Reviewerly/exHarmony huggingface dataset:
Sign Language-to-Text Dictionary with Lightweight Transformer Models

The recent advances in deep learning have been beneficial to automatic sign language recognition (SLR). However, free-to-access, usable, and accessible tools are still not widely available to the deaf community. The need for a sign language-to-text dictionary was raised by a bilingual deaf school in Belgium and linguist experts in sign languages (SL) in order to improve the autonomy of students. To meet that need, an efficient SLR system was built based on a specific transformer model. The proposed system is able to recognize 700 different signs, with a top-10 accuracy of 83%. Those results are competitive with other systems in the literature while using 10 times less parameters than existing solutions. The integration of this model into a usable and accessible web application for the dictionary is also introduced. A user-centered human-computer interaction (HCI) methodology was followed to design and implement the user interface. To the best of our knowledge, this is the first publicly released sign language-to-text dictionary using video captured by a standard camera.
Backpropagation of Unrolled Solvers with Folded Optimization

The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this setting is backpropagation through the solution of an optimization problem, which typically lacks a closed form. One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver. While flexible and general, unrolling can encounter accuracy and efficiency issues in practice. These issues can be avoided by analytical differentiation of the optimization, but current frameworks impose rigid requirements on the optimization problem's form. This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation. Additionally, it proposes a unifying view of unrolling and analytical differentiation through optimization mappings. Experiments over various model-based learning tasks demonstrate the advantages of the approach both computationally and in terms of enhanced expressiveness.
3D Surface Super-resolution from Enhanced 2D Normal Images: A Multimodal-driven Variational AutoEncoder Approach

3D surface super-resolution is an important technical tool in virtual reality, and it is also a research hotspot in computer vision. Due to the unstructured and irregular nature of 3D object data, it is usually difficult to obtain high-quality surface details and geometry textures via a low-cost hardware setup. In this paper, we establish a multimodal-driven variational autoencoder (mmVAE) framework to perform 3D surface enhancement based on 2D normal images. To fully leverage the multimodal learning, we investigate a multimodal Gaussian mixture model (mmGMM) to align and fuse the latent feature representations from different modalities, and further propose a cross-scale encoder-decoder structure to reconstruct high-resolution normal images. Experimental results on several benchmark datasets demonstrate that our method delivers promising surface geometry structures and details in comparison with competitive advances.
Tractable Diversity: Scalable Multiperspective Ontology Management via Standpoint EL

The tractability of the lightweight description logic EL has allowed for the construction of large and widely used ontologies that support semantic interoperability. However, comprehensive domains with a broad user base are often at odds with strong axiomatisations otherwise useful for inferencing, since these are usually context dependent and subject to diverging perspectives. In this paper we introduce Standpoint EL, a multi-modal extension of EL that allows for the integrated representation of domain knowledge relative to diverse, possibly conflicting standpoints (or contexts), which can be hierarchically organised and put in relation to each other. We establish that Standpoint EL still exhibits EL's favourable PTime standard reasoning, whereas introducing additional features like empty standpoints, rigid roles, and nominals makes standard reasoning tasks intractable.
WBFlow: Few-shot White Balance for sRGB Images via Reversible Neural Flows

The sRGB white balance methods aim to correct the nonlinear color cast of sRGB images without accessing raw values. Although existing methods have achieved increasingly better results, their generalization to sRGB images from multiple cameras is still under explored. In this paper, we propose the network named WBFlow that not only performs superior white balance for sRGB images but also generalizes well to multiple cameras. Specifically, we take advantage of neural flow to ensure the reversibility of WBFlow, which enables lossless rendering of color cast sRGB images back to pseudo raw features for linear white balancing and thus achieves superior performance. Furthermore, inspired by camera transformation approaches, we have designed a camera transformation (CT) in pseudo raw feature space to generalize WBFlow for different cameras via few shot learning. By utilizing a few sRGB images from an untrained camera, our WBFlow can perform well on this camera by learning the camera specific parameters of CT. Extensive experiments show that WBFlow achieves superior camera generalization and accuracy on three public datasets as well as our rendered multiple camera sRGB dataset. Our code is available at https://github.com/ChunxiaoLe/WBFlow.
Gradient-Based Mixed Planning with Symbolic and Numeric Action Parameters (Extended Abstract)

Dealing with planning problems with both logical relations and numeric changes in real-world dynamic environments is challenging. Existing numeric planning systems for the problem often discretize numeric variables or impose convex constraints on numeric variables, which harms the performance when solving problems, especially when the problems contain obstacles and non-linear numeric effects. In this work, we propose a novel algorithm framework to solve numeric planning problems mixed with logical relations and numeric changes based on gradient descent. We cast the numeric planning with logical relations and numeric changes as an optimization problem. Specifically, we extend the syntax to allow parameters of action models to be either objects or real-valued numbers, which enhances the ability to model real-world numeric effects. Based on the extended modeling language, we propose a gradient-based framework to simultaneously optimize numeric parameters and compute appropriate actions to form candidate plans. The gradient-based framework is composed of an algorithmic heuristic module based on propositional operations to select actions and generate constraints for gradient descent, an algorithmic transition module to update states to the next ones, and a loss module to compute loss. We repeatedly minimize loss by updating numeric parameters and compute candidate plans until it converges into a valid plan for the planning problem.
Exploring Safety Supervision for Continual Test-time Domain Adaptation

Continual test-time domain adaptation aims to adapt a source pre-trained model to a continually changing target domain without using any source data. Unfortunately, existing methods based on pseudo-label learning suffer from the changing target domain environment, and the quality of generated pseudo-labels is attenuated due to the domain shift, leading to instantaneous negative learning and long-term knowledge forgetting. To solve these problems, in this paper, we propose a simple yet effective framework for exploring safety supervision with three elaborate strategies: Label Safety, Sample Safety, and Parameter Safety. Firstly, to select reliable pseudo-labels, we define and adjust the confidence threshold in a self-adaptive manner according to the test-time learning status. Secondly, a soft-weighted contrastive learning module is presented to explore the highly-correlated samples and discriminate uncorrelated ones, improving the instantaneous efficiency of the model. Finally, we frame a Soft Weight Alignment strategy to normalize the distance between the parameters of the adapted model and the source pre-trained model, which alleviates the long-term problem of knowledge forgetting and significantly improves the accuracy of the adapted model in the late adaptation stage. Extensive experimental results demonstrate that our method achieves state-of-the-art performance on several benchmark datasets.
GeNAS: Neural Architecture Search with Better Generalization

Neural Architecture Search (NAS) aims to automatically excavate the optimal network architecture with superior test performance. Recent neural architecture search (NAS) approaches rely on validation loss or accuracy to find the superior network for the target data. In this paper, we investigate a new neural architecture search measure for excavating architectures with better generalization. We demonstrate that the flatness of the loss surface can be a promising proxy for predicting the generalization capability of neural network architectures. We evaluate our proposed method on various search spaces, showing similar or even better performance compared to the state-of-the-art NAS methods. Notably, the resultant architecture found by flatness measure generalizes robustly to various shifts in data distribution (e.g. ImageNet-V2,-A,-O), as well as various tasks such as object detection and semantic segmentation.
StackFLOW: Monocular Human-Object Reconstruction by Stacked Normalizing Flow with Offset

Modeling and capturing the 3D spatial arrangement of the human and the object is the key to perceiving 3D human-object interaction from monocular images. In this work, we propose to use the Human-Object Offset between anchors which are densely sampled from the surface of human mesh and object mesh to represent human-object spatial relation. Compared with previous works which use contact map or implicit distance filed to encode 3D human-object spatial relations, our method is a simple and efficient way to encode the highly detailed spatial correlation between the human and object. Based on this representation, we propose Stacked Normalizing Flow (StackFLOW) to infer the posterior distribution of human-object spatial relations from the image. During the optimization stage, we finetune the human body pose and object 6D pose by maximizing the likelihood of samples based on this posterior distribution and minimizing the 2D-3D corresponding reprojection loss. Extensive experimental results show that our method achieves impressive results on two challenging benchmarks, BEHAVE and InterCap datasets. Our code has been publicly available at https://github.com/MoChen-bop/StackFLOW.
Towards Utilitarian Online Learning -- A Review of Online Algorithms in Open Feature Space

Human intelligence comes from the capability to describe and make sense of the world surrounding us, often in a lifelong manner. Online Learning (OL) allows a model to simulate this capability, which involves processing data in sequence, making predictions, and learning from predictive errors. However, traditional OL assumes a fixed set of features to describe data, which can be restrictive. In reality, new features may emerge and old features may vanish or become obsolete, leading to an open feature space. This dynamism can be caused by more advanced or outdated technology for sensing the world, or it can be a natural process of evolution. This paper reviews recent breakthroughs that strived to enable OL in open feature spaces, referred to as Utilitarian Online Learning (UOL). We taxonomize existing UOL models into three categories, analyze their pros and cons, and discuss their application scenarios. We also benchmark the performance of representative UOL models, highlighting open problems, challenges, and potential future directions of this emerging topic.
Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning

In semi-supervised domain adaptation (SSDA), a few labeled target samples of each class help the model to transfer knowledge representation from the fully labeled source domain to the target domain. Many existing methods ignore the benefits of making full use of the labeled target samples from multi-level. To make better use of this additional data, we propose a novel Prototype-based Multi-level Learning (ProML) framework to better tap the potential of labeled target samples. To achieve intra-domain adaptation, we first introduce a pseudo-label aggregation based on the intra-domain optimal transport to help the model align the feature distribution of unlabeled target samples and the prototype. At the inter-domain level, we propose a cross-domain alignment loss to help the model use the target prototype for cross-domain knowledge transfer. We further propose a dual consistency based on prototype similarity and linear classifier to promote discriminative learning of compact target feature representation at the batch level. Extensive experiments on three datasets, including DomainNet, VisDA2017, and Office-Home, demonstrate that our proposed method achieves state-of-the-art performance in SSDA. Our code is available at https://github.com/bupt-ai-cz/ProML.
FEAMOE: Fair, Explainable and Adaptive Mixture of Experts

Three key properties that are desired of trustworthy machine learning models deployed in high-stakes environments are fairness, explainability, and an ability to account for various kinds of "drift". While drifts in model accuracy have been widely investigated, drifts in fairness metrics over time remain largely unexplored. In this paper, we propose FEAMOE, a novel "mixture-of-experts" inspired framework aimed at learning fairer, more interpretable models that can also rapidly adjust to drifts in both the accuracy and the fairness of a classifier. We illustrate our framework for three popular fairness measures and demonstrate how drift can be handled with respect to these fairness constraints. Experiments on multiple datasets show that our framework as applied to a mixture of linear experts is able to perform comparably to neural networks in terms of accuracy while producing fairer models. We then use the large-scale HMDA dataset and show that various models trained on HMDA demonstrate drift and FEAMOE can ably handle these drifts with respect to all the considered fairness measures and maintain model accuracy. We also prove that the proposed framework allows for producing fast Shapley value explanations, which makes computationally efficient feature attribution based explanations of model decisions readily available via FEAMOE.
A Large-Scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement

Film, a classic image style, is culturally significant to the whole photographic industry since it marks the birth of photography. However, film photography is time-consuming and expensive, necessitating a more efficient method for collecting film-style photographs. Numerous datasets that have emerged in the field of image enhancement so far are not film-specific. In order to facilitate film-based image stylization research, we construct FilmSet, a large-scale and high-quality film style dataset. Our dataset includes three different film types and more than 5000 in-the-wild high resolution images. Inspired by the features of FilmSet images, we propose a novel framework called FilmNet based on Laplacian Pyramid for stylizing images across frequency bands and achieving film style outcomes. Experiments reveal that the performance of our model is superior than state-of-the-art techniques. The link of our dataset and code is https://github.com/CXH-Research/FilmNet.
The Analysis Between Batak Toba and English In Question Words

The purpose of this research is to identify the similarities and differences between English and Toba Batak question terms. Qualitative descriptive research method. Methods of data collection including documentation. Descriptive analysis is used by researchers to examine the data. There are 5 question words in English: "What", "Where", "Who", "How", and "Why". There are seven interrogative words in the Toba Batak language: "Aha", "Ise", didia, ,"Boasa, "Boha, Piga, Beha." The findings of this study are to reveal six correlations or similarities between English and Toba Batak words. test.However, there are also differences between question words in English and Toba Batak.
Discounting in Strategy Logic

Discounting is an important dimension in multi-agent systems as long as we want to reason about strategies and time. It is a key aspect in economics as it captures the intuition that the far-away future is not as important as the near future. Traditional verification techniques allow to check whether there is a winning strategy for a group of agents but they do not take into account the fact that satisfying a goal sooner is different from satisfying it after a long wait. In this paper, we augment Strategy Logic with future discounting over a set of discounted functions D, denoted SL[D]. We consider “until” operators with discounting functions: the satisfaction value of a specification in SL[D] is a value in [0, 1], where the longer it takes to fulfill requirements, the smaller the satisfaction value is. We motivate our approach with classical examples from Game Theory and study the complexity of model-checking SL[D]-formulas.
Towards Semantics- and Domain-Aware Adversarial Attacks

Language models are known to be vulnerable to textual adversarial attacks, which add human-imperceptible perturbations to the input to mislead DNNs. It is thus imperative to devise effective attack algorithms to identify the deficiencies of DNNs before real-world deployment. However, existing word-level attacks have two major deficiencies: (1) They may change the semantics of the original sentence. (2) The generated adversarial sample can appear unnatural to humans due to the introduction of out-of-domain substitute words. In this paper, to address such drawbacks, we propose a semantics- and domain-aware word-level attack method. Specifically, we greedily replace the important words in a sentence with the ones suggested by a language model. The language model is trained to be semantics- and domain-aware via contrastive learning and in-domain pre-training. Furthermore, to balance the quality of adversarial examples and the attack success rate, we propose an iterative updating framework to optimize the contrastive learning loss and the in-domain pre-training loss in circular order. Comprehensive experimental comparisons confirm the superiority of our approach. Notably, compared with state-of-the-art benchmarks, our strategy can achieve over 3\% improvement in attack success rates and 9.8\% improvement in the quality of adversarial examples.
PENGEMBANGAN MEDIA CERITA BERGAMBAR PADA MATERI DONGENG SISWA KELAS II SEKOLAH DASAR

This research was conducted at SD Negeri 6 Muara Telang, Banyuasin district, to find out the Development of Pictorial Story Media on Fairy Tales for Grade II Elementary School Students in Grade II students. The problem in this research is that the media used is still not attractive and innovative. The purpose of this study was to describe the validity and practicality of the results of the development of Pictorial Story Media on Fairytale Material for Grade II Elementary School Students. This type of research includes development research. The method used is the development research method or called Research and Development, and uses the ADDIE development model. The results of the development of picture story media at SD Negeri 6 Muara Telang, Banyuasin district. Based on the results of the data analysis which was distributed to the three experts, the score of linguists was 95% in the very practical category, media experts 96% in the very practical category and material experts 100% in the very practical category, meaning that the media can be concluded that the validity of the results of developing picture story media in material Fairy Tales for class II Elementary School students are very valid, and based on the results of the questionnaire distributed to students, a score of 76% is obtained, it can be concluded that practical and practical value criteria based on table 6, it can be concluded that Picture Story media is practical for use by the teacher as one of the media in the teaching and learning process in dogeng material at II SD Negeri 6 Muara Telang.
Development of the Internal Quality Assurance Information System Prototype at Syiah Kuala University

Quality Assurance System of Higher Education requires educational institution to have a system which capable of assuring the quality of academic activities implementation. Information systems are used to manage information and data in the university environment and aim to improve the efficiency and effectiveness of university management in achieving its goals. There are 22 information systems in Syiah Kuala University, but it does not mean that it facilitates the quality assurance processes there. This because the system is not connected each other. This condition makes the distribution data is very difficult to be monitored and caused unsystematic audits activity. This study aims to design a Quality Assurance Information System that can be integrated with all the existing system in USK University. The method used in designing the prototyping system is the Prototype Model. Based on the results of interviews regarding to the Internal Quality Assurance System in USK University, the function of information system that is built must be able to meet the needs of several parties involved, not only to guarantee the overall quality required by the university, but also to the level of study programs that can provide benefits from the system built. The result of designing system that successfully created is the quality assurance system can be integrated to the existing system in USK University. System performance testing is carried out using the System Usability Scale and obtains the SUS value of 77 which mean that the quality assurance information system designed has a fairly good usability level. Keywords: Information System, Internal Quality Assurance System, Quality, Prototype, System Usability Scale.
Maintaining Consistency in Managing Campaign Through Websites at KapanLagi Youniverse

This internship report is intended to explain the author's activities in the internship process with KLY (Emtek Digital) through the MBKM program organized by the Ministry of Education and Culture. This program aims to provide opportunities for college students to experiencing work professionally in a company, understanding the company's work culture, gaining various knowledge and skills needed in the world of work. This internship report contains the author's activities as part of the Campaign Manager Intern including the tasks within the Campaign Management division and some of the main tasks that was trusted by the mentor for author to do. As for this report, it will explain author's activities as a Campaign Manager for Kapanlagi Youniverse in collaboration with several teams from other divisions. The MBKM program trains author to be able to implemented the skills that have learned such as effective communication, project management, and analytical thinking into work related to managing campaigns.