Reviewerly/exHarmony huggingface dataset:
Development of Interactive Videos with Transportation Stories for PAUD

This study aims to develop a valid and practical interactive video media product for children’s stories with the theme of transportation. This research development method was combined with the ADDIE model and Tessmer’s formative evaluation. The first data collection technique used observation to assess the child’s behavior and then used a walkthrough to see the value of the validity of a product. The object of this research was the Interactive Video of Children’s Stories with the theme of transportation which went through a one-to-one trial stage (3 children) and small group (9 children), with the conclusion that the Interactive Video of children’s stories on the transportation theme was categorized as valid and practical. Validity was reflected in the results of the validator’s assessment, where the interactive video of students had been revised according to the validator’s suggestions and comments. Practically it was illustrated from the results of the trial, where all students can use interactive video stories with the theme of transportation that students can use well. Based on the results of the interactive video analysis, the story of the transportation developed theme has the appropriateness of learning for children and the suitability of the needs and development of children aged 5-6 years. In addition, it is in accordance with the observation assessment indicators, namely enthusiasm in using media. From this research, there is a novelty in using interactive video media with the theme of transportation which was made into an interesting story. It contributes to the world of early childhood education in the form of additional types of interactive learning media.
From Individual Card Catalogues to the National Union Catalogue: A Story of the Computerisation of Polish Church Libraries

The Development of Church Libraries before the Era of Personal ComputersAs in the whole of Western Europe, church libraries were the first historical book collections in Poland.Their establishment dates back to the adoption of Christianity in 966.The oldest are the cathedral or chapter libraries in Cracow (inventory of 1110), Gniezno, Wrocław, Poznan and Plock, all of which had been collecting manuscripts since the 11th century.The oldest Benedictine monastery libraries were also created in the 11th century, in Tyniec, Mogilno and Lubin.In modern times, the 16th century was the period of the greatest development of church libraries in Poland.This was the period of Poland's political power and the so-called 'golden age' of national literature.That same period saw the popularisation of printing, influencing the spread of the Reformation, and especially the reforms initiated by the Council of Trent.1The 17th century wars against Sweden, the suppression of the Jesuits in 1773, and the subsequent secularisation of monasteries wrought havoc with many book collections.In 1795 Poland was finally partitioned between Austria, Prussia and Russia, so the secularisation of monastic libraries was carried out by the respective authorities.This resulted not only in the partial destruction and dispersal of these book collections, but also in their frequent relocation outside the Polish territory.During these partitions, the Catholic Church -the only independent institution on Polish soil -was concerned with preserving national cultural heritage in that it acted as a collective publisher and ran libraries.The 19th century reorganisation of the structure of Polish dioceses and the establishment of
EST-GAN: Enhancing Style Transfer GANs with Intermediate Game Render Passes

In this paper we introduce EST-GAN, an approach to improve the realism of frames from unsophisticated game scenes. For this purpose, several Generative Adversarial Networks (GANs) structures are applied, which are extended to handle intermediate game render passes generated by conventional rendering pipelines. We present an image-to-image translation method that transforms simple low-poly game scenes into the style of an elaborately produced video game. Through our experiments, we show that the involvement of G-buffer information has a significant impact on the results of these translations and the usage of these leads to a reduction in artifacts.
Peningkatan Pengetahuan dan Kemampuan Penanggulangan Bencana Banjir melalui Sosialisasi di Guru dan Siswa di Kota Bekasi

According to Law Number 24 of 2007, Flood is an event or condition where an area or land is submerged due to an increased volume of water. The impact of floods often causes human casualties, physical losses, property losses, social, economic losses, environmental damage, infrastructure damage, and psychological impacts. Floods occur in almost all parts of Indonesia, especially during the rainy season in areas with high rainfall intensity. AL-Fajar Islamic College, Jatiasih District, Bekasi City, West Java Province is one of the educational facilities that has and is often affected by floods in the area.The problem of limited information and public knowledge about flood disaster management is a vulnerability factor for the risks and impacts of the flood disaster itself. This requires socialization of flood management for communities affected by flood disasters. The purpose of this activity is to increase the knowledge and ability of the community in dealing with and reducing disaster risk. The results of this activity are believed to provide increased knowledge, ability and preparedness in dealing with threats and minimizing the impact of flood disasters.
DESKRIPSI LITERASI DIGITAL PESERTA DIDIK DI SMA MUHAMMADIYAH 1 PONTIANAK

This study aims to decribe the digital literacy profile of students at SMA Muhammadiyah 1 Pontianak. The form of research used is survey. The sample in this study was selected through a saturated sampling technique (all members of the population) totaling 100 students. The research instrument using a questionnaire consists of 22 statements, 4 aspects and 12 indicators. The results showed that the digital literacy skills of students in the attitude aspect of the attitude indicator towards online learning were 62%, the socio-emotional aspect of the indicator was 59% teamwork, network sharing 70%, ethics 73%, legal literacy 71% and self-preservation 82%. The technical aspects of cognition and discovery indicators are 78% each, 63% presentation. The cognitive aspect of the analysis indicator is 65%, evaluation is 72% and creativity is 74%. In general, the four aspects of digital literacy skills are categorized as quite good, except for the socio-emotional aspect of the team work indicator of 59% which is still quite lacking. The lack of ability in these indicators is caused by students having difficulty communicating with group friends in online learning.
Analysis of Generic Structure and Recount Text Understanding in Eighth Grade Students

This research is motivated by many challenges faced and often occur when learning online during a pandemic like this. Many of the schools where I conduct teaching and learning activities have experienced the same problem, namely, students' inability to comprehend the material and limited communication between teachers and students. My goal in carrying out this activity at the junior high school where I teach, particularly in English subjects, is to make lessons more enjoyable and understandable for students. The method I employ is to provide material in the form of a video that incorporates animation with the goal of increasing students' interest in seeing the material, particularly the Recount Text material. Many students continue to struggle with the material they have been given, and some students are less careful in completing the assignments they have been given
Creating a list of word alignments from parallel Russian simplification data

This work describes the development of a list of monolingual word alignments taken from parallel Russian simplification data. This word lists can be used in such lexical simplification tasks as rule-based simplification applications and lexically constrained decoding for neural machine translation models. Moreover, they constitute a valuable source of information for developing educational materials for teaching Russian as a second/foreign language. In this work, a word list was compiled automatically and post-edited by human experts. The resulting list contains 1409 word pairs in which each "complex" word has an equivalent "simpler" (shorter, more frequent, modern, international) synonym. We studied the contents of the word list by comparing the frequencies of the words in the pairs and their levels in the special CEFR-graded vocabulary lists for learners of Russian as a foreign language. The evaluation demonstrated that lexical simplification by means of single-word synonym replacement does not occur often in the adapted texts. The resulting list also illustrates the peculiarities of the lexical simplification task for L2 learners, such as the choice of a less frequent but international word.
The Impact of the Group Maturity on the Software Development Team Effectiveness: an Experience Report

Project-Based Learning (PBL) is an active learning method in which students gain knowledge and skills by exploring real-world problems for an extended period of time. Existing studies point out that soft skills related to working in groups, such as communication skills, teamwork skills, and problem-solving skills, can be enhanced through PBL. Aspects of the group are fundamental to the success of any software project. In this context, this work seeks to investigate the group’s maturity and its relationship with the effectiveness of Agile Software Development teams in PBL. To do so, we conduct a study in the context of a discipline of the Software Engineering graduate course in which PBL is adopted. At the end of the semester, a questionnaire was applied to eight teams of graduating students, involving a total of 29 students, to measure aspects of the group’s maturity, later these aspects were correlated with the effectiveness of the teams. The obtained results point out that group maturity and team effectiveness are directly correlated. The results also show three aspects of group maturity that, if improved, will possibly improve the effectiveness of teams in software development: mutual trust, backup behavior and cohesiveness. Furthermore, from this experience we were able to derive some assumptions and directions for supporting instructors to stimulate the improvement of the soft skills relevant to both team effectiveness and software industry when applying PLB in their classes.
Joint Language Semantic and Structure Embedding for Knowledge Graph Completion

The task of completing knowledge triplets has broad downstream applications. Both structural and semantic information plays an important role in knowledge graph completion. Unlike previous approaches that rely on either the structures or semantics of the knowledge graphs, we propose to jointly embed the semantics in the natural language description of the knowledge triplets with their structure information. Our method embeds knowledge graphs for the completion task via fine-tuning pre-trained language models with respect to a probabilistic structured loss, where the forward pass of the language models captures semantics and the loss reconstructs structures. Our extensive experiments on a variety of knowledge graph benchmarks have demonstrated the state-of-the-art performance of our method. We also show that our method can significantly improve the performance in a low-resource regime, thanks to the better use of semantics. The code and datasets are available at https://github.com/pkusjh/LASS.
MECCANO: A Multimodal Egocentric Dataset for Humans Behavior Understanding in the Industrial-like Domain

Wearable cameras allow to acquire images and videos from the user's perspective. These data can be processed to understand humans behavior. Despite human behavior analysis has been thoroughly investigated in third person vision, it is still understudied in egocentric settings and in particular in industrial scenarios. To encourage research in this field, we present MECCANO, a multimodal dataset of egocentric videos to study humans behavior understanding in industrial-like settings. The multimodality is characterized by the presence of gaze signals, depth maps and RGB videos acquired simultaneously with a custom headset. The dataset has been explicitly labeled for fundamental tasks in the context of human behavior understanding from a first person view, such as recognizing and anticipating human-object interactions. With the MECCANO dataset, we explored five different tasks including 1) Action Recognition, 2) Active Objects Detection and Recognition, 3) Egocentric Human-Objects Interaction Detection, 4) Action Anticipation and 5) Next-Active Objects Detection. We propose a benchmark aimed to study human behavior in the considered industrial-like scenario which demonstrates that the investigated tasks and the considered scenario are challenging for state-of-the-art algorithms. To support research in this field, we publicy release the dataset at https://iplab.dmi.unict.it/MECCANO/.
HiPart: Hierarchical Divisive Clustering Toolbox

This paper presents the HiPart package, an open-source native python library that provides efficient and interpret-able implementations of divisive hierarchical clustering algorithms. HiPart supports interactive visualizations for the manipulation of the execution steps allowing the direct intervention of the clustering outcome. This package is highly suited for Big Data applications as the focus has been given to the computational efficiency of the implemented clustering methodologies. The dependencies used are either Python build-in packages or highly maintained stable external packages. The software is provided under the MIT license. The package's source code and documentation can be found at https://github.com/panagiotisanagnostou/HiPart.
Is Stochastic Gradient Descent Near Optimal?

The success of neural networks over the past decade has established them as effective models for many relevant data generating processes. Statistical theory on neural networks indicates graceful scaling of sample complexity. For example, Joen & Van Roy (arXiv:2203.00246) demonstrate that, when data is generated by a ReLU teacher network with $W$ parameters, an optimal learner needs only $\tilde{O}(W/\epsilon)$ samples to attain expected error $\epsilon$. However, existing computational theory suggests that, even for single-hidden-layer teacher networks, to attain small error for all such teacher networks, the computation required to achieve this sample complexity is intractable. In this work, we fit single-hidden-layer neural networks to data generated by single-hidden-layer ReLU teacher networks with parameters drawn from a natural distribution. We demonstrate that stochastic gradient descent (SGD) with automated width selection attains small expected error with a number of samples and total number of queries both nearly linear in the input dimension and width. This suggests that SGD nearly achieves the information-theoretic sample complexity bounds of Joen & Van Roy (arXiv:2203.00246) in a computationally efficient manner. An important difference between our positive empirical results and the negative theoretical results is that the latter address worst-case error of deterministic algorithms, while our analysis centers on expected error of a stochastic algorithm.
Distribution inference risks: Identifying and mitigating sources of leakage

A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the goal of an adversary is to infer distributional information about the training data. So far, research on distribution inference has focused on demonstrating successful attacks, with little attention given to identifying the potential causes of the leakage and to proposing mitigations. To bridge this gap, as our main contribution, we theoretically and empirically analyze the sources of information leakage that allows an adversary to perpetrate distribution inference attacks. We identify three sources of leakage: (1) memorizing specific information about the $\mathbb{E}[Y|X]$ (expected label given the feature values) of interest to the adversary, (2) wrong inductive bias of the model, and (3) finiteness of the training data. Next, based on our analysis, we propose principled mitigation techniques against distribution inference attacks. Specifically, we demonstrate that causal learning techniques are more resilient to a particular type of distribution inference risk termed distributional membership inference than associative learning methods. And lastly, we present a formalization of distribution inference that allows for reasoning about more general adversaries than was previously possible.
Propagators and Virtual Particles

AbstractSo, if a gauge wave takes some potential out of the original wave, how can any wave be sustainable? After a while, any wave producing gauge waves would have no energy left. So, we discuss how that works. We will discover how gauge waves are not true waves and what that means. Then we arrive at the concept of virtual particles and see why they can be described as a disturbance of the field, rather than a real field quantum. We explain how they are not on the mass-shell as explained earlier. Finally, we understand how they are produced and re-absorbed, as well as how a quantum actually consists of a cloud of virtual particles adding up to the field quantum. We revisit the way a force gets created from the virtual particle perspective and see why that is similar to the wave picture. We show how virtual particles exist in the vacuum, which is therefore never completely empty. In discussing this we introduce Feynman diagrams and explain what a propagator is. We introduce anti-particles and explain why they have an opposite charge and why they move backwards in time. Finally, we discuss the consequences for the arrow of time
Revisiting Rolling Shutter Bundle Adjustment: Toward Accurate and Fast Solution

We propose a robust and fast bundle adjustment solution that estimates the 6-DoF pose of the camera and the geometry of the environment based on measurements from a rolling shutter (RS) camera. This tackles the challenges in the existing works, namely relying on additional sensors, high frame rate video as input, restrictive assumptions on camera motion, readout direction, and poor efficiency. To this end, we first investigate the influence of normalization to the image point on RSBA performance and show its better approximation in modelling the real 6-DoF camera motion. Then we present a novel analytical model for the visual residual covariance, which can be used to standardize the reprojection error during the optimization, consequently improving the overall accuracy. More importantly, the combination of normalization and covariance standardization weighting in RSBA (NW-RSBA) can avoid common planar degeneracy without needing to constrain the filming manner. Besides, we propose an acceleration strategy for NW-RSBA based on the sparsity of its Jacobian matrix and Schur complement. The extensive synthetic and real data experiments verify the effectiveness and efficiency of the proposed solution over the state-of-the-art works. We also demonstrate the proposed method can be easily implemented and plug-in famous GSSfM and GSSLAM systems as completed RSSfM and RSSLAM solutions.
Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective

While reinforcement learning (RL) methods that learn an internal model of the environment have the potential to be more sample efficient than their model-free counterparts, learning to model raw observations from high dimensional sensors can be challenging. Prior work has addressed this challenge by learning low-dimensional representation of observations through auxiliary objectives, such as reconstruction or value prediction. However, the alignment between these auxiliary objectives and the RL objective is often unclear. In this work, we propose a single objective which jointly optimizes a latent-space model and policy to achieve high returns while remaining self-consistent. This objective is a lower bound on expected returns. Unlike prior bounds for model-based RL on policy exploration or model guarantees, our bound is directly on the overall RL objective. We demonstrate that the resulting algorithm matches or improves the sample-efficiency of the best prior model-based and model-free RL methods. While sample efficient methods typically are computationally demanding, our method attains the performance of SAC in about 50% less wall-clock time.
Random Fourier Features for Asymmetric Kernels

The random Fourier features (RFFs) method is a powerful and popular technique in kernel approximation for scalability of kernel methods. The theoretical foundation of RFFs is based on the Bochner theorem that relates symmetric, positive definite (PD) functions to probability measures. This condition naturally excludes asymmetric functions with a wide range applications in practice, e.g., directed graphs, conditional probability, and asymmetric kernels. Nevertheless, understanding asymmetric functions (kernels) and its scalability via RFFs is unclear both theoretically and empirically. In this paper, we introduce a complex measure with the real and imaginary parts corresponding to four finite positive measures, which expands the application scope of the Bochner theorem. By doing so, this framework allows for handling classical symmetric, PD kernels via one positive measure; symmetric, non-positive definite kernels via signed measures; and asymmetric kernels via complex measures, thereby unifying them into a general framework by RFFs, named AsK-RFFs. Such approximation scheme via complex measures enjoys theoretical guarantees in the perspective of the uniform convergence. In algorithmic implementation, to speed up the kernel approximation process, which is expensive due to the calculation of total mass, we employ a subset-based fast estimation method that optimizes total masses on a sub-training set, which enjoys computational efficiency in high dimensions. Our AsK-RFFs method is empirically validated on several typical large-scale datasets and achieves promising kernel approximation performance, which demonstrate the effectiveness of AsK-RFFs.
NeuCEPT: Locally Discover Neural Networks' Mechanism via Critical Neurons Identification with Precision Guarantee

Despite recent studies on understanding deep neural networks (DNNs), there exists numerous questions on how DNNs generate their predictions. Especially, given similar predictions on different input samples, are the underlying mechanisms generating those predictions the same? In this work, we propose NeuCEPT, a method to locally discover critical neurons that play a major role in the model's predictions and identify model's mechanisms in generating those predictions. We first formulate a critical neurons identification problem as maximizing a sequence of mutual-information objectives and provide a theoretical framework to efficiently solve for critical neurons while keeping the precision under control. NeuCEPT next heuristically learns different model's mechanisms in an unsupervised manner. Our experimental results show that neurons identified by NeuCEPT not only have strong influence on the model's predictions but also hold meaningful information about model's mechanisms.