Reviewerly/exHarmony huggingface dataset:
Shortest Path Planning of UAV for Target Tracking and Obstacle Avoidance in 3D Environment

This paper focuses on planning the shortest path of the unmanned aerial vehicle (UAV) for tracking ground target under kinematics constraints, wind effects, and obstacle avoidance requirements in complex three-dimensional environment. First, the path planning mission is modeled in detail by formulating the UAV point-mass model, the target model, the obstacle model, etc. Then, the shortest path guidance vector field (SPGVF) is proposed to optimize UAV trajectory. According to the target motion information, the SPGVF algorithm can guide UAV to the optimal observation height in the vertical plane, as well as converge to the standoff distance along the tangent of the objective circle in the horizontal plane. Third, obstacle avoidance strategy is proposed to plan the collision-free path. By combining the SPGVF and obstacle avoidance strategy, the missions of tracking target and avoiding obstacles can be performed with the feasible shortest path simultaneously. Finally, the simulation results show that the hybrid method can generate the shortest path in various scenarios.
Monitoring and analysis of athletes’ local body movement status based on BP neural network

At present, the body recognition detection of athletes is mostly technical recognition, and the detection of exercise state is less, and the related research is basically blank. Based on this, based on BP neural network algorithm, this study develops athletes’ motion capture based on wearable inertial sensors, and builds a wireless signal transmission scheme based on sensor system. At the same time, this paper constructs the coordinate system to complete the attitude angle settlement and motion recognition and combines the athlete’s actual situation to establish the athlete’s limb trajectory calculation model and analyzes the athletes’ movement patterns. In addition, this paper combines neural network algorithm to analyze, and builds a neural network based athlete body motion recognition model, and analyzes the model effectiveness through simulation system. Studies have shown that when using time domain features+trajectory features as neural network inputs, the hand recognition rate is somewhat improved compared to the use of only time domain features as neural network inputs. It can be seen that the algorithm model of this study has certain validity and can be used as a reference for subsequent related research gradient theory.
Faceted Text Segmentation via Multitask Learning

Text segmentation is a fundamental step in natural language processing (NLP) and information retrieval (IR) tasks. Most existing approaches do not explicitly take into account the facet information of documents for segmentation. Text segmentation and facet annotation are often addressed as separate problems, but they operate in a common input space. This article proposes FTS, which is a novel model for faceted text segmentation via multitask learning (MTL). FTS models faceted text segmentation as an MTL problem with text segmentation and facet annotation. This model employs the bidirectional long short-term memory (Bi-LSTM) network to learn the feature representation of sentences within a document. The feature representation is shared and adjusted with common parameters by MTL, which can help an optimization model to learn a better-shared and robust feature representation from text segmentation to facet annotation. Moreover, the text segmentation is modeled as a sequence tagging task using LSTM with a conditional random fields (CRFs) classification layer. Extensive experiments are conducted on five data sets from five domains: data structure, data mining, computer network, solid mechanics, and crystallography. The results indicate that the FTS model outperforms several highly cited and state-of-the-art approaches related to text segmentation and facet annotation.
Mobile Robot Path Planning Based on Improved Ant Colony Optimization Algorithm

ACO (ant colony algorithm) is a kind of bionic optimization algorithm developed in recent decades, which has shown its excellent performance and great development potential in solving many complex problems. Q-learning, a type of reinforcement learning, has gained increasing popularity in autonomous mobile robot path recently. In order to effectively solve mobile robot path planning problem in obstacle avoidance environment, a path planning model and search algorithm based on improved ant colony optimization algorithm are proposed. The incentive model of reinforcement learning mechanism is introduced with the volatilization of pheromone concentration, establishing dynamic volatile function table.The group intelligent search iterative process of global position selection and local position selection is exploited to combine the volatilization of pheromone concentration with ant colony algorithm, dynamically adjusting the empirical parameter of the reward function by strengthening the data training experiment of Q-learning. To determine the constant parameters for simulation experiment, once the distance between the robot and the obstacle is less than a certain thresholds value, the 0-1 random number is used to randomly adjust the moving direction, avoiding the occurrence of mobile robot path matching deadlock. The study case shows that the proposed algorithm is proved to be better efficient and effective, thereby improving the search intensity and accuracy of the mobile robot path planning problem. And the experimental simulation shows that the proposed model and algorithm effectively solve mobile robot path planning problem that the parameter selection and the actual scene cannot be adapted in real time in traditional path planning problem.
Provenance Inference for Instagram Photos Through Device Fingerprinting

Sensor pattern noise (SPN) has been extensively studied in the scientific community and has found its applications in many practical scenarios in the law-enforcement sector. However, the emergence of photo-sharing social networking sites (SNSs) poses new challenges to SPN-based digital image provenance analysis. One particular issue is that the SNSs' built-in image editing tools tend to inflict distortion on SPNs. One well-known example of such tools is the image filters on Instagram. We observed that some Instagram image filters manipulate the high-frequency bands of the images and hence damage the SPNs, making source-oriented clustering (SOC) of the filtered images unsatisfactory. To address this issue, we propose to first separate the images processed by different filters beforehand into two groups, with Group Malignant (M) containing the filters that significantly distort SPNs and Group Benign (B) covering the other filters that have no significant impact on SPNs. We then cluster the images processed by Group B filters and calculate the centroid of each cluster, with one centroid representing the reference SPN of the corresponding camera. Finally, we use the centroid of each cluster to attract the images processed by the Group M filters in order to complete the SOC task. To identify the filter applied to each image so as to facilitate the clustering, a convolutional neural network based filter-oriented image classifier is proposed. Tested on 19,332 images processed by 18 different filters, the classifier delivers a very promising accuracy of 98.5%. Moreover, compared to the F1-measure of 47.74% by directly clustering on 1,800 filtered images, our proposed clustering framework achieves a much higher F1-measure of 90.33%.
Design of UGV Trajectory Tracking Controller in UGV-UAV Cooperation

In the leader-following formation of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), when the UAV moves in the direction of the UGV motion constraint, traditional PID controller has a series problem, such as poor stability, large overshot. These problems affect the formation maintenance. In order to solve these problems, the following research is carried out. Firstly, the kinematic model and dynamical model of the UGV are analyzed. Secondly, an improved expert PID target tracking control algorithm is proposed which adjusts PID parameters online according to the knowledge base to improve response speed and stability. To build the knowledge base, lots of experiments are carried out. The problem of sudden change of the control amount when switching parameters is solved by the cubic function fitting smoothing. Finally, a UAV-UGV indoor experimental platform is built to verify the effectiveness of the proposed method, and the experimental result shows that the improved expert PID based target tracking control algorithm has superior response speed and stability.
Utility of Neural Embeddings in Semantic Similarity of Text Data

Hendre, Manik Mukherjee, Prasenjit Godse, ManishSemantic similarity plays an important role in understanding the context of text data. In this paper, semantic similarity between large text data is computed using different neural embeddings. we review the utility of different deep neural embeddings for text data representation. Most of the earlier papers have studied the semantic similarity of text by using individual word embeddings. In this paper, we have evaluated the neural embedding techniques on large text data with the help of Essay Dataset. We have used recent neural embedding methods such as Google Sentence Encoder, ELMo, and GloVe along with traditional similarity metrics including TF-IDF and Jaccard Index for experimental investigation. Experimental evaluation in this research paper shows that Google Sentence Encoder and ELMo embeddings perform best on semantic similarity task.
Cryptanalysis of two similar chaos-based image encryption schemes

Chaos based image encryption cryptosystems have garnered much attraction in the recent past. In this regard, recently, two chaos-based image encryption algorithms have been proposed. Both the cryptosystems exhibit very good cryptographic metrics. The first cryptosystem utilizes cryptographic tweaks and other cryptographic primitives to encrypt digital images. This cryptosystem also relies on the idea that a tweak can make the cryptosystem secure. Our paper points out that merely using a tweak doesn't make the cryptosystem secure and additionally, the structure and stages in the algorithm bear inability to preserve confidentiality of sensitive data. The second cryptosystem is an enhancement of another cryptosystem. Our work indicates that the enhancement in the second cryptosystem is also weak and allows an adversary to retrieve the exact key itself. Both the cryptosystems have similar weaknesses and don't follow the guidelines laid down by National Institute of Standards and Technology (NIST). It is observed that both the cryptosystems can be broken, to reveal the exact chaotic sequence used for encryption, using a Chosen Plaintext Attack. From this work, it is realized that the weaknesses in the methods can be partially avoided by strictly following the rules laid down by the cryptographic community and NIST. Apart from this, it is expected that future cryptosystems follow all the suggestions mentioned in our work so that, before the cryptosystem is out for attack by cryptanalysts, it is scrutinized sufficiently.
Design and Implementation of Number Gesture Recognition System Based on Kinect

The paper designs a number gesture recognition system based on Kinect. The system uses the depth image captured by Kinect sensor to extract gesture information for recognition. In the process of recognition, the AdaBoost algorithm in machine learning is adopted firstly, by the help of Visual Gesture Builder to train the number gesture classifier,and export the classifiers to the recognition program to recognize the current gesture. Finally, numbers are outputted according to the recognized gesture, and the basic functions of number gesture recognition system are realized. Through the experimental verification, the recognition system has enough accuracy, and the system is suitable for various factors such as environment, personnel, etc., which has a certain practical value.
Clustering-informed cinematic astrophysical data visualization with application to the Moon-forming terrestrial synestia

Scientific visualization tools are currently not optimized to create cinematic, production-quality representations of numerical data for the purpose of science communication. In our pipeline Estra, we outline a step-by-step process from a raw simulation into a finished render as a way to teach non-experts in the field of visualization how to achieve production-quality outputs on their own. We demonstrate feasibility of using the visual effects software Houdini for cinematic astrophysical data visualization, informed by machine learning clustering algorithms. To demonstrate the capabilities of this pipeline, we used a post-impact, thermally-equilibrated Moon-forming synestia from Lock et al., (2018). Our approach aims to identify “physically interpretable” clusters, where clusters identified in an appropriate phase space (e.g. here we use a temperature–entropy phase–space) correspond to physically meaningful structures within the simulation data. Clustering results can then be used to highlight these structures by informing the color-mapping process in a simplified Houdini software shading network, where dissimilar phase–space clusters are mapped to different color values for easier visual identification. Cluster information can also be used in 3D position space, via Houdini’s Scene View, to aid in physical cluster finding, simulation prototyping, and data exploration. Our clustering-based renders are compared to those created by the Advanced Visualization Lab (AVL) team for the full dome show “Imagine the Moon” as proof of concept. With Estra, scientists have a tool to create their own production-quality, data-driven visualizations.
Deep spatio-frequency saliency detection

Despite the wide success in many vision tasks, it is still challenging for Convolutional Neural Networks (CNNs) to perform saliency detection due to their limited receptive fields and lack of enough discriminative contexts until very late layers. In this paper, beyond spatial convolution, we propose a Spatio-Frequency Network (SFNet) that exploits spatio-frequency clues to effectively enlarge the receptive fields of CNN layers and more importantly, strengthen their spatial discrimination for better saliency detection. In particular, the proposed SFNet contains a carefully designed Frequency Residual Module (FRM) that captures the holistic representation of the whole image within the frequency domain. The FRM leverages discrete and inverse discrete wavelet transformation to alternatively transfer global spatial features into frequency domains, to assist fast and accurate salient object detection. Besides, SFNet also includes an Aggregation of Frequency and Spatial Feature (AFSF) module to jointly integrate the two domain features guided by saliency results in a top-down manner. In this way, the aggregation features per layer contain rich holistic contexts, and the network can eventually explore more complete salient object parts and details by progressively integrating saliency predictions. Extensive experiments on six widely-used saliency detection datasets clearly demonstrate the advantages of our proposed model compared with state-of-the-arts.
Energy Efficient Pupil Tracking Based on Rule Distillation of Cascade Regression Forest

As the demand for human-friendly computing increases, research on pupil tracking to facilitate human–machine interactions (HCIs) is being actively conducted. Several successful pupil tracking approaches have been developed using images and a deep neural network (DNN). However, common DNN-based methods not only require tremendous computing power and energy consumption for learning and prediction; they also have a demerit in that an interpretation is impossible because a black-box model with an unknown prediction process is applied. In this study, we propose a lightweight pupil tracking algorithm for on-device machine learning (ML) using a fast and accurate cascade deep regression forest (RF) instead of a DNN. Pupil estimation is applied in a coarse-to-fine manner in a layer-by-layer RF structure, and each RF is simplified using the proposed rule distillation algorithm for removing unimportant rules constituting the RF. The goal of the proposed algorithm is to produce a more transparent and adoptable model for application to on-device ML systems, while maintaining a precise pupil tracking performance. Our proposed method experimentally achieves an outstanding speed, a reduction in the number of parameters, and a better pupil tracking performance compared to several other state-of-the-art methods using only a CPU.
HPart and Condition Extraction from Aircraft Maintenance Records

Aircraft maintenance records contain vital information about airplane parts and their conditions in free-form text that are crucial health indicators of an aircraft. Extraction of these types of information is essential to improve safety, and lower lifecycle maintenance cost, and to minimize downtime and spare parts inventory. The task, however, is challenging as it is a domain-specific knowledge discovery problem that poses unique challenges in the field of information extraction which have not been studied much. This paper discusses these unique issues and challenges and how we approach them by adapting an advanced deep learning technique that has been widely used for information extraction tasks in other domains. The proposed system has good performance on extracting part names and conditions from noisy texts and is shown to be effective in processing data sets across diverse types of aircraft systems.
The logic of partial supposition

Abstract According to orthodoxy, there are two basic moods of supposition: indicative and subjunctive. The most popular formalizations of the corresponding norms of suppositional judgement are given by Bayesian conditionalization and Lewisian imaging, respectively. It is well known that Bayesian conditionalization can be generalized (via Jeffrey conditionalization) to provide a model for the norms of partial indicative supposition. This raises the question of whether imaging can likewise be generalized to model the norms of ‘partial subjunctive supposition’. The present article casts doubt on whether the most natural generalizations of imaging are able to provide a plausible account of the norms of partial subjunctive supposition .
The time-dependent Schrödinger equation in non-integer dimensions for constrained quantum motion

We propose a theoretical model, based on a generalized Schrödinger equation, to study the behavior of a constrained quantum system in non-integer, lower than two-dimensional space. The non-integer dimensional space is formed as a product space X×Y, comprising x-coordinate with a Hausdorff measure of dimension α1=D−1 (1<D<2) and y-coordinate with the Lebesgue measure of dimension of length (α2=1). Geometric constraints are set at y=0. Two different approaches to find the Green's function are employed, both giving the same form in terms of the Fox H-function. For D=2, the solution for two-dimensional quantum motion on a comb is recovered.
Performance analysis of distance teaching classroom based on machine learning and virtual reality

In remote intelligent teaching, the facial expression features can be recorded in time through facial recognition, which is convenient for teachers to judge the learning status of students in time and helps teachers to change teaching strategies in a timely manner. Based on this, this study applies machine learning and virtual reality technology to distance classroom teaching. Moreover, this study uses different channels to automatically learn global and local features related to facial expression recognition tasks. In addition, this study integrates the soft attention mechanism into the proposed model so that the model automatically learns the feature maps that are more important for facial expression recognition and the salient regions within the feature maps. At the same time, this study performs weighted fusion on the features extracted from different branches, and uses the fused features to re-recognize student features. Finally, this study analyzes the results of this paper through control experiments. The research results show that the algorithm proposed in this paper has good performance and can be applied to the distance teaching system.
Recursive Modified Pattern Search on High-Dimensional Simplex : A Blackbox Optimization Technique

In this paper, a novel derivative-free pattern search based algorithm for Black-box optimization is proposed over a simplex constrained parameter space. At each iteration, starting from the current solution, new possible set of solutions are found by adding a set of derived step-size vectors to the initial starting point. While deriving these step-size vectors, precautions and adjustments are considered so that the set of new possible solution points still remain within the simplex constrained space. Thus, no extra time is spent in evaluating the (possibly expensive) objective function at infeasible points (points outside the unit-simplex space); which being the primary motivation of designing a customized optimization algorithm specifically when the parameters belong to a unit-simplex. While minimizing any objective function of m parameters, within each iteration, the objective function is evaluated at 2m new possible solution points. So, upto 2m parallel threads can be incorporated which makes the computation even faster while optimizing expensive objective functions over high-dimensional parameter space. Once a local minimum is discovered, in order to find a better solution, a novel ‘re-start’ strategy is considered to increase the likelihood of finding a better solution. Unlike existing pattern search based methods, a sparsity control parameter is introduced which can be used to induce sparsity in the solution in case the solution is expected to be sparse in prior. A comparative study of the performances of the proposed algorithm and other existing algorithms are shown for a few low, moderate and high-dimensional optimization problems. Upto 338 folds improvement in computation time is achieved using the proposed algorithm over Genetic algorithm along with better solution. The proposed algorithm is used to estimate the simultaneous quantiles of North Atlantic Hurricane velocities during 1981–2006 by maximizing a non-closed form likelihood function with (possibly) multiple maximums.
Fusion of Angle Measurements from Hull Mounted and Towed Array Sensors

Two sensor arrays, hull-mounted array, and towed array sensors are considered for bearings-only tracking. An algorithm is designed to combine the information obtained as bearing (angle) measurements from both sensor arrays to give a better solution. Using data from two different sensor arrays reduces the problem of observability and the observer need not follow the S-maneuver to attain observability of the process. The performance of the fusion algorithm is comparable to that of theoretical Cramer–Rao lower bound and with that of the algorithm when bearing measurements from a single sensor array are considered. Different filters are used for analyzing both algorithms. Monte Carlo runs need to be done to evaluate the performance of algorithms more accurately. Also, the performance of the fusion algorithm is evaluated in terms of solution convergence time.