Reviewerly/exHarmony huggingface dataset:
Why Change My Design: Explaining Poorly Constructed Visualization Designs with Explorable Explanations

Although visualization tools are widely available and accessible, not everyone knows the best practices and guidelines for creating accurate and honest visual representations of data. Numerous books and articles have been written to expose the misleading potential of poorly constructed charts and teach people how to avoid being deceived by them or making their own mistakes. These readings use various rhetorical devices to explain the concepts to their readers. In our analysis of a collection of books, online materials, and a design workshop, we identified six common explanation methods. To assess the effectiveness of these methods, we conducted two crowdsourced studies (each with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> = 125) to evaluate their ability to teach and persuade people to make design changes. In addition to these existing methods, we brought in the idea of Explorable Explanations, which allows readers to experiment with different chart settings and observe how the changes are reflected in the visualization. While we did not find significant differences across explanation methods, the results of our experiments indicate that, following the exposure to the explanations, the participants showed improved proficiency in identifying deceptive charts and were more receptive to proposed alterations of the visualization design. We discovered that participants were willing to accept more than 60% of the proposed adjustments in the persuasiveness assessment. Nevertheless, we found no significant differences among different explanation methods in convincing participants to accept the modifications.
NTDA: Noise-Tolerant Data Augmentation for Document-Level Event Argument Extraction

Event argument extraction (EAE), aiming at identifying event arguments over multiple sentences, mainly faces data sparsity problem. Cross-domain data augmentation can leverage annotated data to augment training data, but always encounters the issue of noise. The noise mainly consists of two aspects: boundary annotation differences and domain knowledge discrepancy, which may significantly impact the effectiveness of data augmentation. In this paper, we propose a new framework NTDA (Noise-Tolerant Data Augmentation) to solve the above two issues. For annotation differences, we introduce region-based loss function to mitigate the model’s sensitivity towards entity boundaries. To address the knowledge discrepancy problem, we propose a dynamic data selection strategy. Additionally, we further combine the two denoising techniques. Through conducting comprehensive experiments on three datasets, we have demonstrated the superior effectiveness of our framework compared to previous methods.
DIVI: Dynamically Interactive Visualization

Dynamically Interactive Visualization (DIVI) is a novel approach for orchestrating interactions within and across static visualizations. DIVI deconstructs Scalable Vector Graphics charts at runtime to infer content and coordinate user input, decoupling interaction from specification logic. This decoupling allows interactions to extend and compose freely across different tools, chart types, and analysis goals. DIVI exploits positional relations of marks to detect chart components such as axes and legends, reconstruct scales and view encodings, and infer data fields. DIVI then enumerates candidate transformations across inferred data to perform linking between views. To support dynamic interaction without prior specification, we introduce a taxonomy that formalizes the space of standard interactions by chart element, interaction type, and input event. We demonstrate DIVI's usefulness for rapid data exploration and analysis through a usability study with 13 participants and a diverse gallery of dynamically interactive visualizations, including single chart, multi-view, and cross-tool configurations.
FeatRacer: Locating Features Through Assisted Traceability

Locating features is one of the most common software development activities. It is typically done during maintenance and evolution, when developers need to identify the exact places in a codebase where specific features are implemented. Unfortunately, locating features is laborious and error-prone, since feature knowledge fades, projects are developed by different developers, and features are often scattered across the codebase. Recognizing the need, many <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">automated feature location techniques</i> have been proposed, which try to retroactively recover features, i.e., very domain-specific information from the codebase. Unfortunately, such techniques require large training datasets, only recover coarse-grained locations and produce too many false positives to be useful in practice. An alternative is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recording features during development</i> , when they are still fresh in a developer's mind. However, recording is easily forgotten and also costly, especially when the software evolves and such recordings need to be updated. We address the infamous <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feature location problem</i> (a.k.a., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">concern location</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">concept assignment problem</i> ) differently. We present FeatRacer, which combines feature recording and automated feature location in a way that allows developers to proactively and continuously record features and their locations during development, while addressing the shortcomings of both strategies. Specifically, FeatRacer relies on embedded code annotations and a machine-learning-based recommender system. When a developer forgets to annotate, FeatRacer reminds the developer about potentially missing features, which it learned from the feature recording practices in the project at hand. FeatRacer also facilitates fine-grained locations as decided by the developer. Our evaluation shows that FeatRacer outperforms traditional automated feature location based on Latent Semantic Indexing (LSI) and Linear Discriminant Analysis (LDA)—two of the most common methods to realize such techniques—when predicting features for 4,650 commit changesets from the histories of 16 open-source projects spanning an average of three years between 1985 and 2015. Compared to the traditional techniques, FeatRacer showed a 3x higher precision and a 4.5x higher recall, with an average precision and recall of 89.6% among all 16 projects. It can accurately predict feature locations within the first five commits of our evaluation projects, being effective already for small datasets. FeatRacer takes on average 1.9ms to learn from past code fragments of a project, and 0.002ms to predict forgotten feature annotations in new code.
A Scene-Text Synthesis Engine Achieved Through Learning From Decomposed Real-World Data

Scene-text image synthesis techniques that aim to naturally compose text instances on background scene images are very appealing for training deep neural networks due to their ability to provide accurate and comprehensive annotation information. Prior studies have explored generating synthetic text images on two-dimensional and three-dimensional surfaces using rules derived from real-world observations. Some of these studies have proposed generating scene-text images through learning; however, owing to the absence of a suitable training dataset, unsupervised frameworks have been explored to learn from existing real-world data, which might not yield reliable performance. To ease this dilemma and facilitate research on learning-based scene text synthesis, we introduce DecompST, a real-world dataset prepared from some public benchmarks, containing three types of annotations: quadrilateral-level BBoxes, stroke-level text masks, and text-erased images. Leveraging the DecompST dataset, we propose a Learning-Based Text Synthesis engine (LBTS) that includes a text location proposal network (TLPNet) and a text appearance adaptation network (TAANet). TLPNet first predicts the suitable regions for text embedding, after which TAANet adaptively adjusts the geometry and color of the text instance to match the background context. After training, those networks can be integrated and utilized to generate the synthetic dataset for scene text analysis tasks. Comprehensive experiments were conducted to validate the effectiveness of the proposed LBTS along with existing methods, and the experimental results indicate the proposed LBTS can generate better pretraining data for scene text detectors. Our dataset and code are made available at: https://github.com/iiclab/DecompST.
SRFormer: Efficient Yet Powerful Transformer Network for Single Image Super Resolution

Recent breakthroughs in single image super resolution have investigated the potential of deep Convolutional Neural Networks (CNNs) to improve performance. However, CNNs based models suffer from their limited fields and their inability to adapt to the input content. Recently, Transformer based models were presented, which demonstrated major performance gains in Natural Language Processing and Vision tasks while mitigating the drawbacks of CNNs. Nevertheless, Transformer computational complexity can increase quadratically for high-resolution images, and the fact that it ignores the original structures of the image by converting them to the 1D structure can make it problematic to capture the local context information and adapt it for real-time applications. In this paper, we present, SRFormer, an efficient yet powerful Transformer-based architecture, by making several key designs in the building of Transformer blocks and Transformer layers that allow us to consider the original structure of the image (i.e., 2D structure) while capturing both local and global dependencies without raising computational demands or memory consumption. We also present a Gated Multi-Layer Perceptron (MLP) Feature Fusion module to aggregate the features of different stages of Transformer blocks by focusing on inter-spatial relationships while adding minor computational costs to the network. We have conducted extensive experiments on several super-resolution benchmark datasets to evaluate our approach. SRFormer demonstrates superior performance compared to state-of-the-art methods from both Transformer and Convolutional networks, with an improvement margin of 0.1 ~ 0.53 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dB</i> . Furthermore, while SRFormer has almost the same model size, it outperforms SwinIR by 0.47% and inference time by half the time of SwinIR. The code will be available on GitHub.
Revelio: A Modular and Effective Framework for Reproducible Training and Evaluation of Morphing Attack Detectors

Morphing Attack, i.e. the elusion of face verification systems through a facial morphing operation between a criminal and an accomplice, has recently emerged as a serious security threat. Despite the importance of this kind of attack, the development and comparison of Morphing Attack Detection (MAD) methods is still a challenging task, especially with deep learning approaches. Specifically, the lack of public datasets, the absence of common training and validation protocols, and the limited release of public source code hamper the reproducibility and objective comparison of new MAD systems. Usually, these aspects are mainly due to privacy concerns, that limit data transfers and storage, and to the recent introduction of the MAD task. Therefore, in this paper, we propose and publicly release <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Revelio</i> , a modular framework for the reproducible development and evaluation of MAD systems. We include an overview of the modules, and describe the plugin system providing the possibility of extending native components with new functionalities. An extensive cross-datasets experimental evaluation is conducted to validate the framework and the performance of trained models on several publicly-released datasets, and to deeply analyze the main challenges in the MAD task based on single input images. We also propose a new metric, namely WAED, to summarize in a single value the error-based metrics commonly used in the MAD task, computed over different datasets, thus facilitating the comparative evaluation of different approaches. Finally, by exploiting Revelio, a new state-of-the-art MAD model (on SOTAMD single-image benchmark) is proposed and released.
Accelerating Super-Resolution Network Inference via Sensitivity-Based Weight Sparsity Allocation

Weight sparsification has been extensively studied in image classification and object detection to accelerate network inference. However, for image generation tasks, such as image super-resolution, forcing some weights to zeros is a non-trivial task that typically causes significant degradation in restoration quality, that is, peak signal-to-noise (PSNR). In this study, we first introduce a sensitivity metric to measure PSNR degradation for layer-wise sparsity changes and observe that the sensitivities vary significantly across network layers. We demonstrate that a uniform sparsity allocation method generally causes a non-negligible decrease in accuracy, that is, approximately 0.17 dB, for 65% of the zero weights. In addition, finding an optimal solution to the sparsity allocation problem is not feasible because the design space is exponential with respect to the number of weights and layers. To address this problem, we proposed a simple yet effective sparsity allocation method based on layer-wise sensitivity. The experimental results demonstrate that the proposed method achieves up to 35% computation reduction with an average accuracy drop of 0.02 dB varying between 0.01 to 0.04 dB across the well-known datasets Set5, Set14, B100, and Urban100. Moreover, when integrated with the activation sparsity SMSR, the proposed method reduced the computation by 46% on average.
DB-HRNet: Dual Branch High-Resolution Network for Human Pose Estimation

High-resolution networks have demonstrated significant advantages in multi-scale feature extraction for human pose estimation task.However, this often leads to the problems of large network parameter quantity and high computational complexity. On the other hand, some existing lightweight high-resolution networks have lower model parameter quantity and computational complexity, but they lack accuracy in handling keypoint position information.To address these issues, this study proposes a lightweight High-resolution network called DB-HRNet (Dual Branch High-Resolution Network) for human pose estimation.Specifically. based on HRNet, a more efficient network framework is designed as the backbone framework of DB-HRNet. Additionally, we propose a position-sensitive basic module called DBBlock (Dual Branch Block) to enhance the model’s ability to capture positional information accurately, as the basic block of DB-HRNet.Finally, we propose a Pose Refine Module to establish the connections between keypoints and make the final adjustments to the model’s output.To validate the effectiveness of DB-HRNet, we conduct experimental studies on the COCO2017 and MPII human pose estimation datasets, comparing DB-HRNet with several advanced existing lightweight human pose estimation models. The results demonstrate that the proposed network model achieves a 1.8 points improvement in detection accuracy on the COCO2017 dataset compared to the advanced lightweight high-resolution network Dite-HRNet, while also achieving a 38% increase in model inference speed.
Natural Image Decay With a Decay Effects Generator

We present a novel framework for simulating time-varying decay effects for natural images. Conventional methods assume the input image includes enough decay information and uses the color or texture information of the decayed regions to transfer its effect to the non-decayed regions. Unlike these approaches, our framework generates diverse patterns of decay effects by leveraging a decay effects generator network without referencing the decay features of the input image, which allows us to handle more general images with non-decayed objects. Our decay generator network is formed by a style-based generative adversarial network with an arbitrary-sized stationary texture generation mechanism that allows us to synthesize various sizes of decay textures. This arbitrary-sized stationary texture generation is necessary to synthesize photo-realistic decay effects since the appropriate resolutions of the decay textures depend on those of the target objects. We construct a novel decay texture image dataset that contains various types of decay texture images, such as mossy and rust, to train the decay generator network. We show that our framework is able to synthesize diverse decay effects on various non-decayed objects without using additional decayed object images.
SSPNet: Predicting Visual Saliency Shifts

When images undergo quality degradation caused by editing, compression or transmission, their saliency tends to shift away from its original position. Saliency shifts indicate visual behaviour change and therefore contain vital information regarding perception of visual content and its distortions. Given a pristine image and its distorted format, we want to be able to detect saliency shifts induced by distortions. The resulting saliency shift map (SSM) can be used to identify the region and degree of visual distraction caused by distortions, and consequently to perceptually optimise image coding or enhancement algorithms. To this end, we first create a largest-of-its-kind eye-tracking database, comprising 60 pristine images and their associated 540 distorted formats viewed by 96 subjects. We then propose a computational model to predict the saliency shift map (SSM), utilising transformers and convolutional neural networks. Experimental results demonstrate that the proposed model is highly effective in detecting distortion-induced saliency shifts in natural images.
Cloud Computing Product Service Scheme Recommendation System Based on a Hierarchical Knowledge Graph

It is difficult for users to understand the complex cloud product information for product selection. Using this information to recommend satisfactory cloud products is a challenge. Previous studies focused on similar information of users and products while neglecting relevance; therefore, they could not create recommendation approaches that account for functional dependencies among cloud products. To overcome this challenge, this study proposes a cloud product set recommendation model based on a hierarchical knowledge graph (KG) with a pre-post correlation of product functionality. There are two main contributions: First, we construct a cloud product functionality and performance KG using the dependency information of layers and entities to represent complicated pre-post logical connections. The KG was designed according to the cloud service model. Second, we designed an improved PageRank algorithm to obtain the importance weight for each functionality and performance, which replaces the original average method with the proportion of connection weight. We considered the release time of the functionality, launch time of the product, and last update time of the product as crucial factors in the recommendation score to reflect the importance of the functionality and current development stage of the product. Finally, our method recommends a product set based on the weighted scores from the above results. In addition, we constructed a cloud product functionality dataset containing 339 functionalities. The experimental results show that the proposed method can generate a closely related set of products, leading to improved accuracy and higher satisfaction compared to mainstream methods.
Toward Privacy-Supporting Fall Detection via Deep Unsupervised RGB2Depth Adaptation

Fall detection is a vital task in health monitoring, as it allows the system to trigger an alert and therefore enable faster interventions when a person experiences a fall. Although most previous approaches rely on standard RGB video data, such detailed appearance-aware monitoring poses significant privacy concerns. Depth sensors, on the other hand, are better at preserving privacy as they merely capture the distance of objects from the sensor or camera, omitting color and texture information. In this article, we introduce a privacy-supporting solution that makes the RGB-trained model applicable in the depth domain and utilizes depth data at test time for fall detection. To achieve cross-modal fall detection, we present an unsupervised RGB domain to the depth domain (RGB2Depth) cross-modal domain adaptation approach that leverages labeled RGB data and unlabeled depth data during training. Our proposed pipeline incorporates an intermediate domain module (IDM) for feature bridging, modality adversarial loss for modality discrimination, classification loss for pseudo-labeled depth data and labeled source data, triplet loss that considers both the source and target domains, and a novel adaptive loss weight adjustment method for improved coordination among various losses. Our approach achieves state-of-the-art results in the unsupervised RGB2Depth domain adaptation task for fall detection. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/1015206533/privacy_supporting_fall_detection</uri> .
HiFiSketch: High Fidelity Face Photo-Sketch Synthesis and Manipulation

With the rapid development of generative adversarial networks, face photo-sketch synthesis has achieved promising performance and playing an increasingly important role in law enforcement as well as entertainment. However, most of the existing methods only work under the condition of no interference, and lack of generalization ability in wild scenes. The fidelity of the images generated by the existing methods are insufficient, and the manipulation ability according to text description is unavailable. Directly applying existing text-based image manipulation methods on face photo-sketch scenario may lead to severe distortions due to the cross-domain challenges. Therefore, we propose a novel cross-domain face photo-sketch synthesis framework named HiFiSketch, a network that learns to adjust the weights of generators for high-fidelity synthesis and manipulation. It can realize the translation of images between the photo domain and the sketch domain, and modify results according to the text input in the meanwhile. We further propose a cross-domain loss function, which can effectively preserve facial details during face photo-sketch synthesis. Extensive experiments on four public face sketch datasets show the superiority of our method compared to existing methods. We further present text-based face photo-sketch manipulation and sequential face photo-sketch manipulation for the first time to demonstrate the effectiveness of our method on high fidelity face photo-sketch synthesis and manipulation.