Reviewerly/exHarmony huggingface dataset:
An Accurate and Stable Extrinsic Calibration for a Camera and a 1D Laser Range Finder

Due to low cost and ease of manufacture, the combined sensor consisted of a camera and a 1D laser range finder is commonly used in many engineering applications. Especially in aerospace field, this type of combined sensor is important to earth observation and deep space exploration. This article proposes a novel extrinsic calibration method to get the accurate and stable calibration results of camera-1D laser range finder combined sensor. Firstly, an accuracy criterion named with directional comprehensive criterion is proposed to solve the less constraint in traditional calibration methods, which can evaluate the inconsistencies in the 1D laser ranging direction and the mean reprojection error in the 2D image plane direction fully. Then, according to the proposed directional comprehensive criterion, a novel robust extrinsic calibration framework is presented, which removes the observation data with large errors automatically and utilizes a stability criterion as the termination condition. The simulation experiments and real data experiments reveal that the proposed method is effective, which is superior to the existing methods.
An analogue of the Riemann Hypothesis via quantum walks

We consider an analogue of the well-known Riemann Hypothesis based on quantum walks on graphs with the help of the Konno–Sato theorem. Furthermore, we give some examples for complete, cycle, and star graphs.
Staking, Token Pricing, and Crypto Carry

The phenomenal rise of cryptocurrencies and decentralized finance have prominently featured ``staking'': Besides offering a convenience yield for transactions as digital media of exchange, tokens are frequently staked (and slashed) for base-layer consensus generation or for incentivizing economic activities and platform development, and consequently earn stakers rewards in the same tokens. To provide insights into the economics of staking and its asset pricing implications, we build a continuous-time model of a token-based economy where agents heterogeneous in wealth dynamically solve their wealth allocation (stake-transact-consume) problems. We cast the interactions as a mean field game with stochastic control and systematic shocks, which underscores aggregate staking ratio as a key variable linking staking to token pricing and equilibrium reward rate. Empirical findings on all major stakable tokens corroborate the model predictions. In particular, staking ratio has a positive correlation with reward rates in the cross section and has a negative correlation in the time series. Higher reward rates attract greater future staking, increasing an individuals' staking allocation and the staking ratio in aggregate, which in turn predicts positive excess returns. Finally, we use transaction convenience to rationalize violations of the uncovered interest rate parity and significant carry premia (e.g., a long-short carry yields a Sharpe ratio of 1.6) in the cryptocurrency data.
Biomechanical Research on Special Ability of Long Jump Take-Off Muscle Based on Multisource Information Fusion

For athletes of different sports, their muscle activities in different sports will show different special characteristics according to different sports. The purpose of this paper is to study the technology of multi-information fusion and to study the athletes of the long jump competition. The relevant research on the muscle specific ability test methods of the athletes in the long jump competition is of great significance to the study of the long jump. This paper proposes a long jumper's special muscle ability experiment, using the now very popular multi-information fusion technology, to conduct in-depth research on the biomechanics of the long jumper's special ability of the take-off muscle, which can provide an effective scientific basis for the special ability level and special strength of the long jumper's take-off sports. The results of the study show that the hip joint of the take-off leg produces greater hip extensor torque during the take-off action of long jump, and the active contraction ability of the knee flexor group is very important for taking off and avoiding damage to the posterior femoral muscle group. The change range of the pressure center is between 51.26% and 74.35%, which has great application value in actual training.
Social Movement Prediction from Bangla Social Media Data Using Gated Recurrent Unit Neural Network

Social networking platforms have become an essential part of our everyday life. These sites contain more data than any other platform now. Analyzing these social interactions can prove to be a key factor in predicting social movements. In this research work, we used the Gated Recurrent Unit (GRU) Neural Network for analyzing Bangla social media data to predict social movements. We prepared a dataset that represents an equal number of negative and positive notions about social movements. We gathered data from Twitter, which is currently one of the most renowned social networking platforms around the globe. We showed how hyper-parameters optimization can be profitable to attain the best probable outcome from GRU-RNN. This research work will help to figure out whether any social movement is going to take place or not and also help the government or the law enforcement agency to fulfill the public demands or take necessary steps long before the social movements take place.
Relaxed Attention: A Simple Method to Boost Performance of End-to-End Automatic Speech Recognition

Recently, attention-based encoder-decoder (AED) models have shown high performance for end-to-end automatic speech recognition (ASR) across several tasks. Addressing overconfidence in such models, in this paper we introduce the concept of relaxed attention, which is a simple gradual injection of a uniform distribution to the encoder-decoder attention weights during training that is easily implemented with two lines of code. We investigate the effect of relaxed attention across different AED model architectures and two prominent ASR tasks, Wall Street Journal (WSJ) and Librispeech. We found that transformers trained with relaxed attention outperform the standard baseline models consistently during decoding with external language models. On WSJ, we set a new benchmark for transformer-based end-to-end speech recognition with a word error rate of 3.65%, outperforming state of the art (4.20%) by 13.1% relative, while introducing only a single hyperparameter.
Thyroid Discharge Summary NER Based on BERT-BiLSTM-CRF Model

The text study of Chinese thyroid discharge summary plays an important role in predicting thyroid disease. The results of named entity recognition are crucial for later text structuring. In recent common sequence annotation tasks, the contextual semantic issues of words in the text cannot be captured because the encoding of words is static, which can lead to a poor entity recognition rate. In this paper, a method combining Bert model with BiLstm-CRF is proposed to achieve a relatively higher recognition rate.
Peer effects of the young returnee scientists: Evidence from the state key laboratories in China

Abstract Attracting overseas scientists back to their home countries is an important scientific policy in the era of globalization. The returnee scientists not only have the potential to make high academic achievements by themselves but are also expected to influence other scientists. Using a data-set of 1,006 scientists from Chinese State Key Laboratories, this study finds that recruiting young returnee scientists has a positive effect on the number of their peer’s highly-cited articles. It is also found that the peer effect is not achieved through the promotion of international collaborations of local scientists. It may be realized through other mechanisms, such as knowledge spillovers. As shown in the heterogeneity analysis, the adaptation of returnee scientists and the absorptive capacity of local scientists have an impact on the peer effects of the returnees, which reflects that knowledge dissemination and absorption play significant roles in the returnee’s peer effect.
A Buffer-Based Adaptive Bitrate Approach in Wireless Networks With Iterative Correction

Most providers of video streaming are interested in improving quality of user experience across various wireless network circumstances. Many buffer-based ABR algorithms have been proposed to adjust the bitrate according to the buffer occupancy at the client player. Though keeping the buffer occupancy stable, such ABR algorithms cannot provide satisfied responsiveness to bandwidth dynamics. To solve the problem, we propose an ABR algorithm BBA+ that iteratively corrects the mapping function between bitrate and buffer occupancy according to network throughput. The results show that BBA+ effectively improves the average bitrate and reduces the rebuffering ratio.
Abstract: Guided Filter Regularization for Improved Disentanglement of Shape and Appearance in Diffeomorphic Autoencoders

The disentanglement of shape and appearance is a prominent computer vision task, that has become relevant in the medical imaging domain in recent years. Medical images are often acquired in different hospitals, by different devices and using different parameters, resulting in varying intensity profiles. However, when performing population-based analysis over various datasets, e.g. from different hospitals, it is important to be able to distinguish between changes in the anatomical shapes and device-dependent intensity changes.
A Dim-small Target Real-time Detection Method Based on Enhanced YOLO

The identification of dim-small targets has been employed in various industries, including transportation, medical, and military, thanks to the rapid development of artificial intelligence and remote sensing drone technology. Meanwhile, the UAV has become one of the major ways to locate and detect ground remote sensing sensitive targets due to its characteristics of low cost, high flexibility, excellent vision in the air and wide detection range. Although deep learning-based target identification algorithms like Yolo (you only watch once) and SSD (single-lens multi-box detector) have achieved good target detection results, it is still challenging to detect weak targets in real time and effectively. To address this issue, this paper proposes a pre-processing method for processing data sets to increase feature diversity and invariance, as well as to improve the performance of the YOLO model when dealing with dim-small targets. The experimental results show that the proposed method outperforms the original Yolo algorithm in terms of detection accuracy and robustness in remote sensing target detection. This method still has the characteristics of real-time in low configuration hardware environment, so it is of practical significance for the popularization and application of the algorithm.
3D Human Pose and Shape Reconstruction From Videos via Confidence-Aware Temporal Feature Aggregation

Estimating 3D human body shapes and poses from videos is a challenging computer vision task. The intrinsic temporal information embedded in adjacent frames is helpful in making accurate estimations. Existing approaches learn temporal features of the target frames simply by aggregating features of their adjacent frames, using off-the-shelf deep neural networks. Consequently these approaches cannot explicitly and effectively use the correlations between adjacent frames to help infer the parameters of the target frames. In this paper, we propose a novel framework that can measure the correlations amongst adjacent frames in the form of an estimated confidence metric. The confidence value will indicate to what extent the adjacent frames can help predict the target frames’ 3D shapes and poses. Based on the estimated confidence values, temporally aggregated features are then obtained by adaptively allocating different weights to the temporal predicted features from the adjacent frames. The final 3D shapes and poses are estimated by regressing from the temporally aggregated features. Experimental results on three benchmark datasets show that the proposed method outperforms state-of-the-art approaches (even without the motion priors involved in training). In particular, the proposed method is more robust against corrupted frames.
Sequence Analysis and Feature Extraction of Sports Images Using Recurrent Neural Network

Image sequence analysis is attracting significant attention at present, but its principles and techniques have rarely been applied to the field of sports biomechanics. As far as the technology of automatic recognition of joint points by computers is concerned, it is still in the experimental stage. The purpose of this paper is to study and analyze the sequence analysis and feature extraction of sports images based on cyclic neural network. This paper puts forward the basic concepts of sports image sequence analysis and feature extraction and analyzes the importance of sports in this context. As the experimental results demonstrates, the application rate of detecting human motion by using template matching technology detection is between 15% and 47%, while the accuracy of image sequence analysis method has increased from 17% to about 65%. Generally speaking, although the template matching technology detection method is more popular than the image sequence analysis method at the beginning, the popularity of the image sequence analysis method is significantly higher than that of the template matching technology detection method after time precipitation. Therefore, it is very important to study the sequence analysis and feature extraction of sports image based on cyclic neural network.
SPNet: Learning Stereo Matching With Slanted Plane Aggregation

Matching cost aggregation plays a critical role for the stereo matching task. Existing CNN-based methods commonly use 3D convolutions to aggregate matching costs from a local 3D space. However, the high computational cost of 3D convolutions limits their applications. Traditional methods show that a slanted support window in 3D space can help to aggregate matching costs from informative regions, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> the surfaces of objects. Motivated by this idea, we propose a SPNet with differentiable slanted plane aggregation. Our slanted plane aggregation layers aggregate matching costs from a learnable slanted plane in a local 3D space to reduce computational and memory costs. Experimental results show that our slanted plane aggregation layers can learn to fit the surfaces of objects and effectively aggregate matching costs. Comparison with previous stereo matching methods shows that our network achieves competitive performance with higher efficiency.
A Supervised ML Biometric Continuous Authentication System for Industry 4.0

Continuous authentication (CA) is a promising approach to authenticate workers and avoid security breaches in the industry, especially in Industry 4.0, where most interaction between workers and devices takes place. However, introducing CA in industries raises the following unsolved questions regarding machine learning (ML) models: its precision and performance; its robustness; and the issue about if or when to retrain the models. To answer these questions, this article explores these issues with a proposed supervised versus nonsupervised ML-based CA system that uses sensors, applications statistics, or speaker data collected by the operator’s devices. Experiments show supervised models with equal error rates of 7.28% using sensors data, 9.29% with statistics, and 0.31% with voice, a significant improvement of 71.97, 62.14, and 97.08%, respectively, over unsupervised models. Voice is the most robust dimension when adding new workers, with less than 2% of false acceptance rate even if workforce size is doubled.
Path Integral Framework for Characterizing and Controlling Decoherence Induced by Nonstationary Environments on a Quantum Probe

A framework to characterize and control the influence of out-of-equilibrium environments on a quantum device is presented: A methodology inspired by field theory with relevance to quantum technologies.
Orthogonal invariant Lagrange-Fourier moments for image recognition

The orthogonal moments have recently achieved outstanding predictive performance, and become an indispensable tool in a wide range of pattern recognition applications, including the image classification and object detection. Using the orthogonal Lagrange polynomials, we present in this paper three new sets of discrete orthogonal moments and their invariants to translation, scaling and rotation (TSR) for image representation and recognition: the orthogonal Lagrange-Fourier moments (LFMs) for the gray-scale images, the multi-channel orthogonal Lagrange-Fourier moments (MLFMs) and the quaternion orthogonal Lagrange-Fourier moments (QLFMs) for the color images. These orthogonal moments are presented in polar coordinates. We present a set of numerical experiments in the field of classification and pattern recognition to evaluate the performance of the proposed invariant moments. From a comparative study with the recent orthogonal invariant moments, we conclude that our approach is very promising in the pattern recognition field and image analysis.
Agency of Interactive Environment in Shaping Users’ Behaviour Through Actor–Network Theory

The paper defines the role of interactive space in social relationships using Actor–network theory as a methodological tool. It examines interactive space from Actor–network theory perspective, as one of the actors in a heterogeneous network consisting of human and non-human components with their own agency. This methodology allows to consider interactive space as an active participant of social relationship, communicating with other participants of society using non-verbal means of communication. Applying Actor–network theory allows to understand the rules of communication between users and the space, evaluates responsiveness to the system signals and defines participants' stages of involvement into this communication. A full-scale interactive prototype was designed and built as a proof-of-concept to analyse the influence of interactive space on human behaviour and space perception, examining this communication in real time and assessing human responsiveness to different types of signals from the space (light/sound/motion).
Proof of Work Is a Stigmergic Consensus Algorithm: Unlocking Its Potential

The proof-of-work (PoW) algorithm has been initially used in Bitcoin and is now one of the mainstream consensus algorithms to create immutable ledgers of transactions. However, it is still not very well understood, and as such, its entire strength remains undervalued. While some researchers classify it as a Nakamoto consensus algorithm, some other researchers do not even consider it as a consensus algorithm. In this article, the objective is to shed light on classifying PoW. To this end, we make a theoretical analysis from a coordination perspective, and then we conclude that PoW is a stigmergic consensus algorithm where the trace left by an action in the blockchain through indirect coordination of agents stimulates a subsequent action and eventually creates a single chain of blocks. In other words, PoW is a form of stigmergic coordination toward emergent behavior of a blockchain system. Based on this identification, we further analyze the fundamental properties of PoW. Finally, we reengineer PoW by improving its missing stigmergic mechanisms, and we provide a discussion.
Geometric Adversarial Attacks and Defenses on 3D Point Clouds

Deep neural networks are prone to adversarial examples that maliciously alter the network’s outcome. Due to the increasing popularity of 3D sensors in safety-critical systems and the vast deployment of deep learning models for 3D point sets, there is a growing interest in adversarial attacks and defenses for such models. So far, the research has focused on the semantic level, namely, deep point cloud classifiers. However, point clouds are also widely used in a geometric-related form that includes encoding and reconstructing the geometry. In this work, we are the first to consider the problem of adversarial examples at a geometric level. In this setting, the question is how to craft a small change to a clean source point cloud that leads, after passing through an autoencoder model, to the reconstruction of a different target shape. Our attack is in sharp contrast to existing semantic attacks on 3D point clouds. While such works aim to modify the predicted label by a classifier, we alter the entire reconstructed geometry. Additionally, we demonstrate the robustness of our attack in the case of defense, where we show that remnant characteristics of the target shape are still present at the output after applying the defense to the adversarial input. Our code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Research on Road Traffic Moving Target Detection Method Based on Sequential Inter Frame Difference and Optical Flow Method

In order to realize the effective detection of moving objects in intelligent transportation environment, the proposed method of motion detection with higher accuracy and efficiency by combining the sequence frame difference and optical flow algorithm. Firstly, adaptive median filter and histogram equalization are used to preprocess video frames. Then, the preprocessed image is processed by pairwise sequence difference, and the sum of the difference results is calculated. Next, the optical flow method is used to process the differential video sequence to accurately detect and recognize the moving target. Finally, the proposed algorithm is compared with background difference method, optical flow method and inter frame difference method, and the detection effect and accuracy of moving objects in different scenes are analyzed. The experimental results show that the overall performance of the proposed algorithm is stable, the overall contour recognition of moving objects is high, the average true positive rate of moving objects detection is above 0.9.
Secure Industrial IoT Task Containerization With Deadline Constraint: A Stackelberg Game Approach

Industrial IoT (IIoT) accomplishes digital manufacturing that incorporates various devices, simulators, and tools with multiple sensors. These sensors provide sufficient data to coordinate and monitor industrial systems. IIoT requires dedicated supporting devices for an application to execute a given task in maximum allowable response time. The requirement of dedicated devices increases system cost. Task containerization is a process of exploiting available resources of the host machines to meet out varying demands of different applications. It avoids the additional cost to buy dedicated IIoT devices while adding new applications. This article proposes an approach to securely process a given IIoT task within an allowable response time. We use the game theory approach to estimate the fractions of the task to be containerized on the machines. Next, the estimated fractions for each machine maximize the system utility. Finally, we illustrate the experimental results to validate the performance of the proposed approach.
Metal surface 3D imaging based on convolutional autoencoder and light plane calibration

Convolutional autoencoders play an important role in computer vision and image processing. In this paper, an optical stripe extraction method based on convolutional autoencoder is proposed to reconstruct reflective metal surfaces. A six-layer convolutional autoencoder network is constructed to extract the center line of the light stripe. In addition, combined with the center line extraction algorithm and line-structured light calibration algorithm, a good reconstruction effect under reflective conditions is obtained. Experimental results show that the relative error of the 3D imaging method based on the convolutional autoencoder is decreased from 6.233% to 2.183% compared with the traditional line-structured laser 3D imaging method.