Reviewerly/exHarmony huggingface dataset:
What to Learn, and How: Toward Effective Learning from Rationales

Learning from rationales seeks to augment model prediction accuracy using humanannotated rationales (i.e.subsets of input tokens) that justify their chosen labels, often in the form of intermediate or multitask supervision.While intuitive, this idea has proven elusive in practice.We make two observations about human rationales via empirical analyses: 1) maximizing rationale supervision accuracy is not necessarily the optimal objective for improving model accuracy; 2) human rationales vary in whether they provide sufficient information for the model to exploit for prediction.Building on these insights, we propose several novel loss functions and learning strategies, and evaluate their effectiveness on three datasets with human rationales.Our results demonstrate consistent improvements over baselines in both label and rationale accuracy, including a 3% accuracy improvement on MultiRC.Our work highlights the importance of understanding properties of human explanations and exploiting them accordingly in model training.
PEDAGOGICAL INTERACTION IN THE «TEACHER-STUDENT» DIAD IN ONLINE LEARNING CONDITIONS

The article presents the results of a questionnaire survey of modern students regarding their educational needs and expectations in the context of forced online learning caused by the COVID-19 pandemic. The generalization of the educational needs and expectations of modern students, as well as the tendencies characteristic of them in teaching, made it possible to identify a range of problematic accents, which relate to: coordination and management on the part of teachers of the process of mastering the educational material by students; factors that enhance learning motivation, as well as factors that demotivate; the needs of modern students for the presentation of educational content; attitude to forms and methods of teaching in the context of modern changes; features of transformation of communication between students and teachers; feedback, etc. The study revealed some features of the interaction of modern students with the educational environment, their learning needs and expectations: openness to new forms and methods of teaching, the desire to express and manifest their position, the desire for interaction and exchange of views, the desire to be heard about the expected format or presentation of educational information, the desire to do what is interesting and have space for the implementation of their own educational path. Modern students expect from the teacher an interactive presentation of lecture material that has a problematic and exploratory nature. Based on the results of the survey, as well as on current trends characteristic of higher education in the context of transformational changes and forced distance learning caused by the COVID-19 pandemic, the ways of optimal pedagogical interaction in the teacher-student dyad are outlined.
Measurement of electric-field noise from interchangeable samples with a trapped-ion sensor

We demonstrate the use of a single trapped ion as a sensor to probe electric-field noise from interchangeable test surfaces. As proof of principle, we measure the magnitude and distance dependence of electric-field noise from two ion-trap-like samples with patterned Au electrodes. This trapped-ion sensor could be combined with other surface characterization tools to help elucidate the mechanisms that give rise to electric-field noise from ion-trap surfaces. Such noise presents a significant hurdle for performing large-scale trapped-ion quantum computations.
Problems of assessing students’ knowledge in mathematics using LMS Moodle

The widespread introduction of the LMS Moodle educational environment into the educational process in the framework of distance learning and recommendations to use the abovementioned resource for full-time education served as the prerequisites for this study. The authors believe that the knowledge assessment system for distance learning cannot be automatically applied to assess students of other forms of education, but should be adapted to them. The purpose of the research was to study the issues of assessment in the Moodle environment, to develop a methodology for using it to assess the knowledge of students of economic bachelor’s degree in Mathematics. To achieve the goal, scientific literature on this topic was studied, pedagogical design, experiment and student survey were used as research methods. As a result, the authors of the article designed a control and measuring system for assessing knowledge in mathematics in LMS Moodle and the procedure for intermediate certification using this system. The experience of introducing a knowledge assessment system in the first year of the Financial University under the Government of the Russian Federation and a questionnaire survey showed the specificity of approaches to assessing students’ knowledge in an electronic environment in Mathematics. To control the academic achievements of students, the authors consider it necessary to check additionally the performance of the teacher with the subsequent correction of points. The novelty of the research lies in the development of a methodology for using LMS Moodle for assessing students’ knowledge in mathematics in the course of full-time education.
Mutually unbiased measurements with arbitrary purity

Mutually unbiased measurements are a generalization of mutually unbiased bases in which the measurement operators need not to be rank one projectors. In a d-dimensional space, the purity of measurement elements ranges from 1/d for the measurement operators corresponding to maximally mixed states to 1 for the rank one projectors. In this contribution, we provide a class of MUM that encompasses the full range of purity. Similar to the MUB in which the operators corresponding to different outcomes of the same measurement commute mutually, our class of MUM possesses this sense of compatibility within each measurement. The spectra of these MUMs provide a way to construct a class of d-dimensional orthogonal matrices which leave the vector of equal components invariant. Based on this property, and by using the MUM-based entanglement witnesses, we examine the minimal number of measurements needed to detect entanglement of bipartite states. For a general bipartite pure state, we need only two MUMs: The first one assigns a zero mean value for all pure states; however, a complementary measurement is needed to give a negative mean value for entangled states. Interestingly, the amount of this negative value is proportional to an entanglement monotone.
Preliminary results of the impact of 3D-visualization resources in the area of graphic expression on the motivation of university students

Abstract Augmented reality and virtual reality are innovative technologies applied to the area of graphic expression with increasing influence on the teaching–learning process. Although these innovative resources enable new forms of teaching, it remains unclear how these artificial applications can impact students’ motivation. The aim of this paper was to evaluate how virtual exercises increase the motivation level in different typologies of university students. The sample was composed of graduate (master’s degree) and undergraduate students (three engineering degrees) of the University of Cordoba. These tools were available to students through four devices: mobile phones, tablets, computers and virtual reality goggles. The motivation of the students was evaluated through the modified Instructional Materials Motivation Survey by the attention, relevance, confidence and satisfaction motivational model. The results obtained through a 5-point Likert scale showed that these innovative resources significantly improved the students’ motivation level, especially concerning the ‘relevance’ aspect (M = 4.01; SD = 0.98). The virtual resources also increased the understanding of the exercises and their spatial vision (M = 3.80; SD = 1.14). Of the total sample, 63.83% students considered the virtual reality goggles as the most suitable device to visualize graphic expression exercises.
Construction of Seismic Cloud Platform Based on OpenStack

Abstract Based on virtualization technology, cloud computing realizes resource sharing according to its application to meet business requirements. Such a platform can effectively save resources and provide customers with more quality services. Due to the large amount of seismic data processing and that of computing tasks of seismic cloud platform, the whole cloud computing technology and cloud computing platform construction need to be ensured. At present, there is no large-scale application of the current computing platform, it mainly focuses on the construction and research of private platform. Based on the standard system of OpenStack, this study first analyzes the current situation and problems of seismic cloud platform, then designs the seismic cloud platform, and finally elaborates the construction, deployment and functional application of the platform from the perspective of heterogeneous cloud.
Creative thinking as methodical category of foreign language learning

This article is devoted to the development and improvement of the creative thinking approach of foreign language learning, namely speaking process. In that sense, the authors of the given article consider creative thinking as method and technique of foreign language learning and means of communicative competence development. The authors also describe the estimation system of the given method and its content in a form of methodical algorithm and theoretical principles of foreign language learning using creative thinking as an approach. The aim of the given article to develop a new approach for better development of foreign language communication, especially in skills of speaking, to be based on unique thinking as a technique in learning. The authors of the given article also developed and presented notion called “creativization” as methodical and theoretical category in foreign language learning process comprising some theoretical aspects of the given foreign language learning approach including its own specifics and conceptual points described in the given article. Creativization is also described by the authors of the given article as a technique in foreign language learning especially concerning speaking. The notion “creativization” presents concept ideas and basic principles of creative thinking approach in foreign language learning in a form of several methodical principles and theoretical aspects.
On the non-Markovianity of quantum semi-Markov processes

The non-Markovianity of the stochastic process called the quantum semi-Markov (QSM) process is studied using a recently proposed quantification of memory based on the deviation from semigroup evolution, that provides a unified description of divisible and indivisible channels. This is shown to bring out the property of QSM processes to exhibit memory effects even in the CP-divisible regime, in agreement with an earlier result. An operational meaning to the non-Markovian nature of semi-Markov processes is also provided.
Spherical Distributions Used in Evolutionary Algorithms

Performance of evolutionary algorithms in real space is evaluated by local measures such as success probability and expected progress. In high-dimensional landscapes, most algorithms rely on the normal multi-variate, easy to assemble from independent, identically distributed components. This paper analyzes a different distribution, also spherical, yet with dependent components and compact support: uniform in the sphere. Under a simple setting of the parameters, two algorithms are compared on a quadratic fitness function. The success probability and the expected progress of the algorithm with uniform distribution are proved to dominate their normal mutation counterparts by order n!!.
Measurement and Analysis of IC EMI synchronous signal

In many cases, the analysis of Electromagnetic Interference (EMI) analyzing in frequency domain is very important. However, in the digital era, examples of analysis in the time domain is getting more attentions. In time domain, the signals should be carefully captured within the same time interval. Otherwise, the correlation between the signals cannot be compared. Therefore, it is very important to capture the signal synchronously. Thus, the accurate synchronous cutting of different signals is very important procedure for time domain signal acquisition. But it will be affected by noise and cannot be cut accurately. So we propose a new method for such issue. We create an ideal square wave based on the synchronous cutting signal so that the synchronous cutting is not affected by noise.
DDPG-Based Edge Resource Management for Coal Mine Surveillance Video Analysis in Cloud-Edge Cooperation Framework

Intelligent video surveillance is important to ensure production safety in coal mines, while cloud-edge cooperation is an effective means to improve the performance of intelligent video monitoring. However, in edge layers, incorrect resource allocation of computing and network resources will result in the waste of resources and low real-time performance. In this paper, a DDPG-Based (Deep deterministic policy gradient-based) edge resource allocation method for cloud-edge cooperation framework is proposed. Firstly, the cloud-edge cooperation framework is designed for different tasks. Secondly, the joint minimizing problem of latency and bandwidth usage caused by edge computing is modeled. To quickly solve the joint optimization problem, we convert it to MDP (Markov Decision Process). In addition, ESPN (Edge status perception network) is proposed, which enhances the ability of feature perception and action output of DDPG. Finally, DDPG-ESPN is proposed to solve the joint optimization problem. Simulation results show that compared with other methods, DDPG-ESPN improves the real-time performance and bandwidth usage by up to 18.88% and 42.81% respectively.
Dance Tempo Estimation Using a Single Leg-Attached 3D Accelerometer

We present a methodology that enables dance tempo estimation through the acquisition of 3D accelerometer signals using a single wearable inertial device positioned on the dancer's leg. Our tempo estimation method is based on enhanced multiple resonators, implemented with comb feedback filters. To validate the methodology, we focus on the versatile solo jazz dance style. Including a variety of dance moves, with different leg activation patterns and rhythmical variations, solo jazz provides for a highly critical validation environment. We consider 15 different solo jazz dance moves, with different leg activation patterns, assembled in a sequence of 5 repetitions of each, giving 65 moves altogether. A professional and a recreational dancer performed this assembly in a controlled environment, following eight dancing tempos, dictated by a metronome, and ranging from 80 bpm to 220 bpm with 20 bpm increment steps. We show that with appropriate enhancements and using single leg signals, the comb filter bank provides for accurate dance tempo estimates for all moves and rhythmical variations considered. Dance tempo estimates for the overall assembles match strongly the dictated tempo-the difference being at most 1 bpm for all measurement instances is within the limits of the established beat onset stability of the used metronome. Results further show that this accuracy is achievable for shorter dancing excerpts, comprising four dance moves, corresponding to one music phrase, and as such enables real-time feedback. By providing for a dancer's tempo quality and consistency assessment, the presented methodology has the potential of supporting the learning process, classifying individual level of experience, and assessing overall performance. It is extendable to other dance styles and sport motion in general where cyclical patterns occur.
Pri-AwaRE: Tool Support for priority-aware decision-making under uncertainty

The main objective of decision-making in a self-adaptive system (SAS) is to continuously satisfy its requirements under environmental uncertainty. As the run-time context changes, the system may need to re-configure itself by making trade-offs between the non-functional requirements (NFRs) based on their individual priorities for satisfaction. We demonstrate Pri-AwaRE as an approach to support priority-aware decision-making in SASs by providing explicit runtime modelling and reasoning of individual priorities of NFRs. The approach also supports autonomous tuning of the priorities under dynamic situations to maintain the required satisfaction levels of NFRs. In this paper, we showcase how Pri-AwaRE is used in a substantial industrial case of Remote Mirroring using a simulation tool called RDMSim. Our results show that Pri-AwaRE offers the required satisfaction levels of NFRs by autonomously tuning of NFRs’ priorities according to new runtime environmental contexts.
A Study of Deep Neural Networks Transfer Learning For Fault Diagnosis Applications

Intelligent fault diagnosis utilizing deep learning algorithms has been widely investigated recently. Although previous results demonstrated excellent performance, features learned by Deep Neural Networks (DNN) are part of a large black box. Consequently, lack of understanding of underlying physical meanings embedded within the features can lead to poor performance when applied to different but related datasets i.e. transfer learning applications. This study will investigate the transfer learning performance of a Convolution Neural Network (CNN) considering 4 different operating conditions. Utilizing the Case Western Reserve University (CWRU) bearing dataset, the CNN will be trained to classify 12 classes. Each class represents a unique differentfault scenario with varying severity i.e. inner race fault of 0.007”, 0.014” diameter. Initially, zero load data will be utilized for model training and the model will be tuned until testing accuracy above 99% is obtained. The model performance will be evaluated by feeding vibration data collected when the load is varied to 1, 2 and 3 HP. Initial results indicated that the classification accuracy will degrade substantially. Hence, this paper will visualize convolution kernels in time and frequency domains and will investigate the influence of changing loads on fault characteristics, network classification mechanism and activation strength.
Towards Improving Code Stylometry Analysis in Underground Forums

Abstract Code Stylometry has emerged as a powerful mechanism to identify programmers. While there have been significant advances in the field, existing mechanisms underperform in challenging domains. One such domain is studying the provenance of code shared in underground forums, where code posts tend to have small or incomplete source code fragments. This paper proposes a method designed to deal with the idiosyncrasies of code snippets shared in these forums. Our system fuses a forum-specific learning pipeline with Conformal Prediction to generate predictions with precise confidence levels as a novelty. We see that identifying unreliable code snippets is paramount to generate high-accuracy predictions, and this is a task where traditional learning settings fail. Overall, our method performs as twice as well as the state-of-the-art in a constrained setting with a large number of authors (i.e., 100). When dealing with a smaller number of authors (i.e., 20), it performs at high accuracy (89%). We also evaluate our work on an open-world assumption and see that our method is more effective at retaining samples.
Sequences consistency feature learning for video‐based person re‐identification

Video-based person re-identification aims to match pedestrians from video sequences across non-overlapping cameras. A major challenge of the person re-identification is the serious intra-class distance caused by cropped frames variation in video sequences. To address this issue, a novel sequences consistency feature learning (SCFL) framework for video-based person re-identification is proposed. Specifically, SCFL utilizes a deep neural network and the proposed sequences consistency loss to learn sequences-invariant features for each pedestrian, which decreases the intra-class distance across the partial occlusions, inaccurate detection and viewpoint variation. During the training process, SCFL slices the video-level features and computes the cosine similarity between disjoint sequences features pairs of the same pedestrian as the sequences consistency loss to minimize the intra-class distance. The experiments demonstrate that our approach improves the performance of the existing deep networks and achieves competitive performance on the large-scale benchmark datasets including MARS and DukeMTMC-VideoReID.
Reliability Analysis of Hydrodynamic System for Robot Configuration

The failure tree and J-M model method are lack of analysis of the importance of each component model, which leads to the low reliability of the analysis results. In view of this problem, a Monte Carlo method based on the shape of the English long-distance robot is proposed. In view of the configuration of the robot, the realization process of the robot shape fluid dynamics system is analyzed. The frequency of accident is determined by Monte Carlo simulation, which is used as the reliability index of the system. In MATLAB, the reliability of the shape fluid dynamic system of robot is analyzed by Monte Carlo method. The system importance name and parameters are determined. The parameter conforms to the statistical function of random variables of each corresponding probability distribution function. According to the parameters, the function of the structure is established. The system is divided into reliable state, failure state and limit state with 0 as the dividing point, and the actual failure probability of the system is calculated. The numerical solution of log domain is simulated by the method of statistical calculation of random variables, and the actual failure probability is expressed by normal distribution function. The experimental results show that the actual failure probability of the method is lower than 5% under any working load, and the reliability of the analysis results is high.
Artificial Intelligence and Business Leadership Development: Applications and Solutions

Artificial intelligence (AI) is the promise of today and future businesses. Any leadership development model that ignores AI could miss out on modern business tools, technology, and resources. Though evaluations in developing business leaders present a positive relationship between AI and leadership development (Husain, 2017; Reese, 2018; Hosanagar, 2019), not many studies have been conducted in these areas. With Roomba Robots listening to social media and iRobot’s identifying customers and reaching out to them through private channels (Carr, 2011), a question arises: will AI be required to use business leadership practices in solving applicable challenges, or it will just be a marketing tool? Leadem (2017) quoted Colin Angle, iRobot’s founder, and CEO in an Entrepreneur Magazine, “I have been able to remain CEO, not because of the fact I was CEO yesterday, but because I've worked very hard to listen, learn and evolve in the seat." Developing business leaders could be rooted in AI knowledge, applicability, challenges, and solutions while paying attention to the three keywords of listening, learning, and evolving in leadership.