Reviewerly/exHarmony huggingface dataset:
CAT-Det: Contrastively Augmented Transformer for Multimodal 3D Object Detection

In autonomous driving, LiDAR point-clouds and RGB images are two major data modalities with complementary cues for 3D object detection. However, it is quite difficult to sufficiently use them, due to large inter-modal discrepancies. To address this issue, we propose a novel framework, namely Contrastively Augmented Transformer for multi-modal 3D object Detection (CAT-Det). Specifically, CAT-Det adopts a two-stream structure consisting of a Pointformer (PT) branch, an Imageformer (IT) branch along with a Cross-Modal Transformer (CMT) module. PT, IT and CMT jointly encode intra-modal and inter-modal long-range contexts for representing an object, thus fully exploring multi-modal information for detection. Furthermore, we propose an effective One-way Multimodal Data Augmentation (OMDA) approach via hierarchical contrastive learning at both the point and object levels, significantly improving the accuracy only by augmenting point-clouds, which is free from complex generation of paired samples of the two modalities. Extensive experiments on the KITTI benchmark show that CAT-Det achieves a new state-of-the-art, highlighting its effectiveness.
PTTR: Relational 3D Point Cloud Object Tracking with Transformer

In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud. Motivated by the success of transformers, we propose Point Tracking TRansformer (PTTR), which efficiently predicts high-quality 3D tracking results in a coarse-to-fine manner with the help of transformer operations. PTTR consists of three novel designs. 1) Instead of random sampling, we design Relation-Aware Sampling to preserve relevant points to given templates during subsampling. 2) Furthermore, we propose a Point Relation Transformer (PRT) consisting of a self-attention and a cross-attention module. The global self-attention operation captures long-range dependencies to enhance encoded point features for the search area and the template, respectively. Subsequently, we generate the coarse tracking results by matching the two sets of point features via cross-attention. 3) Based on the coarse tracking results, we employ a novel Prediction Refinement Module to obtain the final refined prediction. In addition, we create a large-scale point cloud single object tracking benchmark based on the Waymo Open Dataset. Extensive experiments show that PTTR achieves superior point cloud tracking in both accuracy and efficiency. Our code is available at https://github.com/Jasonkks/PTTR.
Spiking Transformers for Event-based Single Object Tracking

Event-based cameras bring a unique capability to tracking, being able to function in challenging real-world conditions as a direct result of their high temporal resolution and high dynamic range. These imagers capture events asynchronously that encode rich temporal and spatial information. However, effectively extracting this information from events remains an open challenge. In this work, we propose a spiking transformer network, STNet, for single object tracking. STNet dynamically extracts and fuses information from both temporal and spatial domains. In particular, the proposed architecture features a transformer module to provide global spatial information and a spiking neural network (SNN) module for extracting temporal cues. The spiking threshold of the SNN module is dynamically adjusted based on the statistical cues of the spatial information, which we find essential in providing robust SNN features. We fuse both feature branches dynamically with a novel cross-domain attention fusion algorithm. Extensive experiments on three event-based datasets, FE240hz, EED and VisEvent validate that the proposed STNet outperforms existing state-of-the-art methods in both tracking accuracy and speed with a significant margin. The code and pretrained models are at https://github.com/Jee-King/CVPR2022_STNet.
Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds

3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance matching. However, LiDAR point clouds are usually textureless and incomplete, which hinders effective appearance matching. Besides, previous methods greatly overlook the critical motion clues among targets. In this work, beyond 3D Siamese tracking, we introduce a motion-centric paradigm to handle 3D SOT from a new perspective. Following this paradigm, we propose a matching-free two-stage tracker M2-Track. At the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> -stage, M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Track localizes the target within successive frames via motion transformation. Then it refines the target box through motion-assisted shape completion at the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> -stage. Extensive experiments confirm that M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Track significantly outperforms previous state-of-the-arts on three large-scale datasets while running at 57FPS (~ 8%, ~ 17% and ~ 22% precision gains on KITTI, NuScenes, and Waymo Open Dataset respectively). Further analysis verifies each component's effectiveness and shows the motioncentric paradigm's promising potential when combined with appearance matching. Code will be made available at https://github.com/Ghostish/Open3DSOT.
Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization

In this paper, Energy Valley Optimizer (EVO) is proposed as a novel metaheuristic algorithm inspired by advanced physics principles regarding stability and different modes of particle decay. Twenty unconstrained mathematical test functions are utilized in different dimensions to evaluate the proposed algorithm's performance. For statistical purposes, 100 independent optimization runs are conducted to determine the statistical measurements, including the mean, standard deviation, and the required number of objective function evaluations, by considering a predefined stopping criterion. Some well-known statistical analyses are also used for comparative purposes, including the Kolmogorov-Smirnov, Wilcoxon, and Kruskal-Wallis analysis. Besides, the latest Competitions on Evolutionary Computation (CEC), regarding real-world optimization, are also considered for comparing the results of the EVO to the most successful state-of-the-art algorithms. The results demonstrate that the proposed algorithm can provide competitive and outstanding results in dealing with complex benchmarks and real-world problems.
YOLO-Extract: Improved YOLOv5 for Aircraft Object Detection in Remote Sensing Images

Compared with natural images, remote sensing targets have small and dense target shapes as well as complex target backgrounds. As a result, insufficient detection accuracy and target location cannot be accurately identified. So, this paper proposes the YOLO-extract algorithm based on the YOLOv5 algorithm. Firstly, The YOLO-extract algorithm optimized the model structure of the YOLOv5 algorithm. The YOLO-extract algorithm not only deleted the feature layer and prediction head with poor feature extraction ability but also a new feature extractor with stronger feature extraction ability was integrated into the network. At the same time, YOLO-extract borrowed the idea of residual network to integrate Coordinate Attention into the network. Secondly, the mixed dilated convolution was combined with the redesigned residual structure to enhance the feature and location information extraction ability of the shallow layer of the model and optimize the feature extraction ability of the model for different scale targets. Finally, drawing on the idea of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -IoU Loss, Focal- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> EIoU Loss was designed to replace CIoU Loss, which makes the model bounding box regression faster and the loss lower. The experimental results on the test data set show that compared with the YOLOv5 algorithm, the YOLO-extract algorithm has a faster convergence speed, reduces the calculation amount by 45.3GFLOPs and the number of parameters by 10.526M, but increases the mAP by 8.1% and the detection speed by 3 times.
People, places, and time: a large-scale, longitudinal study of transformed avatars and environmental context in group interaction in the metaverse

Abstract As the metaverse expands, understanding how people use virtual reality to learn and connect is increasingly important. We used the Transformed Social Interaction paradigm (Bailenson et al., 2004) to examine different avatar identities and environments over time. In Study 1 (n = 81), entitativity, presence, enjoyment, and realism increased over 8 weeks. Avatars that resembled participants increased synchrony, similarities in moment-to-moment nonverbal behaviors between participants. Moreover, self-avatars increased self-presence and realism, but decreased enjoyment, compared to uniform avatars. In Study 2 (n = 137), participants cycled through 192 unique virtual environments. As visible space increased, so did nonverbal synchrony, perceived restorativeness, entitativity, pleasure, arousal, self- and spatial presence, enjoyment, and realism. Outdoor environments increased perceived restorativeness and enjoyment more than indoor environments. Self-presence and realism increased over time in both studies. We discuss implications of avatar appearance and environmental context on social behavior in classroom contexts over time.
SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation

Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on an unlabeled target domain by utilizing the supervised model trained on a labeled source domain. One popular solution is self-training, which retrains the model with pseudo labels on target instances. Plenty of approaches tend to alleviate noisy pseudo labels, however, they ignore the intrinsic connection of the training data, i.e., intra-class compactness and inter-class dispersion between pixel representations across and within domains. In consequence, they struggle to handle cross-domain semantic variations and fail to build a well-structured embedding space, leading to less discrimination and poor generalization. In this work, we propose emantic-Guided Pixel Contrast (SePiCo), a novel one-stage adaptation framework that highlights the semantic concepts of individual pixels to promote learning of class-discriminative and class-balanced pixel representations across domains, eventually boosting the performance of self-training methods. Specifically, to explore proper semantic concepts, we first investigate a centroid-aware pixel contrast that employs the category centroids of the entire source domain or a single source image to guide the learning of discriminative features. Considering the possible lack of category diversity in semantic concepts, we then blaze a trail of distributional perspective to involve a sufficient quantity of instances, namely distribution-aware pixel contrast, in which we approximate the true distribution of each semantic category from the statistics of labeled source data. Moreover, such an optimization objective can derive a closed-form upper bound by implicitly involving an infinite number of (dis)similar pairs, making it computationally efficient. Extensive experiments show that SePiCo not only helps stabilize training but also yields discriminative representations, making significant progress on both synthetic-to-real and daytime-to-nighttime adaptation scenarios. The code and models are available at https://github.com/BIT-DA/SePiCo.
Hyperparameter Search for Machine Learning Algorithms for Optimizing the Computational Complexity

For machine learning algorithms, fine-tuning hyperparameters is a computational challenge due to the large size of the problem space. An efficient strategy for adjusting hyperparameters can be established with the use of the greedy search and Swarm intelligence algorithms. The Random Search and Grid Search optimization techniques show promise and efficiency for this task. The small population of solutions used at the outset, and the costly goal functions used by these searches, can lead to slow convergence or execution time in some cases. In this research, we propose using the machine learning model known as Support Vector Machine and optimizing it using four distinct algorithms—the Ant Bee Colony Algorithm, the Genetic Algorithm, the Whale Optimization, and the Particle Swarm Optimization—to evaluate the computational cost of SVM after hyper-tuning. Computational complexity comparisons of these optimization algorithms were performed to determine the most effective strategies for hyperparameter tuning. It was found that the Genetic Algorithm had a lower temporal complexity than other algorithms.
Artificial Intelligence as a Disruptive Technology—A Systematic Literature Review

The greatest technological changes in our lives are predicted to be brought about by Artificial Intelligence (AI). Together with the Internet of Things (IoT), blockchain, and several others, AI is considered to be the most disruptive technology, and has impacted numerous sectors, such as healthcare (medicine), business, agriculture, education, and urban development. The present research aims to achieve the following: identify how disruptive technologies have evolved over time and their current acceptation (1); extract the most prominent disruptive technologies, besides AI, that are in use today (2); and elaborate on the domains that were impacted by AI and how this occurred (3). Based on a sentiment analysis of the titles and abstracts, the results reveal that the majority of recent publications have a positive connotation with regard to the disruptive impact of edge technologies, and that the most prominent examples (the top five) are AI, the IoT, blockchain, 5G, and 3D printing. The disruptive effects of AI technology are still changing how people interact in the corporate, consumer, and professional sectors, while 5G and other mobile technologies will become highly disruptive and will genuinely revolutionize the landscape in all sectors in the upcoming years.
Digital governance: A conceptual framework and research agenda

The rapid expansion of digital technologies has paved the way for new forms of organizing, facilitated by increased data and knowledge exchange between individuals and organizations. However, this poses major new challenges for designing effective governance mechanisms. This paper highlights the critical role of digital governance in facilitating digitally enabled exchange relationships. To this end, we propose a typology of analog, augmented, and automated governance modes, each associated with specific control, coordination, incentive, and trust mechanisms. Additionally, we provide a heuristic for determining the optimal governance choice via the interplay of transactivity (i.e., the contributors, connections, and consistency in an exchange network) and corresponding governance costs. Our study advances the governance literature by defining digital governance as a distinct form and outlining key governance mechanisms and choices in the digital era. Finally, we identify avenues for future research in this field.
Coarse-to-Fine Video Instance Segmentation With Factorized Conditional Appearance Flows

We introduce a novel method using a new generative model that automatically learns effective representations of the target and background appearance to detect, segment and track each instance in a video sequence. Differently from current discriminative tracking-by-detection solutions, our proposed hierarchical structural embedding learning can predict more high-quality masks with accurate boundary details over spatio-temporal space via the normalizing flows. We formulate the instance inference procedure as a hierarchical spatio-temporal embedded learning across time and space. Given the video clip, our method first coarsely locates pixels belonging to a particular instance with Gaussian distribution and then builds a novel mixing distribution to promote the instance boundary by fusing hierarchical appearance embedding information in a coarse-to-fine manner. For the mixing distribution, we utilize a factorization condition normalized flow fashion to estimate the distribution parameters to improve the segmentation performance. Comprehensive qualitative, quantitative, and ablation experiments are performed on three representative video instance segmentation benchmarks (i.e., YouTube-VIS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">19</inf> , YouTube-VIS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</inf> , and OVIS) and the effectiveness of the proposed method is demonstrated. More impressively, the superior performance of our model on an unsupervised video object segmentation dataset (i.e., DAVIS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">19</inf> ) proves its generalizability. Our algorithm implementations are publicly available at https://github.com/zyqin19/HEVis.
A novel image-to-knowledge inference approach for automatically diagnosing tumors

Breast cancer is one of the most vulnerable malignant tumors for women in the world, which seriously threatens women's life and health. Breast ultrasound imaging technology is widely used in clinical breast cancer detection due to its advantages of high safety, real-time and convenience. However, breast ultrasound diagnosis relies on experienced diagnostic medical sonographers to read the images and requires high-quality imaging of ultrasound images. On the one hand, reading the breast ultrasound images manually by the doctors is time-consuming and burdensome. On the other hand, cultivating an ultrasonographer is a costly process. The development of computer-aided diagnostic systems in recent years is a solution to these problems to some extent. More and more computer-aided diagnostic systems for breast ultrasound are being used in clinical practice, and their sensitivity and accuracy are higher than those of less senior ultrasonographers, which reduces the workload of physicians and improves diagnostic efficiency to a certain extent. Existing assisted diagnosis systems mainly use texture information such as LBP statistical histograms or deep features extracted from deep networks as the basis for diagnosis, which is not in line with the diagnosis mode of physicians and does not employ medical knowledge for diagnosis, making it difficult to make trade-offs in terms of interpretability and diagnostic performance. In this work, a new interpretable reasoning paradigm from images to knowledge is proposed. Based on this paradigm, interpretable features are firstly perceived as the medical knowledge from the images. The perceived features may be inaccurately identified, then the interpretable features are further amended to obtain high-quality knowledge. Finally, the knowledge is constructed into a knowledge graph and the diagnosis results are obtained by interpretable knowledge inference on the knowledge graph. The experimental results demonstrate that our approach achieves a trade-off in interpretability and diagnostic performance compared to the mainstream diagnostic systems based on deep learning methods and those based on traditional machine learning methods.
Imagic: Text-Based Real Image Editing with Diffusion Models

Text-conditioned image editing has recently attracted considerable interest. However, most methods are currently limited to one of the following: specific editing types (e.g., object overlay, style transfer), synthetically generated images, or requiring multiple input images of a common object. In this paper we demonstrate, for the very first time, the ability to apply complex (e.g., non-rigid) text-based semantic edits to a single real image. For example, we can change the posture and composition of one or multiple objects inside an image, while preserving its original characteristics. Our method can make a standing dog sit down, cause a bird to spread its wings, etc. – each within its single high-resolution user-provided natural image. Contrary to previous work, our proposed method requires only a single input image and a target text (the desired edit). It operates on real images, and does not require any additional inputs (such as image masks or additional views of the object). Our method, called Imagic, leverages a pre-trained text-to-image diffusion model for this task. It produces a text embedding that aligns with both the input image and the target text, while fine-tuning the diffusion model to capture the image-specific appearance. We demonstrate the quality and versatility of Imagic on numerous inputs from various domains, showcasing a plethora of high quality complex semantic image edits, all within a single unified framework. To better assess performance, we introduce TEdBench, a highly challenging image editing benchmark. We conduct a user study, whose findings show that human raters prefer Imagic to previous leading editing methods on TEdBench.
Assessing Dimensions Influencing IoT Implementation Readiness in Industries: A Fuzzy DEMATEL and Fuzzy AHP Analysis

The rapid growth of the information age and digital technologies has ushered in innovative concepts such as Industry 4.0, smart health, digital services, and smart cities. The Internet of Things (IoT) technology has emerged as a crucial driver across domains, engaging businesses, platforms, and industries. The IoT encompasses a holistic ecosystem and a value chain that necessitate evaluating influential dimensions for successful implementation. This research applies Fuzzy DEMATEL and Fuzzy AHP methods to rank and examine dimensions affecting IoT implementation readiness. Findings underscore the significance of organizational factors, hard infrastructures, and soft infrastructures as critical dimensions. Attention to strengthening organizational aspects and developing reliable infrastructures facilitates successful IoT integration. Additionally, while relatively less significant, environmental factors, security and privacy, data analytics, and customer and training dimensions contribute to industry readiness. The combined results provide insights for decision-makers and stakeholders involved in IoT implementation, guiding the development of appropriate strategies, resource allocation, and enhancing operational efficiency. By comprehending the interrelationships and prioritizing influential factors, industries can effectively prepare for successful IoT implementation.