Reviewerly/exHarmony huggingface dataset:
RANCANG BANGUN ANIMASI PERCAKAPAN BAHASA INGGRIS BERBASIS 2D MENGGUNAKAN TEKNIK FRAME BY FRAME

This research focuses on the Animation Design of “English Conversation”2D Based Using Frame By Frame Technique. The purpose of this research is to facilitate teacher in delivering English conversation material and to increase interest student learning and increase enthusiasm for learning. The method used is the method research and development (R&amp;D) which can be defined as “a scientific way of researching, designing, producing and testing questionnaires and product validity that have been generated".The results showed that this animated video was successfully made (designed and built) to make it easier for language teachersbased on the percentage of validation results that is 92.30% with very decent criteria and can increase students' interest in learninglearning and enthusiasm in learn English conversation based on the percentage of learning questionnaire results without using animated videos, which is 43%with less criteria and the percentage of results from learning questionnaires using animated videos is 84% with decent criteria, so it can be declared increased 41%.
ANALISIS TINGKAT KEPUASAN PENGGUNA TERHADAP PENERAPAN LEARNING MANAGEMENT SYSTEM (LMS) UJIAN ONLINE MENGGUNAKAN METODE E-SERVQUAL

Muhammadiyah Bangkinang High School. SMA Muhammadiyah Bangkinag is one of the schools under the Muhammadiyah organization. The Learning Management System (LMS) owned by this school is Muhammadiyah Online Learning (MOLA). The method used in this research is the Electronic Service Quality (E-Servqual) method. This e-Servqual method has seven dimensions, namely efficiency, reliability, responsiveness, privacy,fulfillment, compensation and contact. The purpose of this study was to determine how much influence the quality of the online MOLA exam LMS on user satisfaction. The sampling technique in this research is using random sampling technique. From the resultsof the F test as a whole, the calculated F value is 34,159 &gt; F table 2,10, so it can be concluded that H1 is accepted which means that there is an effect of the E-Servqual variable (X) having a simultaneous (simultaneous) effect on the user satisfaction variable (Y). ). For the results of R square has a value of 0.727, this means that the effect of the E-Servqual variable together or simultaneously in explaining the dependent variable, namely the user satisfaction variable (Y) is 72%. Based on the results of data processing, it shows that the level of user satisfaction with the implementation of the Learning Management System (LMS) online exam according to students is not satisfactory because there are variables that have negative values.
A New Approach of an Image Watermarking Technique Based on DWT DCT and SVD, ARNOLD

There has been rising use of SVD, the digital watermarking tools in change domain has really evolved. It is based on DWT, DCT and SVD, we prepare a new watermarking system for algorithmic image. Our experimental results show that this practice combines the advantages of these three transforms. We can see compared with the SVD and the DCT plus SVD system the projected method has stronger robustness and faster speed in implanting and extracting.
JNDMix: JND-Based Data Augmentation for No-reference Image Quality Assessment

Despite substantial progress in no-reference image quality assessment (NR-IQA), previous training models often suffer from over-fitting due to the limited scale of used datasets, resulting in model performance bottlenecks. To tackle this challenge, we explore the potential of leveraging data augmentation to improve data efficiency and enhance model robustness. However, most existing data augmentation methods incur a serious issue, namely that it alters the image quality and leads to training images mismatching with their original labels. Additionally, although only a few data augmentation methods are available for NR-IQA task, their ability to enrich dataset diversity is still insufficient. To address these issues, we propose a effective and general data augmentation based on just noticeable difference (JND) noise mixing for NR-IQA task, named JNDMix. In detail, we randomly inject the JND noise, imperceptible to the human visual system (HVS), into the training image without any adjustment to its label. Extensive experiments demonstrate that JNDMix significantly improves the performance and data efficiency of various state-of-the-art NR-IQA models and the commonly used baseline models, as well as the generalization ability. More importantly, JNDMix facilitates MANIQA to achieve the state-of-the-art performance on LIVEC and KonIQ-10k.
TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation

In federated learning, a large number of users collaborate to learn a global model. They alternate local computations and communication with a distant server. Communication, which can be slow and costly, is the main bottleneck in this setting. In addition to communication-efficiency, a robust algorithm should allow for partial participation, the desirable feature that not all clients need to participate to every round of the training process. To reduce the communication load and therefore accelerate distributed gradient descent, two strategies are popular: 1) communicate less frequently; that is, perform several iterations of local computations between the communication rounds; and 2) communicate compressed information instead of full-dimensional vectors. We propose TAMUNA, the first algorithm for distributed optimization and federated learning, which harnesses these two strategies jointly and allows for partial participation. TAMUNA converges linearly to an exact solution in the strongly convex setting, with a doubly accelerated rate: it provably benefits from the two acceleration mechanisms provided by local training and compression, namely a better dependency on the condition number of the functions and on the model dimension, respectively.
Unsupervised Layer-wise Score Aggregation for Textual OOD Detection

Out-of-distribution (OOD) detection is a rapidly growing field due to new robustness and security requirements driven by an increased number of AI-based systems. Existing OOD textual detectors often rely on an anomaly score (e.g., Mahalanobis distance) computed on the embedding output of the last layer of the encoder. In this work, we observe that OOD detection performance varies greatly depending on the task and layer output. More importantly, we show that the usual choice (the last layer) is rarely the best one for OOD detection and that far better results could be achieved if the best layer were picked. To leverage this observation, we propose a data-driven, unsupervised method to combine layer-wise anomaly scores. In addition, we extend classical textual OOD benchmarks by including classification tasks with a greater number of classes (up to 77), which reflects more realistic settings. On this augmented benchmark, we show that the proposed post-aggregation methods achieve robust and consistent results while removing manual feature selection altogether. Their performance achieves near oracle's best layer performance.
All-microwave leakage reduction units for quantum error correction with superconducting transmon qubits

Minimizing leakage from computational states is a challenge when using many-level systems like superconducting quantum circuits as qubits. We realize and extend the quantum-hardware-efficient, all-microwave leakage reduction unit (LRU) for transmons in a circuit QED architecture proposed by Battistel et al. This LRU effectively reduces leakage in the second- and third-excited transmon states with up to $99\% $ efficacy in $220~\mathrm{ns}$, with minimum impact on the qubit subspace. As a first application in the context of quantum error correction, we demonstrate the ability of multiple simultaneous LRUs to reduce the error detection rate and to suppress leakage buildup within $1\%$ in data and ancilla qubits over 50 cycles of a weight-2 parity measurement.
Can discrete information extraction prompts generalize across language models?

We study whether automatically-induced prompts that effectively extract information from a language model can also be used, out-of-the-box, to probe other language models for the same information. After confirming that discrete prompts induced with the AutoPrompt algorithm outperform manual and semi-manual prompts on the slot-filling task, we demonstrate a drop in performance for AutoPrompt prompts learned on a model and tested on another. We introduce a way to induce prompts by mixing language models at training time that results in prompts that generalize well across models. We conduct an extensive analysis of the induced prompts, finding that the more general prompts include a larger proportion of existing English words and have a less order-dependent and more uniform distribution of information across their component tokens. Our work provides preliminary evidence that it's possible to generate discrete prompts that can be induced once and used with a number of different models, and gives insights on the properties characterizing such prompts.
General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment

Compared to 2D images, 3D point clouds are much more sensitive to rotations. We expect the point features describing certain patterns to keep invariant to the rotation transformation. There are many recent SOTA works dedicated to rotation-invariant learning for 3D point clouds. However, current rotation-invariant methods lack generalizability on the point clouds in the open scenes due to the reliance on the global distribution, \ie the global scene and backgrounds. Considering that the output activation is a function of the pattern and its orientation, we need to eliminate the effect of the orientation.In this paper, inspired by the idea that the network weights can be considered a set of points distributed in the same 3D space as the input points, we propose Weight-Feature Alignment (WFA) to construct a local Invariant Reference Frame (IRF) via aligning the features with the principal axes of the network weights. Our WFA algorithm provides a general solution for the point clouds of all scenes. WFA ensures the model achieves the target that the response activity is a necessary and sufficient condition of the pattern matching degree. Practically, we perform experiments on the point clouds of both single objects and open large-range scenes. The results suggest that our method almost bridges the gap between rotation invariance learning and normal methods.
Interactive Face Video Coding: A Generative Compression Framework

In this paper, we propose a novel framework for Interactive Face Video Coding (IFVC), which allows humans to interact with the intrinsic visual representations instead of the signals. The proposed solution enjoys several distinct advantages, including ultra-compact representation, low delay interaction, and vivid expression and headpose animation. In particular, we propose the Internal Dimension Increase (IDI) based representation, greatly enhancing the fidelity and flexibility in rendering the appearance while maintaining reasonable representation cost. By leveraging strong statistical regularities, the visual signals can be effectively projected into controllable semantics in the three dimensional space (e.g., mouth motion, eye blinking, head rotation and head translation), which are compressed and transmitted. The editable bitstream, which naturally supports the interactivity at the semantic level, can synthesize the face frames via the strong inference ability of the deep generative model. Experimental results have demonstrated the performance superiority and application prospects of our proposed IFVC scheme. In particular, the proposed scheme not only outperforms the state-of-the-art video coding standard Versatile Video Coding (VVC) and the latest generative compression schemes in terms of rate-distortion performance for face videos, but also enables the interactive coding without introducing additional manipulation processes. Furthermore, the proposed framework is expected to shed lights on the future design of the digital human communication in the metaverse.
Unsupervised OmniMVS: Efficient Omnidirectional Depth Inference via Establishing Pseudo-Stereo Supervision

Omnidirectional multi-view stereo (MVS) vision is attractive for its ultra-wide field-of-view (FoV), enabling machines to perceive 360{\deg} 3D surroundings. However, the existing solutions require expensive dense depth labels for supervision, making them impractical in real-world applications. In this paper, we propose the first unsupervised omnidirectional MVS framework based on multiple fisheye images. To this end, we project all images to a virtual view center and composite two panoramic images with spherical geometry from two pairs of back-to-back fisheye images. The two 360{\deg} images formulate a stereo pair with a special pose, and the photometric consistency is leveraged to establish the unsupervised constraint, which we term "Pseudo-Stereo Supervision". In addition, we propose Un-OmniMVS, an efficient unsupervised omnidirectional MVS network, to facilitate the inference speed with two efficient components. First, a novel feature extractor with frequency attention is proposed to simultaneously capture the non-local Fourier features and local spatial features, explicitly facilitating the feature representation. Then, a variance-based light cost volume is put forward to reduce the computational complexity. Experiments exhibit that the performance of our unsupervised solution is competitive to that of the state-of-the-art (SoTA) supervised methods with better generalization in real-world data.
Adversarial examples within the training distribution: A widespread challenge

Despite a plethora of proposed theories, understanding why deep neural networks are susceptible to adversarial attacks remains an open question. A promising recent strand of research investigates adversarial attacks within the training data distribution, providing a more stringent and worrisome definition for these attacks. These theories posit that the key issue is that in high dimensional datasets, most data points are close to the ground-truth class boundaries. This has been shown in theory for some simple data distributions, but it is unclear if this theory is relevant in practice. Here, we demonstrate the existence of in-distribution adversarial examples for object recognition. This result provides evidence supporting theories attributing adversarial examples to the proximity of data to ground-truth class boundaries, and calls into question other theories which do not account for this more stringent definition of adversarial attacks. These experiments are enabled by our novel gradient-free, evolutionary strategies (ES) based approach for finding in-distribution adversarial examples in 3D rendered objects, which we call CMA-Search.
Stability-based Generalization Analysis for Mixtures of Pointwise and Pairwise Learning

Recently, some mixture algorithms of pointwise and pairwise learning (PPL) have been formulated by employing the hybrid error metric of "pointwise loss + pairwise loss" and have shown empirical effectiveness on feature selection, ranking and recommendation tasks. However, to the best of our knowledge, the learning theory foundation of PPL has not been touched in the existing works. In this paper, we try to fill this theoretical gap by investigating the generalization properties of PPL. After extending the definitions of algorithmic stability to the PPL setting, we establish the high-probability generalization bounds for uniformly stable PPL algorithms. Moreover, explicit convergence rates of stochastic gradient descent (SGD) and regularized risk minimization (RRM) for PPL are stated by developing the stability analysis technique of pairwise learning. In addition, the refined generalization bounds of PPL are obtained by replacing uniform stability with on-average stability.
Social4Rec: Distilling User Preference from Social Graph for Video Recommendation in Tencent

Despite recommender systems play a key role in network content platforms, mining the user's interests is still a significant challenge. Existing works predict the user interest by utilizing user behaviors, i.e., clicks, views, etc., but current solutions are ineffective when users perform unsettled activities. The latter ones involve new users, which have few activities of any kind, and sparse users who have low-frequency behaviors. We uniformly describe both these user-types as "cold users", which are very common but often neglected in network content platforms. To address this issue, we enhance the representation of the user interest by combining his social interest, e.g., friendship, following bloggers, interest groups, etc., with the activity behaviors. Thus, in this work, we present a novel algorithm entitled SocialNet, which adopts a two-stage method to progressively extract the coarse-grained and fine-grained social interest. Our technique then concatenates SocialNet's output with the original user representation to get the final user representation that combines behavior interests and social interests. Offline experiments on Tencent video's recommender system demonstrate the superiority over the baseline behavior-based model. The online experiment also shows a significant performance improvement in clicks and view time in the real-world recommendation system. The source code is available at https://github.com/Social4Rec/SocialNet.
Digital identity architectures: comparing goals and vulnerabilities

Digital identity systems have the promise of efficiently facilitating access to services for a nation's citizens while increasing security and convenience. There are many possible system architectures, each with strengths and weaknesses that should be carefully considered. This report first establishes a set of goals and vulnerabilities faced by any identity system, then evaluates the trade-offs of common digital identity architectures, principally comparing centralised and decentralised systems.
Online Learning: Signal Integration and Augmented Reality Helping Children with Down Syndrome

Purpose: This study focuses on how online learning can aid in the development of the Augmented Reality (AR) technology, "Mar-Care”, which possesses features that will attract elementary students to learn Makaton. Makaton is a Sign Language whose inception dates back to around 1972/73 and it was meant to be used by people with learning disabilities to help develop language and communication skills. This study online learning involves AR technology and the use of Padlet as e-learning platforms a forum for students to discuss and express their opinions.&#x0D; Design: The methods used throughout the production of Mar-Care application technology education learning online tools are the Padlet platform.&#x0D; Findings: Visual representation of the basic hand gestures through 3D animation is shown on the Padlet to explain online learning&#x0D; Research limitations: This study has developed an online medium for online learning.&#x0D; Practical implications: A large number of software used to produce online learning tools successfully make the discussion space between students and teachers.&#x0D; Paper type: Research Paper
On the Metrics for Evaluating Monocular Depth Estimation

Monocular Depth Estimation (MDE) is performed to produce 3D information that can be used in downstream tasks such as those related to on-board perception for Autonomous Vehicles (AVs) or driver assistance. Therefore, a relevant arising question is whether the standard metrics for MDE assessment are a good indicator of the accuracy of future MDE-based driving-related perception tasks. We address this question in this paper. In particular, we take the task of 3D object detection on point clouds as a proxy of on-board perception. We train and test state-of-the-art 3D object detectors using 3D point clouds coming from MDE models. We confront the ranking of object detection results with the ranking given by the depth estimation metrics of the MDE models. We conclude that, indeed, MDE evaluation metrics give rise to a ranking of methods that reflects relatively well the 3D object detection results we may expect. Among the different metrics, the absolute relative (abs-rel) error seems to be the best for that purpose.
Differentiable Arbitrating in Zero-sum Markov Games

We initiate the study of how to perturb the reward in a zero-sum Markov game with two players to induce a desirable Nash equilibrium, namely arbitrating. Such a problem admits a bi-level optimization formulation. The lower level requires solving the Nash equilibrium under a given reward function, which makes the overall problem challenging to optimize in an end-to-end way. We propose a backpropagation scheme that differentiates through the Nash equilibrium, which provides the gradient feedback for the upper level. In particular, our method only requires a black-box solver for the (regularized) Nash equilibrium (NE). We develop the convergence analysis for the proposed framework with proper black-box NE solvers and demonstrate the empirical successes in two multi-agent reinforcement learning (MARL) environments.
Quantum routing in planar graph using perfect state transfer

In this article, we consider a spin-spin interaction network governed by $XX + YY$ Hamiltonian. The vertices and edges of the network represent the spin objects and their interactions, respectively. We take a privilege to switch on or off any interaction, that assists us to perform multiple perfect state transfers in a graph simultaneously. We also build up a salable network allowing quantum communication between two arbitrary vertices. Later we utilize the combinatorial characteristics of hypercube graphs to propose a static routing schema to communicate simultaneously between a set of senders and a set of receivers in a planar network. Our construction is new and significantly powerful. We elaborate multiple examples of planar graphs supporting quantum routing where classical routing is not possible.
Classification of Booster Vaccination Symptoms Using Naive Bayes Algorithm and C4.5

Covid-19 is a respiratory infection that is transmitted through the air. The first case was reported on March 2, 2020, to be precise in Depok, West Java, Indonesia. To reduce the number of corona virus sufferers, the government has made various efforts including policies to limit activities outside the home, online learning, work from home, and even worship activities. To reduce the number of people infected with the Covid-19 virus, efforts are being made, one of which is the provision of vaccines. In this study, the types of booster vaccines are Pfizer and AstraZeneca. Due to the symptoms caused by the condition of the patient after vaccination, the researchers used the Naive Bayes Algorithm and C4.5 methods with attributes including gender, age, comorbidities (comorbidities), temperature, blood pressure, Covid 19 survivors &gt; 1 month, pregnant condition, type of vaccine. primer and booster vaccine types which aim to get the highest accuracy value between the two algorithm methods which are tested using cross validation on the RapidMiner Studio tool. And obtained the Naive Bayes algorithm method with the highest accuracy value of 78.82%. &#x0D; Keywords: Covid 19, booster, AEFI, Naive Bayes, C4.5, Rapid Miner