Reviewerly/exHarmony huggingface dataset:
A Robust Asymmetric Mixture Model for Image Segmentation

Image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. The goal of segmentation is to simplify and change the representation of an image into something that is more meaningful and easier to analyze. Gaussian mixture model (GMM) and Student's-t mixture model (SMM) are well known and simple tools for image segmentation, but these methods suffer from several drawbacks. GMM and SMM have very poor results in the case of asymmetric distribution. In this paper, we present a new asymmetric mixture model for image segmentation. The proposed model uses Student's t-distribution that has heavier tails, multivariate asymmetric distribution and it is more robust than Gaussian. Simulation results demonstrate that our proposed method has a better performance than clustering methods which based on finite mixture models with probability density functions.
Development of a Real-Time Object Tracking System Using Computer Vision and Pan-Tilt Control

This paper presents an investigation of the development of a real-time object tracking system that utilizes computer vision techniques and incorporates pan-tilt control mechanisms. The system architecture includes hardware components such as a camera and a pan-tilt mechanism, and software components such as object detection and tracking algorithms. To achieve efficient and accurate tracking, the Kalman filter, which uses a state-space model, was included in the object tracking process. The proposed system is implemented and evaluated through experiments to demonstrate its feasibility and effectiveness in real-world scenarios. The results indicate that the proposed system is capable of achieving real-time and accurate object tracking, demonstrating its potential for practical applications in various fields.
SALτ: efficiently stopping TAR by improving priors estimates

Abstract In high recall retrieval tasks, human experts review a large pool of documents with the goal of satisfying an information need. Documents are prioritized for review through an active learning policy, and the process is usually referred to as Technology-Assisted Review (TAR). TAR tasks also aim to stop the review process once the target recall is achieved to minimize the annotation cost. In this paper, we introduce a new stopping rule called SAL $$_\tau ^R$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mrow /> <mml:mi>τ</mml:mi> <mml:mi>R</mml:mi> </mml:msubsup> </mml:math> (SLD for Active Learning), a modified version of the Saerens–Latinne–Decaestecker algorithm (SLD) that has been adapted for use in active learning. Experiments show that our algorithm stops the review well ahead of the current state-of-the-art methods, while providing the same guarantees of achieving the target recall.
Constraints on metallogenic temperature and mineralization style from reflection color of sphalerite

The relationship between the color of sphalerite and trace elements has aroused the interest of researchers since a long time, but most of the study is still in the qualitative stage. In this contribution, we made attempt to reveal the relationship between the reflection color and its trace element, in order to discuss the genetic significance of various reflection colors of sphalerite through the quantitative study of reflection color of sphalerite. Based on LA-ICP-MS analysis and microspectrophotometer analysis of sphalerite samples in different types of deposits, the elements with significant correlation with the reflection color index were found and analyzed by factor analysis. Different genetic types of sphalerites have different color index, The average reflection color indices of each type sample are as follows, sphalerite from Mengya’a deposit: λd=477.3nm, Pe=0.046, Rvis=17.23%; sphalerite from Zhaxikang deposit: λd=477.2nm, Pe=0.043, Rvis=16.70%; sphalerite from Nanmushu deposit: λd=476.4nm, Pe=0.038, Rvis=16.55%. The reflection color index of sphalerite is correlated with the content of Mn, Fe, Ga, Ge, Ag, Cd, In, Tl, Pb. The results of factor analysis indicate that sphalerite with high reflectivity is more abundant in Fe, Mn and In, while the sphalerite with low reflectivity is more abundant in Ge, Tl, Pb and Ag. Furthermore, the reflection color index of sphalerite can be used to distinguish the genetic type of deposits. Based on an obvious linear relationship between the reflection color index of sphalerite and its mineralization temperature, we propose a new empirical equation: T(°C)= 61.4*Rvis+1.1*λd+6000*Pe-1533 which can be used as a geological thermometer.
Normalizing flows for conditional independence testing

Abstract Detecting conditional independencies plays a key role in several statistical and machine learning tasks, especially in causal discovery algorithms, yet it remains a highly challenging problem due to dimensionality and complex relationships presented in data. In this study, we introduce LCIT (Latent representation-based Conditional Independence Test)—a novel method for conditional independence testing based on representation learning. Our main contribution involves a hypothesis testing framework in which to test for the independence between X and Y given Z , we first learn to infer the latent representations of target variables X and Y that contain no information about the conditioning variable Z . The latent variables are then investigated for any significant remaining dependencies, which can be performed using a conventional correlation test. Moreover, LCIT can also handle discrete and mixed-type data in general by converting discrete variables into the continuous domain via variational dequantization. The empirical evaluations show that LCIT outperforms several state-of-the-art baselines consistently under different evaluation metrics, and is able to adapt really well to both nonlinear, high-dimensional, and mixed data settings on a diverse collection of synthetic and real data sets.
Optimizing Reservoir Computing Based on an Alternating Input-Driven Spin-Torque Oscillator

Neurons in the brain show nonlinear oscillatorlike behaviors, inspiring the development of neuromorphic computing with spin-torque oscillators for their low power consumption and high integration density. The time-multiplexing technique enables utilizing only one device to achieve neuromorphic computing, further reducing power consumption and simplifying device fabrication. However, optimizing such systems remains a challenge and needs further development. A controversial hypothesis claims that dynamic systems possess the optimal computational capacity to address the information at the dynamical phase transition. Here, we apply this hypothesis to improve a reservoir computing system based on an alternating input-driven spin-torque oscillator. The input-driven property of reservoir computing allows us to exploit the dynamics of a spin-torque oscillator by manipulating the input stream. By tuning the ac amplitude in inputs, the system can exhibit diversified dynamic regimes indicated by the largest Lyapunov exponent and echo-state property spectra. Our findings demonstrate that the proper configuration of ac inputs for reservoir computing based on a single spin-torque oscillator can adjust its information-processing capacity profile and enhance its computational performance for specific tasks.
Attitudes Towards the Development of Good Practices with Augmented Reality in Secondary Education Teachers in Spain

Abstract The attitude of the teaching staff is positioned as a fundamental aspect for the development of good training practices. These good practices are essential when applied within an innovative techno-pedagogical methodology: augmented reality in education. The objectives of this study are to analyze the development of good teaching practices with augmented reality and to discover the factors that influence their quality. A descriptive and correlational design has been carried out. A total of 1490 Spanish Secondary Education teachers have participated. The instrument used was the adaptation to the Spanish context of the questionnaire of the Attitude Scale of Augmented Reality Applications. The results reveal that teachers show a positive attitude towards the use of augmented reality. As for the aspects that influence the good attitude of teachers are age, the number of devices teachers use, the time they dedicate to technological resources and teaching experience. However, ICT training is what determines a direct influence on the attitude of teachers, as well as satisfaction with reliability.
Superposition-state generation of nonorthogonal wavelike and particlelike states without ancillary qubits

The aim of this article is to generate the superposition state of nonorthogonal wavelike and particlelike states without ancillary qubits. Although it has been demonstrated that wavelike and particlelike states can be nonorthogonal, it remains unclear whether a superposition state of the two states can be achieved without the use of orthogonal ancillary qubits. To address this question, we employ a designed double-layer Mach-Zehnder interferometer that could continuously morph the single-photon state from particlelike to wavelike. Through both a theoretical analysis and experimental demonstration, we show that the nonorthogonality of wavelike and particlelike states can be maintained without ancillary qubits. The results showed that the two states are naturally nonorthogonal and can be directly utilized. As a variant of the delayed-choice experiment, our research will contribute to a deeper understanding of the nature of quantum objects, and also provide a different framework for discussions on wave-particle duality.
Semantic Coherence Analysis of English Texts Based on Sentence Semantic Graphs

With the reform of China's education industry, more and more universities are using computers to conduct examinations. For the automatic correction of essays as subjective questions, existing automatic English text scoring systems suffer from insufficient extraction of coherence information and low accuracy when analysing text coherence. Therefore, this paper proposes an unsupervised semantic coherence analysis model for English texts based on sentence semantic graphs, taking Chinese students' English compositions as the research context. Guided by the semantic coherence theory, the English text is represented as a sentence semantic graph, and an improved VF2 subgraph matching algorithm is used to mine the frequently occurring subgraph patterns in the sentence semantic graph. After that, the set of frequent subgraphs is generated by filtering the subgraph patterns according to their frequencies, and the subgraph frequency of each frequent subgraph is calculated separately. Finally, the distribution characteristics of frequent subgraphs and the semantic values of subgraphs in the sentence semantic graphs are extracted to quantify the overall coherence quality of English texts. The experimental results show that the model proposed in this paper has higher accuracy and practical value compared with the current methods of coherence analysis.
Air Channel Planning Based on Improved Deep Q-Learning and Artificial Potential Fields

With the rapid advancement of unmanned aerial vehicle (UAV) technology, the widespread utilization of UAVs poses significant challenges to urban low-altitude safety and airspace management. In the coming future, the quantity of drones is expected to experience a substantial surge. Effectively regulating the flight behavior of UAVs has become an urgent and imperative issue that needs to be addressed. Hence, this paper proposes a standardized approach to UAV flight through the design of an air channel network. The air channel network comprises numerous single air channels, and this study focuses on investigating the characteristics of a single air channel. To achieve optimal outcomes, the concept of the artificial potential field algorithm is integrated into the deep Q-learning algorithm during the establishment of a single air channel. By improving the action space and reward mechanism, the resulting single air channel enables efficient avoidance of various buildings and obstacles. Finally, the algorithm is assessed through comprehensive simulation experiments, demonstrating its effective fulfillment of the aforementioned requirements.
Coverage Path Planning Method for Agricultural Spraying UAV in Arbitrary Polygon Area

In the coverage path planning (CPP) problem of an agricultural spraying UAV, a margin reduction algorithm was designed first to address special situations such as ditches and channels within the operational terrain. Regarding the particularity of a concave polygon area, an algorithm based on topology mapping was developed to judge the concave points of the concave polygon area, and the path with special concave points was scheduled. For the evaluation of the pesticide spraying mission, the flight distance and extra coverage ratio were the most appropriate optimization objectives. Therefore, this paper selected these two indicators to form a fitness function, then found the optimal operating heading angle of the mission after iterative optimization. Finally, the CPP for an agricultural spraying UAV in an arbitrary polygon area under the optimal heading angle was realized. The simulation and flight test results showed that the CPP method could significantly shorten the flight distance and reduce additional coverage, then avoid energy consumption and pesticide waste. In addition, the engineering practicability of the method was verified in this paper. This method can be popularized and widely used for an agricultural spraying UAV, which has great engineering application value.
Soft Semi-Supervised Deep Learning-Based Clustering

Semi-supervised clustering typically relies on both labeled and unlabeled data to guide the learning process towards the optimal data partition and to prevent falling into local minima. However, researchers’ efforts made to improve existing semi-supervised clustering approaches are relatively scarce compared to the contributions made to enhance the state-of-the-art fully unsupervised clustering approaches. In this paper, we propose a novel semi-supervised deep clustering approach, named Soft Constrained Deep Clustering (SC-DEC), that aims to address the limitations exhibited by existing semi-supervised clustering approaches. Specifically, the proposed approach leverages a deep neural network architecture and generates fuzzy membership degrees that better reflect the true partition of the data. In particular, the proposed approach uses side-information and formulates it as a set of soft pairwise constraints to supervise the machine learning process. This supervision information is expressed using rather relaxed constraints named “should-link” constraints. Such constraints determine whether the pairs of data instances should be assigned to the same or different cluster(s). In fact, the clustering task was formulated as an optimization problem via the minimization of a novel objective function. Moreover, the proposed approach’s performance was assessed via extensive experiments using benchmark datasets. Furthermore, the proposed approach was compared to relevant state-of-the-art clustering algorithms, and the obtained results demonstrate the impact of using minimal previous knowledge about the data in improving the overall clustering performance.
Aerial Torsional Work Utilizing a Multirotor UAV with Add-on Thrust Vectoring Device

Aerial manipulation aims to combine the versatility and the agility of aerial platforms with the manipulation capabilities of robotic arms. Their fast deployment allows for their implementation in maintenance tasks and support during disaster situations. However, the under-actuated nature of multirotor UAVs limits the magnitude and direction of the forces an aerial vehicle can safely exert during manipulation tasks. In this paper, the problems associated with UAVs and torsional tasks constraints regarding valve turning are addressed. An add-on thrust vectoring device which enhances manipulation options available to a conventional multirotor UAV is developed and described. The proposed system allows for a partial decoupling of the attitude and velocity vector of a multirotor. This permits stable translational flight and higher torque capabilities for torsional tasks. The separation of attitude and the velocity vector that allows for the design of a passive mechanism for valve operation is presented in this paper as well. The experimental results illustrate the forces and torques that can be generated in the evaluated operation modes.
Local Dependency-Enhanced Graph Convolutional Network for Aspect-Based Sentiment Analysis

The task of aspect-based sentiment analysis (ABSA) is to detect the sentiment polarity toward given aspects. Contemporary methods predominantly utilize graph neural networks and incorporate attention mechanisms to dynamically connect aspect terms with their surrounding contexts, resulting in more informative feature representations. However, these methods only consider whether there are dependencies between words when introducing dependencies, ignoring that dependencies between different sentiment words have different effects. Neglecting this could introduce noise and negatively impact the model’s performance. To overcome this limitation, we introduce a novel approach called the local dependency-enhanced graph convolutional network (LDEGCN). Our method combines semantic information and dependency relationships to better capture the affective relationships between words. Specifically, we integrate sentiment knowledge from SenticNet to enrich the sentence’s dependency graph and thoroughly explore the dependency types between contexts and aspects to focus on particular dependency types. The local context weight (LCW) method is employed on the dependency-enhanced graph to emphasize the importance of local contexts, thereby mitigating the issue of long-distance dependencies. Through extensive evaluations of five public datasets, the LDEGCN model demonstrates significant improvements over mainstream models.
A Numerical Method-Based Analysis of the Structural Deformation Behaviour of a Turkish String Instrument (Cura Baglama) under Varying String Tensions

This study focuses on the structural design analysis of a cura baglama, a traditional Turkish string instrument that does not have in place a regulated set of manufacturing standards to follow. The aim therefore is to introduce a structural deformation analysis for a sample cura baglama in three different string tensions via a numerical method-based engineering analysis technique. The three-dimensional solid model of a sample cura baglama was created using a 3D scanner and parametric 3D solid modelling software. Based on experimental frequency analysis, structural deformation analyses of the instrument were conducted using finite element method-based engineering simulation techniques. The simulation results revealed useful visual and numerical outputs related to the deformation behaviour of the instrument under pre-defined boundary conditions. A maximum deformation of 0.223 mm on the soundboard (at the D3 tune) and a maximum equivalent stress of 18.325 MPa on the bridge (at the D3 tune) were calculated. The outputs of this research contribute to further research into the usage of numerical method-based deformation simulation studies related to the standardisation, development, and preservation of such traditional string instruments.
Effectiveness of Powtoon Learning Media in Increasing Students' English Grammar Knowledge

The purpose of this study was to ascertain how Pow Toon learning materials work to improve students' English grammar knowledge. This study was carried out in Medan, Indonesia. Twenty eighth-grade kids served as the study's participants. Using Pow Toon learning resources, students' results on the English Grammar (basic present tense) test increased with each test. The pre-test had an average score of 57.66, the first cycle's test had an average of 74.33, and the second cycle's exam had an average of 90.66. The percentage of students passing their English Grammar tests (simple present tense) shows an increase; in the pre-test, 5% of students met the Maximum Completeness Criteria (KKM). 45% of students passed the first cycle test, whereas 100% of students passed the second cycle test. This indicates that using Pow Toon educational materials can help students learn more about English Grammar (simple present tense). Pow Toon learning materials engage students in the learning process, make the subject matter simple to write about and comprehend, and inspire them to use English Grammar (simple present tense) in discussions to explain daily activities.
Initial Structure Design and Optimization of an Automotive Remote Head-Up Display

Aiming at the problems of difficult construction and occlusion of the initial structure of an automotive augmented reality head-up display (AR-HUD), a method to quickly build the initial structure of an automotive AR-HUD is proposed. Firstly, the position of the mirrors in the initial structure is calculated based on the Rodrigues rotation formula. Secondly, the position of the mirrors is restricted by constraints during optimization to prevent the problem of structural occlusion. Finally, a virtual image display system with a visual distance of 7.5 m and a field of view angle of 10° × 5° is designed. The image quality analysis of the optimized system shows that the light spots in each field of view are all within Airy spots. At the spatial cutoff frequency of the virtual image plane of the optical system, the modulation transfer function (MTF) value of the full field of view is basically greater than 0.5, and the distortion is less than 1%. Finally, using the image for simulation, the results of the simulation image are satisfying, which proves the validity and feasibility of the structural design. It provides a useful reference for the structural design of the remote head-up display system.
Improved Rapid-Expanding-Random-Tree-Based Trajectory Planning on Drill ARM of Anchor Drilling Robots

Permanent highway support in deep coal mines now depends on the anchor drilling robot’s drill arm. The drilling arm’s trajectory planning using the conventional RRT (rapid-expanding random tree) algorithm is inefficient and has crooked, rough paths. To improve the accuracy of path planning, we propose an improved RRT algorithm. Firstly, the kinematic model of the drill arm of the drill and anchor robot was established, and the improved DH solution parameters and the positive solution of the drill arm kinematics were solved. The end effector’s attainable working space was calculated using the Monte Carlo approach. Additionally, to address the problem of the slow running speed of the RRT algorithm, an artificial potential field factor was introduced to construct virtual force fields at obstacle and target points and calculate the potential field map for the entire reachable workspace to improve the speed of the sampling points close to the target point. At the same time, the greedy approach and the three-time B-sample curve-fitting method were used simultaneously to remove unnecessary points and carry out smooth path processing in order to improve the quality of the drill arm trajectory. This was carried out in order to solve the issue of rough pathways generated by the RRT algorithm. Finally, 50 time-sampling comparison experiments were conducted on 2D and 3D maps. The experimental results showed that the improved RRT algorithm improved the average sampling speed by 20% and reduced the average path length by 14% compared with the RRT algorithm, which verified the feasibility and effectiveness of this improved RRT algorithm. The improved RRT algorithm generates more efficient and smoother paths, which can improve the intelligence of the support process by integrating and automating drilling and anchoring and providing reliable support for coal mine intelligence.