Reviewerly/exHarmony huggingface dataset:
Avoiding Negative Side Effects due to Incomplete Knowledge of AI Systems

Abstract Autonomous agents acting in the real‐world often operate based on models that ignore certain aspects of the environment. The incompleteness of any given model – handcrafted or machine acquired – is inevitable due to practical limitations of any modeling technique for complex real‐world settings. Due to the limited fidelity of its model, an agent's actions may have unexpected, undesirable consequences during execution. Learning to recognize and avoid such negative side effects (NSEs) of an agent's actions is critical to improve the safety and reliability of autonomous systems. Mitigating NSEs is an emerging research topic that is attracting increased attention due to the rapid growth in the deployment of AI systems and their broad societal impacts. This article provides a comprehensive overview of different forms of NSEs and the recent research efforts to address them. We identify key characteristics of NSEs, highlight the challenges in avoiding NSEs, and discuss recently developed approaches, contrasting their benefits and limitations. The article concludes with a discussion of open questions and suggestions for future research directions.
Virtual Reality as a Media for Learn Animal Diversity for Students

In the educational process, students face various problems, one of which is the difficulty in understanding the material due to its complexity of the material. New technology-based media are being introduced that help students in the learning process, such as virtual reality (VR). This research aims to produce a product in the form of virtual reality development media as a learning medium for animal diversity. This type of research is developed using a 4D model design. The data collection method uses non-test with research instruments, namely expert validation questionnaires and user validation questionnaires. As well as data analysis techniques using descriptive analysis to describe the results of questionnaires distributed to experts, practitioners, and students as research subjects consisting of media experts, material experts, practitioners, and students. The results of the product feasibility tests carried out on media experts, material experts, and students get many positive responses. So it can be concluded that the virtual reality media of animal diversity is suitable for learning. This media makes it easy for students to understand the variety of animals and provides an attraction for students so that an increased desire to learn arises.
Semantic similarity analysis between the student's reports with the authoritative sources

Knowledge Building as a pedagogy supports collaborative work between students to improve ideas. The result of knowledge-building discourse in student communities is the development of academic artifacts. Student reports are academic artifacts that can result from a knowledge-building process during their learning. However, the automatic analysis of student reports is quite challenging due to the documents’ length and written language. Given this context, our goal is to analyze the semantic similarity between student reports and curriculum literature using the information coverage measure based on the Skip-gram word2vec model. Word2vec is a well-known set of techniques that provides a way to create a representation of words to predict the nearby words between every word and its context to capture the internal semantic and syntactic information. In this work, through the experimental comparison between the vocabulary list, the best similarity value is 0.90 using word2vec. The results show that each student report was aligned with each document in the curriculum literature using the information coverage measure based on word2vec.
Research is formalized curiosity: Use of technology in the classroom is that enough virtual reality project

There is always concern about the way technology is use in the classroom and if it really help the students learning process, some class could still be just learning concepts and remembering information with the use of a learning platform LMS to support teaching, in some cases consistent in using applications like Kahoot, Mentimeter, Quizlet, etc. with the sole porpoise of creating quizzes and exams, there are so many misconception about using technology in the classroom like that the use of it is solely for sciences classes, that a considerable investment in equipment has to be done, and that the teacher implementing it has to be a tech savvy. At Tecnológico de Monterrey by the participation in NOVUS Initiative for the financing of educational innovation projects was a path of discovery of discovered the importance of the interaction with other teachers, peers and students and the ability to discover new and feasible opportunities, to try to be inquiring and curious and be able to get out of the ordinary and of your comfort zone and most important that using technology is not always translate into students’ academic success or better learning and understanding. The first project with technology in the science class was to bring into the biology classroom 3D SHINECON virtual reality glasses compatible with iPhone, Android and Windows from 4.7 to 6 Inches to study Human Anatomy using different 3D Virtual Reality applications to make an impact on the results that the students obtained in a test for this subject. In relation to the grade of the quizzes applied, there was no significant difference between the control group and the groups that use de VR technology. The question that arouses weather the use of technology in the classroom was of some value for academic achievement? What went wrong? What needs to be modified to obtain our academic objectives?
From the perspective of students' needs: Improve the information construction under the smart campus in the Chinese Universities

With the development of science and technology and the impact of the COVID-19 in recent two years, the concept of the smart campus has gradually played an increasingly important role in universities of China, especially for students. Smart campus is defined as physical space and information space are organically connected, so that anyone, anytime and anywhere can easily obtain resources and services. To better enable students to benefit from the construction of the smart campus in the post-epidemic era, this paper investigates Chinese students and analyzes the data after the average classification by using the statistical method of the linear regression model. Finally, it discusses the relationship between the students' needs and the score of smart campus construction to put forward suggestions for the construction of the smart campus in universities in China
Action Recognition and Temporal Localization for Wushu Routine Competition

To pursue the unification and standardization of technical actions, promote the standardization of the wushu competition scoring system, and help wushu enter the Olympic Games, this paper adopts a method based on computer vision algorithms for action recognition and localization of the actions of the international long fist routine v3, and the results show that the application of this method can better slice and dice action clips from the original video. Combining this method with the competition scoring rules, it is promising to create an automatic scoring referee system to automate the scoring of martial arts routine competitions.
PCKT: Problem Composition in Knowledge Tracking

With the rapid development of online education, personalized education becomes more and more important. The emergence of knowledge tracking (KT) has made a significant breakthrough in personalized education. In recent years, researchers have achieved some good results by building the KT model to search information and relationships between problems and skills. However, in reality, problems are sometimes similar. The skills may be exactly the same between two problems, but only the data is different. Therefore, it is extremely important to take this into account when making predictions. To address this problem, we construct a Problem Composition Knowledge Tracking Model (PCKT). In PCKT, problems are devided into corresponding data and skills, and connected by certain relation. Experiments demonstrated our PCKT achieved state-of-the-art results on two public datasets.
The Predictive Effect of Eye-Movement Behavior on Reading Performance in Online Reading Contexts

Educational data collection and analysis based on Webcam's non-invasive eye-tracking technology is important for understanding complex cognitive processes. This study investigates how eye-movement behavior during reading predicts reading performance. In this study, eye-movement data were collected from 32 learners while reading and answering questions based on an online reading platform through an experimental method. The researchers grouped learners based on whether they scored on each topic separately. Based on this, the researcher investigated the basic performance of eye-movement and the predictive effect on reading performance on each question. The conclusions of the study are as follows. (a) A number of some key eye-movement indicators can have better reading prediction. It is important to select appropriate and meaningful eye-movement indicators based on the research questions. (b) The division of scored and unscored groups based on different question is carried out separately to achieve a more fine-grained analysis. In the future study, the combination of NLP can be considered to deepen the mining between behavior and cognition in the reading process.
Design and Implementation of the Teacher-student Dialogue Automatic Speech Recognition Tool Based on Classroom Verbal Characteristics

Speech behavior is a tool for teachers and students to express and communicate in the classroom, and analyzing the content of classroom teacher-student dialogue is one of the key basic technologies to break through the automatic analysis of teaching videos. However, the current discourse research tools rely on the universal speech recognition cloud platform, and there are problems such as confusion between teacher-student role and unclear discourse boundaries for classroom teacher-student dialogue recognition. Therefore, this paper designs and develops teacher-student dialogue recognition tools after classifing and encoding the auditory information data and summarizing the general rules of speech characteristics in classroom teaching videos. To achieve the purpose of analyzing classroom teaching videos, education researchers can use the tool to intercept teaching videos, obtain boundary points of teacher-student dialogue, and recognize their speech content. Experimental results show that the tool has stable performance, which can quickly and accurately process audio signals, segment and cluster teacher and student voiceprint features. Finally, it produce structured text containing the time point of the discourse boundary, the teacher-student role label and the discourse content, which will provide data support for analyzing further classroom teaching video behavior.
Satisfaction with virtual simulation learning and academic performance in the context of COVID-19 in nursing students at a public university - Peru

Universities worldwide had to close their doors due to the Covid-19 health emergency, and from face-to-face classes go to the virtual modality, being the theoretical/practical nursing career to carry out the virtual simulation, through their homes. The objective of the research was to describe the satisfaction of virtual simulation learning and academic performance, in the context of Covid-19, in nursing students at a Public University-2021. The survey was applied to 186 students of the Faculty of Nursing, of the semesters: III, IV, VII and VIII of the Public University of Ica-Peru. It was a mixed approach study, applying a 15-question Likert scale questionnaire, presenting the physical format. It is concluded that young students are satisfied with virtual simulation learning and it is not related to the grades obtained by a group in their academic performance.
Perceptions of MLearning Student Generated Activities for STEM in Educational Paradigm at Malaysian Universities

The emergence of mobile learning, or mLearning, gives students hope to become more motivated to learn STEM by bringing their personal mobile devices. This study investigated Malaysian students' STEM mobile learning experiences through student-generated activities in learning STEM. STEM has the lowest uptake for Malaysian students as the subjects are difficult to understand. Therefore, this study embarked on the methodology, which was mainly qualitative in order to have an in-depth inquiry through focus group discussions with students from universities in Malaysia. The findings were themes developed from the data gained prior to student-generated activities. Next, the themes that emerged after the student-generated activities. Finally, mLearning best practices for STEM had been proposed for student-generated activities. Likewise, the main contribution of this study was insights from the Malaysian universities and lecturers’ perspectives for mLearning student-generated activities in STEM subjects. The implications of this study are the best practices for mLearning at Malaysian universities.
Research and Practice of SQL-OJ based on blockchain for the second class of database course

The second class has become an important part of practical teaching in colleges, which plays an important role in improving students' creative and practical skills. In view of the existing problems in the implementation of the second class, this paper takes the database course as an example and builds a SQL-OJ practice platform based on blockchain, which can trace the learning of students in the second class and enable autonomous management of grades based on smart contract. The teaching practice verifies that the combination of blockchain technology and the second class practice platform can improve the reliability of the platform information and can motivate students, which is an effective solution for the second class practice.
An Approach to Digital Skills and Educational Practices in the Faculty of Education of the Corporación Universitaria Minuto De Dios, Bogotá Headquarters – Presential

The article corresponds to the project called "Incidence of Digital Competences in the Educational Practice of Teachers of the Faculty of Education of the Minuto de Dios University Corporation Headquarters" whose objective is to recognize the institutional culture in digital competencies of the teachers that make up the Faculty of Education. It is a cross-sectional study in a non-experimental design, 145 responses were obtained to the "CheckIn" form of the teaching digital competence framework "DigCompEdu". The results of the characterization show that 77% of teachers are in the highest categories of the checkIn (B2 - 49% and C1 28%), from this the idea of objectively evaluating their performance arises, considering that the applied instrument is self-diagnostic. A semi-structured interview was designed to compare the results in the next phase of the project.
On the Use of the Matrix-Variate Tail-Inflated Normal Distribution for Parsimonious Mixture Modeling

Recent advances in the matrix-variate model-based clustering literature have shown the growing interest for this kind of data modelization. In this framework, finite mixture models constitute a powerful clustering technique, despite the fact that they tend to suffer from overparameterization problems because of the high number of parameters to be estimated. To cope with this issue, parsimonious matrix-variate normal mixtures have been recently proposed in the literature. However, for many real phenomena, the tails of the mixture components of such models are lighter than required, with a direct effect on the corresponding fitting results. Thus, in this paper we introduce a family of 196 parsimonious mixture models based on the matrix-variate tail-inflated normal distribution, an elliptical heavy-tailed generalization of the matrix-variate normal distribution. Parsimony is reached by applying the well-known eigen-decomposition of the component scale matrices, as well as by allowing the tailedness parameters of the mixture components to be tied across groups. An AECM algorithm for parameter estimation is presented. The proposed models are then fitted to simulated and real data. Comparisons with parsimonious matrix-variate normal mixtures are also provided.
The Italian Social Mood on Economy Index During the Covid-19 Crisis

Since 2016, Istat has published the Social Mood on Economy Index (SMEI), an experimental high-frequency sentiment index derived from public tweets in Italian. Since the economic shock produced by the Covid-19 pandemic has not significantly affected the SMEI series, we wondered to what extent the SMEI could grasp the change in the mood due to the pandemic. We produced alternative sentiment indicators, and we compared them to nontraditional high-frequency series to assess the coherence of the SMEI. An alternative index, calculated by introducing pandemic-related terms in the lexicon used for sentiment analysis, better grasped the negative economic trend during the pandemic. We concluded that a continuous adaptation of the dictionary in lexicon-based techniques could improve the coherence.
Soft Action Priors: Towards Robust Policy Transfer

Despite success in many challenging problems, reinforcement learning (RL) is still confronted with sample inefficiency, which can be mitigated by introducing prior knowledge to agents. However, many transfer techniques in reinforcement learning make the limiting assumption that the teacher is an expert. In this paper, we use the action prior from the Reinforcement Learning as Inference framework - that is, a distribution over actions at each state which resembles a teacher policy, rather than a Bayesian prior - to recover state-of-the-art policy distillation techniques. Then, we propose a class of adaptive methods that can robustly exploit action priors by combining reward shaping and auxiliary regularization losses. In contrast to prior work, we develop algorithms for leveraging suboptimal action priors that may nevertheless impart valuable knowledge - which we call soft action priors. The proposed algorithms adapt by adjusting the strength of teacher feedback according to an estimate of the teacher's usefulness in each state. We perform tabular experiments, which show that the proposed methods achieve state-of-the-art performance, surpassing it when learning from suboptimal priors. Finally, we demonstrate the robustness of the adaptive algorithms in continuous action deep RL problems, in which adaptive algorithms considerably improved stability when compared to existing policy distillation methods.
Automated patent classification for crop protection via domain adaptation

Abstract Patents show how technology evolves in most scientific fields over time. The best way to use this valuable knowledge base is to use efficient and effective information retrieval and searches for related prior art. Patent classification, that is, assigning a patent to one or more predefined categories, is a fundamental step towards synthesizing the information content of an invention. To this end, architectures based on Transformers, especially those derived from the BERT family have already been proposed in the literature and they have shown remarkable results by setting a new state‐of‐the‐art performance for the classification task. Here, we study how domain adaptation can push the performance boundaries in patent classification by rigorously evaluating and implementing a collection of recent transfer learning techniques, for example, domain‐adaptive pretraining and adapters. Our analysis shows how leveraging these advancements enables the development of state‐of‐the‐art models with increased precision, recall, and F 1‐score. We base our evaluation on both standard patent classification datasets derived from patent offices‐defined code hierarchies and more practical real‐world use‐case scenarios containing labels from the agrochemical industrial domain. The application of these domain adapted techniques to patent classification in a multilingual setting is also examined and evaluated.
Sequential and parallel sliding window algorithms for multiplying large integers

The cost of some primitive operations such as multiplication and division significantly affects the running time of some applications in science and engineering especially when the size of data is big. In this study, we propose a formula to minimize the cost of the multiplication operation on integer numbers when the size of each integer and the number of integers to be multiplied are large. Based on a window strategy, we derived, theoretically, the best size of window for minimizing the cost of multiplying a sequence of big integers. To the best of our knowledge, no previous studies have proposed this formula. Then we designed three efficient algorithms, one sequential and two parallel. Experimental studies were conducted using three parameters: (1) the size of integer, (2) the size of a sequence, and (3) the number of threads. The results of a comparison with the best known previous algorithms showed that (1) the designed sequential algorithm is faster than the previous algorithms with an improvement greater than 64%, (2) the designed parallel algorithms are faster than the best known parallel algorithm with an improvement greater than 50% on the average, and (3) the designed parallel algorithms have super linear speed up.
Robustly Learning General Mixtures of Gaussians

This work represents a natural coalescence of two important lines of work — learning mixtures of Gaussians and algorithmic robust statistics. In particular, we give the first provably robust algorithm for learning mixtures of any constant number of Gaussians. We require only mild assumptions on the mixing weights and that the total variation distance between components is bounded away from zero. At the heart of our algorithm is a new method for proving a type of dimension-independent polynomial identifiability — which we call robust identifiability — through applying a carefully chosen sequence of differential operations to certain generating functions that not only encode the parameters we would like to learn but also the system of polynomial equations we would like to solve. We show how the symbolic identities we derive can be directly used to analyze a natural sum-of-squares relaxation.
Learning to Track Multiple Radar Targets with Long Short-Term Memory Networks

Radar multitarget tracking in a dense clutter environment remains a complex problem to be solved. Most existing solutions still rely on complex motion models and prior distribution knowledge. In this paper, a new online tracking method based on a long short-term memory (LSTM) network is proposed. It combines state prediction, measurement association, and trajectory management functions in an end-to-end manner. We employ LSTM networks to model target motion and trajectory associations, relying on their strong learning ability to learn target motion properties and long-term dependence of trajectory associations from noisy data. Moreover, to address the problem of missing appearance information of radar targets, we propose an architecture based on the LSTM network to calculate similarity function by extracting long-term motion features. And the similarity is applied to trajectory associations to improve their robustness. Our proposed method is validated in simulation scenarios and achieves good results.