Reviewerly/exHarmony huggingface dataset:
Global temporal typing patterns in foreign language writing: exploring language proficiency through recurrence quantification analysis (RQA)

Abstract Recurrence quantification analysis (RQA) is a time-series analysis method that uses autocorrelation properties of typing data to detect regularities within the writing process. The following paper first gives a detailed introduction to RQA and its application to time series data. We then apply RQA to keystroke logging data of first and foreign language writing to illustrate how outcome measures of RQA can be understood as skill-driven constraints on keyboard typing performance. Forty native German students performed two prompted writing assignments, one in German and one in English, a standardized copy task, and a standardized English placement test. We assumed more fluent and skilled writing to reveal more structured typing time series patterns. Accordingly, we expected writing in a well-mastered first language to coincide with higher values in relevant RQA measures as compared to writing in a foreign language. Results of mixed model ANOVAs confirmed our hypothesis. We further observed that RQA measures tend to be higher, thus indicating more structured data, whenever parameters of pause, burst, and revision analyses indicate more fluent writing. Multiple regression analyses revealed that, in addition to typing skills, language proficiency significantly predicts outcomes of RQA. Thus, the present data emphasize RQA being a valuable resource for studying time series data that yields meaningful information about the effort a writer must exert during text production.
Efficient Supervised Image Clustering Based on Density Division and Graph Neural Networks

In recent research, supervised image clustering based on Graph Neural Networks (GNN) connectivity prediction has demonstrated considerable improvements over traditional clustering algorithms. However, existing supervised image clustering algorithms are usually time-consuming and limit their applications. In order to infer the connectivity between image instances, they usually created a subgraph for each image instance. Due to the creation and process of a large number of subgraphs as the input of GNN, the computation overheads are enormous. To address the high computation overhead problem in the GNN connectivity prediction, we present a time-efficient and effective GNN-based supervised clustering framework based on density division namely DDC-GNN. DDC-GNN divides all image instances into high-density parts and low-density parts, and only performs GNN subgraph connectivity prediction on the low-density parts, resulting in a significant reduction in redundant calculations. We test two typical models in the GNN connectivity prediction module in the DDC-GNN framework, which are the graph convolutional networks (GCN)-based model and the graph auto-encoder (GAE)-based model. Meanwhile, adaptive subgraphs are generated to ensure sufficient contextual information extraction for low-density parts instead of the fixed-size subgraphs. According to the experiments on different datasets, DDC-GNN achieves higher accuracy and is almost five times quicker than those without the density division strategy.
A highly efficient garbage pick-up embedded system based on improved SSD neural network using robotic arms

With the social evolution, economic development, and continuously improved living standards, the dramatically increasing garbage produced by human beings has seriously affected our living environment. There are 3 main ways to dispose of garbage: sanitary landfill, incineration, or recycling. At present, a huge amount of labor resources is required for pre-sorting before garbage disposal, which greatly reduces efficiency, increases costs, and even leads to direct incineration without sorting. Hence, this study proposes a solution scenario of how to use object detection technology for garbage sorting. With the development of the deep learning theory, object detection technology has been widely used in all fields, thus, how to find target objects accurately and rapidly is one of the key technologies. This paper proposes a highly efficient garbage pick-up embedded system, where detection is optimized based on the Single Shot MultiBox Detector (SSD) neural network architecture and reduced model parameters. The experimental verification scenario was conducted in a dynamic environment integrating a robotic arm with a conveyor belt simulated by an electronic rotating turntable. The experimental results show that the modified model can accurately identify garbage types, with a significant speed of 27.8 FPS (Frames Per Second) on NVidia Jetson TX2, and an accuracy rate of approximately 87%.
Cross-form efficient attention pyramidal network for semantic image segmentation

Although convolutional neural networks (CNNs) are leading the way in semantic segmentation, standard methods still have some flaws. First, there is feature redundancy and less discriminating feature representations. Second, the number of effective multi-scale features is limited. In this paper, we aim to solve these constraints with the proposed network that utilizes two effective pre-trained models as an encoder. We develop a cross-form attention pyramid that acquires semantically rich multi-scale information from local and global priors. A spatial-wise attention module is introduced to further enhance the segmentation findings. It highlights more discriminating regions of low-level features to focus on significant location information. We demonstrate the efficacy of the proposed network on three datasets, including IDD Lite, PASCAL VOC 2012, and CamVid. Our model achieves a mIoU score of 70.7% on the IDD Lite, 83.98% on the PASCAL VOC 2012, and 73.8% on the CamVid dataset.
Measuring internal inequality in capsule networks for supervised anomaly detection

In this paper we explore the use of income inequality metrics such as Gini or Palma coefficients as a tool to identify anomalies via capsule networks. We demonstrate how the interplay between primary and class capsules gives rise to differences in behavior regarding anomalous and normal input which can be exploited to detect anomalies. Our setup for anomaly detection requires supervision in a form of known outliers. We derive several criteria for capsule networks and apply them to a number of Computer Vision benchmark datasets (MNIST, Fashion-MNIST, Kuzushiji-MNIST and CIFAR10), as well as to the dataset of skin lesion images (HAM10000) and the dataset of CRISPR-Cas9 off-target pairs. The proposed methods outperform the competitors in the majority of considered cases.
An online astronomy activity embedded with REACT strategy for preservice primary school teachers

Abstract The purpose of this study was to provide an online astronomy activity using the Stellarium program as a virtual planetarium. The implementation was conducted with 30 preservice primary school teachers (PPTs) who participated in a non-formal workshop that included interactive, technology-based astronomy practices embedded with the R elate, E ngage, A pplying, C ooperating, T ransferring strategy. The activity featured in the implementation covers the zodiac constellations and common related misconceptions, the duration of the Sun in the constellations, and the concepts of astronomy, astrology, and star clusters. At the end of the implementation, an online-survey was conducted to elicit feedback from the PPTs. According to the results of this study, PPTs enjoyed using Stellarium as a virtual planetarium. The PPTs were aware of certain astronomical concepts and misconceptions, stated that the activity content related to daily life, and that they gained skills related to astronomy teaching. The PPTs reported being satisfied with working in groups, and actively communicating actively with their peers. They suggested adding mythological stories while teaching the constellations. In addition, they proposed increased preliminary preparation and practice prior to the activity so as to gain basic pre-skills in using a virtual environment.
Jacobian Estimation with Adaptive Kalman Filter for Uncalibrated Visual Servoing

An uncalibrated visual servo method based on Jacobian estimation with adaptive Kalman filter (AKF) is proposed in this paper. With less or no priori knowledge of the parameters of robotic manipulator and camera, the presented method introduces the projective Jacobian matrix estimated by an adaptive Kalman filter for noise covariance recursive estimation. By doing this, the Jacobian estimation adaptability can be greatly improved to achieve better tracking performance in UVS system. Finally, the simulation experiments are performed to evaluate the performance of the proposed method, indicating better performance compared with state-of-the-art UVS methods using standard Kalman filter (SKF).
Visual Alignment Method by Merging 2D/3D Data in Robot Automatic Drilling

Aiming at the inconsistency between the coordinate system of target object and the theoretical coordinate system in robot automatic drilling, a visual alignment method by merging 2D/3D data is proposed. The 2D image data and 3D point cloud were acquired by the 3D Imaging System with Structured Light. An improved adaptive threshold and morphological processing algorithm were used to extract the edge features of datum holes. Edge discrimination and Euclidean clustering algorithm was used to calculate the projection points of the holes. Based on binocular stereo vision, the matching relationship between points in left and right images were established through the projection points, and the 3D coordinates of the center of the datum hole were obtained. The coordinate system transformation of the workpiece under the vision system was completed by the theoretical coordinates and the actual coordinates. Experimental results showed that the positioning accuracy of the datum hole of the proposed method was 0.05 mm, which was better than 10% of the traditional method that includes Canny edge detection and epipolar constraint.
UAV Path Planning Based on DDQN for Mountain Rescue

The high calculation cost and low success rate of strategy generation caused by unknown environment and emergencies, highly limited the usage of traditional UAV path planning methods. A path planning method based on DDQN algorithm is proposed to solve the mountain rescue problem in a complex environment. First, the state space and action space of the UAV are determined, then the reward function is designed based on the positional relationship between target, obstacle and UAV, and finally the DDQN reinforcement learning model based on the greedy strategy is established. Simulation experiments in the two scenarios confirm that the DDQN algorithm can not only achieve path planning for rescuing static goals, but also realize fast-tracking of moving targets through quadratic programming. At the same time, compared with the results of the DQN algorithm, it shows that the method has faster network convergence and better path planning ability.
Context Dual-Branch Attention Network for Depth Completion of Transparent Object

With the rise in popularity of RGB-D cameras, vision-based robot approaches that rely on the depth information given by RGB-D cameras are gaining favor. However, because of their reflection and refraction features, transparent objects, which are a prevalent part of our daily lives, are difficult to distinguish and locate with an RGB-D camera. To overcome this issue, we present DCTNet, a novel technique for depth completion of transparent objects, in this study. DCTNet is a dual-branch approach that uses a single RGB-D picture to complete the depth of a transparent end-to-end. We apply MSSA, a multi-scale spatial attention technique, to fuse distinct branch feature maps to improve the depth completion results even more. Experiments show that when compared to ClearGrasp, our approach produces much better performance and improves inference speed.
FH-YOLOv4 with Constrained Aspect Ratio Loss for Video Face Detection and Public Safety

Video face detection is a crucial first step in many facial recognition and face analysis systems. It should serve postprocessing steps as much as possible while satisfying high-accuracy real-time detection. In this paper, we first introduce the constrained aspect ratio loss (CARLoss) for better facial boxes regression and incorporate it into the modified FH-YOLOv4, then the IoU Tracker-based video face image deduplication algorithm is proposed on the detection level. Extensive experiments and comparative tests show the effectiveness of our method.
Reappraisal of 75 Years of Exploration for Atomic Minerals in India and the Way Forward

In India, Atomic Minerals Directorate for Exploration and Research (AMD), the oldest unit under the Department of Atomic Energy (DAE) is mandated with the exploration and augmentation of atomic mineral(s) resources of the country to support the Nuclear Power Programme (NPP) of India. AMD, till date have established four (04) generations of uranium metallogeny in India, distributed in 46 deposits and several other occurrences, major share of which are contributed by the globally unique, stratabound carbonate hosted, syn-diagenetic Tummalapalle Group of deposit (∼59%), metamorphite type deposits along the Singhbhum Shear Zone (∼21%), unconformity related deposits in northern part of Cuddapah Basin (∼5%) and sandstone type deposits in Cretaceous Mahadek Basin (∼6%). Besides these, metasomatite type deposits along the North Delhi Fold Belt (4%) and granite related-structurally controlled, high grade deposits in Bhima Basin (2%) along with some small hydrothermal/metamorphite/migmatite type deposits in Aravalli, Kotri-Dongargarh, Higher Himalaya belts and Chhotanagpur Granite Gneiss Complex contribute to the national inventory which stands at 3,76,000 tonne uranium oxide. AMD have stockpiled substantial quantities of Nb-Ta, Be and Li minerals needed for the NPP of India from prospective pegmatite belts in the country. AMD have established immense potential for REE mineralization in carbonatite complexes of Ambadungar in Gujarat and per-alkaline granite-rhyolite of Siwana Ring Complex, Barmer district, Rajasthan. Exploration along the coastal and riverine placers of the country has established 130 heavy mineral placer deposits with substantial thorium and REE resources. The progressive advancements by AMD through adaptation of integrated multidisciplinary exploration techniques coupled with major thrust in airborne/ground geophysics and expansion of state-of-the-art analytical facilities have facilitated systematic planning for future augmentation of atomic mineral(s) resources. Exploration inputs are envisaged to be intensified in the brownfield target areas for resource augmentation while R&D and phase-wise exploration inputs will be focused for developing the identified greenfield areas following a planned roadmap to ensure self-sufficiency in atomic mineral resources for sustained growth of the NPP to reach the target of net zero carbon emissions by 2070.
Digital Economy and Digital Transformation

The chapter's main objective is to analyze the digital economy and its characteristics. In a time of overlapping disruptions, the chapter describes the fast growth of the digital environment, the emergence of digital platforms, the diffusion of innovative digital technologies, and the related development of Industry 4.0, acknowledging the need to embrace ideas and concepts from multiple fields and integrate different research areas. The other related theme is digital transformation, which implies a structural change of the economies and organizations requiring a technological and cultural shift. In addition, the chapter's analysis highlights that digital transformation and its profound changes can generate imbalances if not addressed with awareness and the right mastery of digital skills. Reaping the digital revolution's benefits, and avoiding its pitfalls, will require organizations and economies to manage an unprecedented structural transformation for which the world seems still unprepared.
The Effects of MS PowerPoint on Students at a Malaysian University in the English for Professionals (EFP) Classrooms

The use of Microsoft PowerPoint (a form of multimedia) presentations in classroom instruction has significantly increased globally on both online and offline platforms. This study examines the perception of “English for Professionals (EFP)” students toward the usage of MS PowerPoint in the classrooms for delivering lectures and the effects of MS PowerPoint on students' learning. Questionnaires were used to conduct a quantitative survey. Descriptive analysis was used to analyze the results of this quantitative research. The results illustrate the students' positive attitude toward lectures presented with MS PowerPoint slides. While the majority of the students were receptive, a significant percentage held an indifferent view. The implication is that while MS PowerPoint is a dominant tool in teaching, there is a need to understand the factors behind the neutral reception, whether they are in the tool itself, the application by the educators, or the expertise of the educators in developing the slides.
Two-stream signed directed graph convolutional network for link prediction

Using graph neural networks(GNNs) to transfer and enhance the richness of node information has played an important role in link prediction. However, most traditional GNNs only consider undirected graphs or unsigned graphs, which is limited for information extraction. In order to obtain a richer node representation, we propose a Two-Stream Signed Directed Graph Convolution Network(2S-SDGCN). We consider both the sign and direction when aggregating, which can characterize the topological structure information of the graph. To combine the information together, we conduct a new two-stream network, one for extracting latent factor and transfer pattern features, and the other for obtaining directional positive and negative features. We use five real social networks as datasets to verify the effectiveness of our model. These datasets are usually used as benchmarks to verify the effectiveness of signed network embedding. Experiments show that our model outperforms the existing methods.
Analysis of the Structure of Aerobics Complete Sets of Movements Based on Genetic Algorithm

As an independent sport, aerobics is a popular and highly popular sport that integrates group gymnastics, dance, music, fitness, and entertainment. With the continuous development of global fitness fever and happy sports in recent years, the classification of aerobics has become more and more extensive. The development of fitness aerobics and competitive aerobics conforms to the trend of the times. In the course of aerobics teaching, action structure analysis is essential, and the performance of decomposing action image recognition has a direct impact on the effect of aerobics teaching. Therefore, it is important to study an effective aerobics complete set of action structure analysis method. In order to solve the problem that the traditional method is easily affected by the movement rate, lighting conditions, occlusion, complex background, etc., resulting in poor robustness of the recognition results, this paper takes the complete set of aerobics movements as the initial population and then designs the adaptation of the aerobics movements. The degree function is used to realize the structural analysis of aerobics complete sets of movements through the genetic algorithm. First, the video sequence is divided into several segments by using the time energy pyramid, and the human body target for aerobics exercise is extracted by the background subtraction method, and the decomposition actions of aerobics are obtained. By calculating the fitness of different actions, the fitness of the complete set of actions is obtained and then realize the analysis of aerobics complete sets of movements. The structural analysis of aerobics complete sets of movements can not only improve the choreography efficiency of complete sets of movements, but also promote the improvement of the training level of aerobics athletes, and help coaches make relative adjustments and changes in teaching on the basis of the original training of athletes.
Predicting 3-DoF motions of a moored barge by machine learning

The real-time prediction of a floating platform or a vessel is essential for motion-sensitive maritime activities. It can enhance the performance of motion compensation system and provide useful early-warning information. In this paper, we apply a machine learning technique to predict the surge, heave, and pitch motions of a moored rectangular barge excited by an irregular wave, which is purely based on the motion data. The dataset came from a model test performed in the deep-water ocean basin, at Shanghai Jiao Tong University, China. Using the trained machine learning model, the predictions of 3-DoF (degrees of freedom) motions can extend two to four wave cycles into the future with good accuracy. It shows great potential for applying the machine learning technique to forecast the motions of offshore platforms or vessels.
Online Clustering

Online clustering algorithms play a critical role in data science, especially with the advantages regarding time, memory usage and complexity, while maintaining a high performance compared to traditional clustering methods. This tutorial serves, first, as a survey on online machine learning and, in particular, data stream clustering methods. During this tutorial, state-of-the-art algorithms and the associated core research threads will be presented by identifying different categories based on distance, density grids and hidden statistical models. Clustering validity indices, an important part of the clustering process which are usually neglected or replaced with classification metrics, resulting in misleading interpretation of final results, will also be deeply investigated.
Generalized Deep Mixed Models

We introduce generalized deep mixed model (GDMix), a class of machine learning models for large-scale recommender systems that combines the power of deep neural networks and the efficiency of logistic regression. GDMix leverages state-of-the-art deep neural networks (DNNs) as the global models (fixed effects), and further improves the performance by adding entity-specific personalized models (random effects). For instance, the click response from a particular user m to a job posting j may consist of contributions from a DNN model common to all users and job postings, a model specific to the user m and a model specific to the job j. GDMix models not only possess powerful modeling capabilities but also enjoy high training efficiency especially for web-scale recommender systems. We demonstrate the capabilities by detailing their use in Feed and Ads recommendation at LinkedIn. The source code for the GDMix training framework is available at https://github.com/linkedin/gdmix https://github.com/linkedin/gdmix under the BSD-2-Clause License.