Reviewerly/exHarmony huggingface dataset:
MSEVA : A System for Multimodal Short Videos Emotion Visual Analysis

YouTube Shorts, a new section launched by YouTube in 2021, is a direct competitor to short video platforms like TikTok. It reflects the rising demand for short video content among online users. Social media platforms are often flooded with short videos that capture different perspectives and emotions on hot events. These videos can go viral and have a significant impact on the public's mood and views. However, short videos' affective computing was a neglected area of research in the past. Monitoring the public's emotions through these videos requires a lot of time and effort, which may not be enough to prevent undesirable outcomes. In this paper, we create the first multimodal dataset of short video news covering hot events. We also propose an automatic technique for audio segmenting and transcribing. In addition, we improve the accuracy of the multimodal affective computing model by about 4.17% by optimizing it. Moreover, a novel system MSEVA for emotion analysis of short videos is proposed. Achieving good results on the bili-news dataset, the MSEVA system applies the multimodal emotion analysis method in the real world. It is helpful to conduct timely public opinion guidance and stop the spread of negative emotions. Data and code from our investigations can be accessed at: http://xxx.github.com.
Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data

Federated learning is an emerging distributed machine learning framework aiming at protecting data privacy. Data heterogeneity is one of the core challenges in federated learning, which could severely degrade the convergence rate and prediction performance of deep neural networks. To address this issue, we develop a novel personalized federated learning framework for heterogeneous data, which we refer to as FedSplit. This modeling framework is motivated by the finding that, data in different clients contain both common knowledge and personalized knowledge. Then the hidden elements in each neural layer can be split into the shared and personalized groups. With this decomposition, a novel objective function is established and optimized. We demonstrate FedSplit enjoyers a faster convergence speed than the standard federated learning method both theoretically and empirically. The generalization bound of the FedSplit method is also studied. To practically implement the proposed method on real datasets, factor analysis is introduced to facilitate the decoupling of hidden elements. This leads to a practically implemented model for FedSplit and we further refer to as FedFac. We demonstrated by simulation studies that, using factor analysis can well recover the underlying shared/personalized decomposition. The superior prediction performance of FedFac is further verified empirically by comparison with various state-of-the-art federated learning methods on several real datasets.
Towards a Perceptual Evaluation Framework for Lighting Estimation

Progress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets. While this may appear to be a reasonable approach, we demonstrate that doing so does not correlate to human preference when the estimated lighting is used to relight a virtual scene into a real photograph. To study this, we design a controlled psychophysical experiment where human observers must choose their preference amongst rendered scenes lit using a set of lighting estimation algorithms selected from the recent literature, and use it to analyse how these algorithms perform according to human perception. Then, we demonstrate that none of the most popular IQA metrics from the literature, taken individually, correctly represent human perception. Finally, we show that by learning a combination of existing IQA metrics, we can more accurately represent human preference. This provides a new perceptual framework to help evaluate future lighting estimation algorithms.
MonoGaussianAvatar: Monocular Gaussian Point-based Head Avatar

The ability to animate photo-realistic head avatars reconstructed from monocular portrait video sequences represents a crucial step in bridging the gap between the virtual and real worlds. Recent advancements in head avatar techniques, including explicit 3D morphable meshes (3DMM), point clouds, and neural implicit representation have been exploited for this ongoing research. However, 3DMM-based methods are constrained by their fixed topologies, point-based approaches suffer from a heavy training burden due to the extensive quantity of points involved, and the last ones suffer from limitations in deformation flexibility and rendering efficiency. In response to these challenges, we propose MonoGaussianAvatar (Monocular Gaussian Point-based Head Avatar), a novel approach that harnesses 3D Gaussian point representation coupled with a Gaussian deformation field to learn explicit head avatars from monocular portrait videos. We define our head avatars with Gaussian points characterized by adaptable shapes, enabling flexible topology. These points exhibit movement with a Gaussian deformation field in alignment with the target pose and expression of a person, facilitating efficient deformation. Additionally, the Gaussian points have controllable shape, size, color, and opacity combined with Gaussian splatting, allowing for efficient training and rendering. Experiments demonstrate the superior performance of our method, which achieves state-of-the-art results among previous methods.
Multi-View Unsupervised Image Generation with Cross Attention Guidance

The growing interest in novel view synthesis, driven by Neural Radiance Field (NeRF) models, is hindered by scalability issues due to their reliance on precisely annotated multi-view images. Recent models address this by fine-tuning large text2image diffusion models on synthetic multi-view data. Despite robust zero-shot generalization, they may need post-processing and can face quality issues due to the synthetic-real domain gap. This paper introduces a novel pipeline for unsupervised training of a pose-conditioned diffusion model on single-category datasets. With the help of pretrained self-supervised Vision Transformers (DINOv2), we identify object poses by clustering the dataset through comparing visibility and locations of specific object parts. The pose-conditioned diffusion model, trained on pose labels, and equipped with cross-frame attention at inference time ensures cross-view consistency, that is further aided by our novel hard-attention guidance. Our model, MIRAGE, surpasses prior work in novel view synthesis on real images. Furthermore, MIRAGE is robust to diverse textures and geometries, as demonstrated with our experiments on synthetic images generated with pretrained Stable Diffusion.
OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization

Vision-language pre-training (VLP) models demonstrate impressive abilities in processing both images and text. However, they are vulnerable to multi-modal adversarial examples (AEs). Investigating the generation of high-transferability adversarial examples is crucial for uncovering VLP models' vulnerabilities in practical scenarios. Recent works have indicated that leveraging data augmentation and image-text modal interactions can enhance the transferability of adversarial examples for VLP models significantly. However, they do not consider the optimal alignment problem between dataaugmented image-text pairs. This oversight leads to adversarial examples that are overly tailored to the source model, thus limiting improvements in transferability. In our research, we first explore the interplay between image sets produced through data augmentation and their corresponding text sets. We find that augmented image samples can align optimally with certain texts while exhibiting less relevance to others. Motivated by this, we propose an Optimal Transport-based Adversarial Attack, dubbed OT-Attack. The proposed method formulates the features of image and text sets as two distinct distributions and employs optimal transport theory to determine the most efficient mapping between them. This optimal mapping informs our generation of adversarial examples to effectively counteract the overfitting issues. Extensive experiments across various network architectures and datasets in image-text matching tasks reveal that our OT-Attack outperforms existing state-of-the-art methods in terms of adversarial transferability.
Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Models

Recently, diffusion models have made remarkable progress in text-to-image (T2I) generation, synthesizing images with high fidelity and diverse contents. Despite this advancement, latent space smoothness within diffusion models remains largely unexplored. Smooth latent spaces ensure that a perturbation on an input latent corresponds to a steady change in the output image. This property proves beneficial in downstream tasks, including image interpolation, inversion, and editing. In this work, we expose the non-smoothness of diffusion latent spaces by observing noticeable visual fluctuations resulting from minor latent variations. To tackle this issue, we propose Smooth Diffusion, a new category of diffusion models that can be simultaneously high-performing and smooth. Specifically, we introduce Step-wise Variation Regularization to enforce the proportion between the variations of an arbitrary input latent and that of the output image is a constant at any diffusion training step. In addition, we devise an interpolation standard deviation (ISTD) metric to effectively assess the latent space smoothness of a diffusion model. Extensive quantitative and qualitative experiments demonstrate that Smooth Diffusion stands out as a more desirable solution not only in T2I generation but also across various downstream tasks. Smooth Diffusion is implemented as a plug-and-play Smooth-LoRA to work with various community models. Code is available at https://github.com/SHI-Labs/Smooth-Diffusion.
Content Moderation on Social Media in the EU: Insights From the DSA Transparency Database

The Digital Services Act (DSA) requires large social media platforms in the EU to provide clear and specific information whenever they remove or restrict access to certain content. These "Statements of Reasons" (SoRs) are collected in the DSA Transparency Database to ensure transparency and scrutiny of content moderation decisions of the providers of online platforms. In this work, we empirically analyze 156 million SoRs within an observation period of two months to provide an early look at content moderation decisions of social media platforms in the EU. Our empirical analysis yields the following main findings: (i) There are vast differences in the frequency of content moderation across platforms. For instance, TikTok performs more than 350 times more content moderation decisions per user than X/Twitter. (ii) Content moderation is most commonly applied for text and videos, whereas images and other content formats undergo moderation less frequently. (ii) The primary reasons for moderation include content falling outside the platform's scope of service, illegal/harmful speech, and pornography/sexualized content, with moderation of misinformation being relatively uncommon. (iii) The majority of rule-breaking content is detected and decided upon via automated means rather than manual intervention. However, X/Twitter reports that it relies solely on non-automated methods. (iv) There is significant variation in the content moderation actions taken across platforms. Altogether, our study implies inconsistencies in how social media platforms implement their obligations under the DSA -- resulting in a fragmented outcome that the DSA is meant to avoid. Our findings have important implications for regulators to clarify existing guidelines or lay out more specific rules that ensure common standards on how social media providers handle rule-breaking content on their platforms.
Visual Geometry Grounded Deep Structure From Motion

Structure-from-motion (SfM) is a long-standing problem in the computer vision community, which aims to reconstruct the camera poses and 3D structure of a scene from a set of unconstrained 2D images. Classical frameworks solve this problem in an incremental manner by detecting and matching keypoints, registering images, triangulating 3D points, and conducting bundle adjustment. Recent research efforts have predominantly revolved around harnessing the power of deep learning techniques to enhance specific elements (e.g., keypoint matching), but are still based on the original, non-differentiable pipeline. Instead, we propose a new deep pipeline VGGSfM, where each component is fully differentiable and thus can be trained in an end-to-end manner. To this end, we introduce new mechanisms and simplifications. First, we build on recent advances in deep 2D point tracking to extract reliable pixel-accurate tracks, which eliminates the need for chaining pairwise matches. Furthermore, we recover all cameras simultaneously based on the image and track features instead of gradually registering cameras. Finally, we optimise the cameras and triangulate 3D points via a differentiable bundle adjustment layer. We attain state-of-the-art performance on three popular datasets, CO3D, IMC Phototourism, and ETH3D.
Scaling Laws of Synthetic Images for Model Training ... for Now

Recent significant advances in text-to-image models unlock the possibility of training vision systems using synthetic images, potentially overcoming the difficulty of collecting curated data at scale. It is unclear, however, how these models behave at scale, as more synthetic data is added to the training set. In this paper we study the scaling laws of synthetic images generated by state of the art text-to-image models, for the training of supervised models: image classifiers with label supervision, and CLIP with language supervision. We identify several factors, including text prompts, classifier-free guidance scale, and types of text-to-image models, that significantly affect scaling behavior. After tuning these factors, we observe that synthetic images demonstrate a scaling trend similar to, but slightly less effective than, real images in CLIP training, while they significantly underperform in scaling when training supervised image classifiers. Our analysis indicates that the main reason for this underperformance is the inability of off-the-shelf text-to-image models to generate certain concepts, a limitation that significantly impairs the training of image classifiers. Our findings also suggest that scaling synthetic data can be particularly effective in scenarios such as: (1) when there is a limited supply of real images for a supervised problem (e.g., fewer than 0.5 million images in ImageNet), (2) when the evaluation dataset diverges significantly from the training data, indicating the out-of-distribution scenario, or (3) when synthetic data is used in conjunction with real images, as demonstrated in the training of CLIP models.
A Structural-Clustering Based Active Learning for Graph Neural Networks

In active learning for graph-structured data, Graph Neural Networks (GNNs) have shown effectiveness. However, a common challenge in these applications is the underutilization of crucial structural information. To address this problem, we propose the Structural-Clustering PageRank method for improved Active learning (SPA) specifically designed for graph-structured data. SPA integrates community detection using the SCAN algorithm with the PageRank scoring method for efficient and informative sample selection. SPA prioritizes nodes that are not only informative but also central in structure. Through extensive experiments, SPA demonstrates higher accuracy and macro-F1 score over existing methods across different annotation budgets and achieves significant reductions in query time. In addition, the proposed method only adds two hyperparameters, $\epsilon$ and $\mu$ in the algorithm to finely tune the balance between structural learning and node selection. This simplicity is a key advantage in active learning scenarios, where extensive hyperparameter tuning is often impractical.
I-PHYRE: Interactive Physical Reasoning

Current evaluation protocols predominantly assess physical reasoning in stationary scenes, creating a gap in evaluating agents' abilities to interact with dynamic events. While contemporary methods allow agents to modify initial scene configurations and observe consequences, they lack the capability to interact with events in real time. To address this, we introduce I-PHYRE, a framework that challenges agents to simultaneously exhibit intuitive physical reasoning, multi-step planning, and in-situ intervention. Here, intuitive physical reasoning refers to a quick, approximate understanding of physics to address complex problems; multi-step denotes the need for extensive sequence planning in I-PHYRE, considering each intervention can significantly alter subsequent choices; and in-situ implies the necessity for timely object manipulation within a scene, where minor timing deviations can result in task failure. We formulate four game splits to scrutinize agents' learning and generalization of essential principles of interactive physical reasoning, fostering learning through interaction with representative scenarios. Our exploration involves three planning strategies and examines several supervised and reinforcement agents' zero-shot generalization proficiency on I-PHYRE. The outcomes highlight a notable gap between existing learning algorithms and human performance, emphasizing the imperative for more research in enhancing agents with interactive physical reasoning capabilities. The environment and baselines will be made publicly available.
PENGUJIAN KEAMANAN DENGAN METODE OWASP TOP 10 PADA WEBSITE EFORM HELPDESK

The development of modern technology has had a significant positive impact on various aspects of life. However, along with this progress, the threat from hackers is also increasing. Hackers are individuals or groups with the ability to breach computer systems or networks, whether for illegal purposes, stealing data, or spreading malware. To avoid this, there is a method called penetration testing. Penetration Testing is a series of methods carried out to test the security of a system. The penetration testing process involves analyzing a system to identify potential security vulnerabilities such as system configuration errors, flaws in software or hardware development, and weaknesses in the logic of a process. After conducting a penetration test using the OWASP TOP 10 2021 method on the Eformhelpdesk website, there were six security vulnerabilities identified in the OWASP TOP 10 2021 category, and one vulnerability that did not fall into that category.
Errors Are The Stepping Stones to Learning: Trial-by-Trial Modeling Reveals Overwhelming Evidence for Mediator Retrievals of Previous Errors in Memory Consolidation

Recent studies suggest that errors facilitate learning in certain conditions. Despite this,reinforcement paradigms dominate learning methods, subscribing to the narrative that errorlesslearning is the foundation of an ideal learning environment. If we continue to view learning fromthis restrictive perspective, we may fail to capture and apply the benefits of errors. In this paper,we investigate two potential mechanisms of learning from errors. Participants (N = 61) learnedword pairs in either a study or error trial before taking a final test. Supporting past error learningliterature, errors before a study opportunity led to better performance on a final test. Differencesin reaction times between conditions support the theory that errors increase learning by acting asa mediator, or secondary cue, to the correct answer on subsequent tests. Individual differences inmodel fit using log-likelihood trial-by-trial calculations solidified support for the mediatormethod.
SISTEM PENUNJANG KEPUTUSAN PEMILIHAN SMARTPHONE BERBASIS WEBSITE DENGAN METODE SIMPLE ADDITIVE WEIGTHING

Along with the development of smartphones in Indonesia, people in various circles are very dependent on the use of smartphones. Especially with the current pandemic making people depend on the use of smartphones. With the various brands, types, and specifications of smartphones, people are confused about which smartphones to choose according to their respective needs. From these problems, a website-based decision support system is needed to help and facilitate people in choosing the right smartphone model according to the criteria. The criteria that will be included in this system are price, brand, RAM, storage, camera, battery screen, and features. The purpose of this thesis is to build a system that can help people find smartphones that match the required criteria. This system uses the Simple Additive Weighting (SAW) method which is used to normalize the weights of the inputted criteria and to determine the highest smartphone value as a recommendation option. The results of this study are the creation of a smartphone selection decision support system with the website-based Simple Additive Weighting method that can provide recommendations for smartphone types according to their respective needs.
Pelatihan dan Pendampingan Penggunaan Liveworksheets dalam Pembuatan Lembar Kerja Peserta Didik bagi MGMP Madrasah Tsanawiyah Matematika Rayon 2 Se-Kota Jambi

The purpose of this Community Service (PkM) activity is to maximize the use of the Liveworkssheets platform. In carrying out service, the method used is Asset Based Community-driven Development. The training and mentoring process begins with the first stage being pre-implementation, the second stage is the implementation of research, and the third stage is research evaluation which is divided into five stages (Define, Discovery, Dream, Design, and Deliver or Destiny). This activity was attended by 15 mathematics teachers who are members of the MGMP Mathematics MTs/SMP Rayon 2 Jambi City community. The results of the activities include: 1) Increased understanding from MGMP Mathematics MTs/SMP Rayon 2 Jambi City teachers about the importance of digital literacy. 2)Increased technical knowledge of participants from initially not knowing to knowing after getting training using the Liveworksheets platform. 3)Teachers are interested in using the liveworksheets platform when compiling questions for the purposes of midterm tests or end-of-semester tests because of the ease of compiling and checking students' answers.
THE EFFECTS OF DIFFERENT COLLABORATION TYPES AND LEADERSHIP STYLES UPON GROUP PRODUCTIVITY

The paper studies how different collaboration types affect group productivity.&#x0D; The study provides an experimental investigation of the effects the combinations of collaboration types (by L. Umansky, i.e. individual, consequent and interactive) and leadership styles (by K. Lewin, i.e. autocratic and democratic) upon the group productivity.&#x0D; The experiment described simulates groupwork under different collaboration types and leadership styles. The six experimental stages allowed to register several objective and subjective productivity indicators. The analysis revealed the regularities between different combinations of the collaboration types and the leadership styles on one hand, and the productivity indicators on the other.