Reviewerly/exHarmony huggingface dataset:
MathCityMap Application in Mathematics Learning in Primary School

&lt;p&gt;&lt;em&gt;The background of this research is that the use of digital technology in learning mathematics in schools has not been optimal. The purpose of this research is to improve mathematics learning outcomes by utilizing technology through the MathCityMap application in teaching geometry in elementary schools. Methods This research uses classroom action research which is carried out in two cycles&lt;/em&gt;&lt;strong&gt; &lt;/strong&gt;&lt;em&gt;The subject was a population of 68 grade five students in an elementary school in West Java, using descriptive statistics. Data collection techniques using questionnaires, observations, tests, and, documentation studies. The results of this study indicate an increase in mathematics learning outcomes. In pre-cycle 58.05, it increased in cycle 1 to 72.03 and increased again in cycle 2 to 89.60.The results of observations show that the use of digital technology has the potential to support teachers in facilitating the teaching and learning process of mathematics outdoors so that students gain direct mathematical experience and deal with the real world. Based on these results, MathCityMap is proven to be able to improve mathematics learning outcomes for elementary school students, especially in teaching geometry.&lt;/em&gt;&lt;/p&gt;
Analysis and Discrimination of Insurance Fraud based on Data Mining

Insurance is an important component of the financial system, playing an important role in social stability and ensuring people's livelihoods. With the vigorous develop-ment of the insurance industry this year, automobile insurance fraud has been a fre-quent occurrence. This article achieves effectiv
Study on Strategies for Improving Rural Teachers' Information Technology Application Ability

With the extensive application of information technology in the field of education, the modern education model is developing rapidly in the direction of digitalization and intelligence. However, compared with urban schools, there is a significant lag in technology integration and application in rural education. Due to the limitations of geography, resources and economic conditions, rural teachers are facing the challenge of improving their ability to apply information technology. This not only affects the teaching effect, but also concerns the overall development of rural students and the overall quality of rural education. Based on this, this paper studies the strategies for improving the ability of rural teachers to apply information technology, in order to promote the informatization process of rural education, ensure educational equity, and help the sustainable development of rural education.
Multivariate Analysis ANOVA Based Distribution of Nitrogen Dioxide in Lampung Barat And Way Kanan Regencies

The rise of construction projects and population growth, followed by the expansion of industry and any other sectors such as housing estate, offices, and transportation, can lead to not only economic growth but also negative impacts such as air pollution, including nitrogen dioxide (NO2) pollutants. According to KEP-45/MENLH/10/1997, air pollution can be determined by The Pollutant Standards Index (PSI). The statistical analysis result employing the multivariate ANOVA test with the Anderson-Darling method showed the impact of NO2 concentrations on four location points over two years. Meanwhile, the data result of PSI revealed a high level of ambient air NO2 in Lampung Barat Regency, particularly at the monitoring point of offices. On the other hand, in Way Kanan Regency, the level of ambient air NO2 tends to be safe and secure at the monitoring point of industry and transportation. The factor of sampling location and technique (stage 1: dry season, stage 2: rainy season) did not give a significant effect on the NO2 level due to a p-value ≥ 0,05. However, the sampling point (of monitoring) influenced the NO2 level for p-value ≤ 0,05 in Lampung Barat and Way Kanan Regency in 2019-2020 which was still claimed as safe and secure based on PP No. 41 in the Year 1999, which sets the required limit at 100 g/m3.
An Analysis on Indonesian—English Abstract Translation to Enhance Multicultural Language in Accounting Department, State Polytechnic of Malang

Thesis writing is one compulsory subject which must be completed by students at State Polytechnic of Malang particularly in Accounting Department. Moreover, an abstract is an inseparable part of thesis which must be written by all students. The abstracts must be written both in Indonesian and English. Students have to translate their Indonesian abstract into English. From the preliminary study, it was found out that students have many difficulties in translating their thesis abstract from Indonesian into English. Hence, this research seeks to investigate what type of errors committed by the students during the process of English abstract writing. A descriptive qualitative design was used in this research. Documents in the form of 25 abstracts were used in the data analysis. The results showed that during the process of English abstract writing completion, students tend to have problems in abstract translation, grammar, vocabularies, sentence structures and writing mechanics. Detailed results were presented and discussed in the coming sections, followed by recommendations for further research.&#x0D; Keywords: abstract writing, translation, machine translation, Google Translate
ACM SIGMM Executive Committee Newsletter - 1, 2022

The Special Interest Group in Multimedia of ACM, ACM SIGMM, provides a forum for researchers, engineers, and practitioners in all aspects of multimedia computing, communication, storage, and applications. We do this through our sponsorship and organization of conferences and workshops, supporting student travel to such events, discounted registrations, two regional chapters, recognition of excellence and achievement through an awards scheme, and we inform the Multimedia community of our activities through the SIGMM Records, social media and through mailing lists. Information on joining SIGMM can be found at https://www.acm.org/special-interest-groups/sigs/sigmm.
Low-rank multi-label learning based on nonlinear mapping

The relationship between features and labels, as well as the correlation between labels are two important factors that affect multi-label learning. Many existing studies assume that there is a linear mapping between sample space and label space. However, the linearity assumption often does not hold in practical applications, especially when characterizing the relationship between two high-dimensional spaces. In this paper, a nonlinear mapping is used to describe the relationship between features and labels by mapping the original labels to a low-rank latent label space. With the help of nonlinear mapping, more compact and accurate label information can be mined, effectively overcoming the defect that linear models cannot fit real labels. In particular, the label space is firstly decomposed into a low-rank latent space to reduce the dimension of labels, and then a nonlinear mapping from the sample space to the low-rank latent space is constructed. Considering that the predicted values of similar labels should be closer, a comprehensive manifold regularization term is introduced to learn local and global label-correlation. The proposed multi-label learning method can effectively solve the problem of missing multi-label learning. The experimental results on ten multi-label databases shows that the proposed method has good robustness and is superior to the most advanced multi-label learning methods.
Leveraging Reward Consistency for Interpretable Feature Discovery in Reinforcement Learning

The black-box nature of deep reinforcement learning (RL) hinders them from real-world applications. Therefore, interpreting and explaining RL agents have been active research topics in recent years. Existing methods for post-hoc explanations usually adopt the action matching principle to enable an easy understanding of vision-based RL agents. In this article, it is argued that the commonly used action matching principle is more like an explanation of deep neural networks (DNNs) than the interpretation of RL agents. It may lead to irrelevant or misplaced feature attribution when different DNNs’ outputs lead to the same rewards or different rewards result from the same outputs. Therefore, we propose to consider rewards, the essential objective of RL agents, as the essential objective of interpreting RL agents as well. To ensure reward consistency during interpretable feature discovery, a novel framework (RL interpreting RL, denoted as RL-in-RL) is proposed to solve the gradient disconnection from actions to rewards. We verify and evaluate our method on the Atari 2600 games as well as Duckietown, a challenging self-driving car simulator environment. The results show that our method manages to keep reward (or return) consistency and achieves high-quality feature attribution. Further, a series of analytical experiments validate our assumption of the action matching principle’s limitations.
A New Data Augmentation Method Based on Mixup and Dempster-Shafer Theory

To improve the performance of deep neural networks, the Mixup method has been proposed to alleviate their memorization issues and sensitivity to adversarial samples. This provides networks with better generalization abilities. The learning principle of Mixup is essentially to train deep neural networks for regularization tasks with a convex combination of the original feature vectors and their labels. However, soft labels are generated directly using the mixing ratio without dealing with the uncertain information generated during the mixing process. Therefore, this paper proposes a new data augmentation method based on Mixup and Dempster-Shafer theory called DS-Mixup, which is a regularizer that can express and deal with the uncertainty caused by ambiguity. This method uses interval numbers to generate mass functions of mixed samples to model the distribution of set-valued random variables; then, ambiguous decision spaces are constructed, and soft labels with single-element subsets and multielement subsets are generated to further improve the delineation of decision boundaries during the training process. In addition, an evidence neural network with DS-Mixup is designed in this paper to accomplish recognition or classification tasks. Experimental results obtained on multimedia datasets, including attribute, image, text and signal data, show that the proposed method achieves more effective data augmentation effects and further improves the performance of deep neural networks.
Simple reality [Editorial]

Augmented reality (AR) and virtual reality (VR) have played an important role in our society. AR aims to improve our perception of reality, while VR aims to create a new or artificial reality. There is no doubt that the results achieved in these two areas are remarkable. It is, however, quite surprising that our understanding of reality is not yet complete. In fact, a better understanding of reality will be essential for future developments in these areas.
Focused Contrastive Loss for Classification With Pre-Trained Language Models

Contrastive learning, which learns data representations by contrasting similar and dissimilar instances, has achieved great success in various domains including natural language processing (NLP). Recently, it has been demonstrated that incorporating class labels into contrastive learning, i.e., supervised contrastive learning (SCL), can further enhance the quality of the learned data representations. Although several works have shown empirically that incorporating SCL into classification models leads to better performance, the mechanism of how SCL works for classification is less studied. In this paper, we first investigate how SCL facilitates the classifier learning, where we show that the contrastive region, i.e., the data instances involved in each contrasting operation, has a crucial link to the mechanism of SCL. We reveal that the vanilla SCL is suboptimal since its behavior can be altered by variances in class distributions. Based on this finding, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fo</b> cused <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ontrastive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> oss (FoCL) for classification. Compared with SCL, FoCL defines a finer contrastive region, focusing on the data instances surrounding decision boundaries. We conduct extensive experiments on three NLP tasks: text classification, named entity recognition, and relation extraction. Experimental results show consistent and significant improvements of FoCL over strong baselines on various benchmark datasets, especially in few-shot scenarios.
Katalogizacija u publikaciji (CIP) između teorije i prakse na primeru razvoja CIP-službe u Narodnoj biblioteci Srbije

Catalogisation in publication (CIP) is a set of internationally standardized procedures that allow us to obtain information about the publications of all publishers from one country. In accordance with the basic principles of Universal Bibliographic Control (UBC), the publication is cataloged only once, in the country of publication. Catalogisation in publication produces a CIP record, which is available to all factors of the librarian system. One of the basic tasks of cataloging is to enable regular and timely obtaining of information about new editions. Catalogisation in publication also serves as an announcement for the appearance of new books, which are in the process of publishing, and enables publishers to create catalogs of their own editions and gives insight into the publishing production of other publishers. Catalogisation in publication is of great national importance as it is the primary source of information about a country's publishing production. Catalogisation is also important for controlling the inflow of a required copy. Thirty-four years ago, catalogisation in publication began to be implemented as a joint project of domestic publishers and the National Library of Serbia, as a national catalog center. At the same time, the Matica Srpska Library performed cataloging activities in a publication for publishers from the territory of the Autonomous Province of Vojvodina. Catalogisation in publication is a solution to one of the biggest problems in library and information activities. Thanks to highly qualified staff of various profiles, who apply modern catalogisation and bibliographic principles at the national and international level, catalogisation in publication has been successfully performed in the National Library of Serbia for a full thirty-four years. At the very beginning, the materials were received in the then Department of the Author's Catalog. After that, colleagues from the Department of Author's Catalog and the Department of Subject and Professional Catalog were on duty in one office, and the publication was processed at the reception. Until 2003, the International ISBN Agency was an integral part of the Yugoslav Bibliographic Institute (JUBIN). After the reconstruction of the National Library of Serbia and the abolition of the institutions of the former federal state, all international agencies were transferred to the NBS. At the same time, the subject and professional departments are merged with the author's catalog. From that moment, the conditions were created for the formation of a special unit, ie the CIP service (Cataloging Service) within a single Department of monographic publications. Catalogisation in publication includes all books, regardless of language and script, which achieves the multicultural nature of the entire process.
On prediction of chaotic dynamics in semiconductor lasers by reservoir computing

Studying the chaotic dynamics of semiconductor lasers is of great importance for their applications in random bit generation and secure communication. While considerable effort has been expended towards investigating these chaotic behaviors through numerical simulations and experiments, the accurate prediction of chaotic dynamics from limited observational data remains a challenge. Recent advancements in machine learning, particularly in reservoir computing, have shown promise in capturing and predicting the complex dynamics of semiconductor lasers. However, existing works on laser chaos predictions often suffer from the need for manual parameter optimization. Moreover, the generalizability of the approach remains to be investigated, i.e., concerning the influences of practical laser inherent noise and measurement noise. To address these challenges, we employ an automated optimization approach, i.e., a genetic algorithm, to select optimal reservoir parameters. This allows efficient training of the reservoir network, enabling the prediction of continuous intensity time series and reconstruction of laser dynamics. Furthermore, the impact of inherent laser noise and measurement noise on the prediction of chaotic dynamics is systematically examined through numerical analysis. Simulation results demonstrate the effectiveness and generalizability of the proposed approach in achieving accurate predictions of chaotic dynamics in semiconductor lasers.
Revolution in Engineering Education through Android-Based Learning Media for Mobile Learning: Practicality of Mobile Learning Media to Improve Electrical Measuring Skills in the Industrial Age 4.0

This study examines the practicality of Android-based learning media in improving students’ ability to use electrical measuring instruments in the Industrial Revolution 4.0 era. This study adopted a research and development (R&amp;D) approach using the 4D model. The research process involved practical use tests conducted by lecturers and students using a questionnaire that evaluated convenience, time, and usability. The study’s results revealed that learning Android-based media for mobile learning showed a level of practicality that deserved a thumbs up. Aspects of convenience, time, and use all reach a good level of practicality, so they fall into the ‘practical’ category. Based on research findings, using Android-based learning media for mobile learning provides significant benefits to learning through easy access to learning materials, better interactive and practical visualization, efficiency of learning time, and self-evaluation. The benefits received can be added to understanding the use of electrical measuring instruments. However, the Android-based learning media currently being developed has not been integrated with artificial intelligence (AI), so there are still great opportunities for further research into its integration with education.
Uncertainty in Annual Energy Resulting from Uncertain Irradiance Measurements.

(standard deviation of 1-2%) the uncertainty in annual energy is on the order of a fraction of one percent. We propose a model for a factor that represents uncertainty in modeled annual energy that arises from uncertainty in ground-measured irradiance.
Smoothed Aggregation for Difficult Stretched Mesh and Coefficient Variation Problems.

defines the prolongator smoother, modifications to the filtered matrix, and also direct modifications to the resulting grid transfer operators. Numerical results are given illustrating the potential benefits of the proposed adaptations.
A Data-driven Approach for Improving the Existing Gurson Material Damage Model Using Genetic Programming for Symbolic Regression.

system. The results show that regularizing the GPSR fitness function is critical for generating physically valid models and illustrate how GPSR allows a high level of interpretability compared with other ML approaches. The method of systematic assumption relaxation allows the genera- tion of models that address limiting assumptions found in the Gurson model, and the symbolic forms allow conjecture of decreased material strength due to void interaction and non-symmetric void shapes.
The many faces of Magnetized Liner Inertial Fusion: A data-driven approach to model-free characterization of image metric sensitivities for stagnation imaging applications.

= 139 SCXI images across Nexp = 67 different experiments for subsequent study. The pipeline begins by segmenting images into the stagnated fuel and background using a CNN trained on synthetic images generated from a geometric model of a physical three-dimensional plasma. The resulting segmentation allows for a rules-based registration. Our approach flexibly handles rarely occurring artifacts through minimal user input and avoids the need for extensive hand labelling and augmentation of our experimental dataset that would be needed to train an end-to-end pipeline. Here we also fit background pixels using low-degree polynomials, and perform a statistical assessment of the background and noise properties over the entire image database. Our results provide a guide for choices made in statistical inference models using stagnation image data and can be applied in the generation of synthetic datasets with realistic choices of noise statistics and background models used for machine learning tasks in MagLIF data analysis. We anticipate that the method may be readily extended to automate other MagLIF stagnation imaging applications.
Adaptive High-Arity Logical Activation Functions.

primitives for the manipulation of logical formulas. This layer includes the implementation of efficient unification and substitution application algorithms, structure sharing within the formula database, and efficient access to formulas via arbitrary user-defined properties. The tools are provided in a highly portable form (implemented in Pascal) in order that a diverse community of users may build on them.
Influential Spreaders Identification by Fusing Network Topology

With the development of network science, complex network analysis has received extensive attention. In recent years, influential spreaders identification has become a hot topic in the research of complex networks. In general, influential spreader identification algorithms are mainly divided into centrality-based algorithms and topology-based algorithms. However, centrality-based algorithms have to face the information limitation problem that leads to low accuracy for identifying influential spreaders. Topology-based algorithms have both structural and positional limitation problems that lead to low accuracy for identifying peripheral influential spreaders. In this paper, we focus on improving the situation and propose two influential spreader identification algorithms, NSC (neighborhood structure centrality) and NPC (neighborhood position centrality) from both the perspective of the centrality and the network topology. NSC algorithm collects various types of network structure information through structure embedding and clustering, so as to solve missing network structure information problem. NPC algorithm calculates neighborhood location information by improving the k-shell algorithm to tackle location limitation problem. Experimental results with fourteen baseline algorithms show that our proposed algorithms NSC and NPC can achieve higher accuracy.