Reviewerly/exHarmony huggingface dataset:
ROSEBUD: A Deep Fluvial Segmentation Dataset for Monocular Vision-Based River Navigation and Obstacle Avoidance

Obstacle detection for autonomous navigation through semantic image segmentation using neural networks has grown in popularity for use in unmanned ground and surface vehicles because of its ability to rapidly create a highly accurate pixel-wise classification of complex scenes. Due to the lack of available training data, semantic networks are rarely applied to navigation in complex water scenes such as rivers, creeks, canals, and harbors. This work seeks to address the issue by making a one-of-its-kind River Obstacle Segmentation En-Route By USV Dataset (ROSEBUD) publicly available for use in robotic SLAM applications that map water and non-water entities in fluvial images from the water level. ROSEBUD provides a challenging baseline for surface navigation in complex environments using complex fluvial scenes. The dataset contains 549 images encompassing various water qualities, seasons, and obstacle types that were taken on narrow inland rivers and then hand annotated for use in semantic network training. The difference between the ROSEBUD dataset and existing marine datasets was verified. Two state-of-the-art networks were trained on existing water segmentation datasets and tested for generalization to the ROSEBUD dataset. Results from further training show that modern semantic networks custom made for water recognition, and trained on marine images, can properly segment large areas, but they struggle to properly segment small obstacles in fluvial scenes without further training on the ROSEBUD dataset.
Scheduling Hyperparameters to Improve Generalization: From Centralized SGD to Asynchronous SGD

This paper 1 studies how to schedule hyperparameters to improve generalization of both centralized single-machine stochastic gradient descent (SGD) and distributed asynchronous SGD (ASGD). SGD augmented with momentum variants (e.g., heavy ball momentum (SHB) and Nesterov’s accelerated gradient (NAG)) has been the default optimizer for many tasks, in both centralized and distributed environments. However, many advanced momentum variants, despite empirical advantage over classical SHB/NAG, introduce extra hyperparameters to tune. The error-prone tuning is the main barrier for AutoML. Centralized SGD : We first focus on centralized single-machine SGD and show how to efficiently schedule the hyperparameters of a large class of momentum variants to improve generalization. We propose a unified framework called multistage quasi-hyperbolic momentum (Multistage QHM), which covers a large family of momentum variants as its special cases (e.g. vanilla SGD/SHB/NAG). Existing works mainly focus on only scheduling learning rate α ’s decay, while multistage QHM allows additional varying hyperparameters (e.g., momentum factor), and demonstrates better generalization than only tuning α . We show the convergence of multistage QHM for general nonconvex objectives. Distributed SGD : We then extend our theory to distributed asynchronous SGD (ASGD), in which a parameter server distributes data batches to several worker machines and updates parameters via aggregating batch gradients from workers. We quantify the asynchrony between different workers (i.e., gradient staleness), model the dynamics of asynchronous iterations via a stochastic differential equation (SDE), and then derive a PAC-Bayesian generalization bound for ASGD. As a byproduct, we show how a moderately large learning rate helps ASGD to generalize better. Our tuning strategies have rigorous justifications rather than a blind trial-and-error as we theoretically prove why our tuning strategies could decrease our derived generalization errors in both cases. Our strategies simplify the tuning process and beat competitive optimizers in test accuracy empirically. Our codes are publicly available https://github.com/jsycsjh/centralized-asynchronous-tuning.
Towards Multimodal Search and Visualization of Movies Based on Emotions

Movies are one of the most important and impactful forms of entertainment and a powerful vehicle for culture and education, due to the cognitive and emotional impact on the viewers, and technology has been making them more accessible in pervasive services and devices. As such, the huge amount of movies we can access, and the important role emotions play in our lives, make more pertinent the ability to access, visualize and search movies based on their emotional impact. In this paper, we characterize the challenges and approaches in this scenario, and present interactive means to visualize and search movies based on their dominant and actual emotional impact along the movie, with different models and modalities. In particular through emotional highlights and trajectories, the user's emotional state, or a music being played. Music contributes greatly to the emotional impact of movies and it can also be a trigger to get us into one of them in serendipitous moments.
STEM education in the context of improving the science and mathematics literacy of pupils

Abstract The article considers the problem of STEM education in the context of improving the quality of science and mathematics literacy of pupils. The results of the monitoring study conducted by the “OsvitAnalityka” Analytical Center of Borys Grinchenko Kyiv University in cooperation with the Kyiv City Educational Agency and the Ukrainian Center for Educational Quality Assessment are presented. The state of formation of science and mathematical literacy of pupils as the ability to apply knowledge to solve practical problems for the implementation of STEM education has been established. Examples of practice-oriented integrated tasks in geography, chemistry, biology and physics that reveal the possibilities of STEM education are given. The success of the implementation of individual tasks, which involved subject integration and the use of knowledge in practice is analyzed. The most important factors influencing the quality of STEM education are highlighted: professional level of teachers, material and technical and educational-methodical support, motivation of pupils, practice-oriented content of education. As a result of the monitoring study, recommendations were prepared for general secondary education institutions for further implementation of STEM education.
Moving Object Detection Based on Fusion of Depth Information and RGB Features

The detection of moving objects is one of the key problems in the field of computer vision. It is very important to detect moving objects accurately and rapidly for automatic driving. In this paper, we propose an improved moving object detection method to overcome the disadvantages of the RGB information-only-based method in detecting moving objects that are susceptible to shadow interference and illumination changes by adding depth information. Firstly, a convolutional neural network (CNN) based on the color edge-guided super-resolution reconstruction of depth maps is proposed to perform super-resolution reconstruction of low-resolution depth images obtained by depth cameras. Secondly, the RGB-D moving object detection algorithm is based on fusing the depth information of the same scene with RGB features for detection. Finally, in order to evaluate the effectiveness of the algorithm proposed in this paper, the Middlebury 2005 dataset and the SBM-RGBD dataset are successively used for testing. The experimental results show that our super-resolution reconstruction algorithm achieves the best results among the six commonly used algorithms, and our moving object detection algorithm improves the detection accuracy by up to 18.2%, 9.87% and 40.2% in three scenes, respectively, compared with the original algorithm, and it achieves the best results compared with the other three recent RGB-D-based methods. The algorithm proposed in this paper can better overcome the interference caused by shadow or illumination changes and detect moving objects more accurately.
The use of augmented reality in chemistry lessons in the study of “Oxygen-containing organic compounds” using the mobile application Blippar

Abstract A training kit has been created to study the topic of “Oxygen-containing organic compounds” using augmented reality. We chose the Blippar platform to work, it is quite simple and relatively free. The kit contains markers we have developed, on which, with the camera from the Blippar application, students will be able to observe thematic experiments and molecules of individual substances with the help of augmented reality. There was also a study on the effectiveness of our developments to implement augmented reality in the course of chemistry. The study results showed an increase in pupils’ motivation to study chemistry and their level of interest, facilitating the perception of the theoretical foundations of organic chemistry related to the spatial structure of molecules. Also, the use of augmented reality technologies creates the conditions for improving the level of digital and technological competencies of students, provides support for high quality teaching of chemistry in distance learning.
A multi-sensor deep learning approach for complex daily living activity recognition

With the ever increasing elderly population, human activity trackers can help monitor the daily physical activities performed by the elderly in order to contribute towards improvements in independent living and quality of life.
Decoupling RNN Training and Testing Observation Intervals for Spectrum Sensing Applications

Recurrent neural networks have been shown to outperform other architectures when processing temporally correlated data, such as from wireless communication signals. However, compared to other architectures, such as convolutional neural networks, recurrent neural networks can suffer from drastically longer training and evaluation times due to their inherent sample-by-sample data processing, while traditional usage of both of these architectures assumes a fixed observation interval during both training and testing, the sample-by-sample processing capabilities of recurrent neural networks opens the door for alternative approaches. Rather than assuming that the testing and observation intervals are equivalent, the observation intervals can be "decoupled" or set independently. This can potentially reduce training times and will allow for trained networks to be adapted to different applications without retraining. This work illustrates the benefits and considerations needed when "decoupling" these observation intervals for spectrum sensing applications, using modulation classification as the example use case. The sample-by-sample processing of RNNs also allows for the relaxation of the typical requirement of a fixed time duration of the signals of interest. Allowing for variable observation intervals is important in real-time applications like cognitive radio where decisions need to be made as quickly and accurately as possible as well as in applications like electronic warfare in which the sequence length of the signal of interest may be unknown. This work examines a real-time post-processing method called "just enough" decision making that allows for variable observation intervals. In particular, this work shows that, intuitively, this method can be leveraged to process less data (i.e., shorter observation intervals) for simpler inputs (less complicated signal types or channel conditions). Less intuitively, this works shows that the "decoupling" is dependent on appropriate training to avoid bias and ensure generalization.
Algorithmic power and African indigenous languages: search engine autocomplete and the global multilingual Internet

Predictive language technologies – such as Google Search’s Autocomplete – constitute forms of algorithmic power that reflect and compound global power imbalances between Western technology companies and multilingual Internet users in the global South. Increasing attention is being paid to predictive language technologies and their impacts on individual users and public discourse. However, there is a lack of scholarship on how such technologies interact with African languages. Addressing this gap, the article presents data from experimentation with autocomplete predictions/suggestions for gendered or politicised keywords in Amharic, Kiswahili and Somali. It demonstrates that autocomplete functions for these languages and how users may be exposed to harmful content due to an apparent lack of filtering of problematic ‘predictions’. Drawing on debates on algorithmic power and digital colonialism, the article demonstrates that global power imbalances manifest here not through a lack of online African indigenous language content, but rather in regard to the moderation of content across diverse cultural and linguistic contexts. This raises dilemmas for actors invested in the multilingual Internet between risks of digital surveillance and effective platform oversight, which could prevent algorithmic harms to users engaging with platforms in a myriad of languages and diverse socio-cultural and political environments.
KE-RCNN: Unifying Knowledge-Based Reasoning Into Part-Level Attribute Parsing

Part-level attribute parsing is a fundamental but challenging task, which requires the region-level visual understanding to provide explainable details of body parts. Most existing approaches address this problem by adding a regional convolutional neural network (RCNN) with an attribute prediction head to a two-stage detector, in which attributes of body parts are identified from localwise part boxes. However, localwise part boxes with limit visual clues (i.e., part appearance only) lead to unsatisfying parsing results, since attributes of body parts are highly dependent on comprehensive relations among them. In this article, we propose a knowledge-embedded RCNN (KE-RCNN) to identify attributes by leveraging rich knowledge, including implicit knowledge (e.g., the attribute "above-the-hip" for a shirt requires visual/geometry relations of shirt-hip) and explicit knowledge (e.g., the part of "shorts" cannot have the attribute of "hoodie" or "lining"). Specifically, the KE-RCNN consists of two novel components, that is: 1) implicit knowledge-based encoder (IK-En) and 2) explicit knowledge-based decoder (EK-De). The former is designed to enhance part-level representation by encoding part-part relational contexts into part boxes, and the latter one is proposed to decode attributes with a guidance of prior knowledge about part-attribute relations. In this way, the KE-RCNN is plug-and-play, which can be integrated into any two-stage detectors, for example, Attribute-RCNN, Cascade-RCNN, HRNet-based RCNN, and SwinTransformer-based RCNN. Extensive experiments conducted on two challenging benchmarks, for example, Fashionpedia and Kinetics-TPS, demonstrate the effectiveness and generalizability of the KE-RCNN. In particular, it achieves higher improvements over all existing methods, reaching around 3% of AP allIoU+F1 on Fashionpedia and around 4% of Accp on Kinetics-TPS. Code and models are publicly available at: https://github.com/sota-joson/KE-RCNN.
WatchAuth: User Authentication and Intent Recognition in Mobile Payments using a Smartwatch

In this paper, we show that the tap gesture, performed when a user ‘taps’ a smartwatch onto an NFC-enabled terminal to make a payment, is a biometric capable of implicitly authenticating the user and simultaneously recognising intent-to-pay. The proposed system can be deployed purely in software on the watch without requiring updates to payment terminals. It is agnostic to terminal type and position and the intent recognition portion does not require any training data from the user. To validate the system, we conduct a user study (n=16) to collect wrist motion data from users as they interact with payment terminals and to collect long-term data from a subset of them ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{n}=9$</tex> ) as they perform daily activities. Based on this data, we identify optimum gesture parameters and develop authentication and intent recognition models, for which we achieve EERs of 0.08 and 0.04, respectively.
Wrinkle defects investigation on displacement response in fiber-reinforced composites using grating projection measurement

Wrinkle defects in fiber-reinforced polymer (FRP) composites are often unavoidable due to various causes during the production process, which are also difficult to be rapidly screened by conventional non-destructive testing (NDT) methods. In order to find defects in FRP within non-destructive status, a three-dimensional (3D) finite element analysis (FEA) containing micro mechanical model of wrinkle has been applied to predict the mechanical responses in elastic range. It is found that displacement responses under certain loads are noticeably distorted by wrinkle, especially out-of-plane displacement under tension, which provides a defect inspection guidance for FRP. Therefore, a novel wrinkle defect detection method for FRP based on grating projection measurement is proposed. Combined with the measured point clouds and surface reconstruction algorithm, the full-field out-of-plane displacement distortion of 10 μm measuring precision can be obtained. Tensile tests with a small force are carried out to ensure the wrinkled FRP specimens in the elastic range, the obtained out-of-plane displacement field satisfactorily reflect the wrinkle-induced disturbances. And the measured out-of-plane displacement using grating projection are in good agreement with the predicted responses from numerical approaches.
A near-optimal approach to edge connectivity-based hierarchical graph decomposition

Driven by applications in graph analytics, the problem of efficiently computing all k -edge connected components ( k -ECCs) of a graph G for a user-given k has been extensively and well studied. It is known that the k -ECCs of G for all possible values of k form a hierarchical structure. In this paper, we study the problem of efficiently constructing the hierarchy tree for G which compactly encodes the k -ECCs for all possible k values in space linear to the number of vertices n. All existing approaches construct the hierarchy tree in O (δ( G ) × T KECC ( G ) ) time, where δ( G ) is the degeneracy of G and T KECC ( G ) is the time complexity of computing all k -ECCs of G for a specific k value. To improve the time complexity, we propose a divide-and-conquer approach running in O ((log δ( G )) × T KECC ( G ) ) time, which is optimal up to a logarithmic factor. However, a straightforward implementation of our algorithm would result in a space complexity of O (( m + n ) log δ( G )). As main memory also becomes a scarce resource when processing large-scale graphs, we further propose techniques to optimize the space complexity to 2 m + O ( n log δ( G )), where m is the number of edges in G. Extensive experiments on large real graphs and synthetic graphs demonstrate that our approach outperforms the state-of-the-art approaches by up to 28 times in terms of running time, and by up to 8 times in terms of main memory usage. As a by-product, we also improve the space complexity of computing all k -ECCs for a specific k to 2 m + O ( n ).
Hashing-based affinity matrix for dominant set clustering

• Semi-supervised hashing-based affinity matrix computation method is proposed for DSC. • Unsupervised extension of the HAM-DSC is proposed for unsupervised clustering cases. • HAM-DSC yields the better experimental clustering performance with lowest time cost. Dominant set clustering has been widely used to solve a variety of problems such as image segmentation, video analysis, and image retrieval. However, the key problem of it is the need of a given affinity matrix to provide similarity for every pair of data points. The affinity matrix is either user-given or computed using a pairwise computation, which is impractical for big data situations. Therefore, this work proposes a semi-supervised hashing-based affinity matrix computation method (HAM) for dominant set clustering (HAM-DSC) for cases of semi-supervised clustering where labels are available to a portion of data. The HAM computes affinity matrix efficiently based on Hamming distance between learned hash codes, which preserve semantic similarities effectively. To deal with unsupervised clustering problems, an unsupervised extension of the HAM-DSC is also given in this work. Experimental results on 7 real-world datasets show that the HAM-DSC yields an overall better performance together with lower time costs compared to dominant set clustering using other affinity matrix computation methods.
Mineralogy and K–Ar geochronology of clay alteration associated with uranium mineralization in the Patterson Lake Corridor, Saskatchewan

The Patterson Lake Corridor (PLC) along the southwestern margin of the Athabasca Basin contains high-grade uranium deposits entirely within crystalline basement rocks. Visible–near infrared–shortwave infrared (VNIR–SWIR) spectroscopy measurements were collected on drill core samples from several locations in the PLC. The Triple R and Arrow deposits exhibit downhole spectral trends related to the crystallinity and thermal maturity of clays (illite and kaolinite) and mineralization. The K–Ar dates of silt-and-clay size fractions (10–6 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>μ</mml:mi> </mml:math> m; 6–2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>μ</mml:mi> </mml:math> m; 2–0.6 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>μ</mml:mi> </mml:math> m; 0.6–0.2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>μ</mml:mi> </mml:math> m; &lt;0.2 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>μ</mml:mi> </mml:math> m) from five clay-altered samples decrease with grain size, and span 1608 ± 17 Ma to 1060 ± 14 Ma for the Spitfire discovery ( n = 14) and 1342 ± 17 Ma to 289 ± 4.3 Ma for the Arrow deposit ( n = 4). Alteration assemblages are broadly similar to Athabasca Basin basement-hosted deposits, and K–Ar dates indicate that high-grade uranium mineralization in the PLC reflects remobilization and concentration of primary ores. Integration of geochronology, clay mineralogy and VNIR–SWIR spectral parameters identify fertile fluid conduits when expanded to property or corridor scales, and provide additional evidence that ore grades of the Athabasca Basin deposits reflect several stages of hydrothermal mineralization spanning c. 1000 Ma. Supplementary material: Supporting information document and datasets are available at https://doi.org/10.6084/m9.figshare.c.6033890 Thematic collection: This article is part of the Uranium Fluid Pathways collection available at: https://www.lyellcollection.org/cc/uranium-fluid-pathways
Continuous social distance monitoring in indoor space

The COVID-19 pandemic has caused over 6 million deaths since 2020. To contain the spread of the virus, social distancing is one of the most simple yet effective approaches. Motivated by this, in this paper we study the problem of continuous social distance monitoring (SDM) in indoor space, in which we can monitor and predict the pairwise distances between moving objects (people) in a building in real time. SDM can also serve as the fundamental service for downstream applications, e.g., a mobile alert application that prevents its users from potential close contact with others. To facilitate the monitoring process, we propose a framework that takes the current and future uncertain locations of the objects into account, and finds the object pairs that are close to each other in a near future. We develop efficient algorithms to update the result when object locations update. We carry out experiments on both real and synthetic datasets. The results verify the efficiency and effectiveness of our proposed framework and algorithms.