Reviewerly/exHarmony huggingface dataset:
Pengembangan Sistem Informasi Kudamas (Kuningan Dalam Angka Statistik) Berbasis Android di BPS Kabupaten Kuningan, Jawa Barat

At this time, in everyday life, the Internet can be used in almost all areas of life. Companies of all kinds, organizations, government agencies, educational institutions and individuals use the Internet to search for information, communicate, study, do business, and enjoy entertainment. With the development of internet technology, nowadays many activities can be done electronically, such as using Android applications as a medium of information, where we can get a lot of information that we want to get, so that many people can find the information they need. The method used in the preparation of this report was to conduct internal discussions with field supervisors, then collect data, and conduct literature studies related to making this application. In implementing the design, the application is made using the Java programming language in the Android Studio software. The benefit of making the KUDAMAS android-based application is as a complement to the BPS information media with the aim of being able to easily access information about BPS online and offline. Keywords: Android Studio, BPS, applications, Java, software, internet.
SFCDecomp: Multicriteria Optimized Tool Path Planning in 3D Printing using Space-Filling Curve Based Domain Decomposition

We explore efficient optimization of toolpaths based on multiple criteria for large instances of 3D printing problems. We first show that the minimum turn cost 3D printing problem is NP-hard, even when the region is a simple polygon. We develop SFCDecomp, a space filling curve based decomposition framework to solve large instances of 3D printing problems efficiently by solving these optimization subproblems independently. For the Buddha model, our framework builds toolpaths over a total of 799,716 nodes across 169 layers, and for the Bunny model it builds toolpaths over 812,733 nodes across 360 layers. Building on SFCDecomp, we develop a multicriteria optimization approach for toolpath planning. We demonstrate the utility of our framework by maximizing or minimizing tool path edge overlap between adjacent layers, while jointly minimizing turn costs. Strength testing of a tensile test specimen printed with tool paths that maximize or minimize adjacent layer edge overlaps reveal significant differences in tensile strength between the two classes of prints.
A Study of Digital Interactive Technology and Design Mode Promoting the Learners’ Metacognitive Experience in Smart Education

The effectiveness of learners’ learning in smart education is largely improved by combined with digital interactive technology and design recent years. However, most traditional smart education ignore the importance of improving learners’ metacognition experience by digital interactive technology and design. This paper focuses on the research of digital interactive technology promoting teaching reform to inspire the learners’ metacognition experience, and interactive design mode inspiring the development of smart education platform. Digital interactive technology + interactive design mode + smart education has become an important trend of smart education reform.
Optimization of UAV structure and evaluation of vibrational and fatigue characteristics through simulation studies

Unmanned Aerial Vehicles (UAV) is generic air vehicles that are significantly developed for military and civil purposes. In recent times, advancements in the field of UAVs are exceptional and tremendous. Nevertheless, numerous researches have been performed mainly to reduce the weight of the UAV structure. The flight time and allowable payload rely on the UAV structure's weight, which is considered a significant factor. Hence, in this paper, the UAV model's static structural behavior is cultivated utilizing the morals of Finite Element Analysis (FEA) to determine the total deformation and Von-mises stress. Three different polymer materials, namely Poly Lactic Acid (PLA), Acrylonitrile Butadiene Styrene (ABS), and Polyamide (PA), are chosen for FEA analysis of 3D printed UAV structure. The thrust generated from the motors varies from 2 kg to 5 kg, and an evaluation of structural strength characteristics is performed. The FEA study has shown that maximum deformation and maximum stress are obtained at the propeller casings and the basement of the structure, respectively. After comparing the details on deformation and stress from all three materials, the research also reveals that PLA is the best material for conception. Furthermore, topological optimization is performed on the UAV structure to reduce mass and minimize stress without compromising mechanical strength. The vibrational and fatigue characteristics of optimized UAV structure is examined. The unified body of the UAV frame will reduce the assembly time and make manufacturing much more effortless.
Accuracy in Depth Recovery and 3D Image Synthesis From Single Image Using Multi-Color Filter Aperture and Shallow Depth of Field

A computational 3D image generation using a single view with multi-color filter aperture (MCA) and multi-plane representation is a cost-effective approach and most useful when there is no option to acquire either stereo or multi-views with orientation at all. Although this approach generates 3D perception image that includes multiple objects with both similar and dissimilar colors having occluded by each other, it may be insufficient for virtual/augmented reality applications due to inaccurate depth. In this article, we obtain a more accurate geometric depth estimation by formulating a suitable relationship between inter-objects depth of the 3D scene in the depth-of-field (DoF) zone and its corresponding inter-image plane depths of a 3D perception image in depth-of-focus (DoFo) zone of a given camera under shallow DoF zone constraint. But, this shallow depth zone is configured to be dependent only on the focal distance between the lens and object while the remaining parameters such as aperture diameter, focal length, and sensor sensitivity are held at constant values. All-in-focus 3D perception image is synthesized from multi-plane images (MPIs) by utilizing the inter-image plane depths computed from the disparities caused across the boundaries and its smooth surface from image textures inside the respective boundaries of the 2D MCA image. The 2.1D sketch is used as a semantic segmentation technique to determine the number of objects in the 3D scene as one in-focus region and the rest as out-of-focus regions due to the circle of confusions (CoCs) on the fixed image sensor plane. The same enables both ordering of the image regions and identifying occlusion wherever applicable. An accurate depth 3D image is synthesized, replacing accurate inter-depths in place of inter-depth between MPIs used for 3D perception image. In the end, the paper summarizes few experimental validations for the proposed approach with some salient examples having depth gaps between 0.5cm to 10.5cm.
Adjustable deterministic pseudonymization of speech

While public speech resources become increasingly available, there is a growing interest to preserve the privacy of the speakers, through methods that anonymize the speaker information from speech while preserving the spoken linguistic content. In this paper, a method for pseudonymization (reversible anonymization) of speech is presented, that allows to obfuscate the speaker identity in untranscribed running speech. The approach manipulates the spectro-temporal structure of the speech to simulate a different length and structure of the vocal tract by modifying the formant locations, as well as by altering the pitch and speaking rate. The method is deterministic and partially reversible, and the changes are adjustable on a continuous scale. The method has been evaluated in terms of (i) ABX listening experiments, and (ii) automatic speaker verification and speech recognition. ABX experimental results indicate that the speaker identifiability among forced choice pairs reduced from over 90% to less than 70% through pseudonymization, and that de-pseudonymization was partially effective. An evaluation on the VoicePrivacy 2020 challenge data showed that the proposed approach performs better than the signal processing based baseline method that uses McAdams coefficient and performs slightly worse than the neural source filtering based baseline method. Further analysis showed that the proposed approach: (i) is comparable to the neural source filtering baseline based method in terms of phone posterior feature based objective intelligibility measure, (ii) preserves formant tracks better than the McAdams based method, and (iii) preserves paralinguistic aspects such as dysarthria in several speakers.
Refining BERT Embeddings for Document Hashing via Mutual Information Maximization

Existing unsupervised document hashing methods are mostly established on generative models.Due to the difficulties of capturing long dependency structures, these methods rarely model the raw documents directly, but instead to model the features extracted from them (e.g.bag-of-words (BOW), TFIDF).In this paper, we propose to learn hash codes from BERT embeddings after observing their tremendous successes on downstream tasks.As a first try, we modify existing generative hashing models to accommodate the BERT embeddings.However, little improvement is observed over the codes learned from the old BOW or TFIDF features.We attribute this to the reconstruction requirement in the generative hashing, which will enforce irrelevant information that is abundant in the BERT embeddings also compressed into the codes.To remedy this issue, a new unsupervised hashing paradigm is further proposed based on the mutual information (MI) maximization principle.Specifically, the method first constructs appropriate global and local codes from the documents and then seeks to maximize their mutual information.Experimental results on three benchmark datasets demonstrate that the proposed method is able to generate hash codes that outperform existing ones learned from BOW features by a substantial margin. 1
Volunteering to Help People with Disabilities and Less Mobile People in Transport Higher Education Institutions

The objective of the study was to examine the impact of organisation of volunteer movement on solving of the University tasks and on motivation of the students, particularly considering the sustainable development goals. The attention has been focused on the analysis of the mechanism and methods of implementing the capacity of volunteer movement, from the point of view of possibilities of forming socially significant qualities and value orientations in students that correspond to the needs of society and development of the transport complex, as week as the skills of legal culture and behaviour of future transport employees. A conclusion is drawn about the need to use the opportunities of volunteering as a means of self-development of the student’s personality. The study was particularly dedicated to examining the experience of the Resource Training and Methodological Centre for Training Disabled Persons and Persons with Disabilities created and operating in Russian University of Transport, main directions of increasing the efficiency of activities to create necessary conditions for accessibility of higher education for persons with disabilities in transport universities. Social survey allowed to reveal and assess the correlation of motivation and interest of participants in volunteer movement in training on the programs offered at Russian University of Transport and in participating in volunteering since most important incentives spread among the students comprise desire to help people to feel needed, as well as the desire to master new skills and acquire competences.
Efek Media Zoom Cloud Meeting Terhadap Keaktifan dan Hasil Belajar Bahasa Indonesia Siswa di Masa Pandemi Covid-19

This study aims to analyze the effect of the zoom cloud meeting on the activeness and learning outcomes of Indonesian students during the Covid-19 pandemic. The population of this study amounted to 153 students. This study uses descriptive correlation method. The subjects of this study were students of class VIII.1 MTs Negeri Barru in Barru Regency, as many as 25 students. Sampling was done by using simple random sampling. The instrument used is a test in the form of multiple choice questions. The data analysis technique used was descriptive statistical analysis. The analysis carried out is a statistical test. The tests carried out consisted of the Shapiro-wilk normality test, and hypothesis testing with simple regression analysis. The results of this study indicate (1) The results of students' Indonesian learning before applying the zoom cloud meeting media have not been maximized, indicating that the average value obtained is 59.64%. (2) The effect of the zoom cloud meeting media greatly helps students in activeness and learning outcomes of Indonesian shows that the average value obtained is 80.04%. (3) After implementing the zoom cloud meeting media on the activities and results of learning Indonesian students during the Covid-19 pandemic and before applying the zoom cloud meeting media there was an effect on the process and results shown by the students.
A Collusion-Resistant Blockchain-Enabled Data Sharing Scheme with Decryption Outsourcing under Time Restriction

With the ever-increasing demands on decentralization and transparency of cloud storage, CP-ABE (Ciphertext Policy-Attribute-Based Encryption) has become a promising technology for blockchain-enabled data sharing methods due to its flexibility. However, real-world blockchain applications usually have some special requirements like time restrictions or power limitations. Thus, decryption outsourcing is widely used in data sharing scenarios and also causes concerns about data security. In this paper, we proposed a secure access control scheme based on CP-ABE, which could share contents during a particular time slot in blockchain-enabled data sharing systems. Specifically, we bind the time period with both ciphertexts and the keys to archive the goal of only users who have the required attributes in a particular time slot can decrypt the content. Besides, we use time slots as a token to protect the data and access control scheme when users want to outsource the decryption phase. The security analysis shows that our scheme can provide collusion resistance ability under a time restriction, and performance evaluations indicate that our scheme uses less time in decryption compared to other schemes while ensuring security.
Towards a Corpus of Historical German Plays with Emotion Annotations

In this paper, we present first work-in-progress annotation results of a project investigating computational methods of emotion analysis for historical German plays around 1800. We report on the development of an annotation scheme focussing on the annotation of emotions that are important from a literary studies perspective for this time span as well as on the annotation process we have developed. We annotate emotions expressed or attributed by characters of the plays in the written texts. The scheme consists of 13 hierarchically structured emotion concepts as well as the source (who experiences or attributes the emotion) and target (who or what is the emotion directed towards). We have conducted the annotation of five example plays of our corpus with two annotators per play and report on annotation distributions and agreement statistics. We were able to collect over 6,500 emotion annotations and identified a fair agreement for most concepts around a κ-value of 0.4. We discuss how we plan to improve annotator consistency and continue our work. The results also have implications for similar projects in the context of Digital Humanities.
Understanding Entropic Regularization in GANs

Generative Adversarial Networks (GANs) are a popular method for learning distributions from data by modeling the target distribution as a function of a known distribution. The function, often referred to as the generator, is optimized to minimize a chosen distance measure between the generated and target distributions. One commonly used measure for this purpose is the Wasserstein distance. However, Wasserstein distance is hard to compute and optimize, and in practice entropic regularization techniques are used to facilitate its computation and improve numerical convergence. The influence of regularization on the learned solution, however, remains not well-understood. In this paper, we study how several popular entropic regularizations of Wasserstein distance impact the solution learned by a Wasserstein GAN in a simple benchmark setting where the generator is linear and the target distribution is high-dimensional Gaussian. We show that entropy regularization of Wasserstein distance promotes sparsification of the solution, while replacing the Wasserstein distance with the Sinkhorn divergence recovers the unregularized solution. The significant benefit of both regularization techniques is that they remove the curse of dimensionality suffered by Wasserstein distance. We show that in both cases the optimal generator can be learned to accuracy ∊ with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O$</tex> (1/ ∊ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) samples from the target distribution without requiring to constrain the discriminator. We thus conclude that these regularization techniques can improve the quality of the generator learned from empirical data in a way that is applicable for a large class of distributions.
Word Recognition using Embedded Prototype Subspace Classifiers on a New Imbalanced Dataset

This paper presents an approach towards word recognition based on embedded prototype subspace classification.The purpose of this paper is three-fold. Firstly, a new dataset for word recognition is presented, which is extractedfrom the Esposalles database consisting of the Barcelona cathedral marriage records. Secondly, different clusteringtechniques are evaluated for Embedded Prototype Subspace Classifiers. The dataset, containing 30 different classesof words is heavily imbalanced, and some word classes are very similar, which renders the classification task ratherchallenging. For ease of use, no stratified sampling is done in advance, and the impact of different data splits isevaluated for different clustering techniques. It will be demonstrated that the original clustering technique based onscaling the bandwidth has to be adjusted for this new dataset. Thirdly, an algorithm is therefore proposed that findskclusters, striving to obtain a certain amount of feature points in each cluster, rather than finding some clustersbased on scaling the Silverman’s rule of thumb. Furthermore, Self Organising Maps are also evaluated as both aclustering and embedding technique.
Application of market–basket analysis on healthcare

Abstract Data analysis plays a vital role in the present era as it helps us to understand the patterns by exploring it in meaningful ways. Market—basket is one of the main methods used to find frequently occurring items in a transactional database and many researchers use the Apriori algorithm for this purpose. This paper presents the application of Market Basket Analysis to the healthcare section. The present work tries to find frequent diseases that occur together in an area by using the Apriori algorithm. This could help the residents of an area to be more cautious about the frequently occurring diseases and take all possible precautionary measures to safeguard their health. In addition, it could also help the doctors so that, they are ready with required medications to treat the patients.
Critical Digital Pedagogy and Innovative Model, Revisiting Plato and Kant: An Environmental Approach to Teaching in the Digital Era

Like any kind of innovation, innovation in pedagogy specifically critical digital pedagogy takes new ideas and practices and brings them together in new ways to solve problems which presently do not have adequate solutions.Developing new pedagogical models, however, involves both the identification of problems and testing the old practices and the suggestion of novel practices.The present research evaluates various themes of critical pedagogy, suggests an innovative model, and focuses on intentions when evaluating pedagogies rather than assuming they all have the same purpose.It suggests a critical digital pedagogy framework for the multitude of decisions teachers make to assess their own teaching and the digital literacy of their students.
INTERPRETATION OF HISTORIC STRUCTURE FOR NON-INVASIVE ASSESSMENT USING EYE TRACKING

Abstract. With the aims of ensuring safety and decreasing maintenance costs, previous studies in bridge inspection research have worked to elucidate damage indicators and understand their correspondence to structural deficiency. During this process, understanding how an inspector looks at a structure comprehensively as well as how they localize on damage is vital to examining diagnostic bias and how it can play a role in the preservation and maintenance process. To understand human perception and assess the humaninfrastructure interaction during the feature extraction process, eye tracking can be useful. Eye tracking data can accurately map where a human is looking and what they are focusing on based on metrics such as fixation, saccade, pupil dilation, and scan path. The present research highlights the use of eye tracking metrics for recognizing and inferring human implicit attention and intention while performing a structural inspection. These metrics will be used to learn the behavior of human eyes and how detection tasks can change a person’s overall behavior. A preliminary study has been carried out for damage detection to analyze key features that are important for understanding human-infrastructure interaction during damage assessment. These eye tracking features will lay the foundation for human intent prediction and how an inspector performs inspection on historic structures for existing types of damage. In future, the results of this work will be used to train a machine learning agent for autonomous and reactive decision making.
Computational philosophy in search of boundaries between object and method

The main goal of this paper is to point out a number of methodological difficulties faced by the supporters of different kinds of philosophical computationalism in the formation of their conceptual apparatus. The text examines the beliefs underlying physicalist panсomputationalism, as well as some inaccuracies in the classification of the concept of computation. Particular attention is paid to the principles of hierarchical correlation that underlie social neurosciences that actively use computational methods.