoming widely recognized as the most successful application of the blockchain technology that could be applied into various industries and for different purposes such as e-commerce, energy tradings, assets management, and healthcare services, their implementation has posed several challenges insofar that they could handle large amount of money and digital assets in addition to their ability to manipulate critical data and transactions related information which makes them attractive targets of security threats and attacks that could lead to significant problems like money losses, privacy leakage and data breach. To better deal with such issues, reasoning about the correctness, the safety and the functional accuracy of smart contracts before their deployment on the blockchain network is critical and no important than ever. In this context model checking tools are well adopted for the formal verification of smart contracts in order to assure their execution as parties’ willingness as well as their reliable and secure interaction with users. In this direction, this paper uses Event-B formal verification method to formally model solidity written smart contracts in order to verify and validate their safety, correctness and functional accuracy in addition to their compliance with their specification for given behaviors. The verification is conducted using a model checking tool along which expected safety properties are formalized, validated and judged to be satisfied or unsatisfied. To illustrate the proposed approach, its application to a realistic industrial use case is described.
MEANINGFUL GAMIFICATION IN MOOCS. DESIGNING AND EXAMINING LEARNER ENGAGEMENT IN THE OPEN VIRTUAL MOBILITY LEARNING HUB

I. Buchem1, C. Carlino2, F. Amenduni3, A. Poce3 1Beuth University of Applied Sciences Berlin (GERMANY) 2CINECA (ITALY) 3University Roma Tre (ITALY)
Effective Obstruction to Lifting Tate Classes from Positive Characteristic

We give an algorithm that takes a smooth hypersurface over a number field and computes a p-adic approximation of the obstruction map on the Tate classes of a finite reduction. This gives an upper bound on the “middle Picard number” of the hypersurface. The improvement over existing methods is that our method relies only on a single prime reduction and gives the possibility of cutting down on the dimension of Tate classes by two or more. The obstruction map comes from p-adic variational Hodge conjecture and we rely on the recent advancement by Bloch–Esnault–Kerz to interpret our bounds.
Quantum period finding based on the Bernstein-Vazirani algorithm

Quantum period finding algorithms have been used to analyze symmetric cryptography. For instance, the 3-round Feistel construction and the Even-Mansour construction could be broken in polynomial time by using quantum period finding algorithms. In this paper, we firstly provide a new algorithm for finding the nonzero period of a vectorial function with O(n) quantum queries, which uses the Bernstein-Vazirani algorithm as one step of the subroutine. Afterwards, we compare our algorithm with Simon's algorithm. In some scenarios, such as the Even-Mansour construction and the function satisfying Simon's promise, etc, our algorithm is more efficient than Simon's algorithm with respect to the tradeoff between quantum memory and time. On the other hand, we combine our algorithm with Grover's algorithm for the key-recovery attack on the FX construction. Compared with the Grover-Meets-Simon algorithm proposed by Leander and May at Asiacrypt 2017, the new algorithm could save the quantum memory.
Variability of orographic architecture of Indo-Burmese Ranges (NE India): constraints from morphotectonic and lineament analysis

Activeness of the tectonic and related sub-surface processes control the surface features that lead to variations in topography and lithostratigraphy. To understand the role of active tectonics in shaping the topography, morphotectonic and lineament studies are important. In this study, geological categorization has helped understand the orogenic evolution of the Indo-Burmese Range (IBR), NE India. This is an arcuate hill range that shows many unique topographic characteristics that incited to categorise the entire IBR into different tectonically active domains: Northern (Changlang district of Arunachal Pradesh); Naga Hills; Cachar and Manipur; and Southern (Mizoram). The Northern and Southern domains are more active than the others. Lineament analysis also indicates the presence of active features in the region with NW–SE, NE–SW, ESE–WNW trends are being common. The Northern and Naga Hills domains have mostly E–W younger lineaments whereas the Cachar and Manipur domains rather show N–S younger trends. The Southern Mizoram domain shows a dominance of older N–S lineaments with younger NW–SE lineaments. These variations result from differential stress conditions, i.e. Indian Plate movement and westward stress from the Burma Plate. This study shows how overall variations in tectonic settings can be related to the orogenic evolution.
(Consideration on knowledge acquisition from optimization result of genetic programming)

By devising the production process of industrial products, it will greatly affect the shortening of production time and cost reduction. There is a job shop shceduling which is one of production method. In JSSP(Job Shop Scheduling Problems), machine assignment and job processing order greatly affect production efficiency. In this study, we used dispatching rules. We used GP(genetic programming) to express the priority function of DRs(Dispatching Rules). Moreover, when considering a production system, it is necessary to optimize for multiple purposes. In this study, multi-objective optimization was performed using NSGA-II(Non dominated Sorting Genetic Algorithm) calculate Pareto optimal solutions. Furthermore, clustering was performed using k-means in order to consider Pareto optimal solutions. The distance in k-means is defined in the objective function domain.
Finding Interpretable Concept Spaces in Node Embeddings Using Knowledge Bases

In this paper we propose and study the novel problem of explaining node embeddings by finding embedded human interpretable subspaces in already trained unsupervised node representation embeddings. We use an external knowledge base that is organized as a taxonomy of human-understandable concepts over entities as a guide to identify subspaces in node embeddings learned from an entity graph derived from Wikipedia. We propose a method that given a concept finds a linear transformation to a subspace where the structure of the concept is retained. Our initial experiments show that we obtain low error in finding fine-grained concepts.
Gaussian spatial-polarization entanglement in a folded Mach–Zehnder interferometer

Gaussian spatial-polarization entanglement in a coherent vectorial paraxial light field is studied. Detection of spatial-polarization entanglement through fringe movement on rotation of a linear polarizer, with the light field passing through the polarizer, is outlined. The fringe movement is shown to be a sufficient condition for the detection of spatial-polarization entanglement in coherent paraxial vector light fields. Two Gaussian light fields with a small relative tilt but with significant spatial overlap and with orthogonal polarizations are shown to possess close to 1 ebit of spatial-polarization entanglement. Tunable Gaussian spatial-polarization entanglement is experimentally demonstrated in a folded Mach–Zehnder interferometer.
Quantum walks in periodically kicked circuit QED lattice

We investigate the quantum walks of a single particle in a one-dimensional periodically kicked circuit quantum electrodynamics lattice. It is found that the dynamic process of the quantum walker is affected by the strength of incommensurate potentials and the driven periods of the system. We calculate the mean square displacement to illustrate the dynamic properties of the quantum walks, which shows that the localized process of the quantum walker presents the zero power-law index distribution. By calculating the mean information entropy, we find that the next-nearest-neighbor interactions have a remarkable deviation effects on the quantum walks and make a more stricter parameter condition for the localization of the quantum walker. Moreover, assisted by the lattice-based cavity input-output process, the localized features of circuit quantum electrodynamics lattice can be observed by measuring the average photon number of the cavity field in the steady state.
MULTIMODAL ANALYSIS ON ERTIGA CAR ADVERTISEMENT

Language always refers to communication where spoken and written texts are dominated in human’s life. Communication does not always mean verbal but it is also can be visual. Visual (pictures, gestures, photos, advertisement, and so on) also means text and can deliver message as communication to others. Like verbal, visual also has structures how it delivers the message to audience. Advertisements are the visual objects that human always see for they are dominating in television. The purpose of this research is to investigate the structures of advertisement of Ertiga car. Theories to be used are Kress (2009) and Cheong (2004). Descriptive qualitative was conducted in this research. The source data was the advertisement of a car from Suzuki, Ertiga. After analyzing the data, the researchers found that verbal components (Announcement, Enhancer, Emblem, Tag, Cal-and-visit information) and visual components (Lead, Display and Emblem) were inserted in the advertisement of Ertiga. It means that the advertisement of Ertiga is made in a good way where the purpose of advertisement is to persuade the audience to buy the product. In short, the structures of the advertisement determine the quality of the product.
Association Models to Select the Best Rules for Fuzzy Inference System

In the past decade, the fuzzy inference system (FIS) has seen explosive growth in popularity among the researchers and industrialists. The fuzzy classification system is based on the most knowing process of the FIS in order to map features as inputs to outputs classes. Generally, to make a fuzzy classifier decision it is necessary to select manually the best rules that require the intervention of experts. In the current paper, we propose an automatic method to select the best rules using associations models: Apriority and Filter Associations. The proposed method process in five steps: preprocessing (feature reduction), determining membership function for every input and output, nominal representation of the database basing on the membership functions, call for the association model to select the most important rules, and finally post-processing of the obtained rules. In this work, we lead to appropriate rules set tested on the iris data for the classification task. In addition, the proposed method is highly simple to be implemented to control the even complex system. Our system achieved promising results, which demonstrate the effectiveness of the proposed approach.
Abnormal Moving Speed Detection Using Combination of Kernel Density Estimator and DBSCAN for Coastal Surveillance Radars

In this paper, we investigate the problem of detection of abnormal moving speeds for a coastal surveillance radar. A new definition of normal moving speeds which is based on the historical radar data of maritime targets on the monitoring area is introduced. The historical radar data is mined by the cell-based method, unsupervised machine learning to obtain the vessel normal moving speeds in the monitoring area. Then a logic rule is applied to detect the abnormal targets. The proposed method is tested with real data from a coastal surveillance radar. The test results show that the false alarm rate (FAR) is equal zero. It is also shown that this kind of anomaly detection can be integrated into a coastal surveillance radar for the detection of the maritime illegal activities.