Reviewerly/exHarmony huggingface dataset:
A comprehensive survey of recent trends in deep learning for digital images augmentation

Deep learning proved its efficiency in many fields of computer science such as computer vision, image classifications, object detection, image segmentation, and more. Deep learning models primarily depend on the availability of huge datasets. Without the existence of many images in datasets, different deep learning models will not be able to learn and produce accurate models. Unfortunately, several fields don't have access to large amounts of evidence, such as medical image processing. For example. The world is suffering from the lack of COVID-19 virus datasets, and there is no benchmark dataset from the beginning of 2020. This pandemic was the main motivation of this survey to deliver and discuss the current image data augmentation techniques which can be used to increase the number of images. In this paper, a survey of data augmentation for digital images in deep learning will be presented. The study begins and with the introduction section, which reflects the importance of data augmentation in general. The classical image data augmentation taxonomy and photometric transformation will be presented in the second section. The third section will illustrate the deep learning image data augmentation. Finally, the fourth section will survey the state of the art of using image data augmentation techniques in the different deep learning research and application.
Continuous-variable quantum key distribution with non-Gaussian quantum catalysis

The non-Gaussian operation can be used not only to enhance and distill the entanglement between Gaussian entangled states, but also to improve quantum communications. In this paper, we propose an non-Gaussian continuous-variable quantum key distribution (CVQKD) by using quantum catalysis (QC), which is an intriguing non-Gaussian operation in essence that can be implemented with current technologies. We perform quantum catalysis on both ends of the Einstein-Podolsky-Rosen (EPR) pair prepared by a sender, Alice, and find that for the single-photon QC-CVQKD, the bilateral symmetrical quantum catalysis (BSQC) performs better than the single-side quantum catalysis (SSQC). Attributing to characteristics of integral within an ordered product (IWOP) of operators, we find that the quantum catalysis operation can improve the entanglement property of Gaussian entangled states by enhancing the success probability of non-Gaussian operation, leading to the improvement of the QC-CVQKD system. As a comparison, the QC-CVQKD system involving zero-photon and single-photon quantum catalysis outperforms the previous non-Gaussian CVQKD scheme via photon subtraction in terms of secret key rate, maximal transmission distance and tolerable excess noise.
Pengembangan Bahan Ajar Berbasis Addie Model

Instructional material are an important part in determining the quality of learning. The design of the development of instructional material needs to pay attention to the development model to ensure the quality of instructional material in supporting learning effectiveness, because the development of instuctional material is basically a linear process with the learning process. One of the designs for the development of instructional material that is often used is the ADDIE Model through 5 stages; Analysis, Design, Development, Implementation and Evaluation. The ADDIE model is a learning system design model that shows the basic stages of a learning system that is easy to do.
Application of Landsat-8, Sentinel-2, ASTER and WorldView-3 Spectral Imagery for Exploration of Carbonate-Hosted Pb-Zn Deposits in the Central Iranian Terrane (CIT)

The exploration of carbonate-hosted Pb-Zn mineralization is challenging due to the complex structural-geological settings and costly using geophysical and geochemical techniques. Hydrothermal alteration minerals and structural features are typically associated with this type of mineralization. Application of multi-sensor remote sensing satellite imagery as a fast and inexpensive tool for mapping alteration zones and lithological units associated with carbonate-hosted Pb-Zn deposits is worthwhile. Multiple sources of spectral data derived from different remote sensing sensors can be utilized for detailed mapping a variety of hydrothermal alteration minerals in the visible near infrared (VNIR) and the shortwave infrared (SWIR) regions. In this research, Landsat-8, Sentinel-2, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (WV-3) satellite remote sensing sensors were used for prospecting Zn-Pb mineralization in the central part of the Kashmar–Kerman Tectonic Zone (KKTZ), the Central Iranian Terrane (CIT). The KKTZ has high potential for hosting Pb-Zn mineralization due to its specific geodynamic conditions (folded and thrust belt) and the occurrence of large carbonate platforms. For the processing of the satellite remote sensing datasets, band ratios and principal component analysis (PCA) techniques were adopted and implemented. Fuzzy logic modeling was applied to integrate the thematic layers produced by image processing techniques for generating mineral prospectivity maps of the study area. The spatial distribution of iron oxide/hydroxides, hydroxyl-bearing and carbonate minerals and dolomite were mapped using specialized band ratios and analyzing eigenvector loadings of the PC images. Subsequently, mineral prospectivity maps of the study area were generated by fusing the selected PC thematic layers using fuzzy logic modeling. The most favorable/prospective zones for hydrothermal ore mineralizations and carbonate-hosted Pb-Zn mineralization in the study region were particularly mapped and indicated. Confusion matrix, field reconnaissance and laboratory analysis were carried out to verify the occurrence of alteration zones and highly prospective locations of carbonate-hosted Pb-Zn mineralization in the study area. Results indicate that the spectral data derived from multi-sensor remote sensing satellite datasets can be broadly used for generating remote sensing-based prospectivity maps for exploration of carbonate-hosted Pb-Zn mineralization in many metallogenic provinces around the world.
A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision

Abstract Higher dimensional data such as video and 3D are the leading edge of multimedia retrieval and computer vision research. In this survey, we give a comprehensive overview and key insights into the state of the art of higher dimensional features from deep learning and also traditional approaches. Current approaches are frequently using 3D information from the sensor or are using 3D in modeling and understanding the 3D world. With the growth of prevalent application areas such as 3D games, self-driving automobiles, health monitoring and sports activity training, a wide variety of new sensors have allowed researchers to develop feature description models beyond 2D. Although higher dimensional data enhance the performance of methods on numerous tasks, they can also introduce new challenges and problems. The higher dimensionality of the data often leads to more complicated structures which present additional problems in both extracting meaningful content and in adapting it for current machine learning algorithms. Due to the major importance of the evaluation process, we also present an overview of the current datasets and benchmarks. Moreover, based on more than 330 papers from this study, we present the major challenges and future directions.
Reliable Real‐Time Seismic Signal/Noise Discrimination With Machine Learning

In Earthquake Early Warning (EEW), every sufficiently impulsive signal is potentially the first evidence for an unfolding large earthquake. More often than not, however, impulsive signals are mere nuisance signals. One of the most fundamental - and difficult - tasks in EEW is to rapidly and reliably discriminate real local earthquake signals from all other signals. This discrimination is necessarily based on very little information, typically a few seconds worth of seismic waveforms from a small number of stations. As a result, current EEW systems struggle to avoid discrimination errors, and suffer from false and missed alerts. In this study we show how modern machine learning classifiers can strongly improve real-time signal/noise discrimination. We develop and compare a series of non-linear classifiers with variable architecture depths, including fully connected, convolutional (CNN) and recurrent neural networks, and a model that combines a generative adversarial network with a random forest (GAN+RF). We train all classifiers on the same data set, which includes 374k local earthquake records (M3.0-9.1) and 946k impulsive noise signals. We find that all classifiers outperform existing simple linear classifiers, and that complex models trained directly on the raw signals yield the greatest degree of improvement. Using 3s long waveform snippets, the CNN and the GAN+RF classifiers both reach 99.5% precision and 99.3% recall on an independent validation data set. Most misclassifications stem from impulsive teleseismic records, and from incorrectly labeled records in the data set. Our results suggest that machine learning classifiers can strongly improve the reliability and speed of EEW alerts.
Document-Level Event Argument Extraction by Conditional Generation

Event extraction has long been treated as a sentence-level task in the IE community.We argue that this setting does not match human information seeking behavior and leads to incomplete and uninformative extraction results.We propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates.We also compile a new document-level event extraction benchmark dataset WIKIEVENTS which includes complete event and coreference annotation.On the task of argument extraction, we achieve an absolute gain of 7.6% F1 and 5.7% F1 over the next best model on the RAMS and WIKIEVENTS datasets respectively.On the more challenging task of informative argument extraction, which requires implicit coreference reasoning, we achieve a 9.3% F1 gain over the best baseline.To demonstrate the portability of our model, we also create the first end-to-end zero-shot event extraction framework and achieve 97% of fully supervised model's trigger extraction performance and 82% of the argument extraction performance given only access to 10 out of the 33 types on ACE. 1 Prosecutors say he drove the truck to Geary Lake in Kansas, that 4,000 pounds of ammonium nitrate laced with nitromethane were loaded into the truck there, and that it was driven to Oklahoma City and detonated.Elliott testified that on April 15, McVeigh came into the body shop and reserved the truck, to be picked up at 4pm two days later.Elliott said that McVeigh gave him the $280.32 in exact change after declining to pay an additional amount for insurance.
Multi-Objective Particle Swarm Optimization Based Preprocessing of Multi-Class Extremely Imbalanced Datasets

Today’s datasets are usually very large with many features and making analysis on such datasets is really a tedious task. Especially when performing classification, selecting attributes that are salient for the process is a brainstorming task. It is more difficult when there are many class labels for the target class attribute and hence many researchers have introduced methods to select features for performing classification on multi-class attributes. The process becomes more tedious when the attribute values are imbalanced for which researchers have contributed many methods. But, there is no sufficient research to handle extreme imbalance and feature selection together and hence this paper aims to bridge this gap. Here Particle Swarm Optimization (PSO), an efficient evolutionary algorithm is used to handle imbalanced dataset and feature selection process is also enhanced with the required functionalities. First, Multi-objective Particle Swarm Optimization is used to transform the imbalanced datasets into balanced one and then another version of Multi-objective Particle Swarm Optimization is used to select the significant features. The proposed methodology is applied on eight multi-class extremely imbalanced datasets and the experimental results are found to be better than other existing methods in terms of classification accuracy, G mean, F measure. The results validated by using Friedman test also confirm that the proposed methodology effectively balances the dataset with less number of features than other methods.
The Effect of the Normalization Method Used in Different Sample Sizes on the Success of Artificial Neural Network Model

In this study, it was aimed to compare different normalization methods employed in model developing process via artificial neural networks with different sample sizes. As part of comparison of normalization methods, input variables were set as: work discipline, environmental awareness, instrumental motivation, science self-efficacy, and weekly science learning time that have been covered in PISA 2015, whereas students' Science Literacy level was defined as the output variable. The amount of explained variance and the statistics about the correct classification ratios were used in the comparison of the normalization methods discussed in the study. The dataset was analyzed in Matlab2017b software and both prediction and classification algorithms were used in the study. According to the findings of the study, adjusted min-max normalization method yielded better results in terms of the amount of explained variance in different sample sizes compared to other normalization methods; no significant difference was found in correct classification rates according to the normalization method of the data, which lacked normal distribution and the possibility of overfitting should be taken into consideration when working with small samples in the modelling process of artificial neural network. In addition, it was also found that sample size had a significant effect on both classification and prediction analyzes performed with artificial neural network methods. As a result of the study, it was concluded that with a sample size over 1000, more consistent results can be obtained in the studies performed with artificial neural networks in the field of education.
Securing e-voting based on blockchain in P2P network

Electronic voting (e-voting) is an electronic means for casting and counting votes. It is an efficient and cost-effective way for conducting a voting procedure, which has characteristic of being magnanimous data and real time and requesting high safety. However, concerns on security of networking and privacy of communication for e-voting have been grown. Securing e-voting is very urgent and has becoming a popular topic in the area of communications and networking. We present techniques to exploit blockchain in P2P network to improve the security of e-voting. First, we design a synchronized model of voting records based on distributed ledger technology (DLT) to avoid forgery of votes. Second, we design a user credential model based on elliptic curve cryptography (ECC) to provide authentication and non-repudiation. Third, we design a withdrawal model that allows voters to change their vote before a preset deadline. By integrating the above designs, a blockchain-based e-voting scheme in P2P network is proposed for essential requirements of e-voting process. To prove and verify the scheme, a blockchain-based e-voting system for multiple candidates has been designed on Linux platforms in P2P network. The system involves electronic voting theory, cryptography, and software engineering theory. The implementation result shows that it is a practical and secure e-voting system, which solves the problem on forgery of votes during e-voting. The blockchain-based e-voting system can be applied to a variety of networking applications directly.
TCGL: Temporal Contrastive Graph for Self-Supervised Video Representation Learning

Video self-supervised learning is a challenging task, which requires significant expressive power from the model to leverage rich spatial-temporal knowledge and generate effective supervisory signals from large amounts of unlabeled videos. However, existing methods fail to increase the temporal diversity of unlabeled videos and ignore elaborately modeling multi-scale temporal dependencies in an explicit way. To overcome these limitations, we take advantage of the multi-scale temporal dependencies within videos and propose a novel video self-supervised learning framework named Temporal Contrastive Graph Learning (TCGL), which jointly models the inter-snippet and intra-snippet temporal dependencies for temporal representation learning with a hybrid graph contrastive learning strategy. Specifically, a Spatial-Temporal Knowledge Discovering (STKD) module is first introduced to extract motion-enhanced spatial-temporal representations from videos based on the frequency domain analysis of discrete cosine transform. To explicitly model multi-scale temporal dependencies of unlabeled videos, our TCGL integrates the prior knowledge about the frame and snippet orders into graph structures, i.e., the intra-/inter-snippet Temporal Contrastive Graphs (TCG). Then, specific contrastive learning modules are designed to maximize the agreement between nodes in different graph views. To generate supervisory signals for unlabeled videos, we introduce an Adaptive Snippet Order Prediction (ASOP) module which leverages the relational knowledge among video snippets to learn the global context representation and recalibrate the channel-wise features adaptively. Experimental results demonstrate the superiority of our TCGL over the state-of-the-art methods on large-scale action recognition and video retrieval benchmarks. The code is publicly available at https://github.com/YangLiu9208/TCGL.
ssHealth: Toward Secure, Blockchain-Enabled Healthcare Systems

The future of healthcare systems is being shaped by incorporating emerged technological innovations to drive new models for patient care. By acquiring, integrating, analyzing, and exchanging medical data at different system levels, new practices can be introduced, offering a radical improvement to healthcare services. This article presents a novel smart and secure Healthcare system (ssHealth), which, leveraging advances in edge computing and blockchain technologies, permits epidemics discovering, remote monitoring, and fast emergency response. The proposed system also allows for secure medical data exchange among local healthcare entities, thus realizing the integration of multiple national and international entities and enabling the correlation of critical medical events for, for example, emerging epidemics management and control. In particular, we develop a blockchain-based architecture and enable a flexible configuration thereof, which optimize medical data sharing between different health entities and fulfil the diverse levels of Quality of Service (QoS) that ssHealth may require. Finally, we highlight the benefits of the proposed ssHealth system and possible directions for future research.
Application-Aware Consensus Management for Software-Defined Intelligent Blockchain in IoT

Currently, IoT has become an important carrier of blockchains, which not only makes blockchain more ubiquitous but also improves the security of IoT. Consensus is the core component of blockchains with various forms, which raises the following challenges. Dynamic management and configuration of the consensuses in a blockchain are required because IoT applications have high dynamics. Moreover, an IoT node is usually reutilized by various applications in different blockchains, which means the IoT node should be switched frequently to cross consensuses in different blockchains. To address this, a software-defined blockchain architecture is proposed to realized the dynamic configurations for blockchains. Then a consensus function virtualization approach with application-aware work flow is proposed, which can abstract and manage various consensus resources. Next, a transfer-learning-based intelligent scheme is designed to implement the application- layer packet analysis and perform the efficient management of virtualized consensus resources. Experiment results indicate the feasibility of the proposed scheme. This work is significant in enhancing the flexibility and extendibility of blockchains in IoT.
VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding

We present VideoCLIP, a contrastive approach to pre-train a unified model for zeroshot video and text understanding, without using any labels on downstream tasks.VideoCLIP trains a transformer for video and text by contrasting temporally overlapping positive video-text pairs with hard negatives from nearest neighbor retrieval.Our experiments on a diverse series of downstream tasks, including sequence-level text-video retrieval, VideoQA, token-level action localization, and action segmentation reveal state-ofthe-art performance, surpassing prior work, and in some cases even outperforming supervised approaches.Code is made available at https:
Solving differential equations with neural networks: Applications to the calculation of cosmological phase transitions

Starting from the observation that artificial neural networks are uniquely suited to solving optimisation problems, and most physics problems can be cast as an optimisation task, we introduce a novel way of finding a numerical solution to wide classes of differential equations. We find our approach to be very flexible and stable without relying on trial solutions, and applicable to ordinary, partial and coupled differential equations. We apply our method to the calculation of tunnelling profiles for cosmological phase transitions, which is a problem of relevance for baryogenesis and stochastic gravitational wave spectra. Comparing our solutions with publicly available codes which use numerical methods optimised for the calculation of tunnelling profiles, we find our approach to provide at least as accurate results as these dedicated differential equation solvers, and for some parameter choices even more accurate and reliable solutions. In particular, we compare the neural network approach with two publicly available profile solvers, \texttt{CosmoTransitions} and \texttt{BubbleProfiler}, and give explicit examples where the neural network approach finds the correct solution while dedicated solvers do not. We point out that this approach of using artificial neural networks to solve equations is viable for any problem that can be cast into the form $\mathcal{F}(\vec{x})=0$, and is thus applicable to various other problems in perturbative and non-perturbative quantum field theory.
Traffic flow estimation with data from a video surveillance camera

This study addresses the problem of traffic flow estimation based on the data from a video surveillance camera. Target problem here is formulated as counting and classifying vehicles by their driving direction. This subject area is in early development, and the focus of this work is only one of the busiest crossroads in city Chelyabinsk, Russia. To solve the posed problem, we employed the state-of-the-art Faster R-CNN two-stage detector together with SORT tracker. A simple regions-based heuristic algorithm was used to classify vehicles movement direction. The baseline performance of the Faster R-CNN was enhanced by several modifications: focal loss, adaptive feature pooling, additional mask branch, and anchors optimization. To train and evaluate detector, we gathered 982 video frames with more than 60,000 objects presented in various conditions. The experimental results show that the proposed system can count vehicles and classify their driving direction during weekday rush hours with mean absolute percentage error that is less than 10%. The dataset presented here might be further used by other researches as a challenging test or additional training data.