Reviewerly/exHarmony huggingface dataset:
Pre-registration for Predictive Modeling

Amid rising concerns of reproducibility and generalizability in predictive modeling, we explore the possibility and potential benefits of introducing pre-registration to the field. Despite notable advancements in predictive modeling, spanning core machine learning tasks to various scientific applications, challenges such as overlooked contextual factors, data-dependent decision-making, and unintentional re-use of test data have raised questions about the integrity of results. To address these issues, we propose adapting pre-registration practices from explanatory modeling to predictive modeling. We discuss current best practices in predictive modeling and their limitations, introduce a lightweight pre-registration template, and present a qualitative study with machine learning researchers to gain insight into the effectiveness of pre-registration in preventing biased estimates and promoting more reliable research outcomes. We conclude by exploring the scope of problems that pre-registration can address in predictive modeling and acknowledging its limitations within this context.
Initializing Models with Larger Ones

Weight initialization plays an important role in neural network training. Widely used initialization methods are proposed and evaluated for networks that are trained from scratch. However, the growing number of pretrained models now offers new opportunities for tackling this classical problem of weight initialization. In this work, we introduce weight selection, a method for initializing smaller models by selecting a subset of weights from a pretrained larger model. This enables the transfer of knowledge from pretrained weights to smaller models. Our experiments demonstrate that weight selection can significantly enhance the performance of small models and reduce their training time. Notably, it can also be used together with knowledge distillation. Weight selection offers a new approach to leverage the power of pretrained models in resource-constrained settings, and we hope it can be a useful tool for training small models in the large-model era. Code is available at https://github.com/OscarXZQ/weight-selection.
Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking

Recent work by Power et al. (2022) highlighted a surprising "grokking" phenomenon in learning arithmetic tasks: a neural net first "memorizes" the training set, resulting in perfect training accuracy but near-random test accuracy, and after training for sufficiently longer, it suddenly transitions to perfect test accuracy. This paper studies the grokking phenomenon in theoretical setups and shows that it can be induced by a dichotomy of early and late phase implicit biases. Specifically, when training homogeneous neural nets with large initialization and small weight decay on both classification and regression tasks, we prove that the training process gets trapped at a solution corresponding to a kernel predictor for a long time, and then a very sharp transition to min-norm/max-margin predictors occurs, leading to a dramatic change in test accuracy.
Geometry-Aware Normalizing Wasserstein Flows for Optimal Causal Inference

This paper presents a groundbreaking approach to causal inference by integrating continuous normalizing flows (CNFs) with parametric submodels, enhancing their geometric sensitivity and improving upon traditional Targeted Maximum Likelihood Estimation (TMLE). Our method employs CNFs to refine TMLE, optimizing the Cram\'er-Rao bound and transitioning from a predefined distribution $p_0$ to a data-driven distribution $p_1$. We innovate further by embedding Wasserstein gradient flows within Fokker-Planck equations, thus imposing geometric structures that boost the robustness of CNFs, particularly in optimal transport theory. Our approach addresses the disparity between sample and population distributions, a critical factor in parameter estimation bias. We leverage optimal transport and Wasserstein gradient flows to develop causal inference methodologies with minimal variance in finite-sample settings, outperforming traditional methods like TMLE and AIPW. This novel framework, centered on Wasserstein gradient flows, minimizes variance in efficient influence functions under distribution $p_t$. Preliminary experiments showcase our method's superiority, yielding lower mean-squared errors compared to standard flows, thereby demonstrating the potential of geometry-aware normalizing Wasserstein flows in advancing statistical modeling and inference.
Unsupervised learning architecture based on neural Darwinism and Hopfield networks recognizes symbols with high accuracy

This paper introduces a novel unsupervised learning paradigm inspired by Gerald Edelman's theory of neuronal group selection ("Neural Darwinism"). The presented automaton learns to recognize arbitrary symbols (e.g., letters of an alphabet) when they are presented repeatedly, as they are when children learn to read. On a second hierarchical level, the model creates abstract categories representing the learnt symbols. The fundamental computational unit are simple McCulloch-Pitts neurons arranged into fully-connected groups (Hopfield networks with randomly initialized weights), which are "selected", in an evolutionary sense, through symbol presentation. The learning process is fully tractable and easily interpretable for humans, in contrast to most neural network architectures. Computational properties of Hopfield networks enabling pattern recognition are discussed. In simulations, the model achieves high accuracy in learning the letters of the Latin alphabet, presented as binary patterns on a grid. This paper is a proof of concept with no claims to state-of-the-art performance in letter recognition, but hopefully inspires new thinking in bio-inspired machine learning.
Exploiting Diffusion Prior for Generalizable Pixel-Level Semantic Prediction

Contents generated by recent advanced Text-to-Image (T2I) diffusion models are sometimes too imaginative for existing off-the-shelf dense predictors to estimate due to the immitigable domain gap. We introduce DMP, a pipeline utilizing pre-trained T2I models as a prior for dense prediction tasks. To address the misalignment between deterministic prediction tasks and stochastic T2I models, we reformulate the diffusion process through a sequence of interpolations, establishing a deterministic mapping between input RGB images and output prediction distributions. To preserve generalizability, we use low-rank adaptation to fine-tune pre-trained models. Extensive experiments across five tasks, including 3D property estimation, semantic segmentation, and intrinsic image decomposition, showcase the efficacy of the proposed method. Despite limited-domain training data, the approach yields faithful estimations for arbitrary images, surpassing existing state-of-the-art algorithms.
Just Add $π$! Pose Induced Video Transformers for Understanding Activities of Daily Living

Video transformers have become the de facto standard for human action recognition, yet their exclusive reliance on the RGB modality still limits their adoption in certain domains. One such domain is Activities of Daily Living (ADL), where RGB alone is not sufficient to distinguish between visually similar actions, or actions observed from multiple viewpoints. To facilitate the adoption of video transformers for ADL, we hypothesize that the augmentation of RGB with human pose information, known for its sensitivity to fine-grained motion and multiple viewpoints, is essential. Consequently, we introduce the first Pose Induced Video Transformer: PI-ViT (or $\pi$-ViT), a novel approach that augments the RGB representations learned by video transformers with 2D and 3D pose information. The key elements of $\pi$-ViT are two plug-in modules, 2D Skeleton Induction Module and 3D Skeleton Induction Module, that are responsible for inducing 2D and 3D pose information into the RGB representations. These modules operate by performing pose-aware auxiliary tasks, a design choice that allows $\pi$-ViT to discard the modules during inference. Notably, $\pi$-ViT achieves the state-of-the-art performance on three prominent ADL datasets, encompassing both real-world and large-scale RGB-D datasets, without requiring poses or additional computational overhead at inference.
FreeV:Free Lunch in MultiModal Diffusion U-ViT

&lt;p&gt;This paper reveals the untapped potential of the U-ViT architecture in diffusion models. The study initially explores the contribution of the U-ViT architecture in the visual generation task of multimodal diffusion models and proposes an improvement scheme, ”FreeV”, specifically designed for the U-ViT architecture – marking the first application of the U-Net-based FreeU enhancement framework within the Transformer architecture. The FreeV framework significantly enhances generation quality without requiring additional training or fine-tuning. The key insight of this study lies in balancing the contributions from the backbone network, skip connections, and fused feature maps within the U-ViT to fully leverage the advantages of both components while circumventing the limitations of feature fusion in U-ViT.Project page: https://github.com/GoldenFishes/FreeV &lt;/p&gt;
Analisis Model Pembelajaran Multiliterasi dalam Meningkatkan Kualitas Pemahaman Membaca Siswa

The low level of understanding and writing ability of students causes students to find it difficult to accept, absorb, and understand existing lessons. This will also have an impact on the inability of students to deal with problem solving. Therefore, it is necessary to do an appropriate learning model. However, at this time there are still many educators who do not know an effective learning model to enable students to master reading comprehension skills. The solution in this case is to apply a multiliteracy learning model to hone students' higher-order thinking skills and improve their writing skills. Because this is a competency capital that must be possessed in facing the digital era of the 21st century. The purpose of this research is to find out the multiliteracy learning models that will be applied by educators to students. This research is a type of literature study qualitative research. By using primary sources derived from books and articles related to the multiliteracy learning model. The results of this study are textual, visual, musical, digital literacy models, understanding and skill representation models as well as supporting learning process models that can improve students' reading comprehension and critical thinking.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//
Analisis Strategi Multiliterasi sebagai Pembelajaran PAI di Sekolah/Madrasah

This study aims to analyze multiliteracy learning strategies in schools. The research method used is a literature study with a descriptive and analytical approach. The results of the study show that the use of multiliteracy learning strategies in schools has many benefits, such as improving reading, writing and speaking skills in different languages, as well as increasing the development of digital literacy. In addition, multiliteracy learning strategies can also improve critical and creative thinking skills, and facilitate the integration of different cultures. However, the implementation of this strategy has challenges that must be faced, such as a lack of understanding of the concept of multiliteracy by educators, availability of resources, and difficulties in determining what type of literacy should be taught. Therefore, it is recommended that schools pay attention to this multiliteracy learning strategy so that it can provide a holistic and effective learning experience for students.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//
RAINFALL ANALYSIS FOR CREATING INTENSITY-DURATION-FREQUENCY (IDF) CURVE OF PONTIANAK CITY

Pontianak is a lowland area prone to flooding if it rains with high rainfall intensity. Analysis of the planned flood discharge is needed to overcome the flood problem. The design flood discharge analysis can be calculated using the rational method, which requires rain intensity data in terms of duration and frequency, which can be described as an IDF curve.This research was conducted by calculating the average daily maximum rainfall after conducting consistency and homogeneity tests on the rainfall data used and determining the selected distribution based on the results of statistical parameters. The rain design with various return periods is calculated with the desired distribution that matches Pontianak City. The rainfall intensity calculation uses the Mononobe method because the rainfall data owned is daily rainfall data.The analysis results show that the Log Pearson Type III distribution corresponds to the data distribution in Pontianak. From the IDF curve, it can be seen that high rainfall intensity lasts for a short duration; this can prove the general nature of rain that the faster the rain stays, the higher the rainfall intensity and vice versa. The IDF curve can assist in calculating the design flood discharge.
Investigation of Deep Learning Based Semantic Segmentation Models for Autonomous Vehicles

Semantic segmentation plays a pivotal role in enhancing the perception capabilities of autonomous vehicles and self-driving cars, enabling them to comprehend and navigate complex real-world environments. Numerous techniques have been developed to achieve semantic segmentation. Still, the paper emphasizes the effectiveness of deep learning approaches because they have demonstrated impressive capabilities in capturing intricate patterns and features from images, resulting in highly accurate segmentation results. Although various studies have been conducted in literature, there is needed for a careful investigation and analysis of the existing methods, especially in terms of two critical aspects: accuracy and inference time. To address this need for analysis and investigation, the research focuses on three widely-used deep learning architectures: ResNet, VGG, and MobileNet. By thoroughly evaluating these models based on accuracy and inference time, the study aims to identify the models that strike the best balance between precision and speed. The findings of this study highlight the most accurate and efficient models for semantic segmentation, aiding the development of reliable self-driving technology.
A secure solution based on load-balancing algorithms between regions in the cloud environment

The problem treated in this article is the storage of sensitive data in the cloud environment and how to choose regions and zones to minimize the number of transfer file events. Handling sensitive data in the global internet network many times can increase risks and minimize security levels. Our work consists of scheduling several files on the different regions based on the security and load balancing parameters in the cloud. Each file is characterized by its size. If data is misplaced from the start it will require a transfer from one region to another and sometimes from one area to another. The objective is to find a schedule that assigns these files to the appropriate region ensuring the load balancing executed in each region to guarantee the minimum number of migrations. This problem is NP-hard. A novel model regarding the regional security and load balancing of files in the cloud environment is proposed in this article. This model is based on the component called "Scheduler" which utilizes the proposed algorithms to solve the problem. This model is a secure solution to guarantee an efficient dispersion of the stored files to avoid the most storage in one region. Consequently, damage to this region does not cause a loss of big data. In addition, a novel method called the "Grouping method" is proposed. Several variants of the application of this method are utilized to propose novel algorithms for solving the studied problem. Initially, seven algorithms are proposed in this article. The experimental results show that there is no dominance between these algorithms. Therefore, three combinations of these seven algorithms generate three other algorithms with better results. Based on the dominance rule, only six algorithms are selected to discuss the performance of the proposed algorithms. Four classes of instances are generated to measure and test the performance of algorithms. In total, 1,360 instances are tested. Three metrics are used to assess the algorithms and make a comparison between them. The experimental results show that the best algorithm is the "Best-value of four algorithms" in 86.5% of cases with an average gap of 0.021 and an average running time of 0.0018 s.
Increasing Numeracy Skills as Learning Motivation with Demonstration Methods Using Colorful Ice Cream Sticks for Children Aged 7-8 Years at TPQ Kedung Badak Village

The main purpose of this study is an effort to improve numeracy skills as learning motivation with a demonstration method using colorful ice cream sticks for children aged 7-8 years at TPQ Kedung Badak Village. This research was conducted at TPQ Kedung Badak Sentral Village RT 1 RW 13 Bogor City. This research uses a qualitative approach with a descriptive method. Data collection is done through observation and documentation studies. The object of the study was children aged 7-8 years. The results showed an increase in motivation to learn to count using colorful ice cream sticks. Thus the application of methods and also colorful ice cream stick props has had a very good influence by increasing learning motivation. The material presented was understood by TPQ children, TPQ children's responsibility for the tasks given was high, and helped TPQ children to be actively involved in the learning process.
Evaluating the Effectiveness of Poster &amp; Picture Card Media in Teaching the Water Cycle to Elementary Students

This study explores the validity, practicality, and effectiveness of Postcard learning media in the classroom. Employing a Research and Development approach, the study utilized the ADDIE model, encompassing Analysis, Design, Development, Implementation, and Evaluation stages. The research was conducted with 17 fifth-grade students at SDN 1 Watudandang. The study involved quantitative analysis to assess the Postcard learning method's implementation, employing a variety of data collection techniques. These included validity questionnaires from media and material experts, teacher and student response questionnaires, and pretest-posttest scores. Validity testing revealed that the Postcard learning media were rated as "very valid" by experts, with scores of 91% from material experts and 84% from media experts. Practicality assessment, based on teacher and student response questionnaires, yielded scores of 91% and 88% respectively, placing it in the "very practical" category. The effectiveness of the media was demonstrated through significant differences in student learning outcomes, as evidenced by pretest and posttest scores. The Paired Samples T Test indicated a t-count greater than the t-table, with a significance value of 0.000. Furthermore, the N-Gain score of 0.75 fell into the "high" category, suggesting that the media were "effective enough". In conclusion, the Postcard learning media proved to be both valid and practical, and sufficiently effective for educational use in teaching fifth graders, notably in enhancing their understanding of the subject matter.
Pendampingan Penyusunan Pelaporan Penelitian Tindakan Kelas Bagi Guru MTs Se Pokja Windusari

This community service activity aims to enable teachers to understand and have the skills to write scientific papers. Based on the results of observations and interviews at MTs N 3 Magelang, teachers had difficulty writing proposals and reporting on classroom action research, from determining titles, problems, learning methods, research methods, and writing reports. The results of the workshop on writing classroom action research reports and scientific papers are that participants (teachers) have gained a better understanding of the material contained in classroom action research proposals (PTK). Second, participants have gained an understanding of how to prepare a PTK proposal and publish it in a service journal
Automated Detection and Classification of Soccer Field Objects using YOLOv7 and Computer Vision Techniques

In the last two decades, many technologies have been deployed and utilized in Soccer games (Football) as a result to the huge investment of Federation of International Football Association (FIFA). These technologies aim to monitor and track all soccer match objects including players and the ball itself in order to measure the player performance, and tracking the players’ positions and movements at the field. Latest emerging artificial intelligence and computer vision techniques are being used recently in many systems and deployed in different scenarios. Identifying all field objects automatically has to be the first step in the monitoring process of soccer games. In this paper, we are proposing an automated system that has the ability to detect and track the ball and to detect and classify players and referees on the soccer field. The proposed system implements a detection model using a real-time object detection model YOLOv7 to detect the ball and all humans on the field after building a labeled dataset of 1300 different soccer game frames. It also deploys Improved Color Coherence Vector (ICCV) features to classify all humans on the field to five classes (Team1, Team2, Goalkeeper1, Goalkeeper2, and Referee) using K-Nearest Neighbor algorithm. The proposed system has achieved high accuracy in both the detection and classification modules.
Protecting Your Voice from Speech Synthesis Attacks

In recent years, much attention has been paid to speech synthesis, which aims to generate synthetic speeches in a voice of a target speaker. Although the speech synthesis technique has facilitated a wide spectrum of applications that positively impact our daily lives, it can also be used by attackers to perform speech synthesis attacks. An attacker can use this technique to mimic the voice of a victim and transform arbitrarily chosen text or voice samples into the same content spoken by the victim. To protect a speaker’s voice from speech synthesis attacks, in this paper, we propose two novel defense schemes that can be used by the speaker to process his or her speeches before publishing them on social media platforms or sending them to others. The processed speeches cannot only significantly degrade the performance of speech synthesis systems but also keep the sound of the speaker’s voice so that they can still be used for normal purposes. The desirable performance of the proposed defense schemes is verified through extensive experiments conducted on several real-world speaker recognition (SR) systems and a user study on a public crowdsourcing platform.