Reviewerly/exHarmony huggingface dataset:
Situational Anomaly Detection Using Multi-agent Trajectory Prediction for Terminal Airspace Operations

Most existing methods for trajectory anomaly detection in the air traffic domain have mainly assumed each trajectory to be independent. However, an aircraft’s trajectory is dependent on the trajectories of nearby aircraft, especially in a terminal airspace, as air traffic controllers actively alter the aircraft’s trajectory based on traffic density, separation, and scheduling to maintain the safety and efficiency of aircraft operations. To capture the interaction between flights and find anomalies in relation to the situation, we propose a situational anomaly detection framework based on multi-agent trajectory distribution prediction with an agent-aware attention mechanism. The situational anomalies are defined based on the predicted distribution of the trajectory. The proposed framework is demonstrated with real air traffic surveillance data recorded at Incheon International Airport, South Korea to show its effectiveness in identifying operationally significant anomalies.
Robust Wind-Aware Path Optimization Onboard Small Fixed-wing UAVs

Small, fixed-wing Uncrewed Aerial Vehicles (UAVs) flying in high winds often suffer from large path following deviations, especially on curved segments. Autopilots generally rely on the path following controller to attenuate such disturbances in a feedback manner. This work instead approaches this problem preemptively by incorporating the wind estimate at the path planning stage. Trochoids and “shifted” clothoids are utilized to find the time-optimal path between an initial and final pose, outputting the ground relative trajectory the aircraft must fly. As geometric path following controllers require a solution for the closest point to the aircraft on a respective path, and trochoids and clothoids do not have a closed-form solution to this problem, we propose a computationally lightweight path sampling algorithm to approximate the solution and enable direct integration with existing path following guidance laws. The path sampling and path following algorithms are implemented on open-source autopilot firmware and demonstrated in several relevant software-in-the-loop simulations. The results show that incorporating knowledge of the wind and vehicle dynamics in the planning stage leads to a more repeatable and predictable UAV path following behavior.
Explainable Artificial Intelligence Techniques for the Analysis of Reinforcement Learning in Non-Linear Flight Regimes

Reinforcement Learning is being increasingly applied to flight control tasks, with the objective of developing truly autonomous flying vehicles able to traverse highly variable environments and adapt to unknown situations or possible failures.However, the development of these increasingly complex models and algorithms further reduces our understanding of their inner workings.This can affect the safety and reliability of the algorithms, as it is difficult or even impossible to determine which are their failure characteristics and how they will react in situations never tested before.It is possible to remedy this lack of understading through the development of eXplainable Artifial Intelligence and eXplainable Reinforcement Learning methods like SHapley Additive Explanations.This tool is used to analyze the strategy learnt by an Actor-Critic Incremental Dual Heuristic Programming controller architecture when presented with a pitch rate or roll rate tracking task in non-linear flying conditions, such as at high angles of attack and large sideslip angles.This same controller architecture has been previously explored with the same analysis tool but limited to the nominal linear flight regime, and it was observed that the controller learnt linear control laws, even though its Artificial Neural Networks should be able to approximate any function.Interestingly, it was discovered in this research paper that even in the non-linear flight regime it is still more optimal for this controller architecture to learn quasi-linear control laws, although it seems to continuously modify the linear slope as if it was an extreme case of the gain scheduling technique.
The level of digital literacy ability of elementary school students

Digital literacy skills are mandatory for students to master so that they can process information wisely. The purpose of this research is to determine the level of digital literacy of elementary school students. The research method used in this research is quantitative. The sample of this research is 86 elementary students in Laweyan Sub-district, Surakarta, Indonesia. The data collection technique used is a test that validated by an expert. The data analysis used in this research is descriptive statistical analysis. The result of this study shows that the students’ level of ability in digital literacy is good with the fulfillment of five indicators, namely the use of technology reaches 79.75% or students can analyze the functions of hardware and software in technology, the management of information on digital media reaches 70% or the students can search for valid data sources, social networking reaches 82.25% or students can design and participate in digital technology, online safety reaches 83.33% or students can protect their personal data, and students can identify impacts of technology with percentage reaches 72%. The result of this research can be used as a reference to measure the level of students' digital literacy. Keywords: Digital literacy, students, elementary school
Incorporating A Triple Graph Neural Network with Multiple Implicit Feedback for Social Recommendation

Graph neural networks (GNNs) have been clearly proven to be powerful in recommendation tasks since they can capture high-order user-item interactions and integrate them with rich attributes. However, they are still limited by cold-start problem and data sparsity. Using social relationships to assist recommendation is an effective practice, but it can only moderately alleviate these problems. In addition, rich attributes are often unavailable, which prevents GNNs from being fully effective. Hence, we propose to enrich the model by mining multiple implicit feedback and constructing a triple GCN component. We have noticed that users may be influenced not only by their trusted friends but also by the ratings that already exist. The implicit influence spreads among the item’s previous and potential raters, and do make a difference on future ratings. The implicit influence is analysed on the mechanism of information propagation, and fused with user’s binary implicit attitude, since negative influence propagates as well as the positive one. Furthermore, we leverage explicit feedback, social relationships and multiple implicit feedback in the triple GCN component. Abundant experiments on real-world datasets reveal that our model has improved significantly in the rating prediction task compared with other state-of-the-art methods.
Pedogenesis on Jurassic formations in the Araripe Basin, northeastern Brazil: Weathering and parent material

The Araripe Basin is the largest sedimentary basin in the countryside of Northeast Brazil. Despite the wide diversity of lithostratigraphic, geomorphological, and paleontological studies, little is known about the pedogenesis and weathering. This study aimed to characterize the morphological, physical and chemical attributes, and the spectral behavior of soils in a toposequence over Jurassic formations of the Araripe Basin so as to identify the main pedogenetic processes, as well as their degree of weathering. A toposequence composed of four soil profiles was selected, described, and sampled in a semi-arid depression that is geologically composed of lacustrine (mudstones) and fluvial (sandstones) deposits. The variability of soil attributes such as color, texture, base saturation, clay activity, and spectral behavior (vis-NIR and MIR) reflects the predominant influence of the parent material on the relief factor. The values of Ki index (SiO2/Al2O3) and Fed/Fet ratio indicate a very low degree of weathering, conditioned by a dry climate. From the MIR spectra, absorption features associated with calcite, Mg-silicate, illite, and smectite-illite interstratifications were identified. These minerals were interpreted to have originated authigenically, formed from evaporation processes in lacustrine environments. Despite the smaller influence of the relief and the low action of pedogenetic processes, incipient evidence of clay translocation is observed in the upper third of the landscape.
MVHANet: multi-view hierarchical aggregation network for skeleton-based hand gesture recognition

Skeleton-based gesture recognition (SHGR) is a very challenging task due to the complex articulated topology of hands. Previous works often learn hand characteristics from a single observation viewpoint. However, the various context information hidden in multiple viewpoints is disregarded. To resolve this issue, we propose a novel multi-view hierarchical aggregation network for SHGR. Firstly, two-dimensional non-uniform spatial sampling, a novel strategy forming extrinsic parameter distributions of virtual cameras, is presented to enumerate viewpoints to observe hand skeletons. Afterward, we adopt coordinate transformation to generate multi-view hand skeletons and employ a multi-branch convolutional neural networks to further extract the multi-view features. Furthermore, we exploit a novel hierarchical aggregation network including hierarchical attention architecture and global context modeling to fuse the multi-view features for final classification. Experiments on three benchmarked datasets demonstrate that our work can be competitive with the state-of-the-art methods.
Health persuasion through emoji: How emoji interacted with information source to predict health behaviors in COVID-19 situation

By providing health information through visual communication, public health organizations can effectively guide and persuade people to adopt healthy behaviors, which is critical in the context of public health crises. In this study, drawing upon congruity theory and the premise of visual communication, we examined how information source and emoji may shape people's preventive and self-protective behaviors through perceived fear (PF) and perceived controllability (PC). Using a convenience sample of 210 participants, we conducted a 2 (emoji: with versus without) × 2 (information source: official versus unofficial) between-subject experiment. The results indicated that, compared with nonuse, the use of emoji in information resulted in higher PF, stronger preventive behavioral intention (PBI), and lower PC. In addition, a strong interaction effect was observed between emoji and the source of information on PBI. When emoji were added to health information released by an unofficial organization, the text outperformed that from an official agency in persuading people to adopt preventive behaviors. Furthermore, we determined that PF mediated the effect of emoji on PBI, but only for unofficial information sources. These results provide a reference for enhancing the effectiveness of health information including visual cues, such as emoji.
Personalized online ensemble machine learning with applications for dynamic data streams

In this work we introduce the personalized online super learner (POSL), an online personalizable ensemble machine learning algorithm for streaming data. POSL optimizes predictions with respect to baseline covariates, so personalization can vary from completely individualized, that is, optimization with respect to subject ID, to many individuals, that is, optimization with respect to common baseline covariates. As an online algorithm, POSL learns in real time. As a super learner, POSL is grounded in statistical optimality theory and can leverage a diversity of candidate algorithms, including online algorithms with different training and update times, fixed/offline algorithms that are not updated during POSL's fitting procedure, pooled algorithms that learn from many individuals' time series, and individualized algorithms that learn from within a single time series. POSL's ensembling of the candidates can depend on the amount of data collected, the stationarity of the time series, and the mutual characteristics of a group of time series. Depending on the underlying data-generating process and the information available in the data, POSL is able to adapt to learning across samples, through time, or both. For a range of simulations that reflect realistic forecasting scenarios and in a medical application, we examine the performance of POSL relative to other current ensembling and online learning methods. We show that POSL is able to provide reliable predictions for both short and long time series, and it's able to adjust to changing data-generating environments. We further cultivate POSL's practicality by extending it to settings where time series dynamically enter and exit.
Understanding the cyber-victimization of young people: A test of routine activities theory

Research on cybervictimization focuses on a variety of behaviors. The present study focuses on four behaviors: hacking, having obscene photos shared, bullying, and stalking/trespass to test the Lifestyles Routine Activities Theory (LRAT). Much of the research on cybervictimization uses LRAT to help explain how some groups of individuals are susceptible to becoming victims. We surveyed young adults, aged 18–25, using a paid Qualtrics sample and a convenience sample from a southern university. Using binominal logistic regression, we test the likelihood of victimization across the various behaviors and with the three main elements of LRAT, motivated offender, suitable target, and absence a capable guardian. We found that online dating was the most likely way to be exposed to a motivated offender, that visiting explicit websites made one a suitable target, and knowing how to set privacy settings helped guard against victimization.
ONLINE TESTING: KNOWLEDGE CONTROL DURING DISTANCE LEARNING OF THE ENGLISH LANGUAGE AT A NONLINGUISTIC UNIVERSITY IN CONDITIONS OF FULL-SCALE ARMED AGGRESSION

The article deals with some issues of online testing technology in conditions of full-scale armed aggression, which is increasingly being used in pedagogical practice when teaching such an interdisciplinary subject as a foreign language at a non-linguistic university. The advantages and disadvantages of this method of teaching and controlling students’ knowledge are revealed. An analysis is presented and the vulnerabilities of modern online testing systems are investigated.
Big Data Analytics and Mining for Knowledge Discovery

Big data analytics and mining aims to discover implicit, previously unknown, and potentially useful information and knowledge from big data sets that contain huge volumes of valuable veracious data collected or generated at a high velocity from a wide variety of rich data sources. Among different big data analytic and mining tasks, this chapter focuses on frequent pattern mining. By relying on the MapReduce programming model, researchers only need to specify the “map” and “reduce” functions to discover (organizational) knowledge from (i) big data sets of precise data in a breadth-first manner or depth-first manner and/or from (ii) big data sets of uncertain data. Such a big data analytics process can be sped up by focusing the mining according to the user-specified constraints that express the user interests. The resulting (constrained or unconstrained) frequent patterns mined from big data sets provide users with new insights and a sound understanding of users' patterns. Such (organizational) knowledge is useful is many real-life information science and technology applications.
An improved adaptive triangular mesh-based image warping method

It is of vital importance to stitch the two images into a panorama in many computer vision applications of motion detection and tracking and virtual reality, panoramic photography, and virtual tours. To preserve more local details and with few artifacts in panoramas, this article presents an improved mesh-based joint optimization image stitching model. Since the uniform vertices are usually used in mesh-based warps, we consider the matched feature points and uniform points as grid vertices to strengthen constraints on deformed vertices. Simultaneously, we define an improved energy function and add a color similarity term to perform the alignment. In addition to good alignment and minimal local distortion, a regularization parameter strategy of combining our method with an as-projective-as-possible (APAP) warp is introduced. Then, controlling the proportion of each part by calculating the distance between the vertex and the nearest matched feature point to the vertex. This ensures a more natural stitching effect in non-overlapping areas. A comprehensive evaluation shows that the proposed method achieves more accurate image stitching, with significantly reduced ghosting effects in the overlapping regions and more natural results in the other areas. The comparative experiments demonstrate that the proposed method outperforms the state-of-the-art image stitching warps and achieves higher precision panorama stitching and less distortion in the overlapping. The proposed algorithm illustrates great application potential in image stitching, which can achieve higher precision panoramic image stitching.
An enhanced deep deterministic policy gradient algorithm for intelligent control of robotic arms

Aiming at the poor robustness and adaptability of traditional control methods for different situations, the deep deterministic policy gradient (DDPG) algorithm is improved by designing a hybrid function that includes different rewards superimposed on each other. In addition, the experience replay mechanism of DDPG is also improved by combining priority sampling and uniform sampling to accelerate the DDPG's convergence. Finally, it is verified in the simulation environment that the improved DDPG algorithm can achieve accurate control of the robot arm motion. The experimental results show that the improved DDPG algorithm can converge in a shorter time, and the average success rate in the robotic arm end-reaching task is as high as 91.27%. Compared with the original DDPG algorithm, it has more robust environmental adaptability.
Automatic Detection of Benign/Malignant Tumor in Breast Ultrasound Images using Optimal Features

Breast cancer (BC) is one of the most severe diseases in women. Therefore, a premature diagnosis is necessary for timely detection and treatment execution. Clinical-level diagnosis of BC is normally performed with imaging techniques, and Ultrasound-Imaging (UI) is one of the noninvasive imaging techniques frequently executed to diagnose BC.This research aims to develop an efficient deep-learning framework to detect BC from UI with better accuracy.The executed method consists of the following stages: (i) Data collection and preprocessing, (ii) Deep-features mining with pre-trained VGG16, (iii) Image enhancement using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP), (iv) Firefly-algorithm (FA) supported feature reduction, and (v) Feature integration and classification.The proposed work is tested and executed using 1680 test images (840 benign and 840 malignant) of dimension pixels and implements a binary classifier with 5-fold cross-validation to separate the UI database into the healthy/cancer class.This work implemented FA-supported feature reduction. Moreover, it was found that this scheme helps to achieve a classification accuracy of 98.21% with the KNN classifier.
Structural model and features of the world-class Cuiabá orogenic gold deposit, Rio das Velhas greenstone belt, Quadrilátero Ferrífero region, Brazil

The structural architecture of an orogenic deposit is essential for its metallogenetic understanding, as both subjects are interconnected in this type of environment. Cuiabá gold deposit, located in Archean rocks from Rio das Velhas Greenstone Belt, is the subject of numerous studies focused mainly on metallogenetic issues. From the point of view of structural characterization, two divergent models have been studied: the sheath-fold model and the refolding model. Such hypothesis is different in terms of defining an exploratory research plan and, therefore, the detailed structural characterization is still not well understood. As a way of filling this gap, the main objective of this work is to propose a structural characterization of the entire deposit in order to demonstrate evidence for a new model of tectonic evolution. For this, the methodologies applied for this work were: underground geological mapping in all available ore galleries and definition of geological-structural domains whose main criteria are related to down-plunge view of fold asymmetry and stratigraphic indicators. Structural geology is characterized by 3 deformation phases related to 2 tectonic events. The first event has two ductile-progressive deformation phases (D1 and D2), with NE-SW strike-slip direction of transport associated coaxial fold interference patterns with cylindrical geometry plunging to ESE, which represents the main structure at the mine. The second event is a brittle-ductile deformational phase (D3) with W-verging reverse fault systems, probably associated to Brasiliano Pan-African Orogeny. Gold mineralization is associated with silica and sulfide-rich hydrothermal fluid introduction through D1 and D2 axial-plane foliation. Phase D1 is pyrrhotite-associated mineralization, with coarse gold presence. D2 phase has pyrite-associated mineralization with fine gold. There is no gold in phase D3, although it has late-tectonic sulphides formation. Therefore, there is no field evidence that corroborate the existence of sheath folds for the deposit. Fold interference patterns associated to deposit morphology suggests SW and N prospective targets, with strong possibility of structural connection between other gold deposits, such as Lamego and Descoberto targets.
An Authentication Method for AMBTC Compressed Images Using Dual Embedding Strategies

In this paper, we proposed an efficient authentication method with dual embedment strategies for absolute moment block truncation coding (AMBTC) compressed images. Prior authentication works did not take the smoothness of blocks into account and only used single embedding strategies for embedment, thereby limiting image quality. In the proposed method, blocks were classified as either smooth or complex ones, and dual embedding strategies used for embedment. Respectively, bitmaps and quantized values were embedded with authentication codes, while recognizing that embedment in bitmaps of complex blocks resulted in higher distortion than in smooth blocks. Therefore, authentication codes were embedded into bitmaps of smooth blocks and quantized values of complex blocks. In addition, our method exploited to-be-protected contents to generate authentication codes, thereby providing satisfactory detection results. Experimental results showed that some special tampering, undetected by prior works, were detected by the proposed method and the averaged image quality was significantly improved by at least 1.39 dB.