Reviewerly/exHarmony huggingface dataset:
Developing an interactive pile training module for construction risk management and gaging users’ intentions

Purpose This study aims to develop a novel theoretical model for predicting the users’ intention to use virtual tools designed for construction risk evaluation. Risk evaluation is a vital objective for construction managers. This paper intends to examine critical factors such as potential benefits, motivation, performance expectancy and rich sources of information that may affect users’ intention to use virtual technology. Design/methodology/approach A pile training module (PTM) was developed in a virtual environment to analyze the proposed virtual reality-technology acceptance model (VR-TAM) factors. Further, a questionnaire survey was conducted with the participation of 102 construction professionals in China to validate the proposed VR-TAM model and PTM tool. The retrieved data was computed to test the proposed model by using partial least squares structural equation modeling and the significance of the PTM tool in a virtual environment. Findings The results of this study reveal that high-significance paths represent five relationships between crucial factors affecting users’ intention to use a selected virtual reality (VR) module. Five of seven hypothesis paths were significant with acceptable t -values. By quantitative measurement of high-significance paths, this research has found that each factor under VR-TAM has received significant loadings, with many above the 0.7 threshold mark and others around 0.6. The top factors include “motivation” and “benefits” and have multiplier effects on “intention to use” as the source factors. Practical implications The finding of this study presents crucial factors for VR adoption, and the proposed VR-TAM model contributes to the body of knowledge toward managing construction risk using pre-optimization and understanding in a virtual environment. This study supports Chinese construction company managers in effectively using VR technology in their construction projects for risk assessment and management. Originality/value This study offered the development of a novel VR-TAM integrated with risk assessment techniques for piling processes. Further, the developed model was analyzed by using a survey of Chinese construction professionals to collect perceptions about the modified theoretical model of VR-TAM.
Real-time reading system for pointer meter based on YolactEdge

Despite the extensive deployment of digital instruments in modern times, their stability is challenging to maintain in adverse environmental conditions such as extreme temperatures, pressure, or powerful electromagnetic radiation. Analog meters, owing to their mechanical resilience and electromagnetic impedance, persistently find usage across nuclear power plants, petroleum, and chemical industries. However, under these harsh conditions, manual reading of the instruments may prove to be difficult and dangerous while failing to meet the requirements of real-time monitoring. In recent years, several machine vision-based meter reading systems have been proposed, however, achieving high accuracy through camera-based methods under varying angles and lighting conditions poses significant challenges. Cloud deployment may compromise plant privacy, while edge computing faces limitations in real-time meter reading due to limited computing power. To address these issues, we propose a real-time reading system based on the YolactEdge instance segmentation framework for single-point analog meters. Our system is more accurate than previous studies and is implemented and deployed on the Jetson Xavier NX edge computing device. Our performance evaluation shows that our model outperforms other baselines, with low reference values and relative errors of 0.0237% and 0.0300%, respectively, and an average inference speed of 10.26 FPS with INIT 8 linear acceleration on Nvidia Jetson NX.
Real-Time Evaluation in Online Continual Learning: A New Hope

Current evaluations of Continual Learning (CL) methods typically assume that there is no constraint on training time and computation. This is an unrealistic assumption for any real-world setting, which motivates us to propose: a practical real-time evaluation of continual learning, in which the stream does not wait for the model to complete training before revealing the next data for predictions. To do this, we evaluate current CL methods with respect to their computational costs. We conduct extensive experiments on CLOC, a large-scale dataset containing 39 million time-stamped images with geolocation labels. We show that a simple baseline outperforms state-of-the-art CL methods under this evaluation, questioning the applicability of existing methods in realistic settings. In addition, we explore various CL components commonly used in the literature, including memory sampling strategies and regularization approaches. We find that all considered methods fail to be competitive against our simple baseline. This surprisingly suggests that the majority of existing CL literature is tailored to a specific class of streams that is not practical. We hope that the evaluation we provide will be the first step towards a paradigm shift to consider the computational cost in the development of online continual learning methods.
Enhanced Stable View Synthesis

We introduce an approach to enhance the novel view synthesis from images taken from a freely moving camera. The introduced approach focuses on outdoor scenes where recovering accurate geometric scaffold and camera pose is challenging, leading to inferior results using the state-of-the-art stable view synthesis (SVS) method. SVS and related methods fail for outdoor scenes primarily due to (i) overrelying on the multiview stereo (MVS) for geometric scaffold recovery and (ii) assuming COLMAP computed camera poses as the best possible estimates, despite it being well-studied that MVS 3D reconstruction accuracy is limited to scene disparity and camera-pose accuracy is sensitive to key-point correspondence selection. This work proposes a principled way to enhance novel view synthesis solutions drawing inspiration from the basics of multiple view geometry. By leveraging the complementary behavior of MVS and monocular depth, we arrive at a better scene depth per view for nearby and far points, respectively. Moreover, our approach jointly refines camera poses with image-based rendering via multiple rotation averaging graph optimization. The recovered scene depth and the camera-pose help better view-dependent on-surface feature aggregation of the entire scene. Extensive evaluation of our approach on the popular benchmark dataset, such as Tanks and Temples, shows substantial improvement in view synthesis results compared to the prior art. For instance, our method shows 1.5 dB of PSNR improvement on the Tank and Temples. Similar statistics are observed when tested on other benchmark datasets such as FVS, Mip-NeRF 360, and DTU.
Backdoor Defense via Deconfounded Representation Learning

Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by injecting a few poisoned examples into the training dataset. While extensive efforts have been made to detect and remove backdoors from backdoored DNNs, it is still not clear whether a backdoor-free clean model can be directly obtained from poisoned datasets. In this paper, we first construct a causal graph to model the generation process of poisoned data and find that the backdoor attack acts as the confounder, which brings spurious associations between the input images and target labels, making the model predictions less reliable. Inspired by the causal understanding, we propose the Causality-inspired Backdoor Defense (CBD), to learn deconfounded representations for reliable classification. Specifically, a backdoored model is intentionally trained to capture the confounding effects. The other clean model dedicates to capturing the desired causal effects by minimizing the mutual information with the confounding representations from the backdoored model and employing a sample-wise re-weighting scheme. Extensive experiments on multiple benchmark datasets against 6 state-of-the-art attacks verify that our proposed defense method is effective in reducing backdoor threats while maintaining high accuracy in predicting benign samples. Further analysis shows that CBD can also resist potential adaptive attacks. The code is available at https://github.com/zaixizhang/CBD.
Generating Aligned Pseudo-Supervision from Non-Aligned Data for Image Restoration in Under-Display Camera

Due to the difficulty in collecting large-scale and perfectly aligned paired training data for Under-Display Camera (UDC) image restoration, previous methods resort to monitor-based image systems or simulation-based methods, sacrificing the realness of the data and introducing domain gaps. In this work, we revisit the classic stereo setup for training data collection – capturing two images of the same scene with one UDC and one standard camera. The key idea is to “copy” details from a high-quality reference image and “paste” them on the UDC image. While being able to generate real training pairs, this setting is susceptible to spatial misalignment due to perspective and depth of field changes. The problem is further compounded by the large domain discrepancy between the UDC and normal images, which is unique to UDC restoration. In this paper, we mitigate the non-trivial domain discrepancy and spatial misalignment through a novel Transformer-based framework that generates well-aligned yet high-quality target data for the corresponding UDC input. This is made possible through two carefully designed components, namely, the Domain Alignment Module (DAM) and Geometric Alignment Module (GAM), which encourage robust and accurate discovery of correspondence between the UDC and normal views. Extensive experiments show that high-quality and well-aligned pseudo UDC training pairs are beneficial for training a robust restoration network. Code and the dataset are available at https://github.com/jnjaby/AlignFormer.
Sliced Optimal Partial Transport

Optimal transport (OT) has become exceedingly popular in machine learning, data science, and computer vision. The core assumption in the OT problem is the equal total amount of mass in source and target measures, which limits its application. Optimal Partial Transport (OPT) is a recently proposed solution to this limitation. Similar to the OT problem, the computation of OPT relies on solving a linear programming problem (often in high dimensions), which can become computationally prohibitive. In this paper, we propose an efficient algorithm for calculating the OPT problem between two non-negative measures in one dimension. Next, following the idea of sliced OT distances, we utilize slicing to define the Sliced OPT distance. Finally, we demonstrate the computational and accuracy benefits of the Sliced OPT-based method in various numerical experiments. In particular, we show an application of our proposed Sliced OPT problem in noisy point cloud registration and color adaptation. Our code is available at Github Link.
Learning Human Mesh Recovery in 3D Scenes

We present a novel method for recovering the absolute pose and shape of a human in a pre-scanned scene given a single image. Unlike previous methods that perform scene-aware mesh optimization, we propose to first estimate absolute position and dense scene contacts with a sparse 3D CNN, and later enhance a pretrained human mesh recovery network by cross-attention with the derived 3D scene cues. Joint learning on images and scene geometry enables our method to reduce the ambiguity caused by depth and occlusion, resulting in more reasonable global postures and contacts. Encoding scene-aware cues in the network also allows the proposed method to be optimization-free, and opens up the opportunity for real-time applications. The experiments show that the proposed network is capable of recovering accurate and physically-plausible meshes by a single forward pass and outperforms state-of-the-art methods in terms of both accuracy and speed. Code is available on our project page: https://zju3dv.github.io/sahmr/.
Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection

LiDAR and Radar are two complementary sensing approaches in that LiDAR specializes in capturing an object's 3D shape while Radar provides longer detection ranges as well as velocity hints. Though seemingly natural, how to efficiently combine them for improved feature representation is still unclear. The main challenge arises from that Radar data are extremely sparse and lack height information. Therefore, directly integrating Radar features into LiDAR-centric detection networks is not optimal. In this work, we introduce a bi-directional LiDAR-Radar fusion framework, termed Bi-LRFusion, to tackle the challenges and improve 3D detection for dynamic objects. Technically, Bi-LRFusion involves two steps: first, it enriches Radar's local features by learning important details from the LiDAR branch to alleviate the problems caused by the absence of height information and extreme sparsity; second, it combines LiDAR features with the enhanced Radar features in a unified bird's-eye-view representation. We conduct extensive experiments on nuScenes and ORR datasets, and show that our Bi-LRFusion achieves state-of-the-art performance for detecting dynamic objects. Notably, Radar data in these two datasets have different formats, which demonstrates the generalizability of our method. Codes will be published.
DejaVu: Conditional Regenerative Learning to Enhance Dense Prediction

We present DejaVu, a novel framework which leverages conditional image regeneration as additional supervision during training to improve deep networks for dense prediction tasks such as segmentation, depth estimation, and surface normal prediction. First, we apply redaction to the input image, which removes certain structural information by sparse sampling or selective frequency removal. Next, we use a conditional regenerator, which takes the redacted image and the dense predictions as inputs, and reconstructs the original image by filling in the missing structural information. In the redacted image, structural attributes like boundaries are broken while semantic context is largely preserved. In order to make the regeneration feasible, the conditional generator will then require the structure information from the other input source, i.e., the dense predictions. As such, by including this conditional regeneration objective during training, DejaVu encourages the base network to learn to embed accurate scene structure in its dense prediction. This leads to more accurate predictions with clearer boundaries and better spatial consistency. When it is feasible to leverage additional computation, DejaVu can be extended to incorporate an attention-based regeneration module within the dense prediction network, which further improves accuracy. Through extensive experiments on multiple dense prediction benchmarks such as Cityscapes, COCO, ADE20K, NYUD-v2, and KITTI, we demonstrate the efficacy of employing DejaVu during training, as it out-performs SOTA methods at no added computation cost.
Implementation of a Collision Avoidance System for Machine Tennis Game

Recently, wheelchair tennis has gained worldwide recognition as one of the Paralympic sports. The number of wheelchair tennis players has been increasing year by year and various competitions are held to improve players’ skills around the world. However, the wheelchair tennis is a very challenging sport as players require sophisticated techniques and strength to control the wheelchair. This can be particularly burdensome for beginners or children. In this paper, we consider Omnidirectional Wheelchair Robot for Machine Tennis, which can provide an easy playing environment because it can move in any direction. We propose a collision avoidance system to ensure a safe playing environment. We measured by experiments the position detection accuracy of the proposed system. The experimental results show that the system accuracy was about 93%.
A Two-Stage Crow Search Algorithm for Solving Optimization Problems

This paper proposes a novel metaheuristic technique called two-stage crow search algorithm (TS-CSA).The TS-CSA extends the original crow search algorithm (CSA), that mimics the intelligent foraging behavior of crows.TS-CSA integrates a two-stage search process to improve the CSA in three aspects.First, the two search stages are designed to balance the algorithm's global search and local search capabilities.Second, TS-CSA incorporates a "leaders group" to enable high-quality individuals to guide other individuals and perform global searches by expanding the search space using the best individuals.Third, TS-CSA proposes a new local search strategy for fine-tuning the individuals of the first stage.The proposed TS-CSA and the original CSA are evaluated alongside other metaheuristic algorithms: Grey wolf optimizer (GWO), marine predators algorithm (MPA), and pelican optimization algorithm (POA) using a well-known benchmark of thirteen optimization functions.These functions are commonly employed to assess optimization algorithms due to their complex landscapes and multiple local optima.The results show that the TS-CSA outperforms all competing algorithms, including the original CSA, in terms of convergence and solution quality across all two categories of optimization functions.Furthermore, the proposed algorithm shows potential for solving complex optimization problems across various domains, making it a valuable tool for practitioners and researchers in related optimization fields.
Multiple Random Keys for Image Encryption Based on Sensitivity of a New 6D Chaotic System

This research paper introduces a novel encryption algorithm that utilizes a hyper six-dimensional chaotic system to enhance image security.The algorithm incorporates randomization, switching, diffusion, and XOR operations in a series of stages to ensure robust encryption.Two sets of keys are generated using the chaotic system, adding random and unpredictable elements to enhance the algorithm's security.During the encryption process, the image's color values are transformed into red, green, and blue vectors, enabling precise manipulation at the pixel level.Pseudo-random shuffling, guided by chaotic keys, introduces an additional layer of randomness, making the encrypted image resistant to decryption and tampering attempts.XOR operations further modify the pixel values, while interconnecting the color vectors enhances the encryption's complexity and security.Experimental evaluation of the proposed algorithm demonstrates its efficacy.A high PSNR value of 7.9997 indicates preserved image resolution after encryption.The NPCR value of 99.6002 indicates robust resistance to pixel modifications and the UACI value of 35.3623 showcases uniform pixel distribution.The algorithm also offers a substantial key space of 2 947 , further strengthening its overall security.Notably, its sensitivity of 10 15 highlights its resilience against potential attacks.
Just Noticeable Difference-Aware Per-Scene Bitrate-Laddering for Adaptive Video Streaming

In video streaming applications, a fixed set of bitrate-resolution pairs (known as a bitrate ladder) is typically used during the entire streaming session. However, an optimized bitrate ladder per scene may result in (i) decreased storage or delivery costs or/and (ii) increased Quality of Experience. This paper introduces a Just Noticeable Difference (JND)-aware perscene bitrate ladder prediction scheme (JASLA) for adaptive video-on-demand streaming applications. JASLA predicts jointly optimized resolutions and corresponding constant rate factors (CRFs) using spatial and temporal complexity features for a given set of target bitrates for every scene, which yields an efficient constrained Variable Bitrate encoding. Moreover, bitrate-resolution pairs that yield distortion lower than one JND are eliminated. Experimental results show that, on average, JASLA yields bitrate savings of 34.42% and 42.67% to maintain the same PSNR and VMAF, respectively, compared to the reference HTTP Live Streaming (HLS) bitrate ladder Constant Bitrate encoding using x265 HEVC encoder, where the maximum resolution of streaming is Full HD (1080p). Moreover, a 54.34% average cumulative decrease in storage space is observed.
Sports Visualization in the Wild: The Impact of Technical Factors on User Experience in Augmented Reality Sports Spectating

The maturity of augmented reality (AR) technology allows for expansion into real-world applications, including visualizations for on-site sports spectating. However, it is crucial to understand the factors influencing user experience in AR applications. To optimize user experience, we conducted a user study where participants adjusted parameters to determine noticeable and disruptive values of latency, registration accuracy, and jitter using a mobile indirect AR prototype that simulates a rugby stadium experience. Our findings indicate that latency has the highest disruptive impact on users' experience, with registration accuracy following closely. Furthermore, when noticeable latency, registration accuracy, and jitter were combined, the user experience was negatively affected in a nonlinear, combinatorial manner. This suggests that addressing factors individually is necessary but not enough for successful user experiences. By understanding these factors, developers can optimize AR experiences when creating immersive AR sports experiences and other large-scale AR applications to ensure maximum enjoyment for users.
LLM-Based Interaction for Content Generation: A Case Study on the Perception of Employees in an IT Department

In the past years, AI has seen many advances in the field of NLP. This has led to the emergence of LLMs, such as the now famous GPT-3.5, which revolutionise the way humans can access or generate content. Current studies on LLM-based generative tools are mainly interested in the performance of such tools in generating relevant content (code, text or image). However, ethical concerns related to the design and use of generative tools seem to be growing, impacting the public acceptability for specific tasks. This paper presents a questionnaire survey to identify the intention to use generative tools by employees of an IT company in the context of their work. This survey is based on empirical models measuring intention to use (TAM by Davis, 1989, and UTAUT2 by Venkatesh and al., 2008). Our results indicate a rather average acceptability of generative tools, although the more useful the tool is perceived to be, the higher the intention to use seems to be. Furthermore, our analyses suggest that the frequency of use of generative tools is likely to be a key factor in understanding how employees perceive these tools in the context of their work. Following on from this work, we plan to investigate the nature of the requests that may be made to these tools by specific audiences.