Reviewerly/exHarmony huggingface dataset:
Low-complexity moving object detection algorithm in dynamic background

The scale of the monitoring system is becoming larger and larger. In order to perform intelligent video processing in surveillance systems, we need to detect moving object in image sequence. Some methods in the literature can achieve a valid detection result, but usually they have high computational complexity. In the outdoor scenes, the background is usually dynamic, and the dynamic background makes it difficult to detect moving object. In order to solve these problems, we propose a new method with low computational complexity using mass center coordinate to expand the mask image. The proposed method can remove the interference of dynamic background in the detection. Experiment results show that our method can mask the dynamic background more completely while ensuring fast computation and consuming less hardware resources. The method can be used in massive video intelligent processing.
3D point cloud target detection based on pseudo segmentation for autonomous driving

Object detection plays an important role in autonomous driving. In the past decades, many object detection methods relied on 2D images, losing spatial information due to projecting 3D space into 2D space. Recently, LiDAR has become a popular sensor for 3D point cloud target detection. This paper proposes a new RCNN detection framework based on pseudo segmentation (PS-RCNN). This model is designed to achieve accurate and efficient detection on point cloud by transmitting feature information reversely. The information transmission is supervised by the semantic segmentation task. In order to reduce the difficulty in labeling, a novel algorithm is designed to generate segmentation pseudo-labels. Experimental results conducted on KITTI Dataset and Waymo Open Dataset demonstrate that our model outperforms its counterparts for detecting small objects with a balance between accuracy and efficiency.
Angle based hand gesture recognition using graph convolutional network

Abstract Hand gesture recognition has attracted huge interest in the areas of autonomous driving, human computer systems, gaming and many others. Skeleton based techniques along with graph convolutional networks (GCNs) are being popularly used in this field due to the easy estimation of joint coordinates and better representation capability of graphs. Simple hand skeleton graphs are unable to capture the finer details and complex spatial features of hand gestures. To address these challenges, this work proposes an “angle‐based hand gesture graph convolutional network” (AHG‐GCN). This model introduces two additional types of novel edges in the graph to connect the wrist with each fingertip and finger's base, explicitly capturing their relationship, which plays an important role in differentiating gestures. Besides, novel features for each skeleton joint are designed using the angles formed with fingertip/finger‐base joints and the distance among them to extract semantic correlation and tackle the overfitting problem. Thus, an enhanced set of 25 features for each joint is obtained using these novel techniques. The proposed model achieves 90% and 88% accuracy for 14 and 28 gesture configurations for the DHG 14/28 dataset and, 94.05% and 89.4% accuracy for 14 and 28 gesture configurations for the SHREC 2017 dataset, respectively.
Review of till geochemistry and indicator mineral methods for mineral exploration in glaciated terrain

Since the 1970s, till geochemical and indicator mineral methods for mineral exploration, provenance studies and environmental research in glaciated terrain have been developed, tested and refined. This review summarizes these methods, focusing on field and laboratory methods for till geochemical, indicator mineral and boulder surveys. This review of protocols is intended as a guide for the mineral exploration industry and publicly funded agencies. It summarizes till as a sample medium, and describes the formation of glacial dispersal trains, along with methods for till sample collection, sample processing, matrix geochemistry, indicator mineral analyses, quality assurance/quality control procedures and data reporting and interpretation. The methods described here can be used to conduct reconnaissance- to deposit-scale till sampling surveys to assess mineral resource potential and establish environmental baselines. Supplementary material: Appendix A – Photographs of till types described in text; Appendix B – Logistical and geological factors to consider in till sample survey design; Appendix C – Photographs of glaciate terrain types described in text; and Appendix D – Till sample processing and analysis flow charts are available at https://doi.org/10.6084/m9.figshare.c.6786087 Thematic collection: This article is part of the Reviews in Exploration Geochemistry collection available at: https://www.lyellcollection.org/topic/collections/reviews-in-exploration-geochemistry
Application of K-Nearest Neighbor Classification for Static Webcams Visibility Observation

Visibility observations and accurate forecasts are essential in meteorology, requiring a dense network of observation stations. This paper investigates image processing techniques for object detection and visibility determination using static cameras. It proposes a comprehensive method that includes image preprocessing, landmark identification, and visibility estimation, mirroring the observation process of professional meteorological observers. This study validates the visibility observation procedure using the k-nearest neighbors machine learning method across six locations, including four in the Czech Republic, one in the USA, and one in Germany. By comparing our results with professional observations, the paper demonstrates the suitability of the proposed method for operational application, particularly in foggy and low visibility conditions. This versatile method holds potential for adoption by meteorological services worldwide.
Investigation of dark count rate in NbRe microstrips for single photon detection

Abstract Superconducting microstrip single photon detectors (SMSPDs) received great interest since they are expected to combine the excellent performance of superconducting nanostrip single photon detectors with the possibility to cover large active areas using low-cost fabrication techniques. In this work, we fabricated SMSPDs based on NbRe to investigate the role of vortices in the dark counts events in this innovative material and in devices with micrometer size. We realized devices with different layouts, namely single microstrips and pairs of parallel microstrips. The energy barriers related to the motion of single vortices or vortex–antivortex pairs, responsible of detection events, have been determined and compared with the ones of similar devices based on different materials, such as MoSi, WSi and NbN. The analysis confirms the high potential of NbRe for the realization of superconducting single photon detectors with large areas.
LMCLAEKS: LWE-assisted multi-recipient certificateless authenticated encryption with keyword search

In Industrial Internet of Things (IIoT) environments, interconnected gadgets and sensors produce large-scale, dynamic, and private data by the arrival of the fourth industrial revolution. Such data must be stored by cloud service providers (CSP) and is capable of accessed by consumers. To guarantee data privacy, Public-key Encryption with Keyword Search (PEKS) is a cryptographic primitive designed to resolve this challenge, which is troubled by costly certificate management and key escrow problem. The majority of current PEKS schemes rely on expensive bilinear paring and single-receiver, which are highly vulnerable to keyword guessing attacks (KGA) and have low computational efficiency. Consequently, we propose an Multi-recipient Certificateless Authenticated Encryption with Keyword Search (MCLAEKS) scheme, which has the following advantages: 1) hiding search pattern; 2) data allows multiple consumers; 3) involving no costly bilinear pairing operations; 4) resisting Inside Keyword Guessing Attacks (IKGA). The comparison and analysis of performance indicate that the proposed scheme is more efficient than existing PEKS schemes and is suitable for the IIoT environment.
LJIR: Learning Joint-Action Intrinsic Reward in cooperative multi-agent reinforcement learning

Effective exploration is the key to achieving high returns for reinforcement learning. Agents must explore jointly in multi-agent systems to find the optimal joint policy. Due to the exploration problem and the shared reward, the policy-based multi-agent reinforcement learning algorithms face policy overfitting, which may lead to the joint policy falling into a local optimum. This paper introduces a novel general framework called Learning Joint-Action Intrinsic Reward (LJIR) for improving multi-agent reinforcement learners' joint exploration ability and performance. LJIR observes agents' state and joint actions to learn to construct an intrinsic reward online that can guide effective joint exploration. With the novel combination of Transformer and random network distillation, LJIR selects the novel states to give more intrinsic rewards, which help agents find the best joint actions. LJIR can dynamically adjust the weight of exploration and exploitation during training and keep the policy invariance finally. To ensure LJIR seamlessly adopts existing MARL algorithms, we also provide a flexible combination method for intrinsic and external rewards. Empirical results on the SMAC benchmark show that the proposed method achieves state-of-the-art performance in challenging tasks.
Multiple-instance ensemble for construction of deep heterogeneous committees for high-dimensional low-sample-size data

Deep ensemble learning, where we combine knowledge learned from multiple individual neural networks, has been widely adopted to improve the performance of neural networks in deep learning. This field can be encompassed by committee learning, which includes the construction of neural network cascades. This study focuses on the high-dimensional low-sample-size (HDLS) domain and introduces multiple instance ensemble (MIE) as a novel stacking method for ensembles and cascades. In this study, our proposed approach reformulates the ensemble learning process as a multiple-instance learning problem. We utilise the multiple-instance learning solution of pooling operations to associate feature representations of base neural networks into joint representations as a method of stacking. This study explores various attention mechanisms and proposes two novel committee learning strategies with MIE. In addition, we utilise the capability of MIE to generate pseudo-base neural networks to provide a proof-of-concept for a "growing" neural network cascade that is unbounded by the number of base neural networks. We have shown that our approach provides (1) a class of alternative ensemble methods that performs comparably with various stacking ensemble methods and (2) a novel method for the generation of high-performing "growing" cascades. The approach has also been verified across multiple HDLS datasets, achieving high performance for binary classification tasks in the low-sample size regime.
Online object tracking based interactive attention

By embedding Transformer into the Siamese tracking framework, some Transformer-based Siamese tracking network are proposed, such as TransT and SwinTrack. Nevertheless, the existing Transformer-based Siamese tracking networks do not fully utilize the object information of template branch, and their position encoding methods cannot accurately perceive the object position. Aiming at these problems, a novel Transformer-based Siamese tracking network, which includes feature extraction, feature fusion and prediction head, is proposed in this work. Firstly, an interactive attention calculation module for template branch and search branch is designed to enhance the feature extraction capability of the network for the object region and suppress the background interference. In addition, to address the problem that the existing Transformer-based feature fusion network is not sensitive enough to the object location region, a position encoding method that characterizes the relative distance is proposed to enhance the perception ability of the object location and reduce network parameters. Then, the contrastive loss is introduced to enhance the discriminative ability of the classification layer between foreground and background, and to effectively deal with the interference of similar objects in the background. Extensive experiments with state-of-the-art trackers are carried out on four challenging visual object tracking benchmarks: GOT-10k, LaSOText, TLP, and TrackingNet. Experimental results demonstrate the proposed method is more robust on multiple challenges and can achieve considerable performances with AO of 70.1% on GOT-10k, SUC score of 45.5% on LaSOT ext, 56.9% on TLP, and 79.7% on TrackingNet datasets. It runs faster (7.1G MACs) and occupies less memory (21M), making it more suitable for practical applications.
Generalized multiview regression for feature extraction

Multiview learning (MVL) has attracted considerable attention since an object can be observed from various views. To use the consistency of multiple views, canonical correlation analysis (CCA) is used as a basic technique for analyzing correlated subspaces. However, CCA-based methods are designed to extract the correlation information between each pair of views. In addition, these methods ignore view-specific geometric structures, which may provide effective complementary information. To address the limitations of CCA-based methods and improve the performance of multiview subspace learning methods, a novel MVL framework called generalized multiview regression (GMR) is proposed. It aims at finding a common subspace to preserve the complementary information of each view and maintain consistency among all the views. Specifically, to preserve the view-specific structures, GMR first considers data reconstruction and local geometrical structures. Subsequently, by introducing an orthogonal dictionary, GMR captures the discriminative consistency suitable for classification tasks. Finally, it uses L2,1 as the basic norm to measure errors and regularization, which facilitates robustness and sparsity for feature extraction and selection. An iterative algorithm is designed to solve the proposed GMR. In addition, the convergence and complexity are analyzed theoretically. Extensive experiments on benchmark datasets are conducted to compare the GMR with state-of-the-art or some available multiview methods. The competitive performance implies that GMR is an effective multiview method for learning sparse projections and extracting discriminative and robust features.
Accelerated high-dimensional global optimization: A particle swarm optimizer incorporating homogeneous learning and autophagy mechanisms

The curse of dimensionality often results in either premature convergence or slow convergence in high-dimensional global optimization. This paper proposes the high-speed homogeneous learning-based particle swarm optimizer (HLPSO) to address these challenges. The algorithm is based on the observation that individuals with similar attitudes or beliefs tend to coexist harmoniously in human societies, unlike birds or fish that squeeze or clash in the same space. By incorporating the concept of homogeneity into the optimization process, the algorithm controls particle learning and generates distinct subpopulations for competition and learning. Furthermore, to accelerate the convergence rate, the algorithm employs an autophagy mechanism and dynamic subpopulation diversity, guiding the population towards global optimal solutions while reducing redundant fitness evaluations. The effectiveness of HLPSO is illustrated through its exceptional performance in benchmark test suites, including the IEEE congress on evolutionary computation (CEC) 2008, CEC 2010, and CEC 2013, in both 1000 and 2000 dimensions. These results not only highlight its superiority over several state-of-the-art algorithms but also underscore its aptitude for effectively addressing low-dimensional optimization challenges encountered in CEC 2017.
Research on route planning for solar UAV based on the intelligent optimization algorithm

The solar unmanned aerial vehicle (UAV) route planning needs to comprehensively consider the conversion efficiency of solar cells under the influence of solar ground reflection radiation and sky scattering radiation. On the one hand, it is necessary to consider the cost of radar threat, mileage energy consumption, mountain impact and other costs. On the other hand, it is also necessary to consider the influence of high threat, mountain shadow occlusion cost, and cloud shading cost on solar photovoltaic conversion efficiency. The above problem was solved through using the ant colony intelligent optimization algorithm. By constructing ant colony paths rationally, models of mountain impact cost, high threat, mountain shadow shelter cost, and cloud shading cost were established. The constraints such as the maximum action distance, solar irradiation angle and effective action distance of various costs were introduced into the cost model and exploration factor calculation, and the comprehensive optimization problem of solar UAV route was solved. Finally, the simulation results show that the algorithm path structure is reasonable; the target node can be found independently; the convergence speed can meet the requirements of route planning; the generated route cost is small; the algorithm is reasonable and effective.
Encrypted Traffic Protocol Identification Based on Temporal and Spatial Features

Cryptographic technology is the foundation and key to securing cyberspace, but there are still widespread cases of non-compliance and incorrectness in cryptographic applications, especially commercial cryptographic applications, etc. Detecting the compliance of encryption protocol cipher suites is an important part of carrying out cryptographic evaluation. Aiming at the difficult problems such as insufficient and insignificant extraction of encrypted traffic protocol features and poor effect of encrypted traffic protocol identification model, the concept of network traffic temporal relationship is invoked to comprehensively extract and learn the encrypted traffic protocol temporal features and control the redundant feature weights to highlight the key features in order to improve the identification accuracy. Through comparative experiments, we analyze the influence of temporal and spatial features on recognition effect, fuse spatio-temporal features of traffic, and propose a Transformer and Attention_CNN (TAC) fusion model of encrypted traffic protocol recognition to solve the problem of low accuracy of single feature recognition. The experimental results show that the proposed scheme can effectively distinguish various network protocols and accomplish the purpose of verifying the compliance of cipher suites in encryption protocols.
Modeling and Analysis of UAV Systems With Robot Arm

Aiming at the control problem of unmanned aerial vehicle system with robot arm, the corresponding coordinate system of each joint of the composite system body and operation device is established, and the transformation matrix of the points on each part of the robot arm is deduced. Considering that the operating device stays at rest most of the time and moves only when it needs to operate the object, the system is regarded as a changing static state, and the dynamics of the composite system is derived by using the Newton-Euler method. The system simulation platform of operational quadrotor aircraft based on dynamics model is built in MATLAB environment. The working interval of the operating device and the change of the center of gravity trajectory of the complex system during operation are analyzed, and the effectiveness of the control strategy to reduce the disturbance caused by the movement of the manipulator is verified by simulation experiments
Deep Convolutional Neural Network Algorithm Based on Im2Col and Non-Local Mean Filter

In the big data environment, Deep Convolutional Neural Network is very popular in various fields, but it still has the problems of slow convolution speed and too much noise data. Therefore, how to solve these problems is the direction of our research. We propose a Deep Convolutional Neural Network algorithm based on Im2col and non-local mean Filter (IF-DCNN). The specific method is to use Im2col convolution instead of traditional convolution to accelerate the convolution speed, and use the non-local mean Filter method based on Pearson similarity to remove noise. The experimental results indicate that the proposed method has good performance in Deep Convolutional Neural Network calculation in big data environment, and is suitable for deep convolutional neural network model training of large-scale data sets.
Research on Knowledge Recommendation Technology Based on Domain Knowledge Graph: A Case Study in Aerospace Engine Domain

In order to enhance knowledge reuse in product design and development, we propose a Domain-Specific Knowledge Graph-Based Recommendation Approach (DKGR) in conjunction with the Intelligent Knowledge Management System (IKMS) of an aerospace research institute in Beijing. The DKGR technique leverages the rich semantic relationships within the Domain Knowledge Graph, including product structures, task associations, and knowledge links and incorporates user logs into the DKG. This optimization helps address user matrix sparsity, resulting in improved accuracy and interpretability. Experimental analysis using real-world datasets demonstrates that the DKGR technique achieves an average F1 score of 0.515, compared to 0.343 for traditional recommendation algorithms. It indicates that the DKGR technique provides superior recommendation services in real-world scenarios.
Fragile fingerprint for protecting the integrity of the Vision Transformer

Nowadays, with the rapidly development of deep learning, deep learning models have been widely deployed in various fields and generated significant commercial interest. Some technology companies upload their trained models to cloud servers and serve them to the end-users. Many works have shown that the convolutional neural networks are vulnerable to some model modification attacks, which raise concerns about integrity authentication of the convolutional neural models. Additionally, Transformers based on attention mechanism are now commonly used in computer vision applications, and the need to verify the integrity of ViTs arises if the ViT model is deployed in the safety critical systems. In this paper, we propose a fragile fingerprint method for verifying the integrity of the ViTs, which is based on the targeted adversarial examples. Compared with the existing works, the proposed fingerprint method does not modify the ViTs. We generate some fragile fingerprints, which are classified as the targeted label. In the verification stage, if the fingerprints are successfully classified as targeted label with 100% success rate, we can claim that the ViTs is not modified. Otherwise, when the fingerprint verification success rate is lower than 100%, we can claim that the integrity of ViTs is compromised. Experimental results demonstrate that the fingerprints can effectively verify the integrity of the ViTs when the ViTs is modified by model attacks, even though only a small number of weights of ViTs are changed.