Reviewerly/exHarmony huggingface dataset:
European Digital Library for the Intercultural Dialogue. Interactive Environments for Education, Cultural Heritage and Civic Empowerment

The European Digital Library project encompasses the establishment of a digital community library based on the use of new technologies for the knowledge dissemination in a European framework and the design of an experiential environment for education, cultural heritage fruition and, active participation in civic society. The project operates within the community libraries proposing the development of experiential services and digital applications. The initiative, promoted by the Puglia Region for the activation of Community Libraries within the regional territory, aims to set up an integrated creative environment supporting local development and socio-educational and tourism-cultural actions. In particular, the community library is dedicated to local activation and cultural cohesion, conceived as an advanced library model offering innovative services for the promotion of reading and culture.
Design and Implementation of the Teaching Management System for English Language and Literature

In the entire national culture, language is an inseparable part and it is a microcosm of cultures, and its contents and expressions contain a lot of the cultural customs. Therefore, through the study of the language, students can have a better understanding of the national cultures, and their scope of knowledge will be further expanded. In addition, the understanding of the cultural customs can also improve the “empathic” ability of the students, so that they can correctly analyze and think about the ideas of others, which is of great importance and significance to the improvement of the students’ language application abilities.
Gann Tools for Better Crypto Currency Trading Outcomes

W.D. Gann was a legendary market technician a century ago when loud, clunky wooden boxes hammered out the latest stock quotes on ribbons of tape that collected in piles on the posh floors of Wall Street investment firms. Arcane concepts such as the Golden Ratio and the Square of 91... number spirals and 360 degree angles...cryptic astrological references... an uncanny ability to predict stock prices coupled with rumors of fantastic fortunes...all of these things make up the Gann mystique. Like Elliot Wave, the challenge of Gann angles is that it’s not clear how to apply the rules and the guidelines themselves do not work consistently. Traders have to consider the odds and realize quickly when they are wrong. Like looking forward to a stroll on the sunny side of the street both day and night, rain and shine, a lack of timing will bring disappointment. This paper will demonstrate that in 2018, Gann angles applied correctly to cryptocurrency charts achieved trading outperformance.
Breaking the diffraction limit with nearest neighbor pixel deconvolution

We report an image processing method which is able to break the diffraction limit for single frame images. If an image has a circularly symmetric point spread function (PSF), its spatial resolution can be enhanced by a new image processing method, the nearest neighbor pixel deconvolution (NNPD), as long as its pixel size is much smaller than distribution of the PSF. This method rewrites the PSF in terms of the nearest pixel groups, applies the Shift Theorem of the Fourier Transform to modify the PSF in the Fourier domain, and achieves a higher spatial resolution with the inverse Fourier Transform. The results of our experiment show that this method could enhance image resolution beyond the diffraction limit. We applied this method to two space images, higher resolution with some new features were shown in the processed images.
Handling Imbalanced Time Series Through Ensemble of Classifiers: A Multi-class Approach for Solar Flare Forecasting

Solar flares are huge releases of energy from Sun. They are categorized in five levels of potential damage to Earth (A, B, C, M, and X) and may produce strong impacts to communication systems, threatening human activities dependent on satellites and GPS. Therefore, predicting it in advance may reduce their negative impacts. However, solar flare forecasting has significant challenges: (a) the data is highly imbalanced, (b) adjacent classes are sometimes indiscernible, (c) current approaches perform binary forecasting (aggregating solar flare classes), instead of multi-class, as actually required. So, we aim to tackle these open issues by proposing a new Ensemble method called ECID(Ensemble of classifiers for imbalanced datasets). For each solar flare class, ECID employs a stratified random sampling for training one-class base inducers, strengthen their sensitivity to a given class. Using a modified bootstrap approach, the aggregator method combines the inducers results, enabling multi-class forecasting, which can also be multi-label in case of indiscernible classes. The results showed that ECID is well-suited for forecasting solar flares, achieving a maximum mean of True Positive Rate (TPR) of 96% and a Precision of 87%, in a time horizon of one day.
Three-dimensional shape measurement of specular object with discontinuous surfaces by direct phase measuring deflectometry

Phase measuring deflectometry (PMD) has been widely studied to obtain three-dimensional (3D) shape of specular surfaces. Due to the procedure of slope integration, complicated specular components having discontinuous surfaces cannot be measured by the existing PMD methods. This paper presents a novel Direct PMD (DPMD) method to solve this problem of measuring discontinuous specular objects. A mathematical model is established to directly relate the absolute phase and depth data. Then a hardware measuring system has been set up. The system parameters are calibrated by using a plane mirror and a translating stage. 3D shape of an artificial specular step, a monolithic multi-mirror array having multiple specular surfaces and a reflected diamond distribution surface has been measured. The experimental results verified that the proposed method based on DPMD successfully measured full-field 3D shape of specular objects having discontinuous surfaces accurately and effectively.
Deep learning based automatic signal modulation classification

In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to automatically classify signal modulation more efficiently, which can further help in radio frequency modeling and pattern recognition problem solving. Three different approaches Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) have been deployed and evaluated in the signal modulation classification. In this paper, the signals for training and validation are generated using our MATLAB based RF signal generator, which can simulate various types of modulated signal according to the configuration specification. The numerical results show that CNN network can outperform the DNN and RNN in terms of the signal modulation classification accuracy.
Lessons learned: data mining and aviation explosives detection systems

Prior to the advent of modern deep learning techniques, data mining was already being used for image processing in aviation security. In 2010, the paper “Applying data mining to false alarm reduction in an aviation explosives detection system”, detailed lessons learned from using automated data mining techniques for false alarm identification. The paper included a series of observations and recommendations. Nearly a decade later, deep learning is showing tremendous promise for a variety of image processing problems (in general) and to CT-based explosives detection systems (EDS) in particular. While some risks and shortcomings of deep learning are understood, the particular issues associated with aviation security applications may not be. We revisit the earlier work and see whether it withstands the test of time and still applies. We then combine the earlier work with modern deep learning design guidelines, to form a guide to using deep learning for aviation security.
Leave the Clipboard Behind—Smart, Digital Solutions for Field Data

In response to two consent decrees with the Ohio EPA, the city of Columbus created the Blueprint Columbus Program in 2015. The Blueprint Columbus approach targets to reduce water-in-basement (WIB), sanitary sewer overflows (SSO), and combined sewer overflows (CSO) by implementing lateral lining, downspout redirection, sump pump installation, and green infrastructure (GI) projects. Columbus is currently constructing a GI asset consisting of pervious pavers, regional rain gardens, bio basins, and bumpouts in the urban neighborhood of Clintonville. Monthly maintenance inspections are performed on each GI during the 2-year establishment period to identify areas with repetitive maintenance concerns. To streamline the inspections and quickly disseminate monitoring data, Arcadis developed a mobile survey workflow using available software applications to complete inspections in the field and track GI assets. This paper will provide an overview of the software applications, survey usability, and process for generating GI reports and summary tables.
Properties of federated averaging on highly distributed data

Tactical edge environments are highly distributed with a large number of sensing, computational, and communication nodes spread across large geographical regions, governments, and situated in unique operational environments. In such settings, a large number of observations and actions may occur across a large number of nodes but each node may only have a small number of these data locally. Further, there may be technical as well as policy constraints in aggregating all observations to a single node. Learning from all of the data may uncover critical correlations and insights. However, without having access to all the data, this is not possible. Recently proposed federated averaging approaches allow for learning a single model from data spread across multiple nodes and achieve good results on image classification tasks. However, this still assumes a sizable amount of data on each node and a small number of nodes. This paper investigates the properties of federated averaging for neural networks relative to batch sizes and number of nodes. Experimental results on a human activity dataset finds that (1) accuracy indeed drops as the number of nodes increase but only slightly, however (2) accuracy is highly sensitive to the batch size only in the federated averaging case.
Intellectual Human-Object Classification Using Genetic Algorithm

Video surveillance applications need to segregate human from a variety of objects present in the environment to avoid the performance degradation in their classification. We propose a method to classify human and non-human objects present in the scene using Genetic Algorithm (GA). The initial population (chromosomes) for the genetic algorithm is formed by the coefficients of the Stationary wavelet transform applied for the image and evaluated using the proposed fitness function. Based on the best fitness values, the parents are selected for reproduction followed by mutation to repopulate the generations. The process of producing a new generation and selecting the best member is repeated until the best fitness valued chromosome is met. The Stationary wavelet transform (SWT) has multi resolution property that improves the reconstruction of the image when compared with the conventional Discrete Wavelet Transform (DWT). GA is a global stochastic search and optimization technique that can explore a large solution space and concentrate the search in the regions which lead to fitter structures and hence better solutions are obtained. The proposed method is evaluated by various performance metrics and found better than other state-of-art-methods.
Node Similarity Top-k Query of the Large-Scale Dynamic Graph With Weak Repeated Path Constraint

Node similarity Top-k query has always been a hot topic in the graph field. The existing node similarity Top-k query methods often need to traverse the whole graph, so its iterative calculation process is very inefficient on the large-scale graph. Besides, when the graph changes dynamically, it is difficult to update the query process and results adaptively, leading to the low accuracy of the query results. To solve those problems, in this paper, we propose a node similarity Top-k query method of the large-scale dynamic graph with weak repeated path constraint. In this method, we first divide the similarity relation according to the correlation degree of nodes in the base graph. Then, we put forward the direct similarity and the generic similarity, meanwhile, give their definition and expression, prove the isolation of direct similarity query in the base graph based on the direct similarity relation. And then a quick query method for the direct similarity Top-k attached result set of i step walk is given on the basis of the value of Top-k. When the attached result set of the direct similarity query cannot satisfy the strict Top-k query requirement, by setting weak repeated path multilevel constraint and introducing the idea of the Monte Carlo simulation, we put forward single-edge weaken factor and change the base graph into many single-direction graphs by weak pageRank random walk mechanism, and then realize the cumulative calculation for the generic similarity Top-k complete result set in single-direction graph set. Finally, according to the dynamic characteristics of the graph, based on the principle of local adaptation, we set the trigger update strategy of the base graph and the linkage update strategy of the weak single-direction graph set to ensure the accuracy of the query and minimize the updating maintenance cost. The experiments show that the method in this paper has great advantages in query efficiency, query accuracy, and update efficiency.
A robust framework for tamper detection in digital recorded voice signals

This paper focuses on the tampering detection and pinpointing the manipulated region in digital recorded voice signals via blind semi-fragile watermark embedded in the frequency domain using the Fast Fourier Transform and Quantization Index Modulation-Dither Modulation. The proposed method has been evaluated with several male and female recorded voice signals. The designed method can provide non-audible perceptible difference between the original and the watermarked voice signals obtaining an average Signal-to-Noise Ratio of 44dB, providing resistance to various MP3 compression rates. The simulation results of the parallel implementation of the proposed technique on multicore central processing unit platform has shown efficient performance in a real-time environment.
A Polarimetric SAR and Multispectral Remote Sensing Approach for Mapping Salt Diapirs: Axel Heiberg Island, NU, Canada

Remote sensing has revolutionized resource exploration by enabling quick surveillance of large areas. Quad-polarimetric synthetic aperture radar (SAR) is useful for assessing surface roughness, but few studies have applied it for geological mapping. Located in the Canadian Arctic, Axel Heiberg Island is a suitable site for exploring remote predictive geologic mapping techniques that combine quad-polarimetric SAR and multispectral datasets. The island has extensive rock exposure, with little interference from vegetation and snow in late summer. Axel Heiberg Island has the second highest concentration of salt diapirs globally. As a result, it also hosts extensive secondary salt deposits that have been weathered and precipitated away from their source. Because diapirs frequently provide structural traps for petroleum reservoirs, it is important to distinguish between diapiric and non-diapiric salt during early exploration. This study maps diapirs and secondary salts using multispectral data and characterizes them in polarimetric SAR. Diapirs appear rough in C-Band and L-Band radar, whereas the secondary salts appear smooth at both (cm–dm) scales. Field observations confirm salt diapirs are rough at the millimeter–meter scales, whereas secondary salts precipitate on smoother surfaces. These results show that radar can help differentiate between diapiric and secondary salt exposures, which will assist in future resource exploration.
Research on the Application of Computer Aided Translation CAT in the Field of Translation

With the continuous development of the network information age, how to effectively use modern means to eliminate language barriers between people has become a common problem facing all mankind. Translation is a powerful means to overcome language barriers. However, due to its high cost and slow translation speed, traditional manual translation methods can not meet the needs of the translation market, and machine translation emerges as the times require. Machine Translation (MT) is the process of transforming one natural language into another by computer. Although machine translation is simple and convenient, its accuracy still needs to be improved. At present, it can only be used as an auxiliary means of manual translation. For this reason, computer-aided translation (CAT), that is, computer-aided translation, has attracted people’s attention and favor.
Rating Prediction by Combining User Interest and Friendly Relationship

Due to the popularity of social media, users of these sites are sharing what they’re doing with their friends within numerous social sites. Now, we’ve an enormous quantity of explanations, ratings and comments for native facilities. The data is effective for fresh users to evaluate whether or not the facilities meet their needs before its use. During this paper, suggest an approach for rating prediction by combining user interest, friendly relationship info along with item reputation factor to improve the prediction accuracy. So as to predict ratings, we tend to concentrate on users’ rating behaviors and reputation similarity between items. Within the proposed approach for rating prediction, we tend to fuse five factors like personal interest (items and user’s topics related), similarity of social interest (user interest related), social rating behavior similarity (users’ rating pattern habits related), social rating pattern diffusion (behavior of users diffusions related), and item similarity, this can be deduced by distributing the rating of a user set that represent customers evaluation—into a combined framework of matrix factorization. We tend to perform a number of experiments with the Yelp dataset. Figures in the results show that our approach is having good performance.
The Research on Morpheme-Based Mongolian-Chinese Neural Machine Translation

In view of the rich morphology of Mongolian language and the limited vocabulary of neural machine translation, this paper firstly segmenting Mongolian words from different granularity, which are the segmentation of separates morphological suffixes and the segmentation of Ligatures morphological suffixes. For Chinese, we use word segmentation and word division. Then, we studied the morpheme-based Mongolian-Chinese end-to-end neural machine translation under the framework of bidirectional encoder and attention-based decoder. The experimental results show that the segmentation of Mongolian word effectively solves the data sparsity of Mongolian, and the morpheme-based Mongolian-Chinese neural machine translation model can improve the quality of machine translation. The best NIST and BLEU values of the morpheme-based Mongolian-Chinese Neural Machine Translation results were respectively reached 9.4216 and 0.6320.
Dynamic Reliable Voting in Ensemble Learning

The combination of multiple classifiers can produce an optimal solution than relying on the single learner. However, it is difficult to select the reliable learning algorithms when they have contrasted performances. In this paper, the combination of the supervised learning algorithms is proposed to provide the best decision. Our method transforms a classifier score of training data into a reliable score. Then, a set of reliable candidates is determined through static and dynamic selection. The experimental result of eight datasets shows that our algorithm gives a better average accuracy score compared to the results of the other ensemble methods and the base classifiers.
Codes for straggler mitigation in secure distributed linear regression

We consider the setting of a master server who possesses confidential data (genomic, medical data, etc.) and wants to run linear regression on this data, as part of a machine learning algorithm for example. The master wants to distribute these computations to untrusted workers who have volunteered or are incentivized to help with this task. However, the data must be kept private (in an information theoretic sense) and not revealed to the individual workers. The workers may be busy and will take a random time to finish the task assigned to them. We are interested in reducing the aggregate delay experienced by the master. A known solution is to use a linear secret sharing scheme to divide the data into secret shares on which the workers can compute. However, classical codes can provide straggler mitigation assuming a worst-case scenario of a fixed number of stragglers. We introduce a new family of codes called Staircase codes that allow flexibility in the number of stragglers up to a given maximum, and universally achieve the information theoretic limit on the download cost by the Master, leading to latency reduction. Under the shifted exponential model, we find upper and lower bounds on the Master's mean waiting time. We derive the distribution of the Master's waiting time, and its mean, for systems with up to two stragglers. We show that Staircase codes always outperform classical secret sharing codes. For instance, Staircase codes can lead to up to $59&#37;$ reduction in delay compared to classical secret sharing codes. We validate our results with extensive implementation on Amazon EC2 clusters.
A crowd counting method based on multi-column dilated convolutional neural network

Crowd counting is an important part of crowd analysis, which is of great significance to crowd control and management. The convolutional neural network (CNN) based crowd counting method is widely used to solve the problem of insufficient counting accuracy due to heavy occlusion, background clutters, head scale and perspective changes in crowd scenes. The multi-column convolutional neural network (MCNN) is a CNN-based method for crowd counting, which adapts to head scale variation of crowd scenes by constructing multi-column convolutional neural network composing of three single-column networks corresponding to the convolution kernel with different sizes (large, medium and small). However, as the MCNN network is relatively shallow, its receptive field is also limited, which affects the adaptability to large scale variations. In addition, due to insufficient training data, it is necessary to carry out a pre-training strategies which pre-trains the single-column convolutional neural network individually and combines the cumbersome. In this paper, a crowd counting method based on multi-column dilated convolutional neural network was proposed. Dilated convolution was used to enhance the receptive field of the network, so as to be better adaptive to the head scale variations. The image patches were obtained by randomly clipping from the original training data set images in the process of each iterative training to further expand the training data, while the training could be achieved without tedious pre-training. The experimental results on ShanghaiTech public dataset showed that the accuracy of crowd counting proposed in this paper was better than that of MCNN, which proved that this method is more robust to head scale variations in crowd scenes.