Reviewerly/exHarmony huggingface dataset:
HDA-Net: Hybrid Convolutional Neural Networks for Small Objects Recognization at Airports

Identifying small objects is essential in airport clearances, such as UAVs and bird flocks affecting the safety of the entire airport.Due to small objects and complex backgrounds, it is a formidable task to learn iconic representations from images.Convolution neural networks (CNNs) is the dominant framework in the past few years.Although CNNs can effectively mitigate local redundancy with convolutional kernels, it is also extremely challenging for convolutional kernels to obtain more feature information about the small object.Alternatively, the prediction box loss function has a critical influence on the eventual model performance.To resolve these problems, a revolutionary small object recognition network is proposed in this paper.In the structure, we propose a hybrid convolutional structure, which is able to obtain object features by different convolutional approaches to achieve more information about the small objects.Following that, we use the Double-One Path Aggregation Network (DO-PAN) feature fusion structure to further integrate the small object information.Finally, we propose all sides consistency intersection over union (AIoU) loss, which enables the model to predict the object location more exactly.Combining the three contributions, we designate this structure as HDA-Net.It can simply achieve 96.1% box mAP50 on the UAV-VIT dataset object recognition task, the inference speed on Tesla T4 is 28.46 fps.
ImageNet Challenging Classification with the Raspberry Pis: A Federated Learning Algorithm of Local Stochastic Gradient Descent Models

In this paper, we propose the federated learning algorithm of local stochastic gradient descent (SGD) models at edge devices (i.e. Raspberry Pis) to classify large ImageNet dataset having 1,281,167 images with 1,000 classes. The full very large training dataset is divided into subsets which are stored in local Raspberry Pis. And then, the federated learning algorithm uses Raspberry Pis to train in the incremental and parallel way local SGD models from their own subset without exchanging data. The incremental local SGD tailored on Raspberry Pi sequentially loads small data blocks of its own local training subset to learn local SGD models. In which, the local SGD algorithm uses kmeans to split the data block into k partitions and then it learns in the parallel way SGD models in each data partition to classify the data locally. The numerical test results on Imagenet dataset show that our federated learning algorithm of local SGD models with 4 Raspberry Pis (Broadcom BCM2711, Quad core Cortex-A72 (ARM v8) 64-bit SoC @ 1.5 GHz, 4 GB RAM) is faster than the state-of-the-art linear SVM run on a PC (Intel(R) Core i7–4790 CPU, 3.6 GHz, 4 cores, 32 GB RAM) with the competitive classification accuracy.
SAR Image Super-Resolution based on Artificial Intelligence

High-resolution synthetic aperture radar (SAR) image can provide detailed information of the target, which is a benefit for improving the performance of the following interpretation application. However, the higher the resolution, the more complex the system and the higher the cost. A new challenge is how to obtain high-resolution target images from medium resolution images, by using new technology, such as artificial intelligence. In this paper, a new method is proposed to improve the resolution based on the SRGAN-SSIM. Since SRGAN is developed for nature image super-resolution, which results in a poor performance for SAR image, pre-processing is implemented. A modified Non-Local Means (NLM) is adopted for speckle noise suppression. Then, SRGAN based net is used for super-resolution, and the loss function is optimized according to the SSIM. Finally, the method is verified by Terra-SAR images.
On Reducing the Bias of Random Forest

Random Forest is one of the most popular decision forest building algorithms that uses decision trees as the base classifier. Decision trees for Random Forest are formed from the records of a training data set. This makes the decision trees almost equally biased towards the training data set. In reality, testing data set can be significantly different from the training data set. Thus, to reduce the bias of decision trees and hence of Random Forest, we introduce a random weight for each of the decision trees. We present experimental results on four widely used data sets from the UCI Machine Learning Repository. The experimental results indicate that the proposed technique can reduce the bias of Random Forest to become less sensitive to noisy data.
Stability-driven Contact Reconstruction From Monocular Color Images

Physical contact provides additional constraints for hand-object state reconstruction as well as a basis for further understanding of interaction affordances. Estimating these severely occluded regions from monocular images presents a considerable challenge. Existing methods optimize the hand-object contact driven by distance threshold or prior from contact-labeled datasets. However, due to the number of subjects and objects involved in these indoor datasets being limited, the learned contact patterns could not be generalized easily. Our key idea is to reconstruct the contact pattern directly from monocular images, and then utilize the physical stability criterion in the simulation to optimize it. This criterion is defined by the resultant forces and contact distribution computed by the physics engine. Compared to existing solutions, our framework can be adapted to more personalized hands and diverse object shapes. Furthermore, an interaction dataset with extra physical attributes is created to verify the sim-to-real consistency of our methods. Through comprehensive evaluations, hand-object contact can be reconstructed with both accuracy and stability by the proposed framework.
Optimizing Elimination Templates by Greedy Parameter Search

We propose a new method for constructing elimination templates for efficient polynomial system solving of minimal problems in structure from motion, image matching, and camera tracking. We first construct a particular affine parameterization of the elimination templates for systems with a finite number of distinct solutions. Then, we use a heuristic greedy optimization strategy over the space of parameters to get a template with a small size. We test our method on 34 minimal problems in computer vision. For all of them, we found the templates either of the same or smaller size compared to the state-of-the-art. For some difficult examples, our templates are, e.g., 2.1, 2.5, 3.8, 6.6 times smaller. For the problem of refractive absolute pose estimation with unknown focal length, we have found a template that is 20 times smaller. Our experiments on synthetic data also show that the new solvers are fast and numerically accurate. We also present a fast and numerically accurate solver for the problem of relative pose estimation with unknown common focal length and radial distortion.
The Impact of Communication and Memory in State-Based Potential Game-based Distributed Optimization

In this paper, we discuss the impact of communication and memory-based learners on distributed self-optimization of smart and flexible manufacturing units. Specifically, we employ the recently proposed framework of state-based potential games, which has proven to be successful in allowing distributed optimization in multi-agent systems. We first augment the framework with additional communication capabilities for the individual players and analyze the efficacy of state and action communications within the different players. Second, we incorporate memory states within the learning dynamics of the players and analyze their impact on the learning performance. The proposed method is inspired by the promising results of memory-based reinforcement learning. However, previous studies have rarely dealt with distributed manufacturing control. We believe that it will be important to explore the potential use of the communication and memory-based approaches in manufacturing control with multi-agent settings. Hence, the proposed method is applied to a bulk good laboratory plant providing a thorough experimental analysis of the effect of the various improvements with very encouraging results.
Speed up Object Detection on Gigapixel-level Images with Patch Arrangement

With the appearance of super high-resolution (e.g., gigapixel-level) images, performing efficient object detection on such images becomes an important issue. Most ex-isting works for efficient object detection on high-resolution images focus on generating local patches where objects may exist, and then every patch is detected independently. How-ever, when the image resolution reaches gigapixel-level, they will suffer from a huge time cost for detecting numerous patches. Different from them, we devise a novel patch ar-rangement frameworkfor fast object detection on gigapixel-level images. Under this framework, a Patch Arrangement Network (PAN) is proposed to accelerate the detection by determining which patches could be packed together into a compact canvas. Specifically, PAN consists of (1) a Patch Filter Module (PFM) (2) a Patch Packing Module (PPM). PFM filters patch candidates by learning to select patches between two granularities. Subsequently, from the remaining patches, PPM determines how to pack these patches to-gether into a smaller number of canvases. Meanwhile, it generates an ideal layout of patches on canvas. These can-vases are fed to the detector to get final results. Experiments show that our method could improve the inference speed on gigapixel-level images by 5 x while maintaining great performance.
ActiveZero: Mixed Domain Learning for Active Stereovision with Zero Annotation

Traditional depth sensors generate accurate real world depth estimates that surpass even the most advanced learning approaches trained only on simulation domains. Since ground truth depth is readily available in the simulation domain but quite difficult to obtain in the real domain, we propose a method that leverages the best of both worlds. In this paper we present a new framework, ActiveZero, which is a mixed domain learning solution for active stereovision systems that requires no real world depth annotation. First, we demonstrate the transferability of our method to out-of-distribution real data by using a mixed domain learning strategy. In the simulation domain, we use a combination of supervised disparity loss and self-supervised losses on a shape primitives dataset. By contrast, in the real domain, we only use self-supervised losses on a dataset that is out-of-distribution from either training simulation data or test real data. Second, our method introduces a novel self-supervised loss called temporal IR reprojection to increase the robustness and accuracy of our reprojections in hard-to-perceive regions. Finally, we show how the method can be trained end-to-end and that each module is important for attaining the end result. Extensive qualitative and quantitative evaluations on real data demonstrate state of the art results that can even beat a commercial depth sensor. The codes of ActiveZero are available at: httis://github.com/haosulab/active_zero.
Hierarchical Model Compression via Shape-Edge Representation of Feature Maps—an Enlightenment From the Primate Visual System

The cumbersome computation of deep neural networks (DNNs) limits their practical deployment on resource-constrained mobile multimedia devices. To deploy DNNs on devices with limited computing resources, model compression techniques are leveraged to accelerate the networks, where network pruning can improve the inference efficiency of DNNs by removing redundant weights and structures. As one of the important components of DNNs, the feature maps (FMs) can be leveraged to evaluate the importance of network structures for DNN pruning. However, previous methods neglect to fully explore the characteristics of FMs in network pruning. In this paper, we investigate the high capacity and resource efficient analogy-ventral dual-pathway primates visual system (PVS) to propose a hierarchical pruning framework (dubbed as HPSE). In an efficient PVS, the analog pathway analyzes low-frequency information to facilitate the high-frequency information inference in ventral stream. In HPSE, we extract the low-frequency shape information and high-frequency edge information from FMs to present a novel pruning pipeline that resembles the analysis mechanism of PVS. In particular, we first imitate the analogy pathway to group different FMs in each layer by calculating the shape-feature overlap. Secondly, we leverage the edge information modulated by the grouping results of the first step to prune the network. The effectiveness of HPSE is verified by pruning various DNNs on different benchmarks. For example, for ResNet-56 on CIFAR-10, HPSE reduces 52.9% of FLOPs with a slight accuracy improvement; for ResNet-50 on ImageNet, we achieve 54.3%-FLOPs drop with only 0.49% Top-1 accuracy loss.
Incremental Path Planning Algorithm via Topological Mapping with Metric Gluing

We present an incremental topology-based motion planner that, while planning paths in the configuration space, performs metric gluing on the constructed Vietoris-Rips simplicial complex of each sub-space (voxel). By incrementally capturing topological and geometric information in batches of voxel graphs, our algorithm avoids the time overhead of analyzing the properties of the entire configuration space. We theoretically prove in this paper that the simplices of all voxel graphs joined together are homotopy-equivalent to the union of the simplices in the configuration space. Experiments were carried out in seven different environments using various robots, including the articulated linkage robot, the Kuka YouBot, and the PR2 robot. In all environments, the results show that our algorithm achieves better convergence for path cost and computation time with a memory-efficient roadmap than state-of-the-art methods.
Scale-Aware Spatio-Temporal Relation Learning for Video Anomaly Detection

Recent progress in video anomaly detection (VAD) has shown that feature discrimination is the key to effectively distinguishing anomalies from normal events. We observe that many anomalous events occur in limited local regions, and the severe background noise increases the difficulty of feature learning. In this paper, we propose a scale-aware weakly supervised learning approach to capture local and salient anomalous patterns from the background, using only coarse video-level labels as supervision. We achieve this by segmenting frames into non-overlapping patches and then capturing inconsistencies among different regions through our patch spatial relation (PSR) module, which consists of self-attention mechanisms and dilated convolutions. To address the scale variation of anomalies and enhance the robustness of our method, a multi-scale patch aggregation method is further introduced to enable local-to-global spatial perception by merging features of patches with different scales. Considering the importance of temporal cues, we extend the relation modeling from the spatial domain to the spatio-temporal domain with the help of the existing video temporal relation network to effectively encode the spatio-temporal dynamics in the video. Experimental results show that our proposed method achieves new state-of-the-art performance on UCF-Crime and ShanghaiTech benchmarks. Code are available at https://github.com/nutuniv/SSRL .
Reading to write code

A substantial portion of any software engineer's job is reading code. Despite the criticality of this skill in a budding software engineer, reading code---and more specifically, techniques on how to read code when integrating oneself into a large existing software project---is often neglected in the typical software engineering education. As part of a new professional Master of Software Engineering at the University of California, Irvine, we designed and delivered a "reading to write code" course from the ground up. Titled Reverse Engineering and Modeling, the course introduces students to techniques they can use to become familiar with a large code base, so they are able to make meaningful contributions. In this paper, we briefly introduce the Master program and its underlying philosophy, articulate the course's learning outcomes, present the design of the course, and provide a detailed reflection on our experiences in terms of what went well, what did not go well, what we do not know yet, and what our next steps are in preparing for the forthcoming incarnation of the course in Spring 2022. In so doing, we hope to provide a baseline together with lessons learned for others who may be interested in instituting a similar course at their institution.
PSBCNN: Fine-grained image classification based on pyramid convolution networks and SimAM

Fine-grained image classification has been an important but challenging task due to high intra-class variances and low inter-class variances. Therefore, we propose a fine-grained image classification algorithm based on pyramidal convolutional neural network and 3-D attention module to solve these problems. The algorithm captures different levels of detail in the scene and assigns greater weights to the distinguishing features, allowing the neural network model to focus more on local regions and thus improve the accuracy of fine-grained classification. Qualitative experiments on three benchmark fine-grained datasets demonstrate the superiority of our proposed method.
Residual Adaptive Dense Weight Attention Network for Single Image Super-Resolution

Recently, with the rise and progress of convolutional neural networks (CNNs), CNN-based single image super-resolution (SISR) methods have gained considerable advancement and showed great power for image reconstruction tasks. Never-theless, existing methods cannot dynamically adjust the network according with the input, which greatly impairs the practical performance of the network. To address this issue, a novel residual adaptive dense weight attention network (RADWAN) is proposed consisted of several adaptive residual groups (ARGs) to enhance the generalization performance of the network. Specifically, each ARG contains several adaptive dense weight attention blocks (ADWAB). It generates dense connection coefficients dynamically using an adaptive dense weight block (ADWB) for more accurate feature extraction. Besides, an avg-std channel attention block is further presented to maximize the potential of RADWAN to make the model focus on critical information. To adjustably utilize additional features from shallow and intermediate layers, we introduce adaptive short connections (ASC) and adaptive long connections (ALC) to effectively integrate abundant hier-archical features. Extensive experiments on several datasets have demonstrated the superiority of RADWAN over the state-of-the-art methods in aspects of both quantitative metrics and visual quality.
The Contribution of Blockchain Technology in the Supply Chain Management: The Shipping Industry as an Example

Today, the visibility of information and the traceability of physical flows have become a challenge to the supply chain's smooth operation, especially because they are difficult to process. Several studies have been done in response to this complexity to uncover practical solutions and more effective strategies for delivering services at the level of client expectations. This paper proposes blockchain technology as a solution that has the potential to deliver encouraging results in terms of supply chain sustainability, trust, traceability, and transparency.
Fully Size-Adaptive Sliding Window Method for SAR Image Edge Detection

The local statistics are useful for the synthetic aperture radar (SAR) image processing. However, unreliable statistical results are produced due to insufficient sample size. To overcome the statistical limitation, the appropriate size of sliding window is vital in the image processing. Therefore, a completely size-adaptive sliding window method is proposed in this letter. A method to determine the size boundaries of the sliding window based on the basic size information is firstly proposed. Then, a strategy for changing the sliding window size is used to generate the sliding window size index matrix of the image. Among the many image processing applications that use sliding windows, we choose the SAR image edge detection arbitrarily to verify the effectiveness of the proposed method. And the experimental results indicate that our method makes the original edge detection algorithms improved significantly. Further, the proposed size-adaptive sliding window method has potential for many other image processing tasks that require the use of local statistical information.
Learning from Better Supervision: Self-distillation for Learning with Noisy Labels

The remarkable performance of deep neural networks heavily rely on large-scale datasets with high-quality annotations. Since the data collection process such as web crawling naturally involves unreliable supervision (i.e., noisy label), handling samples with noisy labels has been actively studied. Existing methods in learning with noisy labels (LNL) 1) develop the sampling strategy for filtering out the noisy labels or 2) devise the robust loss function against noisy labels. As a result of these efforts, existing LNL models achieve impressive performance, recording a higher accuracy than the ratio of the clean samples in the dataset. Based on this observation, we propose a self-distillation framework to utilize the prediction of existing LNL models and further improve the performance via rectified distillation; hard pseudo label and feature distillation. Our rectified distillation can be easily applied to existing LNL models, thus we can enjoy their state-of-the-art performances. From extensive evaluations, we confirm that our model is effective on both synthetic and real noisy datasets with state-of-the-art performances on four benchmark datasets.
Investigating YOLOv5 for Search and Rescue Operations Involving UAVs

Mountain recreation has become more popular, with mountaineering, rock climbing, skiing, mountain biking, hiking, and mushroom picking among the most popular sports including desert safari. Despite this tendency, there is currently limited research available explaining the rise in search and rescue as well as the injuries and illnesses that entail aid in tourist-friendly areas. Deep learning has been termed as potentially effective tool for SAR applications. Even if the individual is partially veiled, a trained deep learning system can recognize them from a variety of perspectives. Existing state-of-the-art detectors such as Faster R-CNN, YOLOv4, RetinaNet, and Cascade R-CNN have been investigated in literature on various datasets to simulate rescue scenes with acceptable results. In this research, the YOLOv5L detector is investigated for further investigation on Search and rescue dataset because of its great speed and accuracy, as well as claimed small number of false detections. The results illustrate the highest mean average accuracy and is compared with other detectors.