Reviewerly/exHarmony huggingface dataset:
LIME-Assisted Automatic Target Recognition With SAR Images: Toward Incremental Learning and Explainability

Integrating an automatic target recognition (ATR) system into real-world applications presents a challenge as it may frequently encounter new samples from unseen classes. To overcome this challenge, it is necessary to adopt incremental learning, which enables the continuous acquisition of new knowledge while retaining previous knowledge. This paper introduces a novel, multi-purpose interpretability metric for ATR systems that employ synthetic aperture radar (SAR) images. The metric leverages the Local Interpretable Model-Agnostic Explanation (LIME) algorithm, enhancing human decision-making by providing a secondary measure alongside the conventional classification score. Additionally, the proposed metric is employed to analyze the robustness of Convolutional Neural Networks (CNNs) by examining the impact of target features and irrelevant background correlations on recognition results. Finally, we demonstrate the effectiveness of the proposed metric in the context of incremental learning. By utilizing the proposed interpretability metric, we select exemplars in an incremental learning scenario, resulting in improved performance and showcasing the application potential of our proposed methodology. The network is fine-tuned sequentially with unknown samples recognized by the Openmax classifier and exemplars from the old known classes, which are selected based on the proposed interpretability metric. The effectiveness of this approach is demonstrated using the publicly available MSTAR dataset.
MINA: Multi-Input Network Augmentation for Enhancing Tiny Deep Learning

Network Augmentation (NetAug) is a recent method used to improve the performance of tiny neural networks on large-scale datasets. This method provides additional supervision to tiny models from larger augmented models, mitigating the issue of underfitting. However, the capacity of the augmented models is not fully utilized, resulting in underutilization of resources. In order to fully utilize the capacity of a larger augmented model without exacerbating the underfitting of a tiny model, we propose a new method called Multi-Input Network Augmentation (MINA). MINA converts tiny neural networks into a multi-input configuration, allowing only the augmented model to receive more diverse inputs during training. Additionally, tiny neural network can be converted back into their original single-input configuration after training. Our extensive experiments on large-scale datasets demonstrate that MINA is effective in improving the performance of tiny neural networks. We also demonstrate that MINA is consistently effective in downstream tasks, such as fine-grained image classification tasks and object detection tasks.
Classification and Methods of Acute Lymphoblastic Leukemia Detection Using Neural Network

Leukemia is a cancer of the blood that starts from bone marrow then spreads into the bloodstream and other vital organs. Based on lymphoid or myeloid stem cells becoming cancerous, leukemia can be divided into myeloid leukemia and lymphoblastic leukemia. The EM-algorithm-based method uses statistics techniques to classify three types of leukocytes (i.e., band neutrophils, eosinophils, and lymphocytes). This method projects the image patterns onto lower dimensional subspaces by PCA and uses EM-algorithm to find the maximum likelihood solution for the models with latent variable. The SVM-based method uses the texture, shape, and color as the features to describe leukocytes. This chapter includes blood, introduction of ALL disease with its types, steps of ALL disease detection, detection types of ALL disease detection, and conclusion.
Dynamic 3-D Measurement Based on Temporal Fringe Order Recognition

Efficient and robust fringe order recognition is an important topic in 3D measurement. In this paper, a novel dynamic 3D measurement method based on temporal fringe order recognition is proposed. Using binary defocusing technology, the high-speed measurement system can cyclically project and capture three-step phase-shifting fringe patterns at 120 fps, enabling computation of the wrapped phase from three adjacent deformed patterns. Due to the ultra-short interval, the phase relationship between each interval is explored and established by segmenting the connected domain. With coarse and fine recognition, the temporal fringe order at each interval can be accurately recognized along the time axis. The proposed method can achieve a dynamic 3D reconstruction rate of 120fps while also possessing a similar single-shot feature. Moreover, our temporal fringe order recognition method can realize phase unwrapping without any additional pattern, except for the projection pattern in the first frame reconstruction result. It provides a unique solution to the challenges posed by traditional temporal phase unwrapping methods in which additional patterns must be required. High quality 3D results of dynamic scenes, including isolated objects in motion, left-right motion with a mask held in hand, rotational motion of a Davidian object, and hand-grasping paper, were presented at a frame rate of 120 fps. Experimental results demonstrate its effectiveness and low cumulative effect of spatial and temporal errors.
MSBA-Net: Multiscale Behavior Analysis Network for Random Hand Gesture Authentication

Random hand gesture authentication allows the probe hand gesture types to be inconsistent with the registered ones. While it is highly user-friendly, it poses a significant challenge that requires the authentication model to distill more abstract and complex identity features. Prior efforts on random hand gesture authentication mainly use convolution operations to obtain short-term behavioral information and cannot distill robust behavioral features well. In this paper, we propose a novel Multi-Scale Behavior Analysis Network (MSBA-Net), with a focus on capturing multi-scale behavioral features for random hand gesture authentication, which can simultaneously distill short-term behavioral information and model long-term behavioral relationships in addition to physiological features of hand gestures. In addition, as hand motion can result in inter-frame semantic misalignment, we propose an efficient semantic alignment strategy to mitigate this issue, which helps extract behavior features accurately and improves model performance. MSBA module is a plug-and-play module and could be integrated into existing 2D CNNs to yield a powerful video understanding model (MSBA-Net). Extensive experiments on the SCUT-DHGA dataset demonstrate that our MSBA-Net has compelling advantages over the other 20 state-of-the-art methods. The code is available at https://github.com/SCUT-BIP-Lab/MSBA-Net.
Crosstalk Suppression for Color Phase-Shifting Profilometry Based on Chord Distribution Equalization

Color phase-shifting profilometry encodes the gray fringe pattern of the conventional three-step phase-shifting algorithm into a single-color image as a means of greatly increasing the measurement speed. However, the color crosstalk problem induces phase errors, which affect the measuring accuracy. In this study, a color crosstalk suppression method based on the equalization of the wrapped phase chord distribution is proposed. By using the wrapped phase value as the polar angle, a chord distribution diagram in the polar coordinate can be obtained. The diagram can be regarded as the distribution of the wrapped phase. Then, the polar angle value is used to equalize the chord distribution. After equalization, a uniform chord distribution diagram is retrieved, and it can be used to recover a new wrapped phase. Nonlinear errors caused by the color crosstalk can be suppressed in a manner that corrected the wrapped phase. This method is a completely software-based method for eliminating color crosstalk, which is more applicable and flexible than conventional methods requiring pre-calibration, the construction of a look-up-table of phase errors or additional patterns. Results of simulations and real experiments indicate the ability of the proposed method to effectively correct the nonlinear errors caused by the color crosstalk.
Facing Completely Occluded Short-Term Tracking Based on Correlation Filters

Accurate occlusion tracking is a difficult research in vision-based measurement. Most state-of-the-art approaches exploit suppression of templates learning of filters to avoid occlusion pollution. Such suppression strategies retard in the recognition of target obstruction and lack state processing during the occlusion phase. This paper explores the precise and robust handling of the complete occlusion problem in the framework of discriminative correlation filters. We propose a generative association approach by utilizing raw images of pixels as well as adjacent trajectories for generating robust target descriptors and observable cumulative sequences. The occlusion of the target is identified through the descriptors matching over the time-series, and estimates the transfer of the state by the generating function. Compared to suppression methods, our approach develops the apparent characteristics and cumulative state of the target, not relying on filter templates. This achieves a maximum gain of 5.0 percent of area under the curve precision on the OTB benchmark. Comprehensive evaluations are performed on six datasets: UAV123, TB50, OTB100, LaSOT, VOT2018ST, and VOT2019ST. Our method establishes a new record score in distance precision on the TB50, by exceeding the other 19 state-of-the-art trackers. Also, we reach the state-of-the-art ranks on both the VOT2018ST and VOT2019ST datasets.
A Weighted Least-Squares Algorithm for Wrapped Phase Retrieval in Sinusoidal Fringe Projection Profilometry

The sinusoidal fringe projection profilometry employs the least squares (LS) algorithm as the fundamental method for recovering the wrapped phase distribution from the recorded fringe patterns. However, the intensity noise level of the fringe pattern acquisition process varies and does not satisfy the assumption of constant variance in the LS algorithm. As a result, the LS algorithm cannot obtain the phase results with minimum variance. To this end, a weighted least squares (WLS) algorithm is proposed in this paper to achieve effective suppression of heteroscedasticity and obtain the phase results with minimum variance. In contrast to the LS algorithm, which treats each intensity equally, the WLS algorithm assigns different weights over intensities to counterbalance the change in the variance of noise. The weights are determined according to the variance properties of noise in the measurement system. Simulations and experiments demonstrate that the proposed method effectively improves the accuracy of wrapped phase measurement in sinusoidal fringe projection profilometry.
NSAW: An Efficient and Accurate Transformer for Vehicle LiDAR Object Detection

Nowadays, light detection and ranging (LiDAR) plays an important role in environmental perception technology, however, LiDAR can’t detect some blocked objects, and it can even detect some incorrect and unimportant objects as target objects due to the sparsity and disorder of the point cloud and the limited receiving area. Indeed, the detection accuracy can be improved by setting multiple cameras and LiDAR, but the cost can be unacceptable. In order to resolve these issues, a transformer-based architecture that can connect voxels with each other by neighborhood sparse attention is proposed. A standardized transformer, however, cannot be applied directly to sparse point cloud space because it contains a large number of empty voxels. Hence, a scheme of neighborhood sparse voxel attention to window attention (NSAW) is introduced, which is an efficient and accurate single stride transformer for connecting three-dimensional (3D) backbone to two-dimensional (2D) BEV feature map. In this architecture, neighborhood window attention (NWA) and neighborhood voxel attention (NVA) are proposed to effectively extract features from no-empty voxels, which can achieve 84.30% AP3D(R40) and bring 2.68% AP3D(R40) improvement for Voxel R-CNN on the moderate car class on the KITTI test split. On the premise of computational efficiency, the proposed NSAW demonstrates quite a few improvements over the traditional baselines on KITTI3D dataset.
MetaE2RL: Toward Meta-Reasoning for Energy-Efficient Multigoal Reinforcement Learning With Squeezed-Edge You Only Look Once

Metareasoning shows promise in efficiently using the computational resources of tiny edge devices while performing highly computationally intensive Reinforcement Learning (RL) algorithms.This work proposes MetaE2RL: a hardware-aware framework that incorporates low-power pre-processing solutions and metareasoning to enable the deployment of multi-goal RL on tiny autonomous devices.For this aim, a meta-level is proposed to allocate resources efficiently in real-time by switching between models with different complexities.Moreover, Squeezed Edge YOLO is proposed for energy-efficient object detection in the pre-processing phase.For the experimental results, the proposed Squeezed Edge YOLO was deployed onboard a tiny drone named Crazyflie with GAP8 processor that includes 8 parallel RISC-V cluster cores.We compared latency and power consumption of Squeezed Edge YOLO and a lighter CNN-based model while deploying them separately onboard on GAP8.Experimental results show Squeezed Edge YOLO is 8x smaller than previous work and consumes 541 mW on GAP8 with inference latency of 130 ms.
Vertical Layering of Quantized Neural Networks for Heterogeneous Inference

Although considerable progress has been obtained in neural network quantization for efficient inference, existing methods are not scalable to heterogeneous devices as one dedicated model needs to be trained, transmitted, and stored for one specific hardware setting, incurring considerable costs in model training and maintenance. In this paper, we study a new vertical-layered representation of neural network weights for encapsulating all quantized models into a single one. It represents weights as a group of bits (i.e., vertical layers) organized from the most significant bit (also called the basic layer) to less significant bits (i.e., enhance layers). Hence, a neural network with an arbitrary quantization precision can be obtained by adding corresponding enhance layers to the basic layer. However, we empirically find that models obtained with existing quantization methods suffer severe performance degradation if they are adapted to vertical-layered weight representation. To this end, we propose a simple once quantization-aware training (QAT) scheme for obtaining high-performance vertical-layered models. Our design incorporates a cascade downsampling mechanism with the multi-objective optimization employed to train the shared source model weights such that they can be updated simultaneously, considering the performance of all networks. After the model is trained, to construct a vertical-layered network, the lowest bit-width quantized weights become the basic layer, and every bit dropped along the downsampling process act as an enhance layer. Our design is extensively evaluated on CIFAR-100 and ImageNet datasets. Experiments show that the proposed vertical-layered representation and developed once QAT scheme are effective in embodying multiple quantized networks into a single one and allow one-time training, and it delivers comparable performance as that of quantized models tailored to any specific bit-width.
The Application of Artificial Intelligence Teaching Software in College English Teaching

Abstract This paper first proposes English learning software based on personalized recommendations to enhance students’ interest and motivation in learning college English based on artificial intelligence teaching. Secondly, it consists of constructing a learner model, an English learning resource personalized recommendation algorithm to constitute an English learning resource recommendation model, and using a collaborative decomposition algorithm and a regularization algorithm to integrate the potential, multifaceted trust relationship between users with the traditional scoring matrix, a process that emphasizes that trust between users in English teaching varies by different factors and strengths. Then, a personalized recommendation-based university English learning software was designed, and English learning software applications were analyzed by example. The results showed that the adjusted means of pre and post-test scores of the control group, experimental group one, and experimental group two were 77.37, 80.94, and 75.12, respectively, and the statistical score result F=5.33, P&lt;05 indicated that there was a significant difference in the change of English performance of these three groups, which verified that the teaching software had a facilitating effect on the improvement of English teaching quality.
Hand-Controller Latency and Aiming Accuracy in 6-DOF VR

All virtual reality (VR) systems have some inherent hand-controller latency even when operated locally. In remotely rendered VR, additional latency may be added due to the remote transmission of data, commonly conducted through shared low-capacity channels. Increased latency will negatively affect the performance of the human VR operator, but the level of detriment depends on the given task. This work quantifies the relations between aiming accuracy and hand-controller latency, virtual target speed, and the predictability of the target motion. The tested context involves a target that changes direction multiple times while moving in straight lines. The main conclusions are, given the tested context, first, that the predictability of target motion becomes significantly more important as latency and target speed increase. A significant difference in accuracy is generally observed at latencies beyond approximately 130 ms and at target speeds beyond approximately 3.5°/s. Second, latency starts to significantly impact accuracy at roughly 90 ms and approximately 3.5°/s if the target motion cannot be predicted. If it can, the numbers are approximately 130 ms and 12.7°/s. Finally, reaction times are on average 190–200 ms when the target motion changes to a new and unpredictable direction.
Multiscale Depth Fusion With Contextual Hybrid Enhancement Network for Image Dehazing

Image dehazing is a research focus, however the existing methods do not make enough use of depth information, which leads to poor dehazing effect for large depth scenes. Therefore, according to the relevance between the difference of haze concentration and depth in different regions of the image, it has become a challenging task. To address this challenge, firstly, we propose a novel multiscale depth information fusion enhancement network to improve dehazing ability in scenes with large depth changes. Secondly, our method hierarchically fuses the multiscale features of the depth map with the hazy image. We built a grade-by-grade fusion structure to fuse and enhance contextual features with attention mechanism. Finally, to make our model better adapt to real hazy images, restore the image while maintaining details. We add local prior knowledge supervision to each branch. We compare our model with other state-of-the-art methods on hazy datasets. The experimental results show that our dehazing model not only achieve optimal performance on synthetic images but also show certain advantages in real-world hazy images.
2PCLA: Provable Secure and Privacy Preserving Enhanced Certificateless Authentication Scheme for Distributed Learning

Distributed learning (DL) emerges as machine learning and the Internet of Things develop quickly and widely. As edge servers pre-process and pre-learn the statistics, global servers can reduce costs, improve efficiency and output more precise results. However, to acquire high-quality and adequate data, servers should collect information from a number of end devices, which naturally leads to confidentiality and privacy problems during information transmission. If the private information or the data are compromised by malicious attackers, the users’ security and the network operation will all be in danger. To resolve this thorny challenge, numerous schemes have been put forward, adopting different cryptography technologies and aiming at aspects of security. However, many state-of-the-art schemes can hardly satisfy the security demands and are pointed out to be defective. Lately, Jiang et al. made an effort and proposed a certificateless signature scheme, as well as an authentication scheme for the purpose of solving the privacy issues. Unfortunately, in this paper, we point out that their schemes can hardly resist forgery attacks and ephemeral key leakage attacks. Further, we will propose an improved scheme noted as 2PCLA and change the method of generating the session key. Theoretical analysis and formal security analysis utilizing Tamarin analysis tool are provided to prove the security of 2PCLA scheme. Performance evaluation has been done from both theoretical and experimental perspectives. The assessment results illustrate that 2PCLA can balance security properties with execution efficiency relatively well.
A Regularization Ensemble Based on Levenberg–Marquardt Algorithm for Robot Calibration

This chapter investigates six regularization schemes, such as L1, L2, dropout, elastic, log, and swish. Then, an efficient ensemble incorporates six regularizations to achieve high calibration accuracy. Firstly, Sect. 5.1 discuss the research background of robot calibration. In Sect. 5.2, we introduce six regularized robot calibration schemes and the principle of an ensemble. Then, Sect. 5.3 presents experiments for the proposed ensemble. Lastly, conclusions and future work are summarized in Sect. 5.4.
A Deep-Learning Phase Picker with Calibrated Bayesian-Derived Uncertainties for Earthquakes in the Yellowstone Volcanic Region

ABSTRACT Traditional seismic phase pickers perform poorly during periods of elevated seismicity due to inherent weakness when detecting overlapping earthquake waveforms. This weakness results in incomplete seismic catalogs, particularly deficient in earthquakes that are close in space and time. Supervised deep-learning (DL) pickers allow for improved detection performance and better handle the overlapping waveforms. Here, we present a DL phase-picking procedure specifically trained on Yellowstone seismicity and designed to fit within the University of Utah Seismograph Stations (UUSS) real-time system. We modify and combine existing DL models to label the seismic phases in continuous data and produce better phase arrival times. We use transfer learning to achieve consistency with UUSS analysts while maintaining robust models. To improve the performance during periods of enhanced seismicity, we develop a data augmentation strategy to synthesize waveforms with two nearly coincident P arrivals. We also incorporate a model uncertainty quantification method, Multiple Stochastic Weight Averaging-Gaussian (MultiSWAG), for arrival-time estimates and compare it to dropout—a more standard approach. We use an efficient, model-agnostic method of empirically calibrating the uncertainties to produce meaningful 90% credible intervals. The credible intervals are used downstream in association, location, and quality assessment. For an in-depth evaluation of our automated method, we apply it to continuous data recorded from 25 March to 3 April 2014, on 20 three-component stations and 14 vertical-component stations. This 10-day period contains an Mw 4.8 event, the largest earthquake in the Yellowstone region since 1980. A seismic analyst manually examined more than 1000 located events, including ∼855 previously unidentified, and concluded that only two were incorrect. Finally, we present an analyst-created, high-resolution arrival-time data set, including 651 new arrival times, for one hour of data from station WY.YNR for robust evaluation of missed detections before association. Our method identified 60% of the analyst P picks and 81% of the S picks.