Reviewerly/exHarmony huggingface dataset:
Visual Perception Method Based on Human Pose Estimation for Humanoid Robot Imitating Human Motions

The goal of the system proposed in this paper is to develop functions for humanoid robots to use vision to perceive human behavior and imitate human motions. With that aim, the position of the human joint points is recorded by a key point detector and the motion data is fed to 3D-baseline to estimate the 3D human skeleton. The accuracy of 3D joint point prediction is directly related to the degree of robot's restoration of human motions. Therefore, data augmentation is applied to improve the accuracy of 3D human pose estimation. The experimental results on Human3.6M demonstrate that our method can yield good performance. In addition, the motion mapping between human and humanoid robot is realized by inverse kinematics. A balance strategy based on ankle joint adjustment is proposed to maintain the stability of the robot. Tested in a humanoid robot, the system is able to imitate human movements naturally after observation, which improves the human-computer interaction of the humanoid robot.
Multi-class classification for large datasets with optimized SVM by non-linear kernel function

Abstract Most important part of Support Vector Machines(SVM) are the kernels. Although there are several widely used kernel functions, a carefully designed kernel will help improve the accuracy of SVM. The proposed work aims to develop a new kernel function for a multi-class support vector machine, perform experiments on various data sets, and compare them with other classification methods. Directly it is not possible multiclass classification with SVM. In this proposed work first designed a model for binary class then extended with the one-verses-all approach. Experimental results have proved the efficiency of the new kernel function. The proposed kernel reduces misclassification and time. Other classification methods observed better results for some data sets collected from the UCI repository.
Amaurotic-Entity-Based Consensus Selection in Blockchain-Enabled Industrial IoT

In this article, we propose a dynamic-consensus-based blockchain system—A-Blocks—for efficiently managing the data produced by the sensors in an Industrial Internet of Things (IIoT) environment. Typically, industries deal with a heterogeneous set of data from a diverse range of sensors. Conventional blockchain adoptions are a popular choice in such scenarios for data security while satisfying both transparency and immutability. However, stringent consensus algorithms are inadequate for managing heterogeneous data, especially due to its implicit constraints. For instance, while PoW provides inevitable security and is highly distributive, it is not scalable and requires more energy. In contrast, PoS is energy efficient but has reduced scalability and PBFT is suitable for faster processing. A-Blocks exploits the features of the available consensus algorithms and dynamically selects the best one in real time. It operates in two phases: 1) categorizing the data into groups based on their traits and then 2) selecting the appropriate consensus algorithm. Extensive experimental results using open industrial data sets demonstrate the effectiveness of A-Blocks with 8% CPU and 78% memory consumptions on resource-constrained devices. Furthermore, compared to the existing methods, although A-Blocks increases energy consumption by 11%, it also reduces mining time by 7%.
Generation of a Panorama Compatible with the JPEG 360 International Standard Using a Single PTZ Camera

Recently, the JPEG working group (ISO/IEC JTC1 SC29 WG1) developed an international standard, JPEG 360, that specifies the metadata and functionalities for saving and sharing 360-degree images efficiently to create a more realistic environment in various virtual reality services. We surveyed the metadata formats of existing 360-degree images and compared them to the JPEG 360 metadata format. We found that existing omnidirectional cameras and stitching software packages use formats that are incompatible with the JPEG 360 standard to embed metadata in JPEG image files. This paper proposes an easy-to-use tool for embedding JPEG 360 standard metadata for 360-degree images in JPEG image files using a JPEG-defined box format: the JPEG universal metadata box format. The proposed implementation will help 360-degree cameras and software vendors provide immersive services to users in a standardized manner for various markets, such as entertainment, education, professional training, navigation, and virtual and augmented reality applications. We also propose and develop an economical JPEG 360 standard compatible panoramic image acquisition system from a single PTZ camera with a special-use case of a wide field of view image of a conference or meeting. A remote attendee of the conference/meeting can see the realistic and immersive environment through our PTZ panorama in virtual reality.
A scene segmentation algorithm combining the body and the edge of the object

Scene segmentation is a very challenging task where convolutional neural networks are used in this field and have achieved very good results. Current scene segmentation methods often ignore the internal consistency of the target object, and lack to make full use of global and local context information which leads to the situation of object misclassification. In addition, most of the previous work focused on the segmentation of the main part of the object, however, there are few researches on the quality of the object edge segmentation. In this article, based on the use of flow information to maintain body consistency, the context feature extraction module is designed to fully consider the global and local body context information of the target object, refining the rough feature map in the intermediate stage. So, the misclassification of the target object is reduced. Besides, in the proposed edge attention module, the low-level feature map guided by the global feature and the edge feature map with semantic information obtained by intermediate process are connected to obtain more accurate edge detail information. Finally, the segmentation quality that contains the body part of the noise and the edge details can be improved. This paper not only conducts experiments on the classic FCN, PSPNet, and DeepLabv3+ several mainstream network architectures, but also on the real-time SFNet network structure proposed last year, and the value of mIoU in object and boundary is improved to verify the effectiveness of the method proposed in this paper. Moreover, in order to prove the robustness of the experiment, we conduct experiments on three complex scene segmentation data sets of Cityscapes, CamVid, and KiTTi, and obtained mIoU values of 80.52% on the Cityscapes validation data set, and 71.4%, 56.53% on the Camvid and KITTI test data set, which shows better results when compared with most of the state-of-the-art methods.
Hybrid Optimization Algorithm Based on QUATRE and ABC Algorithms

Artificial bee colony optimization algorithm (ABC) is an optimization algorithm based on swarm intelligence which is obtained by observing the behavior of bees looking for nectar and sharing food information with bees in the hive. QUasi-Affine TRansformation Evolutionary (QUATRE) is an algorithm that uses quasi-affine transformation as an evolution method because ABC has the shortcoming of weak ability to develop new nectar sources, and QUATRE has weak search ability but strong development ability, so this paper combines these two algorithms to a certain extent and proposes an improved artificial bee colony optimization algorithm (QUA-ABC). QUA-ABC is inspired by the location update formula in QUATRE and proposes a new location update formula suitable for ABC. In this study, experiments were conducted using the internationally used CEC2013 data set. The optimization accuracy and convergence speed of QUA-ABC were compared with the original ABC. The results show that the QUA-ABC algorithm has stronger capabilities and better performance.
Analysis of the Hardware Imprecisions for Scalable and Compact Photonic Tensorized Neural Networks

We simulated tensor-train decomposed neural networks realized by Mach-Zehnder interferometer-based scalable photonic neuromorphic devices. The simulation results demonstrate that under practical hardware imprecisions, the TT-decomposed neural networks can achieve >90% test accuracy with 33.6× fewer MZIs than conventional photonic neural network implementations.
Study on Target Detection and Tracking Method of UAV Based on LiDAR

Unmanned aerial vehicle (UAV) may bring potential danger such as hurting people falling accidents, Therefore the surveillance of trajectory of UAV is subjected to extensive attention for avoiding irreversible damage. This paper presents a target detection and tracking method based on LiDAR, in which target detection uses Euclidean distance clustering and particle filter are utilized to achieve target detection and tracking, respectively. Once UAV is detected by the clustering method, the particle filter algorithm is used to track the target. In the experiment, the Livox Mid 40 LiDAR developed by the Livox company is used as the front-end sensor to detect the UAV. Experimental results show that the proposed method could realize the detection and tracking of UAV.
Efficient and Safe Robotic Autonomous Environment Exploration Using Integrated Frontier Detection and Multiple Path Evaluation

Autonomous exploration and remote sensing using robots have gained increasing attention in recent years and aims to maximize information collection regarding the external world without human intervention. However, incomplete frontier detection, an inability to eliminate inefficient frontiers, and incomplete evaluation limit further improvements in autonomous exploration efficiency. This article provides a systematic solution for ground mobile robot exploration with high efficiency. Firstly, an integrated frontier detection and maintenance method is proposed, which incrementally discovers potential frontiers and achieves incremental maintenance of the safe and informative frontiers by updating the distance map locally. Secondly, we propose a novel multiple paths planning method to generate multiple paths from the robot position to the unexplored frontiers. Then, we use the proposed utility function to select the optimal path and improve its smoothness using an iterative optimization strategy. Ultimately, the model predictive control (MPC) method is applied to track the smooth path. Simulation experiments on typical environments demonstrate that compared with the benchmark methods, the proposed method reduce the path length by 27.07% and the exploration time by 27.09% on average. The real-world experimental results also reveal that our proposed method can achieve complete mapping with fewer repetitive paths.
Multi-experts Joint Decision with Adaptive Model Updater for Robust Visual Tracking

Abstract Over these years, correlation filters based trackers have shown edges both in accuracy and speed. However, variations of target appearance caused by heavy occlusion, rotation, background clutters and target deformations are the major challenges for tracking. To solve these problems, many works put on exploiting the power of target representation, such as high-level convolutional features. Nonetheless, these methods make a great compromise between the speed and performance. At the same time, there are few researches on improving the performance of model updater and the ensemble methods. In this paper, a multi-experts joint decision strategy base on kernelized correlation filters is proposed to obtain robust and accurate visual tracking, two trackers with handcrafted features and deep convolutional neural network features are integrated in this framework. We also investigate the mechanism of tracking failure caused by occlusion and background clutters, and propose a novel criterion to evaluate the reliability of samples. Our work includes extending the kernelized correlation filter-based tracker with the capability of handling scale changes as well. The proposed tracker is extensively evaluated on the OTB-2013, OTB-2015 and VOT2015 benchmark datasets. Compared with the state-of-the-art trackers, the distinguished experimental results demonstrate the effectiveness of the proposed framework.
A Modified Argon2i Using a Tweaked Variant of Blake3

Following the idea that the memory-hard hash function Argon2 can be modified to support other compression functions, we propose the use of Blake 3 and a tweaked version of it as the core function. More precisely, we are particularly interested in Argon2i, as the applications we target consist of password-based key derivation functions. We managed to speedup Argon2i in software while keeping it secure. We believe that our proposal could be more suitable in hardware rather than the original Argon2i function. Moreover, based on our construction, we developed an offline encryption application.
Secure ticket-based authentication method for IoT applications

The recent surge in the number of machines, appliances, and services connected to the Internet demands secure processing and transmission of sensory data. Authentication plays a crucial role in a typical security model used in the Internet of Things (IoT), and it protects data communications from various attacks, such as impersonation and denial of service, by verifying and allowing legitimate users to access the IoT resources. However, recent authentication literature has not addressed the need for developing a scalable and efficient authentication method in this field. This paper proposes a secure and anonymous ticket-based authentication method for the IoT. The proposed method protects the network from various security and privacy threats such as data alternation and denial of service while also offering mutual authentication and sensor anonymity. Our security and performance evaluations confirm the improvement.
Cheating with Models

Beliefs and decisions are often based on confronting models with data. What is the largest “fake” correlation that a misspecified model can generate, even when it passes an elementary misspecification test? We study an “analyst” who fits a model, represented by a directed acyclic graph, to an objective (multivariate) Gaussian distribution. We characterize the maximal estimated pairwise correlation for generic Gaussian objective distributions, subject to the constraint that the estimated model preserves the marginal distribution of any individual variable. As the number of model variables grows, the estimated correlation can become arbitrarily close to one regardless of the objective correlation. (JEL D83, C13, C46, C51)
Updating Variational Bayes: fast sequential posterior inference

Variational Bayesian (VB) methods produce posterior inference in a time frame considerably smaller than traditional Markov Chain Monte Carlo approaches. Although the VB posterior is an approximation, it has been shown to produce good parameter estimates and predicted values when a rich classes of approximating distributions are considered. In this paper, we propose the use of recursive algorithms to update a sequence of VB posterior approximations in an online, time series setting, with the computation of each posterior update requiring only the data observed since the previous update. We show how importance sampling can be incorporated into online variational inference allowing the user to trade accuracy for a substantial increase in computational speed. The proposed methods and their properties are detailed in two separate simulation studies. Additionally, two empirical illustrations are provided, including one where a Dirichlet Process Mixture model with a novel posterior dependence structure is repeatedly updated in the context of predicting the future behaviour of vehicles on a stretch of the US Highway 101.
M2N: Mutual constraint network for multi-level unsupervised domain adaptation

Unsupervised domain adaptation (UDA) algorithms aim to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. To tackle this issue, domain-alignment models based on different representation space levels were used to extract knowledge relevant to the ultimate task. Although they achieved remarkable performance, domain shift that poses an evident challenge for adapting the model trained on one domain to another one was ignored. Pseudo labels learning for UDA is therefore proposed to reduce the impact of domain shifts. However, the inevitable label noise severely weakens the model’s capability of feature representations. Inspired by recent studies on deep learning with noisy labels, we propose a Mutual Constraint Network for multi-level UDA (M2N), to mitigate the effects of noisy pseudo labels and learn better feature representation from different space levels. Extensive experimental results on Digits, Office-31, and VisDA-2017, show that our methods can achieve new state-of-the-art performance on three benchmark tasks and improve significantly over prior single-level UDA.
The Innovation and Development of College English Blended Teaching Model in the Network Environment

The development and innovation of network technology and the development needs of teaching itself have accelerated the pace of educational informatization. As the core of the informatization process, the Internet has become one of the indispensable parts of modern language teaching; the blended teaching model was put forward under this situation, and gradually recognized by educational scholars. The purpose of this article is to study the innovative development of college English blended teaching mode under the network environment, and it discussed the research status of blended learning at home and abroad by searching and analyzing relevant materials. In the research process, through a large number of related research materials, the concept of network, college English, and blended teaching models were defined, which laid a theoretical foundation for the design of college English blended teaching models. Using the research method of questionnaire to understand the learning needs of students, the article analyzed the goals and content of college English courses. On this basis, combined with the connotation of blended teaching, the design principles of blended teaching mode are proposed to guide the later blending the design of the learning model. According to the results of the research experiment, nearly 100% of students and teachers hope that the platform supports the uploading of learning materials and assignments, as well as the functions of discussion and communication. Among freshmen and sophomores, more than 75% of students recognize the blended teaching mode.
Emotional Virtual Reality Stroop Task: Pilot Design

Anxiety-inducing and assessment methods in Virtual Reality has been a topic of discussion in recent literature. The importance of the topic is related to the difficulty of getting accurate and timely measurements of anxiety without relying on self-report and breaking the immersion. To this end, the current study utilises the emotional version of a well-established cognitive task; the Stroop Color-Word Task and brings it to Virtual Reality. It consists of three levels; congruent which is used as control and corresponds with no anxiety, incongruent, which corresponds with mild anxiety and emotional, which corresponds with severe anxiety. This pilot serves two functions. The first is to validate the effects of the task using biosignal measurements. The second is to use the bio signal information and the labels to train a machine-learning algorithm. The information collected by the pilot will be used to decide what types of signals and devices to use in the final product, as well as what algorithm and time frame will be better suited for the purpose of accurately determining the user's anxiety level within Virtual Reality without breaking the immersion.
Research on Improved Potential Field Ant Colony Algorithm for UAV Path Planning

In order to solve the problems of slow convergence speed and prone to local optimization in ant colony algorithm, an improved ant colony algorithm of potential field is proposed in this study. Firstly, the method of pheromone affected by artificial potential field is introduced to reduce the blindness of the ant colony. Then, the potential field heuristic information is used to accelerate the convergence speed of ant colony algorithm. Finally, the decrease coefficient of potential field resultant force is used to solve the local optimal problem. Compare with the traditional algorithm, the experiments show that the improved algorithm in this paper can get better results, in terms of path length and the number of convergence iterations, the average search time for convergence is reduced by 44.83% in the 20×20 map.
Motion Saliency-Based Collision Avoidance for Mobile Robots in Dynamic Environments

Obstacle avoidance is a sizable challenge for robots working in a multiple dynamic obstacle environment. Conventional obstacle avoidance methods often require complex calculations to process all dynamic obstacles detected in the scene. Avoiding dangerous moving objects in time is often difficult when obstacles are many. In this article, we propose a robot obstacle avoidance method based on motion saliency for dynamic environments. First, we use segmented dynamic objects to calculate the saliency of dynamic objects and segment dangerous dynamic objects. Then, we use the B-spline curve to predict the movement of dangerous dynamic objects and combine it with the nonlinear model predictive control method to avoid dangerous obstacles in the dynamic environment of the robot. Considering the motion behavior of different dynamic objects, our obstacle avoidance strategy is to generate obstacle-free paths by adjusting the speed of the robot or connecting the centers of the rolling variable-size circles. Finally, we conduct a series of experiments to verify the effectiveness of our method.
Automatic Assembly of Bolts and Nuts Based on Machine Vision Recognition

Abstract In this study, based on the computer vision technology, we developed a recognition system for nuts positioning to complete the automatic bolt assembly part of the automatic production line. The actual image of the nut was captured by an industrial camera, which would be processed by the following edge detection and Hough circle transformation. After that, the coordinates of the nuts were obtained in the pixel scale. Finally, the real position of nuts would be fed back to the robot arm, according to which the automatic assembly of the bolt would be completed. This computer vision based recognition system is an indispensable part for the efficiency and accuracy improvement of automatic production line.
Maximum Likelihood Decoder For Image Watermarking Techniques

The paper extends a general approach for extracting the watermark using a mathematical technique for watermarking using Discrete Wavelet Transform (DWT). Though the watermark may be inserted using quantization or a combination of other transforms, yet the successful recovery of watermarks is a difficult task. It is indeed a challenge for the decoder to recover the watermark when various attacks are applied upon a watermarked image. The corresponding scheme of watermark decoding uses the principle of Maximum Likelihood (ML) estimation and is based upon the statistical modeling of parameters of Gaussian distribution. The watermarking experiments utilizing ML decoder exhibit improved performances over the conventional approaches of watermarking employing correlation detectors. The robustness of such techniques is verified through the values of Bit Error Rate (%).