Reviewerly/exHarmony huggingface dataset:
Research on Multi-level Attention-based Human Pose Estimation

Detecting human key points from a single image is very challenging due to occlusion, blurring, illumination and scale changes. In this paper, this problem is addressed by designing an effective network structure. Since global and local information plays an important role in reasoning about human body structure and invisible keypoints, Multi-level Attention Network (MAN) is proposed. First, compared with traditional multi-resolution networks, it enables multi-resolution feature maps with greater information variance by generating them directly from the highest resolution feature map, which in turn increases the abundance of feature information after final fusion. Secondly, it effectively integrates global and local information in different resolution feature maps through the Feature Alignment Attention Block(FAAB), and intensifies them in a targeted manner. On the COCO dataset, with HRNet (Sun K. et al [1]) as the baseline network, HRNet of inserted MAN improves 1.1-2.3 AP points over the baseline network.
Detection Algorithm of Known Road Signs in Unknown Environment Based on Loop Detection

Aimed at solving the visual-integrated navigation task with a visual camera and inertial measurement unit (IMU) for the intelligent vehicle of long-period and large-scale movement, this paper studies the visual detection algorithms of the known landmarks and historical road signs in the unknown environment based on loop detection, of which the pre-known position can be used to be integrated with the IMU to obtain an accurate navigation error estimation. Experiments based on the WHEELTEe R550 platform are conducted to validate the proposed algorithms and test the performance. The results show that the known landmarks are identified accurately. Performance improvement is determined by comparisons between the proposed and traditional algorithms.
A Method for Evaluating Key Elements Affecting Visual Perception Capabilities of Unmanned Autonomous Systems in Virtual Scenarios

In order to reduce field testing expenses and construct rare extreme scenarios in the real world, it is convenient to evaluate the visual perception capability of unmanned autonomous systems more effectively. In this paper, a virtual base scenario based on a real test site is constructed using tilt photography, and then three target detection models, SSD300, Faster R-CNN and RetinaNet, which are fully trained using the COCO dataset, are used instead of the unmanned autonomous system visual perception module. After that, the dataset was built for testing with different values of influencing elements. The experimental results show that changing the quantified values of the influencing elements can effectively affect the detection effect of the target detection models, i.e., the unmanned autonomous system perception capability can be effectively tested even in virtual scenarios, which can save cost and shorten the cycle time for the development of unmanned autonomous systems.
Region Segmentation Method of Mobile Robot Based on ORB Feature in Dynamic Scene

In the traditional mobile robot SLAM system, the requirements for the scene are generally static, because the dynamic target will have an impact on the mapping process, causing the system to calculate the position of the camera incorrectly, resulting in a decrease in the accuracy of the navigation process, leaving a series of non-static noise caused by dynamic objects, thus causing the system to record the scene incorrectly. In order to reduce the impact of dynamic objects on SLAM system, this paper proposes a dynamic object region segmentation method based on ORB algorithm, which aims to segment the dynamic region from the image before the image is input into slam system, leaving the static region, and then perform DMatch matching for the static region to achieve the effect of reducing the interference of dynamic objects.
Abundant Semantic Information Module for Object Detection

Nowadays, with the high-speed iteration of convolution neural network, the efficient object detector emerges one after another. As an important branch of computer vision, object detection aims to detect where and what the object is. However, nowadays, many detector cannot extract abundant semantic information to discriminate the location and size of the objects, resulting in poor performance of the network. In this paper, a new module is proposed, named Abundant Semantic Information Module (ASIM), to enrich and expand the semantic information of the object with more and larger receptive fields. In ASIM, we blend the extracted feature maps to different degrees with different blending factors and fuse them so that all object information is given full attention. Compared to the baseline method, a wealth of experiments show that our module has achieved a significant performance improvement.
Detection of low, small, and slow targets in the air based on improved YOLOv5

At present, the drone industry is very mature and has been widely used in many fields. While it brings us convenience, it also brings many hidden dangers. Since the target of the UAV is relatively small, the target detection effect of simply using YOLOv5 is poor. Based on this situation, a low-altitude UAV detection algorithm based on improved YOLOv5 is proposed. Targeted improvements have been made to YOLOv5's Backbone, Neck, and Loss. Experiments show that the new method achieves an accuracy of 0.918 and a mAP@.5:.95 of 0.529, which is significantly improved compared with the original.
An adaptive SOM algorithm for PCB NC drilling path planning

An Adaptive Self Organizing Map (ASOM) algorithm is proposed to solve the problem of large-scale PCB drilling path planning. The algorithm initializes the SOM network according to the number and distribution characteristics of PCB holes, adjusts the probability that holes are selected as input vectors according to the density of holes, and constructs the learning neighborhood of holes to adjust the learning efficiency of the SOM network. It effectively shortens the length of the drilling path and improves the solution quality. The ASOM algorithm is verified by several types of problems in TSPLIB, and the results show that the ASOM algorithm is close to the optimal solution when solving the large-scale PCB drilling path planning problem, and is better than the standard SOM algorithm and the extended SOM algorithm.
An adaptive algorithm for non-uniform illumination in large-size workpieces online detection

Large-size workpieces in traditional industrial production have complex lighting environments that cannot meet the requirements for online detection. To improve the quality of images acquired under non-uniform illumination conditions. In this paper, an adaptive algorithm for non-uniform illumination in large-size workpieces online detection is proposed. First, a color space conversion is performed to preserve the color information, and then an improved effective guided filter is used to estimate the illumination information in the value channel, the illumination information is divided into the illuminance layer and the detail layer, 2D adaptive gamma function, and histogram specification are used for the illuminance layer to solve the illumination problem, and an optimized enhancement factor is used for the detail layer to enhance it. Finally, the enhanced image is obtained after fusion. By comparing with MSR, LIME, and FGif algorithms, the proposed algorithm can effectively solve the problem of non-uniform illumination in online detection, not only improving the image quality but also the operating speed that meets the requirements of online detection.
Research on Trajectory Tracking Control of Intelligent Vehicle Based on PSO-MPC Algorithm

In order to solve the problems that low tracking accuracy and slow response speed in the process of intelligent vehicle track tracking, the model predictive controller optimized by particle swarm optimization(PSO-MPC) algorithm is proposed. And the performance of the algorithm is improved by optimizing the weight matrix of the controller in real-time. The model predictive controller with particle swarm optimization is built by Matlab software to test the algorithm effect, and the model of vehicle dynamic is build in Carsim software. The double-shifting track is used as the target trajectory to test the trajectory tracking capability of the vehicle. By analyzing the simulation results, it can be concluded that the PSO-MPC algorithm effectively improves tracking response speed and accuracy comparing with the traditional MPC algorithm.
Self-testing criteria for preparing-measuring qubit system

Self-testing is the high-level security verification of a claimed quantum device, confirming the quantum states prepared in the device and the measurements performed based solely on the observed statistics. The statistical correlations can realize the self-testing of the quantum system in the preparing-and-measuring scenario. However, most of previous studies focused on the self-testing of shared entangled states between devices, at present only a few researches are presented and the existing work can only simultaneously self-test the states and measurements when some witness inequalities reach a maximum violation. We focus on four-state preparation and the selected scenarios of two measurements. In this scenario, Armin Tavakoli et al. [Tavakoli A, Kaniewski J, Vértesi T, Rosset D, Brunner N 2018 &lt;i&gt;Phys. Rev. A&lt;/i&gt; &lt;b&gt;98&lt;/b&gt; 062307] have put forward a criterion based on the dimensional witness violation inequality which can achieve BB84 particles and corresponding Pauli measurements. However, in addition to the maximum violation of the inequality, any statistics with deviation from the maximum deviation cannot be self-tested. Besides, only the BB84 particle preparation and measurements system can be self-tested with that criterion, resulting in a large number of four-state preparation and two measurement systems that cannot be self-tested. Therefore, in this work, in addition to the maximum violation of that dimension inequality, we directly focus on the full observed statistics and further propose some new criteria for self-testing qubit quantum systems in the preparing-and-measiuring scenarios. And the self-testing criteria are proven in an ideal case. We construct a local isometry by using the constructions commonly used in device-independent cases, exchange the target system with the additional system, and realize the self-testing of more qubit state sets and measurement sets than BB84 particles. This meets the requirements for practical experiments to realize various tasks by different quantum state sets. In addition, we perform a robust analysis of the proposed criteria and use fidelity to describe the closeness of the state to the ideal state of the auxiliary system. Finally, an improved dimensional-dependent NPA method is used to optimize the lower bound of the robustness, making the new criteria practical under experimental noise. We use the YALIMP software package in MATLAB and the solver SEDUMI to solve this optimization problem. The present research increases the diversity of qubit state preparations and self-testing of measurement system, which is beneficial to the actual self-testing of different non-entangled single quantum systems.
Experience of Developing Value Orientations of Future Teachers in the Process of Mastering the Bachelor's Program

The article presents a theoretical analysis of approaches to the understanding of value orientations and mechanisms of their formation, reveals the impact of the educational process on the transformation of the complex value orientations of students - future teachers. The article presents the experience of designing the educational process of students in the direction of "Pedagogical education", aimed at promoting the development of value orientations of students - future teachers within the educational modules of the educational program. Particular attention in pedagogical design was paid to the peculiarities of applying the idea of affective area of B. Bloom's taxonomy of learning objectives and such mechanisms of value orientations formation as identification, empathy, individualization and reflection. D.A. Leontiev's "Meaning-Life Orientation Test" method was used to study the peculiarities of the complex of students' value orientations. The sample consisted of 84 students of "Pedagogical education" from the first to the third year. The results of the analysis of the data of the control study revealed positive dynamics in such indicators of value orientations as "satisfaction with the results of their life activities", "degree of independence", "degree of responsibility", "control over your life". Thus, the educational process, designed on the basis of such mechanisms of formation of value orientations, can be considered as a resource for the development of the value sphere of the personality of future teachers.
CITE: Compact Interactive TransformEr for Multilingual Image Captioning

Current state-of-the-art image captioning models generate captions in a single language, requiring a combination of multiple language specific models to build a multilingual image captioning system. However, as the number of supported languages increases, it leads to the parameters of the multilingual image captioning system grow linearly. To tackle this issue, we propose a single Compact Interactive TransformEr (CITE) model, which can describe an image in multiple languages simultaneously, making the captioning system more compact. Specifically, based on the standard Transformer model, we share the encoder and decoder backbone parameters and replace the self-attention sub-layer in the decoder with the interactive attention sub-layer. In addition, we extend the traditional monolingual reinforcement learning mechanism to a multilingual version to promote better description generation. Due to the wide use of Chinese and English, we evaluate the performance of our CITE model by simultaneously generating English and Chinese captions. We expand the image captions of the whole MSCOCO dataset and release a COCO-EN-CN dataset. Extensive experiments on the COCO-EN-CN dataset show that our single CITE model with more parameter-efficient can maintain the competitive performance or even better than the monolingual captioning models.
A YOLO-CDA X-ray Image Detection Algorithm

It is difficult for the YOLO algorithm to accurately detect and locate small target contrabands that are similar to the background and occlude each other in security detection applications. In response to this problem, this paper proposes a YOLO-CDA algorithm, which mainly improves the upsampling module, embeds channel convolution self-attention and spatial convolution self-attention based on the CARAFE structure, and strengthens the new upsampling operator to capture the spatial and channel dependencies between long-distance features, effectively establishes long-distance contextual connections, and ensures the integrity of semantic information for contraband imaging and background distinction and upsampling. At the same time, a new online data augmentation method is introduced in the pre-processing to increase the linear combination of images to alleviate the over fitting phenomenon, and also increase the diversity of input training images. The results show that the proposed YOLO-CDA algorithm mAP0.5:0.95 reaches 76.9%, which is 4.7% higher than the accuracy of the original model algorithm; FPS reaches 63, which meets the real-time detection requirements.
ARNets: Action Recurrent Networks for Human Action Recognition

Recurrent neural networks (RNNs) are powerful and robust neural networks that capture time dynamics via cycles in the graph. Compared to Convolutional Neural Networks (CNNs), RNNs consider both the current and the historical inputs which leads them to the standard approach for practitioners to address machine learning tasks involving sequential data. Compared to the achievements in these sequential learning tasks, RNNs pale in presenting a promising performance on human action recognition. Do we miss something? In this paper, we will conduct a detailed investigation of the RNNs for the human action recognition task. Several alternative recurrent architectures are designed and compared. Even simply, we find that adding the supervision signal at the end of an RNN can provide us with a comparable if not better performance than the previous state-of-the-art. We further analyze the different combinations of modalities and the effect of the length of the sequence. Compared to CNN-based recognition architecture, RNNs focus more on the action itself. Going deeper, we also find that when modeling long and difficult actions, RNNs perform much better than CNNs. It means a complex video action dataset may be required.
Research on Mixing of Virtual and Actual Reality for Motor Augmented Assembly

The effect of the mixing of virtual and actual reality will directly affect the realism and immersion of the augmented assembly system. The correct occlusion relationship and collision interaction between virtual and real objects are crucial to the fusion effect. The existing virtual-real occlusion handling methods cannot be applied to the augmented assembly system with complex contours and changeable scenes. Therefore, an occlusion handling method based on "virtual avatar" of motor equipment is proposed, which has strong robustness and real-time performance. Aiming at the problem that the number of wrong matching pairs of feature points is too large in the 3D registration process, we combine the method of Cosine similarity measurement on the basis of ORB algorithm to improve the accuracy of feature point matching. The current virtual and real object collision detection methods have large computation and low detection accuracy. We propose an octree hierarchical collision detection optimization algorithm based on model surface voxelization representation and volume difference, which improves the detection accuracy on the basis of ensuring computational efficiency. The experimental results show that the proposed methods have high real-time performance and accuracy, and greatly improve the realism and immersion of the augmented assembly system.
Adversarial Recovery Network for Low-Light Person Re-Identification

Person re-identification (re-ID), which is to connect pedestrians with the same identity in different scenarios, has been more widely used in case detection of security. The re-ID datasets and technology have contributed greatly to advancing community research. There are currently no available low-light datasets due to the difficulty of collecting and labeling images of people in low-light scenes. As a result, the vast majority of current re-ID methods do not have the recognition capability in low-light scenes. However, the recognition of persons in the low-light surveillance video has become an important issue to be solved for urban security, which plays an important role in improving the crime-solving rate of public safety departments and protecting people's lives and property. In this paper, we propose an Adversarial Recovery Network (ARN) that, unlike other approaches, enables a re-ID model with low-light recognition capability without the need for low-light data. Specifically, we first degrade the original images to generate multi-level low-light images; then obtain recovered images through the adversarial recovery module, and finally input the recovered images into the CNN network to extract pedestrian recognition knowledge. In addition, to validate the effectiveness of the proposed method, we also propose a low-light re-ID dataset (LOLR). Extensive experiments on LOLR have shown that our method outperforms other competitors by a wide margin. The dataset and code are available online at https://github.com/xl02111/ARN.
Image Super-Resolution with Perceptual Quality Assessment Guidance

Generative Adversarial Networks (GAN) have demonstrated the potential to recover realistic details for single image super-resolution (SISR). However, without considering perceptual metrics in optimization, existing perceptual SR methods could not show stable performance. To further improve the visual quality of super-resolution results, we propose a SISR method driven by image perceptual quality assessment. Through evaluating the generation results of GAN-based SISR and utilizing the evaluation results as the loss function, the generator can be optimized to obtain results with better perceptual quality. Specifically, we use a distorted dataset consisting of images generated by traditional and GAN-based image enhancement algorithms, and present an image perceptual quality assessment method which is highly consistent with human subjective evaluation. Then, we evaluate the generation results of GAN-based SISR and the evaluation result is utilized as the quality constraint to guide SISR model to obtain better subjective perception generation results. Although the proposed method is simple, it is effective and can be easily inserted into the existing SISR method. Extensive experiments show that the proposed method can produce visually pleasing results and achieve the state-of-the-art (SOTA) performance in perceptual metrics.
Procedural Rhythm Game Generation in Virtual Reality

Rhythm games and Virtual Reality (VR) often find themselves coming together through the help of various applications designed with the intent to deliver a unique gameplay experience to individuals who are seeking an immersive virtual environment where there is background music. Popular rhythm games such as Beat Saber make the gameplay experience unique by allowing users to move and slice multicolored tiles to the beat of developer-made “beat maps.” Using music synthesis software, we have designed an application utilizing automatic beat mapping techniques to automatically synthesize rhythm games like Beat Saber in virtual reality to make the game itself more immersive and enjoyable when allowing the user to play any song that they desire, without the need to make the beat maps manually. Through a series of experiment results and user studies, we show that our proposed approach for rhythm game synthesis is immersive and enjoyable when the game objects are automatically synchronized with the background music's rhythm.
Dual-Channel Improved ShuffleNet (DCISN) for Real-time Violence Detection

In this paper, we propose a lightweight deep learning network architecture, named dual-channel improved ShuffleNet (DCISN), for real-time violence detection in videos. The proposed extracts space-time features using two parallel channels like SlowFast networks and adopts newly designed ShuffleNet units to construct lightweight stage modules. Cross-stage connections are introduced to boost the accuracy of the DCISN network. Cascaded depth-wise convolution layers and Squeeze-and-Excitation (SE) block are employed in the newly designed ShuffleNet units to lower computation cost and meanwhile ensure good accuracy. A DCISN model has been designed and evaluated on recognized benchmark datasets, namely Hockey-Fight, Movies-Fight and RWF-2000. The DCISN model has 0.168M parameters and requires only 0.253GFlops in computation cost. Experiment results suggest that, in comparison with reported schemes, the DCISN model achieves competitive accuracy with much lower computation cost.