Reviewerly/exHarmony huggingface dataset:
GroningenNet: Deep Learning for Low-Magnitude Earthquake Detection on a Multi-Level Sensor Network

Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe. The detection of low-magnitude seismic events is essential for microseismic monitoring of hydraulic fracturing, carbon capture and storage, and geothermal operations for hazard detection and mitigation. Moreover, the detection of micro-earthquakes is crucial to understanding the underlying mechanisms of larger earthquakes. Various algorithms, including deep learning methods, have been proposed over the years to detect such low-magnitude events. However, there is still a need for improving the robustness of these methods in discriminating between local sources of noise and weak seismic events. In this study, we propose a convolutional neural network (CNN) to detect seismic events from shallow borehole stations in Groningen, the Netherlands. We train a CNN model to detect low-magnitude earthquakes, harnessing the multi-level sensor configuration of the G-network in Groningen. Each G-network station consists of four geophones at depths of 50, 100, 150, and 200 m. Unlike prior deep learning approaches that use 3-component seismic records only at a single sensor level, we use records from the entire borehole as one training example. This allows us to train the CNN model using moveout patterns of the energy traveling across the borehole sensors to discriminate between events originating in the subsurface and local noise arriving from the surface. We compare the prediction accuracy of our trained CNN model to that of the STA/LTA and template matching algorithms on a two-month continuous record. We demonstrate that the CNN model shows significantly better performance than STA/LTA and template matching in detecting new events missing from the catalog and minimizing false detections. Moreover, we find that using the moveout feature allows us to effectively train our CNN model using only a fraction of the data that would be needed otherwise, saving plenty of manual labor in preparing training labels. The proposed approach can be easily applied to other microseismic monitoring networks with multi-level sensors.
A fast robotic arm gravity compensation updating approach for industrial application using sparse selection and reconstruction

With the increasing complexity of robot tasks in the industrial field, sometimes the manipulator needs to change the payload or replace end-effectors frequently in a period of time, which will cause the change of gravity model. This paper presents a fast gravity compensation updating method based on sparse selection and reconstruction. Instead of using the classic model, feature sets in the formulation of dynamic motion are exploited and the gravity model learning is transformed into a sparse problem. Then, the Alternating Direction Multiplier Method (ADMM) is used to accelerate the process of solving the sparse optimization problem and reconstructing the effective features of the gravity model from the original signal. The merits of our method are that it does not depend on any kinematic and dynamic parameters, and there is no need to redesign the specific excitation trajectory in the gravity model updating. Thus, the updating process avoids heavy work of calibration and simplifies the labor complexity considerably from the conventional analytical methods. The results of various payload experiments on a real 7-DOF manipulator show that the proposed method can update the gravity compensation model efficiently and accurately.
Image Dehazing Based on Pixel Guided CNN with PAM via Graph Cut

Image dehazing is still an open research topic that has been undergoing a lot of development, especially with the renewed interest in machine learning-based methods. A major challenge of the existing dehazing methods is the estimation of transmittance, which is the key element of haze-affected imaging models. Conventional methods are based on a set of assumptions that reduce the solution search space. However, the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image. In this paper we reduce the number of simplified hypotheses in order to attain a more plausible and realistic solution by exploiting a priori knowledge of the ground truth in the proposed method. The proposed method relies on pixel information between the ground truth and haze image to reduce these assumptions. This is achieved by using ground truth and haze image to find the geometric-pixel information through a guided Convolution Neural Networks (CNNs) with a Parallax Attention Mechanism (PAM). It uses the differential pixel-based variance in order to estimate transmittance. The pixel variance uses local and global patches between the assumed ground truth and haze image to refine the transmission map. The transmission map is also improved based on improved Markov random field (MRF) energy functions. We used different images to test the proposed algorithm. The entropy value of the proposed method was 7.43 and 7.39, a percent increase of and , respectively, compared to the best existing results. The increment is similar in other performance quality metrics and this validate its superiority compared to other existing methods in terms of key image quality evaluation metrics. The proposed approach's drawback, an over-reliance on real ground truth images, is also investigated. The proposed method show more details hence yields better images than those from the existing state-of-the-art-methods.
Archives, linked data and the digital humanities: increasing access to digitised and born-digital archives via the semantic web

Abstract Mass digitisation and the exponential growth of born-digital archives over the past two decades have resulted in an enormous volume of archives and archival data being available digitally. This has produced a valuable but under-utilised source of large-scale digital data ripe for interrogation by scholars and practitioners in the Digital Humanities. However, current digitisation approaches fall short of the requirements of digital humanists for structured, integrated, interoperable, and interrogable data. Linked Data provides a viable means of producing such data, creating machine-readable archival data suited to analysis using digital humanities research methods. While a growing body of archival scholarship and praxis has explored Linked Data, its potential to open up digitised and born-digital archives to the Digital Humanities is under-examined. This article approaches Archival Linked Data from the perspective of the Digital Humanities, extrapolating from both archival and digital humanities Linked Data scholarship to identify the benefits to digital humanists of the production and provision of access to Archival Linked Data. It will consider some of the current barriers preventing digital humanists from being able to experience the benefits of Archival Linked Data evidenced, and to fully utilise archives which have been made available digitally. The article argues for increased collaboration between the two disciplines, challenges individuals and institutions to engage with Linked Data, and suggests the incorporation of AI and low-barrier tools such as Wikidata into the Linked Data production workflow in order to scale up the production of Archival Linked Data as a means of increasing access to and utilisation of digitised and born-digital archives.
Comparison Of Market Basket Analysis To Determine Consumer Purchasing Patterns Using Fp-Growth And Apriori Algorithm

The development of technology in the field of business grows rapidly day by day. The use of technology in business can help company deploying best business strategies to compete with others. Companies can take advantage of sales data to discover more information that can help them make decisions. Using data mining approach, companies can process transaction data in order to find out consumer buying patterns. In this paper, the authors implement the association rules mining or often referred to as Market Basket Analysis for transaction data processing using RapidMiner by comparing FP-Growth and Apriori algorithm. The results of this study from 1641 transaction rows data, with minimum support of 0.09 and confidence of 0.9, is that the Fp-Growth needs 6 seconds to produce 19 rules and forms a combination of 3 itemset with a rule strength of 112.66%. and an accuracy of 217%. While the Apriori 30 second algorithm produces 6 rules and forms a combination of 3 items with a rule strength of 52.47% and an accuracy of 46%. From the results of the comparison of algorithms, it can be concluded that the Fp-Growth algorithm is better than the Apriori algorithm.
A hybrid model of stacked autoencoder and modified particle swarm optimization for multivariate chaotic time series forecasting

Phase space reconstruction (PSR) is an effective method for chaotic system modeling, which can reveal the implicit evolution information in a complex system. However, the reconstructed time series tend to have a high dimension and contain some redundant information. It is difficult for a traditional simple model to directly forecast the reconstructed time series. In this paper, we propose a hybrid model of stacked autoencoder (SAE) and modified particle swarm optimization (MPSO) for multivariate chaotic time series forecasting. We utilize SAE to extract the reconstructed time series and adopt feedforward neural network (FNN) to forecast time series. In the proposed hybrid model, the SAE is followed by FNN, and we make the MPSO to train the output weights of the model, which is a large-scale optimization problem. To enhance the generalization ability and prevent over-fitting, we add a regularization item to the objective function when MPSO trains the weights of the model. Experimental results show that MPSO algorithm has advantages in the exploration and exploitation in large-scale optimization problems. Then, experiments on Lorenz time series and two real-world time series datasets verify the effectiveness of the hybrid model in multivariate chaotic time series forecasting.
Model Compression for Communication Efficient Federated Learning

Despite the many advantages of using deep neural networks over shallow networks in various machine learning tasks, their effectiveness is compromised in a federated learning setting due to large storage sizes and high computational resource requirements for training. A large model size can potentially require infeasible amounts of data to be transmitted between the server and clients for training. To address these issues, we investigate the traditional and novel compression techniques to construct sparse models from dense networks whose storage and bandwidth requirements are significantly lower. We do this by separately considering compression techniques for the server model to address downstream communication and the client models to address upstream communication. Both of these play a crucial role in developing and maintaining sparsity across communication cycles. We empirically demonstrate the efficacy of the proposed schemes by testing their performance on standard datasets and verify that they outperform various state-of-the-art baseline schemes in terms of accuracy and communication volume.
Efficient, Private and Robust Federated Learning

Federated learning (FL) has demonstrated tremendous success in various mission-critical large-scale scenarios. However, such promising distributed learning paradigm is still vulnerable to privacy inference and byzantine attacks. The former aims to infer the privacy of target participants involved in training, while the latter focuses on destroying the integrity of the constructed model. To mitigate the above two issues, a few works recently explored unified solutions by utilizing generic secure computation techniques and common byzantine-robust aggregation rules, but there are two major limitations: 1) they suffer from impracticality due to efficiency bottlenecks, and 2) they are still vulnerable to various types of attacks because of model incomprehensiveness.
Differential evolution with dynamic combination based mutation operator and two-level parameter adaptation strategy

Differential evolution (DE) is a simple yet effective algorithm for numerical optimization, and its performance significantly depends on mutation operator and control parameters. Therefore, designing appropriate mutation operator and parameter regulation strategy is an important and necessary task. To improve the performance of DE algorithm, we propose a novel DE variant called DCDE based on a dynamic combination based mutation operator and a two-level parameter regulation strategy. More specifically, the newly proposed mutation operator contains a dynamic base vector that consists of two individuals, one is the current optimal individual while the other, called elite individual, is the best one among three randomly selected individuals, and they are dynamically combined by a weight parameter associated with the evolution process and the ranking status of the elite individual in the current population. Moreover, the scale factor and crossover rate in DCDE depend on the combined effect of a population-level parameter and one individual-level parameter, respectively. Both mutation operator and control parameters in DCDE are designed to achieve an appropriate balance between global exploration ability and local exploitation ability. To evaluate the performance of DCDE, comparison experiments are conducted with five state-of-the-art DE variants and three non-DE algorithms on solving 29 functions in IEEE CEC 2017 benchmark suite. The comparison results indicate that the proposed DCDE is significantly better than, or at least comparable to the adopted competitors.
How augmented reality affected academic achievement in K-12 education – a meta-analysis and thematic-analysis

The purpose of this study is to determine the effect of augmented reality (AR) applications on K-12 students’ academic achievement through meta-analysis and thematic analysis. 40 samples from 35 articles published between 2006 and 2020 were used to examine the impact of AR on academic achievement and to explore the effects of differences in grade level, academic subject, duration of the experiment, and sampling methods. The research results showed that (1) AR has a medium effect on K-12 students’ academic achievement (g = 0.437) according to the Thalheimer and Cook’s effect size classification. (2) academic subject and duration of experiment as moderator variables have a significant influence on whether AR application will enhance K-12 students’ academic achievement, and (3) the advantages of AR in K-12 education include enhancing interest and motivation, offering vivid resources, providing an interactive environment, and improving attention. The disadvantages include integrated method limitations, system function shortcomings, and the causing of physical discomfort problems. The advantages and disadvantages show why AR application has a medium effect on K-12 students’ academic achievement.
WeChat-Based Interactive Translation Mobile Teaching Model

Although the research results of mobile teaching mode at home and abroad have been involved in all aspects of education and teaching and provided a lot of useful reference for subsequent researchers, the lack of research on WeChat interactive translation teaching mode makes the practical research in this field urgent. As China’s education industry continues to pursue international development, it is necessary to train a large number of international talents who have an international perspective, are familiar with international rules, and can participate in international affairs and international competition. However, to become an international talent, you must first have superior English communication skills and be able to deal with various international situations proficiently. The English education goals at the basic education stage are simply beyond reach and can only be cultivated through professional and systematic college English courses. This shows that college English education is at a crucial position in the future development of education. This study attempts to explore how to apply the interactive teaching mode supported by WeChat network platform in translation teaching and English learning of high school students, so as to improve students’ autonomy in translation practice, promote students’ personalized learning, and improve students’ interest and ability in English learning. Based on the investigation of a senior high school in our city, the school began to implement the interactive translation mobile teaching mode based on WeChat in September 2018. The experiment selected the change of students’ English learning achievements in a certain semester and judged whether the mobile teaching mode was conducive to the improvement of learning performance based on t-test. We also need to collect the needs of teachers and parents for the WeChat based mobile teaching mode and judge whether the mode can be promoted. The experimental results show that this teaching mode has an obvious effect on the improvement of students’ English scores, especially in the eighth grade, whose scores have increased from 72.6 to 75. Through the construction of WeChat based interactive translation mobile teaching mode, parents can fully understand the key points of English learning and make English tutoring more targeted and timely. This teaching mode is worthy of promotion in senior high school English course learning.
Modeling behavior sequence for personalized fund recommendation with graphical deep collaborative filtering

Financial services are increasingly personalized due to the rapid development of artificial intelligence. In particular, precision marketing is the ultimate goal in providing financial services. However, precision marketing is hindered by problems in model scalability, cold starts, and insufficient transaction data points for many customers and products. Some researchers have attempted to solve these problems by incorporating deep learning into the collaborative filtering algorithm. This has improved the performance of the traditional matrix factorization algorithm, but it comes at the cost of imposing a limit on the capacity of the recommendation model to capture the complex interaction features. In this paper, we propose a graphical deep collaborative filtering (GraphDCF) algorithm for providing personalized mutual fund recommendations. The graph-structured network is constructed by connecting customer nodes with similar purchases and redeemed trading orders in the sequential view. In this manner, we can model different latent relationships among customers who have similar shopping habits. Subsequently, the corresponding embedding vector for each customer node is generated through an aggregate function based on similarity in transaction behaviors. Finally, to provide personalized recommendations, in addition to customer features and mutual fund attributes, the proposed deep embedded collaborative filtering framework predicts how willing a customer is to purchase a mutual fund. Experimental results on a real-world data set from Taiwan Commercial bank demonstrated indicated that DECF approaches outperformed deep learning methods such as DCF and NCF. The proposed GraphDCF algorithm outperformed (by up to 2.3%) other frequently used approaches.
Vision Based Real Time Monitoring System for Elderly Fall Event Detection Using Deep Learning

Human fall detection plays a vital part in the design of sensor based alarming system, aid physical therapists not only to lessen after fall effect and also to save human life. Accurate and timely identification can offer quick medical services to the injured people and prevent from serious consequences. Several vision-based approaches have been developed by the placement of cameras in diverse everyday environments. At present times, deep learning (DL) models particularly convolutional neural networks (CNNs) have gained much importance in the fall detection tasks. With this motivation, this paper presents a new vision based elderly fall event detection using deep learning (VEFED-DL) model. The proposed VEFED-DL model involves different stages of operations namely preprocessing, feature extraction, classification, and parameter optimization. Primarily, the digital video camera is used to capture the RGB color images and the video is extracted into a set of frames. For improving the image quality and eliminate noise, the frames are processed in three levels namely resizing, augmentation, and min–max based normalization. Besides, MobileNet model is applied as a feature extractor to derive the spatial features that exist in the preprocessed frames. In addition, the extracted spatial features are then fed into the gated recurrent unit (GRU) to extract the temporal dependencies of the human movements. Finally, a group teaching optimization algorithm (GTOA) with stacked autoencoder (SAE) is used as a binary classification model to determine the existence of fall or non-fall events. The GTOA is employed for the parameter optimization of the SAE model in such a way that the detection performance can be enhanced. In order to assess the fall detection performance of the presented VEFED-DL model, a set of simulations take place on the UR fall detection dataset and multiple cameras fall dataset. The experimental outcomes highlighted the superior performance of the presented method over the recent methods.
OPFaceNet: OPtimized Face Recognition Network for noise and occlusion affected face images using Hyperparameters tuned Convolutional Neural Network

Face recognition is considered as important research in computer vision applications, and it is regarded as the basic biometric security system. Research related to face recognition has been done in the past several years. Still, many more challenges associated with this field need to be addressed. Some literary works have designed the face recognition model on the relatively controlled environments; yet, their performance in general settings has been substandard This paper develops an Optimal Face Recognition Network (OPFaceNet) to recognize the face images affected by high noise and occlusion. The feature patterns subjected to noise like LBP, FLBP, and NRLBP are extracted. The average of all three patterns is given to the proposed Convolutional Neural Network (CNN) classifier. As the main contribution, the CNN model is enhanced by optimizing the Fitness Sorted Rider Optimization Algorithm (FS-ROA). This algorithm optimizes the hyperparameters of CNN like Convolutional layer, Pooling Layer, Fully connected layer, number of Hidden layers, and Type of Pooling. Finally, the simulation results show that the system achieves a good recognition rate of 97.2% and is robust against variations in terms of occlusion and noise when benchmarked over diverse datasets.