Reviewerly/exHarmony huggingface dataset:
Late Fusion Multiple Kernel Clustering With Proxy Graph Refinement

Multiple kernel clustering (MKC) optimally utilizes a group of pre-specified base kernels to improve clustering performance. Among existing MKC algorithms, the recently proposed late fusion MKC methods demonstrate promising clustering performance in various applications and enjoy considerable computational acceleration. However, we observe that the kernel partition learning and late fusion processes are separated from each other in the existing mechanism, which may lead to suboptimal solutions and adversely affect the clustering performance. In this article, we propose a novel late fusion multiple kernel clustering with proxy graph refinement (LFMKC-PGR) framework to address these issues. First, we theoretically revisit the connection between late fusion kernel base partition and traditional spectral embedding. Based on this observation, we construct a proxy self-expressive graph from kernel base partitions. The proxy graph in return refines the individual kernel partitions and also captures partition relations in graph structure rather than simple linear transformation. We also provide theoretical connections and considerations between the proposed framework and the multiple kernel subspace clustering. An alternate algorithm with proved convergence is then developed to solve the resultant optimization problem. After that, extensive experiments are conducted on 12 multi-kernel benchmark datasets, and the results demonstrate the effectiveness of our proposed algorithm. The code of the proposed algorithm is publicly available at https://github.com/wangsiwei2010/graphlatefusion_MKC.
Fast-Tracker: A Robust Aerial System for Tracking Agile Target in Cluttered Environments

This paper proposes a systematic solution that uses an unmanned aerial vehicle (UAV) to aggressively and safely track an agile target. It properly handles the challenging situations where the intent of the target and the dense environments are unknown. Our work is divided into two parts: target motion prediction and tracking trajectory planning. The target motion prediction method utilizes target observations to reliably predict its future motion. The tracking trajectory planner follows the hierarchical workflow. A target informed kinody-namic searching method is adopted as the front-end, which heuristically searches for a safe tracking trajectory. The back- end optimizer then refines it into a spatial-temporal optimal trajectory. The proposed solution is integrated into an onboard quadrotor system. We fully test the system in challenging real-world tracking missions. Moreover, benchmark comparisons validate that the proposed method surpasses the cutting-edge methods on time efficiency and tracking effectiveness.
Autonomous underwater robot for underwater image enhancement via multi-scale deformable convolution network with attention mechanism

• We develop a novel encoder-decoder architecture with an attention-based skip fusion integrated into autonomous underwater robot for underwater image enhancement. • We propose a multi-scale deformable convolutional network, which acquires deformable local receptive fields at different scales and enriches the diversity of feature representation. • We adopt a parallel attention block for spatial and channel dimensions to focus on the interest regions of the input feature map. • Our MsDCANet achieves state-of-the-art performance on the recent benchmark in terms of visual effects and object metrics, which is conducive to integrating underwater robots and applications in aquaculture. Underwater images suffer from poor visibility quality, which results from selective attenuation and scattering. These interdependence phenomena together cause image degradation, failing autonomous underwater robots to recognize image contents. To address those problems, we propose a multi-scale deformable convolution network with an attention mechanism (MsDCANet) to enhance the quality of underwater images. The proposed model is generally implemented by an encoder-decoder architecture. Concretely, we first propose a multi-scale deformable convolutional network, which acquires deformable local receptive fields at different scales and enriches the diversity of feature representation. Coupled with spatial and channel attention mechanisms, the task-oriented high-level representations acquired from input feature maps, and the most significant features are highlighted. Considering the contributions of the encoder and decoder functions on the final enhancement task, an attention-based skip fusion scheme is designed to improve the original skip connection, which aims to reconstruct higher quality underwater images. Finally, the proposed model is optimized by a multi-task loss function, including pixel loss and perceptual loss. Experiments on underwater images captured under diverse scenes demonstrate that the proposed model produces visually pleasing results and has strong generalization ability, even significantly outperforming several state-of-the-art methods. Besides, our approach can also improve the performance of vision tasks and be applied to aquaculture development.
Indoor occupancy measurement by the fusion of motion detection and static estimation

The human dimension information in buildings is crucial for energy saving, environment conditioning, human health, and security management. Indoor occupancy information measurement methods based on cameras mainly consist of cameras at the room entrance and interior. However, each method has particular advantages and limitations. In this paper, we propose a four-step occupancy detection and estimation system based on cameras. Firstly, we develop an occupancy detection method to filter the non-occupied frames and provide the switching value. Secondly, we design a motion detection method to detect entering or leaving events at the room entrance. Thirdly, Fully Convolutional Head Detector (FCHD) is used to detect indoor human heads. Finally, considering the occupancy number information depends on the previous results, we propose a fusion estimation method that combines motion detection and static estimation by calculating the Kalman filter and Occupancy Frequency Histogram (OFH). The novel fusion method takes the advantages of two methods and overcomes its own limitations, so that it can compensate each other to reach the high-accuracy results. The accumulative error and the shaking results are cleared, and then some headlike objects are filtered. The proposed fusion method reaches an occupancy detection accuracy of 97.8%; reaches an occupancy estimation Score of 78.52% and 79.18%; reaches an occupancy estimation the Mean Square Error (MSE) of 0.21 and 0.23. The proposed method is novel and effective for fusing these two vision situations through practical experimental verification. The proposed method has a significant potential to save energy and prevent the spreading of coronavirus disease 2019 (COVID-19) in buildings.
Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks

Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. Many researchers have been working on vision-based gesture recognition due to its various applications. This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation. The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out. The proposed model is a light-weight architecture with only 3.7 million training parameters. The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly. The model was trained on 2000 video-clips per class which were separated into 80% training and 20% validation sets. An accuracy of 99% and 97% was achieved on training and testing data, respectively. We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2 + LSTM.
Analog Gaussian Function Circuit: Architectures, Operating Principles and Applications

This review paper explores existing architectures, operating principles, performance metrics and applications of analog Gaussian function circuits. Architectures based on the translinear principle, the bulk-controlled approach, the floating gate approach, the use of multiple differential pairs, compositions of different fundamental blocks and others are considered. Applications involving analog implementations of Machine Learning algorithms, neuromorphic circuits, smart sensor systems and fuzzy/neuro-fuzzy systems are discussed, focusing on the role of the Gaussian function circuit. Finally, a general discussion and concluding remarks are provided.
Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search

Object rearrangement is a widely-applicable and challenging task for robots. Geometric constraints must be carefully examined to avoid collisions and combinatorial issues arise as the number of objects increases. This work studies the algorithmic structure of rearranging uniform objects, where robot-object collisions do not occur but object-object collisions have to be avoided. The objective is minimizing the number of object transfers under the assumption that the robot can manipulate one object at a time. An efficiently computable decomposition of the configuration space is used to create a "region graph", which classifies all continuous paths of equivalent collision possibilities. Based on this compact but rich representation, a complete dynamic programming primitive DFS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DP</inf> performs a recursive depth first search to solve monotone problems quickly, i.e., those instances that do not require objects to be moved first to an intermediate buffer. DFS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DP</inf> is extended to solve single-buffer, non-monotone instances, given a choice of an object and a buffer. This work utilizes these primitives as local planners in an informed search framework for more general, non-monotone instances. The search utilizes partial solutions from the primitives to identify the most promising choice of objects and buffers. Experiments demonstrate that the proposed solution returns near-optimal paths with higher success rate, even for challenging non-monotone instances, than other leading alternatives.
Collaborative and Adversarial Learning of Focused and Dispersive Representations for Semi-supervised Polyp Segmentation

Automatic polyp segmentation from colonoscopy images is an essential step in computer aided diagnosis for colorectal cancer. Most of polyp segmentation methods reported in recent years are based on fully supervised deep learning. However, annotation for polyp images by physicians during the diagnosis is time-consuming and costly. In this paper, we present a novel semi-supervised polyp segmentation via collaborative and adversarial learning of focused and dispersive representations learning model, where focused and dispersive extraction module are used to deal with the diversity of location and shape of polyps. In addition, confidence maps produced by a discriminator in an adversarial training framework shows the effectiveness of leveraging unlabeled data and improving the performance of segmentation network. Consistent regularization is further employed to optimize the segmentation networks to strengthen the representation of the outputs of focused and dispersive extraction module. We also propose an auxiliary adversarial learning method to better leverage unlabeled examples to further improve semantic segmentation accuracy. We conduct extensive experiments on two famous polyp datasets: Kvasir-SEG and CVC-Clinic DB. Experimental results demonstrate the effectiveness of the proposed model, consistently outperforming state-of-the-art semi-supervised segmentation models based on adversarial training and even some advanced fully supervised models.
PyTorchVideo

We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing. The library covers a full stack of video understanding tools including multimodal data loading, transformations, and models that reproduce state-of-the-art performance. PyTorchVideo further supports hardware acceleration that enables real-time inference on mobile devices. The library is based on PyTorch and can be used by any training framework; for example, PyTorchLightning, PySlowFast, or Classy Vision. PyTorchVideo is available at https://pytorchvideo.org/.
MobiHisNet: A Lightweight CNN in Mobile Edge Computing for Histopathological Image Classification

Recent advances in artificial intelligence (AI), especially convolutional neural networks (CNNs), alongside the digitization of histopathological images, have made the computer-aided diagnosis of breast cancer a reality. However, deep learning-based approaches are computationally expensive and have huge parameters, which makes them less affordable for edge devices. In order to make them affordable for edge devices, the whole classification model needs to be compressed while maintaining accuracy. Providing a low-cost solution for histopathological diagnosis in the recent edge-computing world is of utmost importance. Therefore, in this study, we propose “MobiHisNet,” an efficient and lightweight CNN model for histopathological image classification (HIC) based on MobileNet. MobiHisNet was successfully deployed on a Raspberry Pi, as well as three mobile devices, demonstrating its ability to run on a lightweight and portable processor. Our studies indicated that a depth parameter ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gamma = 0.5$ </tex-math></inline-formula> ) and a 16-bit quantization are the optimum parameters for the proposed model while balancing the accuracy, inference time, and memory peak requirements. Compared to the state-of-the-art, pretrained models, MobiHisNet has fewer parameters and calculations, resulting in faster image classification. This renders it more viable for production purposes and applications on edge devices. In addition, MobiHisNet is computationally faster than VGG16, ResNet50, Xception, and InceptionV3 by twenty-seven, eight, six, and five times, respectively. This also outperforms all the baseline models with the moderate model size and FLOP counts. Experiments on breast cancer HIC (BreakHis) data sets show superior performance of MobiHisNet on edge devices in terms of higher accuracy, lesser complexity, and lesser memory requirements. Thus, it has a high potential for deployment in mobile edge devices.
Game-based Sprint retrospectives: multiple action research

In today's fast-paced world of rapid technological change, software development teams need to constantly revise their work practices. Not surprisingly, regular reflection on how to become more effective is perceived as one of the most important principles of Agile Software Development. Nevertheless, running an effective and enjoyable retrospective meeting is still a challenge in real environments. As reported by several studies, the Sprint Retrospective is an agile practice most likely to be implemented improperly or sacrificed when teams perform under pressure to deliver. To facilitate the implementation of the practice, some agile coaches have proposed to set up retrospective meetings in the form of retrospective games. However, there has been little research-based evidence to support the positive effects of retrospective games. Our aim is to investigate whether the adoption of retrospective games can improve retrospective meetings in general and lead to positive societal outcomes. In this paper, we report on an Action Research project in which we implemented six retrospective games in six Scrum teams that had experienced common retrospective problems. The received feedback indicates that the approach helped the teams to mitigate many of the "accidental difficulties" pertaining to the Sprint Retrospective, such as lack of structure, dullness, too many complaints, or unequal participation and made the meetings more productive to some degree. Moreover, depending on their individual preferences, different participants perceived different games as having a positive impact on their communication, motivation-and-involvement, and/or creativity, even though there were others, less numerous, who had an opposite view. The advantages and disadvantages of each game as well as eight lessons learned are presented in the paper.
Faster Coherent Quantum Algorithms for Phase, Energy, and Amplitude Estimation

We consider performing phase estimation under the following conditions: we are given only one copy of the input state, the input state does not have to be an eigenstate of the unitary, and the state must not be measured. Most quantum estimation algorithms make assumptions that make them unsuitable for this 'coherent' setting, leaving only the textbook approach. We present novel algorithms for phase, energy, and amplitude estimation that are both conceptually and computationally simpler than the textbook method, featuring both a smaller query complexity and ancilla footprint. They do not require a quantum Fourier transform, and they do not require a quantum sorting network to compute the median of several estimates. Instead, they use block-encoding techniques to compute the estimate one bit at a time, performing all amplification via singular value transformation. These improved subroutines accelerate the performance of quantum Metropolis sampling and quantum Bayesian inference.
Video Visual Relation Detection via Iterative Inference

The core problem of video visual relation detection (VidVRD) lies in accurately classifying the relation triplets, which comprise of the classes of subject and object entities, and the predicate classes of various relationships between them. Existing VidVRD approaches classify these three relation components in either independent or cascaded manner, thus fail to fully exploit the inter-dependency among them. In order to utilize this inter-dependency in tackling the challenges of visual relation recognition in videos, we propose a novel iterative relation inference approach for VidVRD. We derive our model from the viewpoint of joint relation classification which is light-weight yet effective, and propose a training approach to better learn the dependency knowledge from the likely correct triplet combinations. As such, the proposed inference approach is able to gradually refine each component based on its learnt dependency and the other two's predictions. Our ablation studies show that this iterative relation inference can empirically converge in a few steps and consistently boost the performance over baselines. Further, we incorporate it into a newly designed VidVRD architecture, named VidVRD-II (Iterative Inference), which generalizes well across different datasets. Experiments show that VidVRD-II achieves the start-of-the-art performance on both of ImageNet-VidVRD and VidOR benchmark datasets.
A monocular vision-based decoupling measurement method for plane motion orbits

The plane motion that composed of dynamic displacement and angle is the basis of six-degree-of-freedom motion, and its accurate measurement is especially important for the applications of inertial navigation, pose estimation, and bridge structural healthy analysis. Currently, the commonly used measurement strategies include the laser interferometry, grating ruler-based method, and sensor-based method. However, they are always difficult to accomplish the high-accuracy and efficiency measurement in a broad low-frequency range due to the limitation of their performances. In this study, a monocular vision-based method is investigated to measure the plane motion, which can get the displacement and angle as well as orbit simultaneously by using the Zernike moment method with sub-pixel accuracy and decoupling model. The measurement uncertainties of dynamic displacement and angle are 0.266% and 0.098°, and the resolutions can highly reach to 0.1 um and 0.0001°, respectively. Comparison experiments with the laser interferometry and circular grating method demonstrate the investigated method can accomplish high-accuracy displacement and angle measurements in the ranges of 0.05–20 Hz and 0.001–10 Hz. Meanwhile, the different plane motion orbits can also be measured by the investigated method.
Is Visual Context Really Helpful for Knowledge Graph? A Representation Learning Perspective

Visual modality recently has aroused extensive attention in the fields of knowledge graph and multimedia because a lot of real-world knowledge is multi-modal in nature. However, it is currently unclear to what extent the visual modality can improve the performance of knowledge graph tasks over unimodal models, and equally treating structural and visual features may encode too much irrelevant information from images. In this paper, we probe the utility of the auxiliary visual context from knowledge graph representation learning perspective by designing a Relation Sensitive Multi-modal Embedding model, RSME for short. RSME can automatically encourage or filter the influence of visual context during the representation learning. We also examine the effect of different visual feature encoders. Experimental results validate the superiority of our approach compared to the state-of-the-art methods. On the basis of in-depth analysis, we conclude that under appropriate circumstances models are capable of leveraging the visual input to generate better knowledge graph embeddings and vice versa.
Bayesian optimization of multiclass SVM for efficient diagnosis of erythemato-squamous diseases

Recently, Bayesian Optimization (BO) has emerged as an efficient technique for adjusting the hyperparameters of machine learning models. BO approach develops an alternative mathematical function to efficiently optimize the computation-intensive functions. In this paper, we demonstrate the utility of this approach in hyperparameter optimizations and feature selection for the multiclass support vector machine (SVM). The efficiency of the proposed BO-SVM hybrid model was evaluated in the differential diagnosis of the erythemato-squamous diseases (ESDs) dataset from UCI machine learning repository. The dataset contains the results of clinical and histopathological tests for six different skin diseases. The multiclass problem has been manipulated using four different Error-Correcting Output Codes (ECOC) coding schemes: one-versus-all, binary complete, one-versus-one, and ternary complete. BO has been implemented using the Gaussian process (GP) model with Matérn covariance kernel and expected improvement acquisition function. Our experimental results show that the advantage of the multiclass BO-SVM with 100% and 99.07% training and test classification accuracies respectively. Some basic and practical procedures in model development and evaluation such as normalization, cross-validation, decimal to binary mask conversion, feature selection and a comparison between predictive power of the clinical and histopathological subsets are also referred to.