Reviewerly/exHarmony huggingface dataset:
Perancangan Media Presentasi Alternatif Menggunakan Focusky Pada Matakuliah Jaringan Komputer di Program Studi PTIK UIN Bukittinggi

Abstract— The implemlelntation of computer network lectures in the delivery of learlning materials already uses media such as zoom meetings and pdf files. Basically there is nothing wrong in using the media. However, the authors argue that it would be better if it was comblined with the use of presentation medlia-based technology. Meanwhile students said that the material provided was quite understandable, but there was a feleling of bolredom during learning because the media was monotonous in the form of pdf files only, and the contents of each slide were too dense/many. Based on this, the solution given is to design a media presentation.&#x0D; Research on the deslign of preselntation media using focusky on computer network courses at the PTIK IAIN Bukittinggi study program uses the Research and Development (R&amp;D) method with the DDD-E development model. Media development using the DDD-E model consists of (1) Decide or set progrlam goals and materials, (2) Design or design, namely creating program structures, (3) Develop or developing is prolducing media elements and making media appearance, (4) Evaluate or evaluate, namely checking the entire design and development process. The product tests used in this study are validity tests, practicality tests, and effectiveness tests.&#x0D; The results of the product test that the author did obtained a validity test from 4 validlators obtained a value of 0.85 with a very valid category, a practiclality test from 3 practicators obtained a value of 0.94 with a very high category, and the effectiveness test from 31 assessors obtained a value of 0, 88 with very effectlive category.&#x0D; Keyword: Presentaltion Media, Foclusky, Research and Development (R&amp;D)
GERAKAN LITERASI SEKOLAH: TANTANGAN LITERASI DI ERA DIGITAL DAN CARA MENGATASINYA

In this article, we will explore the concept of the school literacy movement as well as the literacy challenges in the digital age that need to be addressed. In addition, we will describe several effective ways to overcome these challenges in order to create a literacy environment that is inclusive, adaptive and responsive to technological developments. By understanding the challenges of literacy in the digital era and implementing appropriate strategies, the School Literacy Movement can become a driving force that encourages the progress of student literacy in this digital era. The method used in this research is qualitative method. The purpose of this research is to provide an understanding of literacy challenges in the digital era and how to overcome them in the context of the School Literacy Movement. The data sources for this study come from books, journals and reading materials that are relevant to the discussion in this study.
Sistem Keamanan Kendaraan Roda Dua Berbasis Internet Of Things (IOT)

The high rate of theft of two-wheeled vehicles makes vehicle owners afraid to leave their vehicles. Most vehicle owners increase the security of their vehicles by providing a padlock key that is placed on the motorcycle disc.Along with the increasing acts of motorcycle theft, vehicle security must also be improved, so that vehicle owners are not afraid if they have to leave their vehicle. Therefore the author wants to create a security system by utilizing a smartphone as a vehicle control tool remotely. In this study a vehicle security system was designed with fingerprints as keys and realtime monitoring of vehicles via telegram by utilizing Arduino Uno, Relay, FingerPrint Sensor, ESP32 CAM and NodeMCU. This study used an experimental method which directly tested the vehicle. The test was carried out with 10 different fingerprints, 8 finger positions and 5 fingerprint obstacles. The test results show that the system can execute commands with an average delay time of 9.11 seconds and can provide notifications in the form of messages to Telegram and can be monitored in real time when someone tries to activate the vehicle.&#x0D;
INVESTIGATION OF THE PROBLEMS OF PROGRAMMING KNOWLEDGE ASSESSING IN THE CONTEXT OF DISTANCE LEARNING ON THE MOODLE PLATFORM

The article investigates the problems related to assessing students’ knowledge of programming in distance learning and possible ways to solve them. The following problems are considered: the complexity of assessment in the dynamics, fraud during distance assessment, limited interaction and feedback, unequal opportunities for students to access resources and technologies, and the complexity of assessing practical problem-solving skills. The relevance of the work is due to the increased workload of the teacher in distance learning and the difficulty of qualitative assessment of students due to the lack of direct communication. The research conducted in this paper is based on the capabilities of the Moodle platform for online testing of students’ knowledge. The analysis of statistics on the use of different types of Moodle tests shows that most teachers prefer the simplest types of multiple-choice tests with the possibility of mixing questions and answer options. Such a choice does not contribute to the qualitative assessment of programming knowledge. The article investigates the possibilities of automatic test creation and their effectiveness in improving the quality of assessment and preventing fraud during testing, and provides examples of test questions creation. The paper proposes a classification of tasks and test questions for a basic programming course by type. The results of pedagogical experiments of determining the percentage difficulty of different types of tasks of this classification during teaching programming at the Kremenchuk Mykhailo Ostrohradskyi National University using the online learning system krnu.org, built on the Moodle platform, are presented. The article substantiates the effectiveness of using the proposed classification of tasks by teachers to improve the quality and objectivity of assessing the acquired skills and competencies during studying a basic programming course. The article concludes that research on improving assessment tools in distance learning remains relevant and perspective against the background of further development of digital education.
METHODS OF INCREASING THE LEVEL OF EFFICIENCY OF INDEPENDENT WORK OF MILITARY ACADEMY CADETS

The article is devoted to the study of methods of organizing independent work and practical training of cadets in higher military educational institutions, as well as the features and possibilities of introducing new forms of independent work in conditions of martial law, strict compliance with the requirements of military regulations and creating conditions for cadets to obtain quality knowledge, skills and abilities. The article provides an analysis of theoretical research on this topic and indicates the main features of the educational process in a military educational institution of higher education. The relevance of scientific research on the use of the potential of self-control and independent work of cadets of a higher military educational institution is determined by the system of contradictions between: the prevalence of directive methods of educational activity in the higher education system and the inability of first-year students to work effectively independently; declaring the need to saturate the content of education with fundamental scientific values and the lack of a sufficient level of knowledge for freshmen to adapt to study in a military educational institution. Independent work of cadets in the natural sciences contributes to the formation of skills: to systematize information using mathematical methods for solving applied and military-applied problems, to plan and model professional activity, to analyze and critically evaluate professional activity, to think independently, to ensure the process of developing self-organization and self-control skills in cognitive activity. Modernization of modern education involves modernization of the content of higher military education, reorientation of learning goals, restructuring of its structure, introduction of new technologies for studying disciplines, integration and differentiation of knowledge. Therefore, the search for new effective ways to improve the educational process continues in pedagogical science today. Improving the quality of training for cadets is implemented through the development and implementation of new pedagogical technologies and methods, as well as the improvement of existing ones. It is important to teach a person to independently navigate information successfully and use it critically. For this, it is necessary to develop the ability of the future military specialist to think creatively, outside the box, to independently replenish his knowledge. The authors believe that in order to carry out high-quality independent work by cadets in the natural sciences, it must be properly organized under the guidance of commanders and teachers, then it will contribute to the formation of the ability to systematize information, use methods for solving applied and military-special problems, plan and model professional activities , analyze and critically evaluate the results of their activities, think independently, provides the process of developing self-organization skills and self-control of any activity for continuous education throughout life.
KORELASI KEBIASAAN MEMBACA MANGA DENGAN KEMAMPUAN KANJI PADA MAHASISWA BAHASA JEPANG UNIVERSITAS NEGERI PADANG

This study aims to see how strong the relationship between reading manga habits and students' kanji abilities is. The design of this study is a correlation design. The population in this study amounted to 100 students. The sampling technique used in this study was purposive sampling with a sample of 80 students. This study uses a questionnaire to see the habit of reading manga and a test to determine the students' kanji ability. The results of this study are: First, the average value of students' habits in reading manga is 77.33. Second, the students' kanji ability is 82.25. Third, the correlation coefficient of the two variables; manga reading habit and kanji ability is 0.784. Thus, the researcher proves that is rejected and is accepted, meaning that there is a significant relationship between students' habits in reading manga and students' kanji abilities.
Secure Arcade: A Gamified Defense Against Cyber Attacks

In modernity, we continually receive increasingly intricate technologies that allow us to increase our lives convenience and efficiency. Our technology, particularly technology available over the internet, is advancing at unprecedented speed. However, this speed of advancement allows those behind malicious attacks to have an increasingly easier time taking advantage of those who know little about computer security. Unfortunately, education in the computer security field is generally limited only to tertiary education. This research addresses this problem through a gamified web-based application that drives users to reach learning goals to help them become more vigilant internet users: 1. Learn and memorize general computer security terminology, 2. Become familiar with basic cryptography concepts, 3. Learn to recognize potential phishing scams via email quickly, and 4. Learn common attacks on servers and how to deal with them.
A Unified Approach for Text- and Image-guided 4D Scene Generation

Large-scale diffusion generative models are greatly simplifying image, video and 3D asset creation from user-provided text prompts and images. However, the challenging problem of text-to-4D dynamic 3D scene generation with diffusion guidance remains largely unexplored. We propose Dream-in-4D, which features a novel two-stage approach for text-to-4D synthesis, leveraging (1) 3D and 2D diffusion guidance to effectively learn a high-quality static 3D asset in the first stage; (2) a deformable neural radiance field that explicitly disentangles the learned static asset from its deformation, preserving quality during motion learning; and (3) a multi-resolution feature grid for the deformation field with a displacement total variation loss to effectively learn motion with video diffusion guidance in the second stage. Through a user preference study, we demonstrate that our approach significantly advances image and motion quality, 3D consistency and text fidelity for text-to-4D generation compared to baseline approaches. Thanks to its motion-disentangled representation, Dream-in-4D can also be easily adapted for controllable generation where appearance is defined by one or multiple images, without the need to modify the motion learning stage. Thus, our method offers, for the first time, a unified approach for text-to-4D, image-to-4D and personalized 4D generation tasks.
Generative Data Augmentation Improves Scribble-supervised Semantic Segmentation

Recent advances in generative models, such as diffusion models, have made generating high-quality synthetic images widely accessible. Prior works have shown that training on synthetic images improves many perception tasks, such as image classification, object detection, and semantic segmentation. We are the first to explore generative data augmentations for scribble-supervised semantic segmentation. We propose a generative data augmentation method that leverages a ControlNet diffusion model conditioned on semantic scribbles to produce high-quality training data. However, naive implementations of generative data augmentations may inadvertently harm the performance of the downstream segmentor rather than improve it. We leverage classifier-free diffusion guidance to enforce class consistency and introduce encode ratios to trade off data diversity for data realism. Using the guidance scale and encode ratio, we are able to generate a spectrum of high-quality training images. We propose multiple augmentation schemes and find that these schemes significantly impact model performance, especially in the low-data regime. Our framework further reduces the gap between the performance of scribble-supervised segmentation and that of fully-supervised segmentation. We also show that our framework significantly improves segmentation performance on small datasets, even surpassing fully-supervised segmentation.
Vulnerability Analysis of Transformer-based Optical Character Recognition to Adversarial Attacks

Recent advancements in Optical Character Recognition (OCR) have been driven by transformer-based models. OCR systems are critical in numerous high-stakes domains, yet their vulnerability to adversarial attack remains largely uncharted territory, raising concerns about security and compliance with emerging AI regulations. In this work we present a novel framework to assess the resilience of Transformer-based OCR (TrOCR) models. We develop and assess algorithms for both targeted and untargeted attacks. For the untargeted case, we measure the Character Error Rate (CER), while for the targeted case we use the success ratio. We find that TrOCR is highly vulnerable to untargeted attacks and somewhat less vulnerable to targeted attacks. On a benchmark handwriting data set, untargeted attacks can cause a CER of more than 1 without being noticeable to the eye. With a similar perturbation size, targeted attacks can lead to success rates of around $25\%$ -- here we attacked single tokens, requiring TrOCR to output the tenth most likely token from a large vocabulary.
Plug-and-Play, Dense-Label-Free Extraction of Open-Vocabulary Semantic Segmentation from Vision-Language Models

From image-text pairs, large-scale vision-language models (VLMs) learn to implicitly associate image regions with words, which prove effective for tasks like visual question answering. However, leveraging the learned association for open-vocabulary semantic segmentation remains a challenge. In this paper, we propose a simple, yet extremely effective, training-free technique, Plug-and-Play Open-Vocabulary Semantic Segmentation (PnP-OVSS) for this task. PnP-OVSS leverages a VLM with direct text-to-image cross-attention and an image-text matching loss. To balance between over-segmentation and under-segmentation, we introduce Salience Dropout; by iteratively dropping patches that the model is most attentive to, we are able to better resolve the entire extent of the segmentation mask. \shortname{} does not require any neural network training and performs hyperparameter tuning without the need for any segmentation annotations, even for a validation set. PnP-OVSS demonstrates substantial improvements over comparable baselines (+29.4% mIoU on Pascal VOC, +13.2% mIoU on Pascal Context, +14.0% mIoU on MS COCO, and +11.4% mIoU on ADE-20K.) and even outperforms most baselines that conduct additional network training on top of pretrained VLMs. Our codebase is at https://github.com/letitiabanana/PnP-OVSS.
Multi-Scale 3D Gaussian Splatting for Anti-Aliased Rendering

3D Gaussians have recently emerged as a highly efficient representation for 3D reconstruction and rendering. Despite its high rendering quality and speed at high resolutions, they both deteriorate drastically when rendered at lower resolutions or from far away camera position. During low resolution or far away rendering, the pixel size of the image can fall below the Nyquist frequency compared to the screen size of each splatted 3D Gaussian and leads to aliasing effect. The rendering is also drastically slowed down by the sequential alpha blending of more splatted Gaussians per pixel. To address these issues, we propose a multi-scale 3D Gaussian splatting algorithm, which maintains Gaussians at different scales to represent the same scene. Higher-resolution images are rendered with more small Gaussians, and lower-resolution images are rendered with fewer larger Gaussians. With similar training time, our algorithm can achieve 13\%-66\% PSNR and 160\%-2400\% rendering speed improvement at 4$\times$-128$\times$ scale rendering on Mip-NeRF360 dataset compared to the single scale 3D Gaussian splatting.
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play

Recent advances in Competitive Self-Play (CSP) have achieved, or even surpassed, human level performance in complex game environments such as Dota 2 and StarCraft II using Distributed Multi-Agent Reinforcement Learning (MARL). One core component of these methods relies on creating a pool of learning agents -- consisting of the Main Agent, past versions of this agent, and Exploiter Agents -- where Exploiter Agents learn counter-strategies to the Main Agents. A key drawback of these approaches is the large computational cost and physical time that is required to train the system, making them impractical to deploy in highly iterative real-life settings such as video game productions. In this paper, we propose the Minimax Exploiter, a game theoretic approach to exploiting Main Agents that leverages knowledge of its opponents, leading to significant increases in data efficiency. We validate our approach in a diversity of settings, including simple turn based games, the arcade learning environment, and For Honor, a modern video game. The Minimax Exploiter consistently outperforms strong baselines, demonstrating improved stability and data efficiency, leading to a robust CSP-MARL method that is both flexible and easy to deploy.
Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder

Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics. In this paper, we present Set-VAE, a particle-based variational autoencoder (VAE) anomaly detection algorithm. We demonstrate a 2x signal efficiency gain compared with traditional subjettiness-based jet selection. Furthermore, with an eye to the future deployment to trigger systems, we propose the CLIP-VAE, which reduces the inference-time cost of anomaly detection by using the KL-divergence loss as the anomaly score, resulting in a 2x acceleration in latency and reducing the caching requirement.
On Commutative Penalty Functions in Parent-Hamiltonian Constructions

There are several known techniques to construct a Hamiltonian with an expected value that is minimized uniquely by a given quantum state. Common approaches include the parent Hamiltonian construction from matrix product states, building approximate ground state projectors, and, in a common case, developing penalty functions from the generalized Ising model. Here we consider the framework that enables one to engineer exact parent Hamiltonians from commuting polynomials. We derive elementary classification results of quadratic Ising parent Hamiltonians and to generally derive a non-injective parent Hamiltonian construction. We also consider that any $n$-qubit stabilizer state has a commutative parent Hamiltonian with $n+1$ terms and we develop an approach that allows the derivation of parent Hamiltonians by composition of network elements that embed the truth tables of discrete functions into a kernel space. This work presents a unifying framework that captures components of what is known about exact parent Hamiltonians and bridges a few techniques across the domains that are concerned with such constructions.
Entanglement of Quantum States which are Zero on the Symmetric Sector

We consider a quantum system of n qudits and the Clebsch-Gordan decomposition of the associated Hilbert space. In this decomposition, one of the subspaces is the so-called symmetric subspace or symmetric sector, that is, the subspace of all states that are invariant under the action of the symmetric group. We prove that any separable state must have a nonzero component on the symmetric sector, or, equivalently, any state which has zero component on the symmetric sector must be entangled. For the cases of n=2,3 particles, and in arbitrary dimension d, this result can be refined by providing sharp lower bounds on the size of the component of separable states on the symmetric sector. This leads us to identify a class of entanglement witnesses for these systems. We provide an example showing that in the multipartite case, this class of witnesses detects PPT entangled states.
Quantum circuit synthesis via random combinatorial search

We use a random search technique to find quantum gate sequences that implement perfect quantum state preparation or unitary operator synthesis with arbitrary targets. This approach is based on the recent discovery that there is a large multiplicity of quantum circuits that achieve unit fidelity in performing the desired target operation, even at the minimum number of single-qubit and two-qubit gates needed to achieve unit fidelity. We show that the fraction of perfect-fidelity quantum circuits increases rapidly as soon as the circuit size exceeds the minimum circuit size required for achieving unit fidelity. In addition to analyzing the case where the CNOT gate is the elementary two-qubit gate, we analyze the case where the two-qubit gate is the B gate, which can reduce the minimum quantum circuit size. We apply the random search method to the problem of decomposing the 4-qubit Toffoli gate and find a 15 CNOT-gate decomposition.