Reviewerly/exHarmony huggingface dataset:
Broad Minimax Probability Learning System and its Application in Regression Modeling

Broad learning system (BLS), a novel incremental learning algorithm, has attracted more and more attention and application. BLS achieves a perfect balance between learning performance and modeling efficiency based on the mean square error (MSE) criterion. However, MSE assumes that a random error between an observed value and real value is a Gaussian distribution, which may be inconsistent to the real-world situation. Moreover, the distributional assumptions may cast doubt on the generalization and validity of the built BLS model. In this article, we propose a novel distribution-free BLS for regression analysis, called broad minimax probability learning system (BMPLS). Learning from the idea of minimax probability machine regression, BMPLS builds a regression model by maximizing the worst probability of the regression function being within the allowed error range. It adequately utilizes the mean and covariance information of the hidden-space feature without making any distributional assumptions of the random error. In order to further improve the learning performance of the model, a regularized BMPLS (RBMPLS) is proposed by applying elastic-net regularization term to BMPLS. The corresponding optimization algorithm is also designed for calculating the output weights of the model. Multiple experiments based on public datasets were carried out to demonstrate the feasibility of the proposed algorithms.
Plingo: A System for Probabilistic Reasoning in Clingo Based on $$ LP ^{ MLN }$$

We present plingo, an extension of the ASP system clingo with various probabilistic reasoning modes. Plingo is centered upon $$ LP ^{ MLN }$$ , a probabilistic extension of ASP based on a weight scheme from Markov Logic. This choice is motivated by the fact that the core probabilistic reasoning modes can be mapped onto optimization problems and that $$ LP ^{ MLN }$$ may serve as a middle-ground formalism connecting to other probabilistic approaches. As a result, plingo offers three alternative frontends, for $$ LP ^{ MLN }$$ , P-log, and ProbLog. The corresponding input languages and reasoning modes are implemented by means of clingo’s multi-shot and theory solving capabilities. The core of plingo amounts to a re-implementation of $$ LP ^{ MLN }$$ in terms of modern ASP technology, extended by an approximation technique based on a new method for answer set enumeration in the order of optimality. We evaluate plingo’s performance empirically by comparing it to other probabilistic systems.
HairMapper: Removing Hair from Portraits Using GANs

Removing hair from portrait images is challenging due to the complex occlusions between hair and face, as well as the lack of paired portrait data with/without hair. To this end, we present a dataset and a baseline method for removing hair from portrait images using generative adversarial networks (GANs). Our core idea is to train a fully connected network HairMapper to find the direction of hair removal in the latent space of StyleGAN for the training stage. We develop a new separation boundary and diffuse method to generate paired training data for males, and a novel “female-male-bald” pipeline for paired data of females. Experiments show that our method can naturally deal with portrait images with variations on gender, age, etc. We validate the superior performance of our method by comparing it to state-of-the-art methods through extensive experiments and user studies. We also demonstrate its applications in hair design and 3D face reconstruction.
Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation

The integration of Vector Quantised Variational AutoEncoder (VQ-VAE) with autoregressive models as generation part has yielded high-quality results on image generation. However, the autoregressive models will strictly follow the progressive scanning order during the sampling phase. This leads the existing VQ series models to hardly escape the trap of lacking global information. Denoising Diffusion Probabilistic Models (DDPM) in the continuous domain have shown a capability to capture the global context, while generating high-quality images. In the discrete state space, some works have demonstrated the potential to perform text generation and low resolution image generation. We show that with the help of a content-rich discrete visual codebook from VQ-VAE, the discrete diffusion model can also generate high fidelity images with global context, which compensates for the deficiency of the classical autoregressive model along pixel space. Meanwhile, the integration of the discrete VAE with the diffusion model resolves the drawback of conventional autoregressive models being oversized, and the diffusion model which demands excessive time in the sampling process when generating images. It is found that the quality of the generated images is heavily dependent on the discrete visual codebook. Extensive experiments demonstrate that the proposed Vector Quantised Discrete Diffusion Model (VQ-DDM) is able to achieve comparable performance to top-tier methods with low complexity. It also demonstrates outstanding advantages over other vectors quantised with autoregressive models in terms of image inpainting tasks without additional training.
VISOLO: Grid-Based Space-Time Aggregation for Efficient Online Video Instance Segmentation

For online video instance segmentation (VIS), fully utilizing the information from previous frames in an efficient manner is essential for real-time applications. Most previous methods follow a two-stage approach requiring additional computations such as RPN and RoIAlign, and do not fully exploit the available information in the video for all subtasks in VIS. In this paper, we propose a novel single-stage framework for online VIS built based on the grid structured feature representation. The grid-based features allow us to employ fully convolutional networks for real-time processing, and also to easily reuse and share features within different components. We also introduce cooperatively operating modules that aggregate information from available frames, in order to enrich the features for all subtasks in VIS. Our design fully takes advantage of previous information in a grid form for all tasks in VIS in an efficient way, and we achieved the new state-of-the-art accuracy (38.6 AP and 36.9 AP) and speed (40.0 FPS) on YouTube-VIS 2019 and 2021 datasets among online VIS methods. The code is available at https://github.com/SuHoHan95/VISOLQ
Door Opening Strategy for Mobile Manipulator with Constrained Configuration

We address the task of robotic door opening in office environments. This task is important for providing indoor mobility for collaborative mobile manipulators. In our work, we mainly focus on the use of high-level control opportunities and identification of the door parameters from visual and lidar data. We develop a solution, which includes handle recognition, handle twisting, and opening. The position of the handle is identified from stereo images by a neural-network-based method. We divide the opening procedure into two stages: first, handle twisting and slightly opening, and second, wide opening. The first stage is implemented via high-level task-space control of the robotic arm, while the platform is static. The position of the door axis is identified during the slight opening by fitting lidar data to the kinematic model. At the second stage, both the platform and the arm are active. The trajectory of the platform is defined by the model predictive planner in such a way that it avoids pushing the arm into a singular configuration, while the manipulator is operated via high-level impedance control. In our experiments, a mobile manipulator composed from the wheeled platform and the robotic arm was able to open office doors using the proposed approach.
The Effect of Inverter Loading Ratio on Energy Estimate Bias

Subhourly effects, particularly variability in solar irradiance, can lead to underestimation of inverter clipping losses and overestimation of energy in hourly photovoltaic system performance models, particularly for systems with high inverter loading ratios. Direct simulation of this error can be complicated by factors such as the representation of spatial and temporal variability in hourly weather data and transient system conditions. In this work we take an alternative approach using real system power measurements to show that energy predictions from typical industry models suffer from a bias that increases with inverter loading ratio. We also show that this loading ratio-dependent bias is strongly correlated with an empirical subhourly inverter clipping bias derived from real power plant data. Finally, we show that this bias is not necessarily specific to any one model or weather dataset by recreating similar biases with alternatives of each.
Incremental Training of Face Morphing Detectors

Recently, the Face Morphing Attack has emerged as a serious and concrete security threat, and then several Morphing Attack Detectors (MADs) have been proposed in the literature. Unfortunately, most MAD approaches, especially if based on single input images, are not yet mature for real-world deployment, mainly due to low accuracy and limited generalization capabilities with data distribution different from those used for training. While better models and techniques will certainly contribute to advancing the state-of-the-art, one of the main obstacles is on the data side: indeed, training data available to research groups are often limited in size and variety, and privacy issues restrict the possibility of data release and exchange. The proposed approach envisages the incremental training of MADs on a collection of datasets even owned by different research groups, adopting a learning strategy that involves model transfer as an alternative to data sharing. Specifically, in this paper, we propose and publicly release a framework to support the adoption of Continual Learning strategies, enabling an efficient incremental training for MADs on new data that progressively become available at different places or times. Different learning strategies are analyzed and compared in terms of the effectiveness and stability of the results achieved.
Painting the landscape of automotive software in GitHub

The automotive industry has transitioned from being an electromechanical to a software-intensive industry. A current high-end production vehicle contains 100 million+ lines of code surpassing modern airplanes, the Large Hadron Collider, the Android OS, and Facebook's front-end software, in code size by a huge margin. Today, software companies worldwide, including Apple, Google, Huawei, Baidu, and Sony are reportedly working to bring their vehicles to the road. This paper ventures into the automotive software landscape in open source, providing a first glimpse into this multi-disciplinary industry with a long history of closed source development. We paint the landscape of automotive software on GitHub by describing its characteristics and development styles.
Convergent Distributed Actor-Critic Algorithm Based on Gradient Temporal Difference

In this paper a new distributed off-policy Actor-Critic algorithm for reinforcement learning is proposed. It is composed of the Gradient Temporal Difference GTD(1) algorithm at the Critic stage, and a complementary consensus-based exact policy gradient algorithm at the Actor stage, derived from the global objective in the form of a sum of weighted local state-value functions. Weak convergence of the algorithm to the invariant set of a corresponding attached ODE is demonstrated under mild conditions. An experimental verification of the algorithm properties is presented, showing that the algorithm can represent an efficient tool for practice, enabling parallel execution and fusion of local exploration spaces.
Edge-Assisted Real-Time Video Analytics With Spatial–Temporal Redundancy Suppression

Driven by plummeting camera prices and advances of video inference algorithms, video cameras are deployed ubiquitously and organizations usually rely on live video analytics to retrieve key information, such as the locations and identities of target objects. However, analyzing real-time video poses severe challenges to today’s network and computation systems. To balance accuracy, bandwidth usage, and latency, we present EVA, an edge-assisted real-time video analytics framework, which coordinates computationally weak cameras with more powerful edge servers to enable video analytics under the accuracy and latency requirements of applications. EVA treats the region where a target object is located as a fine-grained transmission unit and exploits the redundancies in both spatial and temporal domains to reduce the bandwidth usage. Based on the framework, we design an adaptive offloading algorithm, which coordinates the recognition process between the camera and the server. To adapt to complex environments, we then design a threshold adjustment algorithm to tune the confidence threshold dynamically. Experiments on real-world video feeds show that compared to several recent baselines on multiple video genres, EVA maintains high accuracy while reducing bandwidth usage by up to 90%.
Calibration-Free Multi-view Crowd Counting

Deep learning based multi-view crowd counting (MVCC) has been proposed to handle scenes with large size, in irregular shape or with severe occlusions. The current MVCC methods require camera calibrations in both training and testing, limiting the real application scenarios of MVCC. To extend and apply MVCC to more practical situations, in this paper we propose calibration-free multi-view crowd counting (CF-MVCC), which obtains the scene-level count directly from the density map predictions for each camera view without needing the camera calibrations in the test. Specifically, the proposed CF-MVCC method first estimates the homography matrix to align each pair of camera-views, and then estimates a matching probability map for each camera-view pair. Based on the matching maps of all camera-view pairs, a weight map for each camera view is predicted, which represents how many cameras can reliably see a given pixel in the camera view. Finally, using the weight maps, the total scene-level count is obtained as a simple weighted sum of the density maps for the camera views. Experiments are conducted on several multi-view counting datasets, and promising performance is achieved compared to calibrated MVCC methods that require camera calibrations as input and use scene-level density maps as supervision.
Graph Contrastive Partial Multi-View Clustering

With the diversity of information acquisition, data is stored and transmitted in an increasing number of modalities. Nevertheless, it is not unusual for parts of the data to be lost in some views due to unavoidable acquisition, transmission or storage errors. In this paper, we propose an augmentation-free graph contrastive learning framework to solve the problem of partial multi-view clustering. Notably, we suppose that the representations of similar samples (i.e., belonging to the same cluster) should be similar. This is distinct from the general unsupervised contrastive learning that assumes an image and its augmentations share a similar representation. Specifically, relation graphs are constructed using the nearest neighbors to identify existing similar samples, then the constructed inter-instance relation graphs are transferred to the missing views to build graphs on the corresponding missing data. Subsequently, two main components, within-view graph contrastive learning and cross-view graph consistency learning, are devised to maximize the mutual information of different views within a cluster. The proposed approach elevates instance-level contrastive learning and missing data inference to the cluster-level, effectively mitigating the impact of individual missing data on clustering. Experiments on several challenging datasets demonstrate the superiority of our proposed methods.
Rethinking Monocular Height Estimation From a Classification Task Perspective Leveraging the Vision Transformer

Height estimation from a single remote sensing image has great potential in generating digital surface models (DSM) efficiently for a quick earth surface reconstruction. Recently, convolutional neural networks (CNN) have emerged as a powerful method to deal with this ill-posed problem. Most existing methods formulate height estimation as a regression problem due to the continuity of object height. However, it is difficult for the model to regress the object heights exactly to the ground-truth values with a wide range. In this letter, we reformulate the height estimation task as a classification task to improve the model performance. Specifically, we discretize the continuous ground-truth height into bins and assign each pixel to a single label according to the bin subdivision. In addition, we propose to generate a unique bin subdivision for each input image adaptively by viewing the bin generation as a set-to-set problem. Compared with the fixed bin subdivision method, a specific bin subdivision for each input image makes the model adaptively focus on the height range that is more probable to occur in the scene of the input image. In our experiments, we qualitatively and quantitatively demonstrate that the proposed method outperforms the state-of-the-art approaches on both Vaihingen and Potsdam datasets.
Fisher Pruning for Real-Time UAV Tracking

Despite the wide prospect of applications of unmanned aerial vehicle (UAV)-based tracking in transportation, agriculture, public security, and so on, the limitations of computing resources, battery capacity and maximum load of UAV largely hinder the deployment of deep learning (DL)-based tracking algorithms on UAV. Discriminative correlation filters (DCF)-based trackers because of their high efficiency and low resource-consuming merits have thus stood out in the UAV tracking community. However, the precision of DCF-based trackers is hardly comparable to DL-based ones in complex scenarios due to the limited representation learning ability. Filter pruning is a common technique used to deploy deep networks in edge-devices with low power and constrained resources, without compromising much on the accuracy of the model. It is probably an effective means to improve the efficiency of DL-based trackers and facilitate their deployment on UAVs. However, applying filter pruning to UAV tracking has not been well explored. A simple and effective pruning criterion is very desirable at present and may draw more attention in the UAV tracking community to model compression. In this paper, we propose to exploit Fisher pruning to compress the SiamFC++ model for UAV tracking, resulting in our proposed F-SiamFC++ tracker which demonstrates a remarkable balance between efficiency and precision. Extensive experiments on four UAV benchmarks, including UAV123@10fps, DTB70, UAVDT and Vistrone2018 (VisDrone2018-test-dev), show that the proposed F-SiamFC++ tracker achieves state-of-the-art performance.
CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data

Hyperbolic space can naturally embed hierarchies that often exist in real-world data and semantics. While high-dimensional hyperbolic embeddings lead to better representations, most hyperbolic models utilize low-dimensional embeddings, due to non-trivial optimization and visualization of high-dimensional hyperbolic data. We propose CO-SNE, which extends the Euclidean space visualization tool, t-SNE, to hyperbolic space. Like t-SNE, it converts distances between data points to joint probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of high-dimensional data <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> and low-dimensional embedding <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Y$</tex> . However, unlike Euclidean space, hyperbolic space is inhomogeneous: A volume could contain a lot more points at a location far from the origin. CO-SNE thus uses hyperbolic normal distributions for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> and hyperbolic Cauchy instead of t-SNE's Student's t-distribution for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Y$</tex> , and it additionally seeks to preserve <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> 's individual distances to the Origin in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Y$</tex> . We apply CO-SNE to naturally hyperbolic data and supervisedly learned hyperbolic features. Our results demonstrate that CO-SNE deflates high-dimensional hyperbolic data into a low-dimensional space without losing their hyperbolic characteristics, significantly outperforming popular visualization tools such as PCA, t-SNE, UMAP, and HoroPCA which is also designed for hyperbolic data.
Improving Mirror Fitness through augmented reality technology

Along with the development of sports in the new China, the whole country is actively responding to the national call for a "Healthy China", the fitness industry is booming, and people's physical quality and health levels are improving. The latter situation has brought new opportunities and challenges to fitness, and smart fitness has become an unstoppable trend. Taking the more prominent Mirror Fitness as the research object, it is found that its action error correction feedback is defective and only reminds users in the form of text, which reduces the efficiency of exercise. This paper combines the concept and technology of augmented reality, and researches combining it with the user's mirror image to intuitively reflect the incorrect action area through virtual-real interaction, allowing the user to receive information and correct it in time, which not only reduces exercise risk but also improves exercise efficiency and user experience.
Heterogeneous and Customized Cost-Efficient Reversible Image Degradation for Green IoT

With the large-scale deployment of the Internet of Things (IoT) in daily life, more and more privacy data are collected by IoT devices. These data are not directly physically controlled by users, which may cause privacy concerns. In fact, privacy has become one of the significant problems faced by IoT. In this article, we mainly study the protection of image privacy under the green IoT. We have conducted an in-depth analysis of the green IoT scenario and put forward the scope and corresponding goals that the scheme should have. Motivated by this, a novel image privacy protection scheme is proposed, i.e., heterogeneous and customized cost-efficient reversible image degradation for green IoT. This scheme fully considers the characteristics of privacy and the various users’ diverse requirements to achieve a heterogeneous and customized privacy protection. Meanwhile, cost effectiveness cannot be confined to the efficiency of the direct image processing at the expense of greatly increasing costs in other aspects, such as transmission and reversion. It is mitigated by preserving some visual content in the privacy-protected image. It also improves the image compression efficiency and ensures that the user can select the desired image according to the visual content for reversion. Some experiments have been carried out to demonstrate that this work has achieved the proposed scope and corresponding goals.