Reviewerly/exHarmony huggingface dataset:
Global motion estimation with iterative optimization-based independent univariate model for action recognition

Motion information used in the existed video action recognition schemes is mixing of global motion(GM) and local motion(LM). In fact, GM & LM have their respective semantic concepts. Thus, it is promising to decouple GM and LM from the mixed motions. Numerous efforts have been made on the design of global motion models for video encoding, video dejittering, video denoising, and so on. Nevertheless, some of the models are too basic to cover the camera motions in action recognition while others are over-complicated. In this paper, we focus on the characteristic of the action recognition and propose a novel independent univariate GM model. It ignores camera rotation, which appears rarely in action recognition videos, and represents the GM in x and y direction respectively. Furthermore, GM is position invariant because it is from the universal camera motion. Pixels with global motions are subjected to the same parametric model and pixels with mixed motion can be seen as outliers. Motivated by this, we develop an iterative optimization scheme for GM estimation which removes the outlier points step by step and estimates global motions in a coarse-to-fine manner. Finally, the LM is estimated through a Spatio-temporal threshold-based method. Experimental results demonstrate that the proposed GM model makes a better trade-off between the model complexity and the robustness. And the iterative optimization scheme is more effective than the existed algorithms. The compared experiments using four popular action recognition models on UCF-101 (for action recognition) and NCAA (for group activity recognition) demonstrate that local motions are more effective than the mixed motions.
Students’ Perceptions of Using Microsoft Teams Application in Online Learning During the Covid-19 Pandemic

Abstract The spread of COVID-19 has an impact on changing the learning process at the Faculty of Teacher Training and Education (FKIP) Nusa Nipa University (UNIPA). UNIPA chose to implement online learning using Microsoft Teams. This study aims to determine the FKIP students’ perceptions on the use of Microsoft Teams application in online learning during the COVID-19 pandemic. The sample of this research was 176 FKIP students’ which were spread in 5 study programs. They are Physics Education, Chemistry Education, Biology Education, and Elementary School Teacher Education. This research is qualitative research with data collection techniques were questionnaire and analyzed using Likert scale. The results showed that FKIP UNIPA students had a good perception of using Microsoft Teams. Students hope that this application will continue to be used during online learning with some improvements so that learning using this application can take place more effectively and efficiently.
Recurrent generative adversarial networks for unsupervised WCE video summarization

Wireless capsule endoscopy (WCE) produces amounts of redundant images in one examination, which is very laborious and time-consuming for a physician to review these. It has been extremely needed for a technique that automatically produces a shortened and informative WCE video summary from its original video. This paper considers unsupervised WCE video summarization, and casts it as a sequence-to-sequence learning problem. Our key idea is to learn a deep summarizer network to minimize information loss between training videos and their summaries, in an unsupervised way. To this end, we propose a hybrid yet effective unsupervised WCE video summarization method using long short-term memory (LSTM), variational autoencoder (VAE), pointer network (Ptr-Net), generative adversarial network (GAN), and de-redundancy mechanism (DM) etc. techniques. The proposed model termed Adv-Ptr-Der-SUM adopts a generative adversarial framework, consisting of a summarizer and a discriminator. The summarizer is the VAE-based LSTM architecture with Ptr-Net and DM that aims to learn the conditional probability of output sequence and provide a compact summary. The discriminator is another LSTM aimed at distinguishing between the original video and reconstructed video from the summarizer. The summarizer and discriminator are adversarially trained to optimize the summarizer and produce optimal WCE video summary. Extensive experiments on our WCE-2019-Video dataset show that our model can outperform other video summarization approaches by a large margin in both supervised and unsupervised settings. Also, the proposed model is applied to two public multimedia benchmark datasets, verifying its effectiveness and generality, and demonstrating that it can achieve a competitive result.
Feature-based Distant Domain Transfer Learning

In this paper, we study a not well-investigated but important transfer learning problem termed Distant Domain Transfer Learning (DDTL). This topic is closely related to negative transfer. Unlike conventional transfer learning problems which assume that the source domain and the target domain are more or less similar to each other, DDTL aims to make efficient transfers even when the domains or the tasks are completely different. As an extreme example in image classification, there are only a sufficient amount of unlabeled images of watches, airplanes, and horses in the source domain, and the target domain only has a small set of labeled human face images. Previously, a few instance-based distant domain transfer algorithms were proposed to deal with this type of binary distant domain image classification problems. Yet most existing algorithms are very task-specific and they are only good at binary classification tasks. In this study, we propose a novel feature-based distant domain transfer learning algorithm, which requires only a tiny set of labeled target data and unlabeled source data from completely different domains. Instead of selecting intermediate instances, we introduced Distant Feature Fusion (DFF), a novel feature selection method, to discover general features cross distant domains and tasks by using convolutional autoencoder with a domain distance measurement as a feature extractor. As the novelty of this study, it can effectively handle both distant domain mutil-class image classification and binary image classification problems. More importantly, it has achieved up to 19% higher classification accuracy than "non-transfer" algorithms, and up to 9% higher than existing distant transfer algorithms.
MRP-GAN: Multi-resolution parallel generative adversarial networks for text-to-image synthesis

Synthesizing photographic images from given text descriptions is a challenging problem. Although current methods first synthesize an initial blurred image, then refine the initial image to a high-quality one, the most existing methods are difficult to refine the initial image to an image corresponding to the text description. In this paper, the Multi-resolution Parallel Generative Adversarial Networks for Text-to-Image Synthesis (MRP-GAN) is proposed to generate photographic images. MRP-GAN introduces a Multi-resolution Parallel structure to refine the initial images when the initial images are not synthesized well. The low-resolution semantics are maintained through the whole process by Multi-resolution Parallel structure. Response Gate is designed to fully explore the capability of Multi-resolution Parallel structure by aggregating the outputs of the multi-resolution parallel subnetworks. We also utilize an attention mechanism, named Residual Attention Network, to fine-tune more fine-grained details of the generated images. We evaluate our MRP-GAN model on the CUB and MS-COCO datasets. Extensive experiments demonstrate the state-of-the-art performance of MRP-GAN. Besides, we apply a Multi-resolution Parallel structure in the existing method to verify its transferability.
ESPFNet: An Edge-Aware Spatial Pyramid Fusion Network for Salient Shadow Detection in Aerial Remote Sensing Images

Shadows can hinder image interpretation in aerial remote sensing images. The existing shadow detection methods focus on all shadow regions and detect the shadow regions directly, but they ignore the fact that salient shadows have a more significant effect. In this work, a novel edge-aware spatial pyramid fusion network (ESPFNet) under a multitask learning framework is proposed for salient shadow detection in aerial remote sensing images. ESPFNet has three components: a parallel spatial pyramid (PSP) structure; an edge detection module (EDM); and an edge-aware multibranch integration (EMI). The PSP structure is constructed to extract multiscale features from the input image and fuse them gradually. The EDM then integrates the shallow features and deep features to detect the shadow edges. Finally, the EMI incorporates the edge features with multibranch features, and then concatenates them with the shallow features to generate the salient shadow detection result. The experimental analyses confirm the effectiveness of the ESPFNet method in both the qualitative and quantitative performance, compared to the existing methods, with the F-score reaching 92.04% in the salient shadow test set.
Identifying plant wax inputs in lake sediments using machine learning

This study aims to evaluate whether machine learning techniques can be successfully applied to process the complex information contained within the molecular abundance distributions of plant wax n-alkane and n-alkanoic acid homologous series. We trained five vegetation identification models using plant wax chain length distributions from modern plants in the Mojave Desert (hyperarid) and the San Bernardino Mountains (conifer forest) and previously published data for macrophytes from Blood Pond (USA) and Mt Kenya (Kenya). All vegetation identification models proved accurate (mean classification accuracy = 0.81) at classifying the modern plant wax chain length distributions into desert plants, conifer and macrophyte categories. We then applied the models to fossil waxes extracted from a 76 m lacustrine sediment core drilled in Searles Valley, CA with an approximate age range of 10 to 150 kyrs (SLAPP-SRLS17) to reconstruct the proportion of desert plants, conifer woodland and lake vegetation. We compared our machine learning models with a previously published linear mixing model and validated our modelled plant type distributions by comparing the results with the archaeol caldarchaeol ecometric (ACE), a proxy for lake salinity, measured in the same core. We found a moderate positive correlation (r = 0.40) between the modelled desert plant proportion and high lake salinity in our models as well as a negative correlation (r = –0.45) between modelled macrophyte plants and ACE, validating the ability of the machine learning techniques to detect both xeric and macrophyte plant communities. Our results suggest that machine learning of plant wax molecular abundance distributions has potential to reconstruct past plant communities, given information from two compound classes and highly differentiated vegetation types.
Facial Expression Recognition using Locally Linear Embedding with LBP and HOG Descriptors

Facial expression recognition intervenes in various fields of applications such as human-computer interaction. Despite the fact that several methods are regularly proposed, designing an efficient automatic facial expression recognition method remains challenging. In this paper, we propose a method through which we compare the performance of two common and well-known image descriptors namely Local Binary Patterns and Histogram of Oriented Gradients. Both are used by two distinct manners; global which uses the whole face while the local exploits predefined sub-regions. Moreover, we employ a specific dimensionality reduction technique namely Locally Linear Embedding. As for the recognition part, we choose to employ a multiclass Support Vector Machine classifier for its generalization capabilities in order to recognize the expressed emotion among the six basic ones. Finally, we assess the performances of the proposed method using three different and common datasets namely KDEF, JAFFE and RafD. The obtained results are promising with corresponding recognition rates; 85.48%, 96.05% and 93.54%, respectively.
Infusing Finetuning with Semantic Dependencies

For natural language processing systems, two kinds of evidence support the use of text representations from neural language models "pretrained" on large unannotated corpora: performance on application-inspired benchmarks (Peters et al., 2018, inter alia), and the emergence of syntactic abstractions in those representations (Tenney et al., 2019, inter alia). On the other hand, the lack of grounded supervision calls into question how well these representations can ever capture meaning (Bender and Koller, 2020). We apply novel probes to recent language models -- specifically focusing on predicate-argument structure as operationalized by semantic dependencies (Ivanova et al., 2012) -- and find that, unlike syntax, semantics is not brought to the surface by today's pretrained models. We then use convolutional graph encoders to explicitly incorporate semantic parses into task-specific finetuning, yielding benefits to natural language understanding (NLU) tasks in the GLUE benchmark. This approach demonstrates the potential for general-purpose (rather than task-specific) linguistic supervision, above and beyond conventional pretraining and finetuning. Several diagnostics help to localize the benefits of our approach.
Developing Museum Education Content: AR Blended Learning

Abstract Traditional museums of culture and history are failing to develop or make effective use of augmented reality (AR) technology. To address this deficit, the present study sought to develop online and offline experiential AR learning tools that would enable children to more fully explore museum artefacts. The study approach was based on the Blended Learning Model, which utilises symbiotic online and offline conversions and arrangements to enhance learning effectiveness. First, the study investigated the characteristics and limitations of existing AR technology from the perspective of children’s education by focusing on cultural assets. Second, an educational app with cultural heritage content was designed on the basis of a survey of stakeholders’ perceptions and needs. A blended learning experiment was then conducted, involving five steps (1) making a 3D assembly model (offline); (2) viewing the 3D model through the AR app; (3) learning about the cultural relic from a textbook (offline); (4) exploring the 3D model through the AR Quiz Game (online); and ( 5) visiting the museum (offline). This programme was found to be appropriate for elementary school third and fourth graders, who responded with high levels of flow and interest. The findings suggest that this experiential AR blended learning tool provides a more meaningful experience that motivates children to appreciate the value of cultural/historical assets.
IoT Based Fall Detection Monitoring and Alarm System For Elderly

This paper presents an IoT based fall detection monitoring and alarm system for the elderly using 3-axis Accelerometer. In elderly, injuries induced by fall, becomes fatal sometimes, if timely medical interventions do not take place. Therefore, we aim to design a system to detect the fall and alert the medical experts about the incidents of distress. In the proposed system for detection of falling, the elderly patient's acceleration data are continuously acquired by using a wearable sensor and stored on a cloud server, using an IoT board. To access the stored data, an android application is designed for the medical expert to examine the fall in the elderly patient and provide the desired assistance, if needed. A threshold-based approach for the fall detection has been used to get the sensor data and set the threshold on accelerometer readings. A complete algorithm has been designed for the detection of genuine fall.
Universal image segmentation for optical identification of 2D materials

Machine learning methods are changing the way data is analyzed. One of the most powerful and widespread applications of these techniques is in image segmentation wherein disparate objects of a digital image are partitioned and classified. Here we present an image segmentation program incorporating a series of unsupervised clustering algorithms for the automatic thickness identification of two-dimensional materials from digital optical microscopy images. The program identifies mono- and few-layer flakes of a variety of materials on both opaque and transparent substrates with a pixel accuracy of roughly 95%. Contrasting with previous attempts, application generality is achieved through preservation and analysis of all three digital color channels and Gaussian mixture model fits to arbitrarily shaped data clusters. Our results provide a facile implementation of data clustering for the universal, automatic identification of two-dimensional materials exfoliated onto any substrate.
Tools for Remote Exploration: A Lithium (Li) Dedicated Spectral Library of the Fregeneda–Almendra Aplite–Pegmatite Field

The existence of diagnostic features in the visible and infrared regions makes it possible to use reflectance spectra not only to identify mineral assemblages but also for calibration and classification of satellite images, considering lithological and/or mineral mapping. For this purpose, a consistent spectral library with the target spectra of minerals and rocks is needed. Currently, there is big market pressure for raw materials including lithium (Li) that has driven new satellite image applications for Li exploration. However, there are no reference spectra for petalite (a Li mineral) in large, open spectral datasets. In this work, a spectral library was built exclusively dedicated to Li minerals and Li pegmatite exploration through satellite remote sensing. The database includes field and laboratory spectra collected in the Fregeneda–Almendra region (Spain–Portugal) from (i) distinct Li minerals (spodumene, petalite, lepidolite); (ii) several Li pegmatites and other outcropping lithologies to allow satellite-based lithological mapping; (iii) areas previously misclassified as Li pegmatites using machine learning algorithms to allow comparisons between these regions and the target areas. Ancillary data include (i) sample location and coordinates, (ii) sample conditions, (iii) sample color, (iv) type of face measured, (v) equipment used, and for the laboratory spectra, (vi) sample photographs, (vii) continuum removed spectra files, and (viii) statistics on the main absorption features automatically extracted. The potential future uses of this spectral library are reinforced by its major advantages: (i) data is provided in a universal file format; (ii) it allows users to compare field and laboratory spectra; (iii) a large number of complementary data allow the comparison of shape, asymmetry, and depth of the absorption features of the distinct Li minerals.
A Blockchain-Based Trusted Edge Platform in Edge Computing Environment

Edge computing is a product of the evolution of IoT and the development of cloud computing technology, providing computing, storage, network, and other infrastructure close to users. Compared with the centralized deployment model of traditional cloud computing, edge computing solves the problems of extended communication time and high convergence traffic, providing better support for low latency and high bandwidth services. With the increasing amount of data generated by users and devices in IoT, security and privacy issues in the edge computing environment have become concerns. Blockchain, a security technology developed rapidly in recent years, has been adopted by many industries, such as finance and insurance. With the edge computing capability, deploying blockchain platforms/applications on edge computing platforms can provide security services for network edge environments. Although there are already solutions for integrating edge computing with blockchain in many IoT application scenarios, they slightly lack scalability, portability, and heterogeneous data processing. In this paper, we propose a trusted edge platform to integrate the edge computing framework and blockchain network for building an edge security environment. The proposed platform aims to preserve the data privacy of the edge computing client. The design based on the microservice architecture makes the platform lighter. To improve the portability of the platform, we introduce the Edgex Foundry framework and design an edge application module on the platform to improve the business capability of Edgex. Simultaneously, we designed a series of well-defined security authentication microservices. These microservices use the Hyperledger Fabric blockchain network to build a reliable security mechanism in the edge environment. Finally, we build an edge computing network using different hardware devices and deploy the trusted edge platform on multiple network nodes. The usability of the proposed platform is demonstrated by testing the round-trip time (RTT) of several important workflows. The experimental results demonstrate that the platform can meet the availability requirements in real-world usage scenarios.
As Strong As Its Weakest Link: How to Break Blockchain DApps at RPC Service

operates on a blockchain full node maintained directly by the DApp owner (i.e., an in-house RPC node) or a set of nodes hosted by a third party (i.e., a third-party RPC service) intended to ease DApp deployment.Given the ever-growing blockchain states (e.g., 130 GB and 1.8 TB for a fully synced and an archived Ethereum node, respectively, as of 2018), the RPC service plays an increasingly important role in the DApp ecosystem, scaling DApp clients to low-end mobile devices and web browsers.Major blockchains today flock to roll out RPC supports, which spawn a good number of services in practice, including nine service providers (as is evaluated in this work) supporting the Ethereum's JSON-RPC interface [16], blockchain.info[5] with Bitcoin's JSON-RPC [2], dfuse.io[13] and greymass.com[39] with EOSIO's Chain API [6], stellar.org[46] with Stellar Horizon [23], etc.These services host the majority of DApps; for instance, at least 63% of Ethereum based DApps use one RPC service [10].DApp clients RPC service JSON-RPC Block/tx synchronization Blockchain peers
Simulation Studies as a Tool to Understand Bayes Factors

When social scientists wish to learn about an empirical phenomenon, they perform an experiment. When they wish to learn about a complex numerical phenomenon, they can perform a simulation study. The goal of this Tutorial is twofold. First, it introduces how to set up a simulation study using the relatively simple example of simulating from the prior. Second, it demonstrates how simulation can be used to learn about the Jeffreys-Zellner-Siow (JZS) Bayes factor, a currently popular implementation of the Bayes factor employed in the BayesFactor R package and freeware program JASP. Many technical expositions on Bayes factors exist, but these may be somewhat inaccessible to researchers who are not specialized in statistics. In a step-by-step approach, this Tutorial shows how a simple simulation script can be used to approximate the calculation of the Bayes factor. We explain how a researcher can write such a sampler to approximate Bayes factors in a few lines of code, what the logic is behind the Savage-Dickey method used to visualize Bayes factors, and what the practical differences are for different choices of the prior distribution used to calculate Bayes factors.