Reviewerly/exHarmony huggingface dataset:
GAN-CL: Generative Adversarial Networks for Learning From Complementary Labels

Learning from complementary labels (CLs) is a useful learning paradigm, where the CL specifies the classes that the instance does not belong to, instead of providing the ground truth as in the ordinary supervised learning scenario. In general, although it is less laborious and more efficient to collect CLs compared with ordinary labels, the less informative signal in the complementary supervision is less helpful to learn competent feature representation. Consequently, the final classifier's performance greatly deteriorates. In this article, we leverage generative adversarial networks (GANs) to derive an algorithm GAN-CL to effectively learn from CLs. In addition to the role in original GAN, the discriminator also serves as a normal classifier in GAN-CL, with the objective constructed partly with the complementary information. To further prove the effectiveness of our schema, we study the global optimality of both generator and discriminator for the GAN-CL under mild assumptions. We conduct extensive experiments on benchmark image datasets using deep models, to demonstrate the compelling improvements, compared with state-of-the-art CL learning approaches.
Efficient photometric and geometric stitching approach for remote sensing images based on wavelet transform and local invariant

Image stitching is an active research area in many photogrammetric and remote sensing applications. The main contribution of our work is to regard stitching as a multi-image matching problem that exploits advantages of invariant local features and homographic transformation to overcome photometric, geometric, and perspective distortions. Our proposed approach, referred to as Haar Invariant Feature with PROSAC, consists of three main steps. The first step extracts a set of independent keypoints by an adaptive local feature extractor based on Haar wavelet transform and scale invariant feature transform (SIFT). Then to increase the precision of matched points, we propose to substitute classical matching algorithms by a fast Haar-k-nearest neighbor algorithm and estimate the homography matrix using the Progressive Sample Consensus algorithm. Finally, we warp images according to the projective transformation and blend them with a Laplacian multi-band to create a perfect panorama. Experiments on various images show the effectiveness and the robustness of our approach compared to common image matching techniques such as SIFT, speeded up robust features, optimized random sample consensus with contrario, and image matching by affine simulation.
Learning Logical Reasoning : Improving the Student Model with a Data Driven Approach

In our previous works, we presented Logic-Muse as an Intelligent Tutoring System that helps learners improve logical reasoning skills in multiple contexts. Logic-Muse components were validated and argued by experts throughout the designing process (ITS researchers, logicians and reasoning psychologists). A Bayesian network with expert validation has been developed and used in a Bayesian Knowledge Tracing (BKT) process that allows the inference of the learner’s behaviour. This paper presents an evaluation of the learner components of Logic-Muse. We conducted a study and collected data from nearly 300 students who processed 48 reasoning activities. This data was used in the development a psychometric model, a key element for initializing the learner’s model and for validating and improve the structure of the initial Bayesian network built with experts.
Knowledge and Practice Changes Following a Student Data-Focused Data Management Education Program

Introduction: It is known that graduate students work with research data more intimately than their faculty mentors. Because of this, much data management education is geared toward this population. However, student learning has predominantly been assessed through measures of satisfaction and attendance rather than through evaluating knowledge and skills acquired. This study attempts to advance assessment efforts by asking students to report their knowledge and practice changes before, immediately after, and 6 months following education. Method: Graduate students in science, technology, engineering, and mathematics (STEM) and health science disciplines self-enrolled in an 8-week pilot data management program that used their research projects as the focus for learning. Three surveys were administered (pre-, post-, and 6 months following) to determine changes in students’ knowledge and practices regarding data management skills through self-assessment. The survey consisted of approximately 115 Likert-style questions and covered major aspects of the data life cycle. Results and Discussion: Overall, students increased their data management knowledge and improved their skills in all areas of the data life cycle. Students readily adopted practices for straightforward tasks such as determining storage and improving file naming. Students improved in but struggled with tasks that were more involved, such as sharing data and documenting code. For most of these practices, students consistently implemented them through the 6-month follow-up period. Conclusion: Impact of data management education lasts significantly beyond immediate instruction. In-depth assessment of student knowledge and practices indicates in which areas this education is effective and in which areas it needs further support. It is likely that this effect is due to the program length and focus on implementation.
Mobile Applications for Pandemic Monitoring : Approaches, Challenges and Opportunities

In the context of the COVID-19 pandemic, mobile applications have been widely used and promoted to help prevent the spread of the pandemic. Many countries have proposed these applications with the aim of improving contact tracing and thus helping "flatten the curve" of infection case numbers. However, even though there were many campaigns to promote the use of these apps, their adoption was relatively low, which significantly affected their success. The success of COVID-19 contact tracing apps, in terms of its massive use and effectiveness, seems to have been deeply affected by issues associated with privacy and anonymity and the perception that its potential users have about the cost/benefit. In this paper, we analyze some applications for monitoring and contact tracing of infectious diseases to identify the most common approaches and features. We then proceed to discuss the challenges and risks that may influence the success of this type of applications, and we present some recommendations that can contribute to improve their acceptance. Considering the proposed recommendations, we describe the main features and the development of an app that includes additional functionalities alongside the contact tracing feature, in order to increase user perceived value from the use of the application.
Blockchain Technologies in the Educational Sector: a Reflection on the Topic in the Middle of the Covid-19 Situation

This paper looks at current projects in the field of Blockchain in education, their specific areas of application, possible advantages and weaknesses. Three examples developed by the team of authors are introduced in detail. First: Gallery-Defender a Serious Game, which was adapted to serve as a demonstrator in a stand-alone version to show the possibility to carry out exams directly from within the game and store the grades and meta-data on Blockchain. Second: Art-Quiz, an e-learning tool, which can be integrated into existing LMS systems and map exam results and further data using Blockchain technologies. Both were developed following an iterative design process. And third: The results of a focus group, which simulated the assignment of grades after an oral online exam. The three examples presented here are based on the Blockchain system Ardor/Childchain Ignis, but each demonstrator has a different set of features and approaches.In addition, the integration of various Blockchain solutions was conceptually designed to make a Multi-Chain model possible.
An introduction to the orthonormal wavelet transform on discrete sets

In this paper z-transform theory is used to develop the discrete orthonormal wavelet transform for multidimensional signals. The tone is tutorial and expository. Some rudimentary knowledge of z-transforms and vector spaces is assumed. The wavelet transform of a signal consists of a sequence of inner products of a signal computed against the elements of a complete orthonormal set of basis vectors. The signal is recovered as a weighted sum of the basis vectors. This paper addresses the necessary and sufficient conditions that such a basis must respect. An algorithm for the design of a proper basis is derived from the orthonormality and perfect reconstruction conditions. In the interest of simplicity the case of multidimensional signals is treated separately. The exposition lays bare the structure of hardware and software implementations.
Prospects For the Introduction of Artificial Intelligence Technologies in Higher and General Education

At present, comprehensive research on the implementation of artificial intelligence technologies is of particular relevance. The article is devoted to the implementation of artificial intelligence technologies in the field of education. The perspective directions of using artificial intelligence in the sphere of higher and general education are considered and analyzed. The article actualizes the problem of implementing artificial intelligence technologies in teaching students and schoolchildren. The purpose of the article is to reveal the specifics of the use of artificial intelligence technologies in the field of education. The study is theoretical in nature and includes an analysis of the possibilities of using artificial intelligence technologies at different levels of education. The program of the discipline “Artificial Intelligence Technologies”, developed in the areas of Teacher education training at the Novosibirsk State Pedagogical University, is presented. The possibilities of studying artificial intelligence technologies using the Python language in a school computer science course are considered, which will allow you to move to a new level of learning programming both in schools and in higher education.
Estimating Distributions of Large Graphs from Incomplete Sampled Data

We study the problem of how to estimate the latent in-degree distribution of large directed graphs from random samples, when the samples only indicate the presence of partial incoming edges into nodes and thus their sampled distribution is far from the original one. While this problem can be cast as an inverse problem, it often appears to be ill-posed and leads to poor estimation performance. There have thus been few recent studies to overcome this problem, which include a constrained, penalized weighted least squares estimator and an asymptotic estimator. The recent estimators, however, are computationally expensive or only limited to estimating the tail distribution, and their performance may not be satisfactory. In this paper, we formulate the problem as a maximum-likelihood estimation problem. We then employ the expectation-maximization algorithm to solve this problem and derive a simple iterative estimator, which is easy to implement and computationally fast. Finally, we empirically demonstrate that our estimator is significantly more accurate than the state-of-the-art estimators and it can also be further improved with a proper choice of its parameter.
Support vector machine for the implementation of the fitness function of genetic algorithms

A good number of search and optimization problems, computationally speaking, fall within the "intractable problems" and their complexity is of the exponential order. In this type of problem, genetic algorithms are an excellent alternative to find solutions close to the optimum. One of the phases of the genetic algorithms requires a fitness function, which will allow to select the individuals of the next generation. This aptitude function is generally implemented based on mathematical expressions or functions. However, some of the problems that can be solved with genetic algorithms present the difficulty of requiring subjective information to define the fitness function; above all, when the individual selection process requires an assessment conditioned by sensory aspects or experience. In these cases, defining the fitness function mathematically presents a lot of complexity or becomes unfeasible. The present work aims to find computer solutions to this type of problem, embedding the support vector machines in the implementation of the fitness functions of the genetic algorithms.
Development of Non-Player Character for 3D Kart Racing Game Using Decision Tree

Racing game is one of the genre that’s still popular today. Unity is one of many game engines one can use to develop a racing game. At Unity Asset Store, there is a free template called Micro-Game Karting which can only be played alone. In order to play player versus enemy mode, an artificial intelligence (AI) is needed for directing non-player character (NPC) who acts as the opponent. In racing game, the AI requires the use of movement algorithm and decision making system. For this study, the movement algorithm will use pathfinding. The algorithm is used as a guiding path when NPC is moving and avoiding obstacles in the way. Pathfinding will use waypoint system and raycasting to accomplish it. The decision making technique that will be used is decision tree. It functions as decision maker for NPC so it can determine the correct action to be done at certain time. Result of black box and white box testing showed NPC is functional. As for FPS (frame per second) test, performance suffers 0.2-0.3 FPS decrease for every addition of 2 NPCs. According to lap time test, the developed NPC is faster than ML NPC and driving test showed favorable outcome.
Solving “Limited” Task Allocation Problem for UAVs Based on Optimization Algorithms

With the rapid development of science and technology, unmanned technology has been widely used in many fields. One of the most important applications is in the field of civil and military UAVs. In the field of military UAVs (unmanned aerial vehicles), UAVs usually have to complete a series of tasks. In this series of tasks, there are often some key tasks. Key tasks play an important role, which is highly related to the feasibility of the whole action or task; mission failure sometimes causes incalculable damage. When assigning tasks to UAVs, it is necessary to ensure the accurate implementation of key tasks, so as to ensure the orderly implementation of the overall task. This paper not only successfully solved the previous problems but also comprehensively considered the minimization of resource consumption and the maximization of task revenue in the process of UAV task allocation. On the basis of considering the key system, considering the constraints and multiobjective problems in the UAV task allocation process, the violence allocation algorithm, constraint optimization evolutionary algorithm, PSO algorithm, and greedy algorithm combined with a constraint evolutionary algorithm are improved and optimized; it has been proven that they can solve the above difficulties. At the same time, several comparison experiments have been carried out; the performance and conclusion of the above four algorithms in the “limited” UAV task allocation scheme are analyzed in the experimental part.
The future of work in exploration: A new model to bring people, technology and data together

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Problems of nonparametric goodness-of-fit test application in tasks of measurement results processing

It is argued that in most cases two reasons underlie the incorrect application of nonparametric goodness-of-fit tests in various applications. The first reason is that when testing composite hypotheses and evaluating the parameters of the law for the analyzed sample, classical results associated with testing simple hypotheses are used. When testing composite hypotheses, the distributions of goodness-of-fit statistics are influenced by the form of the observed law F(x, q) corresponding to the hypothesis being tested, by the type and number of estimated parameters, by the estimation method, and in some cases by the value of the shape parameter. The paper shows the influence of all mentiomed factors on the distribution of test statistics. It is emphasized that, when testing composite hypotheses, the neglect, of the fact that the test has lost the property of “freedom from distribution” leads to an increase in the probability of the 2nd kind errors. It is shown that the distribution of the statistics of the test necessary for the formation of a conclusion about the results of testing a composite hypothesis can be found using simulation in an interactive mode directly in the process of testing. The second reason is associated with the presence of round-off errors which can significantly change the distributions of test statistics. The paper shows that asymptotic results when testing simple and composite hypotheses can be used with round -off errors D much less than the standard deviation s of the distribution law of measurement errors and sample sizes n not exceeding some maximum values. For sample sizes larger than these maximum values, the real distributions of the test statistics deviate from asymptotic ones towards larger statistics values. In such situations, the use of asymptotic distributions to arrive at a conclusion about the test results leads to an increase in the probabilities of errors of the 1st kind (to the rejection of a valid hypothesis being tested). It is shown that when the round-off errors and s are commensurable, the distributions of the test statistics deviate from the asymptotic distributions for small n. And as n grows, the situation only gets worse. In the paper, changes in the distributions of statistics under the influence of rounding are demonstrated both when testing both simple and composite hypotheses. It is shown that the only way out that ensures the correctness of conclusions according to the applied tests in such non-standard conditions is the use of real distributions of statistics. This task can be solved interactively (in the process of verification) and rely on computer research technologies and the apparatus of mathematical statistics.
Object Detection using OpenCV and Deep Learning

Object Detection using SSD (Single Shot Detector) and MobileNets are efficient because this technique detects objects quickly with less resourses without sacrificing performance. In this every class of item for which the classification algorithm has been trained generates a bounding box and an annotation describing that class of object. This provides the foundation for creating several types of analytical features such as the volume of traffic in a certain area over time or the entire population in an area is real-time detection and categorization of objects from video data.
HUBUNGAN ANTARA KECERDASAN INTRAPERSONAL DENGAN KECERDASAN EMOSIONAL

Intrapersonal intelligence is a part of emotional intelligence because emotional intelligence has two abilities: intrapersonal skills and interpersonal skills. Therefore, emotional intelligence, it has five elements that are composite rather than intrapersonal intelligence and interpersonal intelligence. Five elements are self-awareness, self-settings, motivation, empathy, and social. The first three elements are part of intrapersonal intelligence, self-awareness, self-settings, and motivation, while the latter two are part of interpersonal intelligence, empathy, and social. Intelligence here is shrewdness and skill.
Case Based Reasoning using K-Nearest Neighbor with Euclidean Distance for Early Diagnosis of Personality Disorder

A personality disorder is a condition of a person with an extreme personality that causes the sufferer to have unhealthy and different thoughts patterns and behavior from other people. The personality disorders discussed in this study consisted of 110 diseases with 300 case data and 68 symptoms. Based on Basic Health Research (Riskesdas) 2018 data, it shows that more than 19 million people aged 15 years and over were affected by mental-emotional disorders. Data from the Statistics Indonesia in 2019 that the population of Indonesia is around 265 million people, while according to the Indonesian Clinical Psychologist Association, the number of verified professional psychologists is 1,599 clinical psychologists out of a total membership of 2,078 as of January 2019. However, this figure does not meet the standards of the World Health Organization (WHO), which is that psychologists serve 30 thousand people. This shows that Indonesia still lacks around 28,970 psychologists. The unequal distribution of professional psychologists has made psychologists need a long time to provide a diagnosis because of the number of patients being inversely proportional to the availability of psychologists in Indonesia. Moreover, there is not enough patient knowledge about the symptoms they feel. This study aims to produce a system for diagnosing personality disorders. This study is a case based reasoning to solve problems that have occurred in previous cases using K-Nearest Neighbor to classify data based on the closest distance using the calculation of the Euclidean Distance. Algorithm testing for the system used the Confusion Matrix test. Based on the results of testing data in the 60 case data using K-nearest Neighbor and the calculation of the Euclidean Distance with a score of K=3, it is known that 60 data have 100% similarity to cases with a personality disorder. Meanwhile, testing new cases with 10 case data that were not in the knowledge base was also conducted showing that 9 cases had 100% similarity to the previous case, while another case had 90% similarity to the previous case.
Missing the Audience. Online Musicking in Times of COVID-19 / Missing the Audience. Online Musicking in Times of COVID-19

The outbreak of COVID-19 has greatly impacted musicians and orchestras. While many orchestras had ample experience in producing and distributing their concerts online, others now had to experiment with new ways of performing online. But how to perform classical music without the rituals and routines of the concert hall? And what skills are required for musicians and orchestras to engage in online musicking? Based on our fieldwork during the experiment Online Musicking, which took place during the first lockdown in the Netherlands in 2020, we explicate the hidden work that goes into producing and distributing a collaborative classical music video, regarding performance qualities, audience participation, and societal relevance. The case of Online Musicking shows the challenges of shaping a digital offer for online audiences, and the value of experimentation for orchestras, which are finding new ways to produce and distribute.