Reviewerly/exHarmony huggingface dataset:
Augmented Reality sebagai Media Pembelajaran Interaktif 3D untuk Meningkatkan Motivasi Belajar Siswa Digital Native

This best practice aims to describe the use of Augmented Reality (AR) as a 3D interactive learning media that has an impact on increasing the learning motivation of digital native students. AR is a technology that can integrate the dimensions of the virtual world with the real world that can be displayed in real time. AR is implemented to convey the earth's material as a planet class X IPS 1 and X IPS 2 at SMA N 2 Wates. The method in writing this best practice uses a qualitative descriptive approach, with the stages in this best practice activity consisting of the preparation stage, implementation stage and evaluation stage. The author gives a questionnaire to students related to the evaluation of the use of AR which is then analyzed and used as the basis for determining conclusions. Based on the results of the questionnaire, it is known that there is an increase in students' learning motivation in participating in geography learning using AR as a 3D interactive learning medium, from before there were no students who had high motivation, after using AR as a medium 65% of students had very high motivation. In addition, students who have high motivation before using AR are only 3% to 23% of students, using five indicators of student learning motivation assessment, namely the desire to succeed, awareness in learning, persevering in completing assignments, never giving up when facing difficulties, interest in learning. in study. Based on these indicators, it is known that students' learning motivation has increased after using AR. Thus, it can be concluded that AR as a 3D interactive media can increase the learning motivation of digital native students.
An interactive tutorial for developing likelihood functions for ACT-R models

Building upon the work in Fisher et al. [2022], we developed a set of tutorial notebooks explaining howto develop likelihood functions for models based upon the ACT-R cognitive architecture [Andersonet al., 2004]. The tutorial notebooks combine text, equations, plots, and annotated computer code tocreate an integrated learning experience. Each tutorial notebook is interactive, allowing the readeradjust model parameters with sliders, and observe the effects on model predictions and the likelihoodfunction. The introductory tutorial explains the benefit of using likelihood functions to evaluatemodels, such as the ability to use Bayesian parameter estimation, and avoiding potential problemswith averaging artifacts. The remainder of the tutorial is organized into two broad sections: (1)background information covering software, mathematical concepts, and statistical concepts, and(2) a series of models ranging from simple to moderately complex. Each model tutorial guidesreaders through the development of the likelihood function, and demonstrates how to estimateparameters with Bayesian parameter estimation. A repo containing the tutorials can be foundat https://github.com/itsdfish/ACTRTutorials.jl. Installation instructions are enclosed within theREADME file.
Prediction error, processing elements, and the development of early linguistic skills: An integrated EDL-PRIMs model

Statistical learning and cognitive processing perspectives independently acknowledge the crucial role of learning in early linguistic acquisition. In the current paper, simultaneous acquirement of lexicons and procedures is implemented by uniformly integrating prediction error-driven learning (EDL) within the primitive information processing element architecture (PRIMs), assuming a statistical learning perspective in learning both forms. The first proof-of-concept simulation study extends the statistical learning perspective in syntactic acquisition based on EDL-PRIMs procedural learning. The second simulation study demonstrates procedural and lexical learning from syntactic, lexical, and hybrid patterns and simulates corresponding skill development by modeling age-related efficiency enhancement. The simulation studies are related to empirical focusing preferences based on operation latencies through a resource availability perspective. The paper discusses the EDL-PRIMs approach and simulated results linking to empirical evidence and computational theories of early linguistic development.
Design and Implementation of a Text Mining-based Tool to Support Scoping Reviews

Among literature reviews, scoping review is a relatively new approach that is increasingly gaining popularity since it helps researchers in defining emerging and multidisciplinary fields.While artificial intelligence for text processing can help researchers in this sense, we still lack clear procedures and tools to improve the reviewing process.Following a design science approach, in this article we propose a novel tool based on natural language processing (NLP) to support scoping review and to visualise its results.The tool (NLP4Scoping) is implemented using open-source software and is made available for reuse on GitHub.Each phase for its proper application is described focusing on the nascent literature stream on innovation management in digital ecosystems.
Real-time Scene Text Detection Based on Global Level and Word Level Features

It is an extremely challenging task to detect arbitrary shape text in natural scenes on high accuracy and efficiency. In this paper, we propose a scene text detection framework, namely GWNet, which mainly includes two modules: Global module and RCNN module. Specifically, Global module improves the adaptive performance of the DB (Differentiable Binarization) module by adding k submodule and shift submodule. Two submodules enhance the adaptability of amplifying factor k, accelerate the convergence of models and help to produce more accurate detection results. RCNN module fuses global-level and word-level features. The word-level label is generated by obtaining the minimum axis-aligned rectangle boxes of the shrunk polygon. In the inference period, GWNet only uses global-level features to output simple polygon detections. Experiments on four benchmark datasets, including the MSRA-TD500, Total-Text, ICDAR2015 and CTW-1500, demonstrate that our GWNet outperforms the state-of-the-art detectors. Specifically, with a backbone of ResNet-50, we achieve an F-measure of 88.6% on MSRA- TD500, 87.9% on Total-Text, 89.2% on ICDAR2015 and 87.5% on CTW-1500.
V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs

Semantic Web or Knowledge Graphs (KG) emerged to one of the most important information source for intelligent systems requiring access to structured knowledge. One of the major challenges is the extraction and processing of unambiguous information from textual data. Following the human perception, overlapping semantic linkages between two named entities become clear due to our common-sense about the context a relationship lives in which is not the case when we look at it from an automatically driven process of a machine. In this work, we are interested in the problem of Relational Resolution within the scope of KGs, i.e, we are investigating the inherent semantic of relationships between entities within a network. We propose a new adaptive AutoEncoder, called V-Coder, to identify relations inherently connecting entities from different domains. Those relations can be considered as being ambiguous and are candidates for disentanglement. Likewise to the Adaptive Learning Theory (ART), our model learns new patterns from the KG by increasing units in a competitive layer without discarding the previous observed patterns whilst learning the quality of each relation separately. The evaluation on real-world datasets of Freebase, Yago and NELL shows that the V-Coder is not only able to recover links from corrupted input data, but also shows that the semantic disclosure of relations in a KG show the tendency to improve link prediction. A semantic evaluation wraps the evaluation up.
Investigating and Explaining the Frequency Bias in Image Classification

CNNs exhibit many behaviors different from humans, one of which is the capability of employing high-frequency components. This paper discusses the frequency bias phenomenon in image classification tasks: the high-frequency components are actually much less exploited than the low- and mid-frequency components. We first investigate the frequency bias phenomenon by presenting two observations on feature discrimination and learning priority. Furthermore, we hypothesize that (i) the spectral density, (ii) class consistency directly affect the frequency bias. Specifically, our investigations verify that the spectral density of datasets mainly affects the learning priority, while the class consistency mainly affects the feature discrimination.
A Tighter Analysis of Spectral Clustering, and Beyond

This work studies the classical spectral clustering algorithm which embeds the vertices of some graph $G=(V_G, E_G)$ into $\mathbb{R}^k$ using $k$ eigenvectors of some matrix of $G$, and applies $k$-means to partition $V_G$ into $k$ clusters. Our first result is a tighter analysis on the performance of spectral clustering, and explains why it works under some much weaker condition than the ones studied in the literature. For the second result, we show that, by applying fewer than $k$ eigenvectors to construct the embedding, spectral clustering is able to produce better output for many practical instances; this result is the first of its kind in spectral clustering. Besides its conceptual and theoretical significance, the practical impact of our work is demonstrated by the empirical analysis on both synthetic and real-world datasets, in which spectral clustering produces comparable or better results with fewer than $k$ eigenvectors.
SISTEM INFORMASIPENDUKUNG KEPUTUSAN PEMILIHAN PELANGGAN TERBAIK MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) PADA MINIMARKET UTAMA KENDAL

The Kendal Main Minimarket, which is located at Sendang Kulon Village, RT 02/02, Kangkung District, Kendal Regency, is an alternative place to shop and look for daily needs. The obstacle faced by the company is the selection of active customers who are given an annual bonus at random, therefore there are still many customers who complain because they feel they often shop but do not get the annual bonus.&#x0D; Based on this research, the authors designed and made the application of the best customer selection decision support system using the Simple Additive Weighting method with the aim of making recording and selecting customers easier and more effective. This application uses notepad++ software as a text editor and MySQL database.
The Effectiveness of “Ibu Pintar” Bookmic on Maternal Knowledge and Attitude toward Complementary Feeding Practice in Children Aged 6-24 Months

Background : Complementary Feeding Practice is an important factor for the fulfilment of children’s nutritional needs. Due to the nutritional imbalance between the energy and nutrient needed by infants and what provided by breastmilk, therefore it should be fulfilled by complementary feeding. One third of toddlers worldwide are in developing countries who obtain adequate complementary feeding, with a standard frequency and variety. Meanwhile, in Indonesia two third or most of the children aged 0-24 months do not obtain appropriate complementary food. The study aims to develop “Ibu Pintar” Bookmic, a media which is valid and effective to improve maternal knowledge and attitude in appropriate complementary feeding practice. Method: The design of the study used 2 methods. The first method was Research and Development to collect user data and subsequently generated a product that can be utilized to improve knowledge and attitude of mothers of toddlers. The second method was quasi experimental design conducted before and after the study without any comparison to apply Paired Sample T-Test by using SPSS . Result: The result indicated that after the treatment with “Ibu Pintar” Bookmic maternal knowledge in complementary feeding practice was improved (Mean= 1.42; SD= 0.67) compared to maternal knowledge in complementary feeding practice before the treatment (Mean= 0.66; SD; 0.77), and the result was statistically significant (p &lt; 0.001). After the treatment with “Ibu Pintar” Bookmic maternal attitude in complementary feeding practice was improved (Mean= 0.76; SD= 0.43) compared to maternal attitude in complementary feeding practice before the treatment (Mean= 0.44; SD; 0.50), and the resulf was statistically significant (p &lt; 0.001). Conclusion : “Ibu Pintar” Bookmic is effective for maternal knowledge and attitude in complementary feeding practice. Keywords : knowledge, attitude, and complementary food. Correspondence: Fifty K. Lucita. Master Program in Public Health. Universitas Negeri Semarang. Email: fifty.kaa@gmail.com. Mobile: +62 858-6922-1419. Journal of Maternal and Child Health (2022), 07(04): 489-497 https://doi.org/10.26911/thejmch.2022.07.04.12
Efficient Exploration via First-Person Behavior Cloning Assisted Rapidly-Exploring Random Trees

Modern day computer games have extremely large state and action spaces. To detect bugs in these games' models, human testers play the games repeatedly to explore the game and find errors in the games. Such gameplay is exhaustive and time consuming. Moreover, since robotics simulators depend on similar methods of model specification and debugging, the problem of finding errors in the model is of interest to the robotics community to ensure robot behaviors and interactions are consistent in simulators. Previous methods have used reinforcement learning arXiv:2103.13798 and search based methods (Chang, 2019, (Chang, 2021) arXiv:1811.06962 including Rapidly-exploring Random Trees (RRT) to explore a game's state-action space to find bugs. However, such search and exploration based methods are not efficient at exploring the state-action space without a pre-defined heuristic. In this work we attempt to combine a human-tester's expertise in solving games, and the RRT's exhaustiveness to search a game's state space efficiently with high coverage. This paper introduces Cloning Assisted RRT (CA-RRT) to test a game through search. We compare our methods to two existing baselines: 1) a weighted-RRT as described by arXiv:1812.03125; 2) human demonstration seeded RRT as described by Chang et. al. We find CA-RRT is applicable to more game maps and explores more game states in fewer tree expansions/iterations when compared to the existing baselines. In each test, CA-RRT reached more states on average in the same number of iterations as weighted-RRT. In our tested environments, CA-RRT reached the same number of states as weighted-RRT by more than 5000 fewer iterations on average, almost a 50% reduction and applied to more scenarios than. Moreover, as a consequence of our first person behavior cloning approach, CA-RRT worked on unseen game maps than just seeding the RRT with human demonstrated states.
CALI: Coarse-to-Fine ALIgnments Based Unsupervised Domain Adaptation of Traversability Prediction for Deployable Autonomous Navigation

Traversability prediction is a fundamental perception capability for autonomous navigation. The diversity of data in different domains imposes significant gaps to the prediction performance of the perception model. In this work, we make efforts to reduce the gaps by proposing a novel coarse-to-fine unsupervised domain adaptation (UDA) model - CALI. Our aim is to transfer the perception model with high data efficiency, eliminate the prohibitively expensive data labeling, and improve the generalization capability during the adaptation from easy-to-obtain source domains to various challenging target domains. We prove that a combination of a coarse alignment and a fine alignment can be beneficial to each other and further design a first-coarse-then-fine alignment process. This proposed work bridges theoretical analyses and algorithm designs, leading to an efficient UDA model with easy and stable training. We show the advantages of our proposed model over multiple baselines in several challenging domain adaptation setups. To further validate the effectiveness of our model, we then combine our perception model with a visual planner to build a navigation system and show the high reliability of our model in complex natural environments where no labeled data is available.
A robot-driven automatic scribing method via three-dimensional measurement sensor

The evaluation of the complex workpiece allowance and the scribing the datum line now extremely rely on manual operation. The manual operation is time-consuming and prone to misjudgment, and has a large blind area. To solve these problems, the robot-driven automatic scribing via three-dimensional (3D) measurement sensor method (RAS3DM) is proposed. In this study, we solve the key technology of calibrating the rigid transformation between the 3D measurement coordinate system and laser scribing coordinate system. And a fast and accurate multiple coordinate systems calibration and unification method is proposed, a fixed calibration target and a mobile calibration target are designed. Hence, a structured-light 3D shape measurement sensor driven by an industrial robot with six-degree of freedom (6-DOF) is utilized to obtain the complete 3D shape of the complex workpiece automatically, and then by comparing the 3D measurement result and the standard CAD model, the laser scribing equipment can automatically scribe the mechanical processing datum line to assist further processing. The experiment results illustrate that the proposed method is accurate and efficient: the complex workpiece allowance evaluation and scribing time is about 5 times faster than that of manual operation, and the scribing accuracy is 0.15mm, which can effectively solve various problems of manual scribing. The proposed RAS3DM can be widely applied in factories and laboratories.
EPISTEMOLOGICAL THINKING ABOUT ACCOUNTING IN THE ERA OF ARTIFICIAL INTELLIGENCE

Purpose: the objective of this study was to position accounting among the branches of human knowledge in order to understand the effects of these changes on it. The central line on which the discussion unfolds is what defines it as a science and whether, as a science, it can cease to exist. Method/approach: we adopted the Theoretical Essay as our methodology. Main findings: the future of accounting is a recent topic in several worldwide journals and there is a unanimous understanding that accounting will not cease to exist, it should only seek in essence its reason for existing to remain competitive in the face of automated processes, acting mainly in the creation, interpretation and data authentication and reporting for decision making and no more manual processes. This study explores the ways in which the accounting sciences will be able to remain at the forefront of knowledge and as a profession of high importance for organizations. Theoretical, practical/social contributions: the evolution of digital technologies such as Internet of Things (IoT), Big Data and Artificial Intelligence for business (BI) has provoked discussions about the future of professions, especially those that present data collection and processing activities. From this perspective, the essay contributes by directing the discussion to accounting and reflecting on the transformation of accounting science with increasing emphasis on report creation, interpretation, and authentication. Originality/relevance: the originality of the study lies in establishing links between accounting science and disruptive technologies.
Values. Uses. Materials: the expression of users in the co-creation through virtual and tangible representation systems

Co-creation [1] aims to involve users at the heart of the design process. The specificity of this approach is to allow for a more detailed identification of the activities and future needs of users through a practice of « making ». But to genuinely participate in design ideation, end-users must be provided with proper representation tools. Therefore, these assumptions lead to two main questions: what kind of ideas do users actually produce in co-creation workshops? And how VR and tangible representation support the expression of those ideas? In this study, we analyze the development of the core concept of a 7-participants groups involved in the design of a University Lobby, supported by a hybrid tangible/virtual representation system. We explore, through the prism of levels of abstraction [Rasmussen 1983], how the core concept is gradually shaped and the roles of tangible and virtual representations, as well as the real space to be designed, in the co-creation process. We show that participants succeed in designing very concrete features but that core decisions are taken on an abstract level. Representations support expression of concrete ideas, but we found no difference between virtual and tangible tools. We conclude on perspectives to deepen this ongoing work.
Relationships Between Rigs and Humanoid and Coveroid Landmarks

3D body scanning, 3D body photography, and motion capture systems, along with Coveroid creation and ideation tools still have significant interoperability gaps and user interface shortfalls.As much as consumers want to trust virtual try-on platforms, and brands want a return on investment of 3d tools via industry key performance indicators like fit-related returns, the trade-offs brands made in repurposing creation tools from the gaming industry remain insufficient for designing Coveroids (virtual apparel or garments) to fit Humanoids (avatars) of consumers.Currently, mesh construction is a painstaking process, involving joint hierarchy definition, binding of mesh vertices to joint centers, and application of weights to each joint.Apparel landmarks are equally as challenging, with many landmarks located on soft tissues that slide over the underlying skeleton and bony references moved by the Coveroid and/or body movement.The most appropriate positions to associate with vertebrae and spinal sections (Cervicale, Thoracic, Lumbar, Sacral) for the rigged model are of discussion.
Procedural texture patterns for encoding changes in color in 2.5D treemap visualizations

Abstract Treemaps depict tree-structured data while maintaining flexibility in mapping data to different visual variables. This work explores how changes in data mapped to color can be represented with rectangular 2.5D treemaps using procedural texture patterns. The patterns are designed to function for both static images and interactive visualizations with animated transitions. During rendering, the procedural texture patterns are superimposed onto the existing color mapping. We present a pattern catalog with seven exemplary patterns having different characteristics in representing the mapped data. This pattern catalog is implemented in a WebGL-based treemap rendering prototype and is evaluated using performance measurements and case studies on two software projects. As a result, this work extends the toolset of visual encodings for 2.5D treemaps by procedural texture patterns to represent changes in color. It serves as a starting point for user-centered evaluation. Graphical abstract
Teaching in preschool during the pandemic: interactions between teacher, students, families and screens

The objective of this paper was to analyze the teaching practice of a preschool teacher in a private school in San Luis Potosi, Mexico, based on the dimensions of effective and represented work. From the analysis of the instructions and the simple self-confrontation, axes of analysis were identified that allowed an approach to the main challenges that the teacher faced with the online education developed during the pandemic by covid-19. The results of the work placed at the center of the discussion the teacher’s challenges and concerns about the ways of teaching online literacy, the relationships with the students’ families when working from home, and the interaction with technology in the virtual classroom. Taken together, the findings broaden the picture of teaching practices during social distancing and contribute to the empirical knowledge of this object of study as a result of the impact generated by the pandemic.