Reviewerly/exHarmony huggingface dataset:
Flexible Spatial and Angular Light Field Super Resolution

A light field contains information in four dimensions, two spatial and two angular. Representing a light field by sampling it with a fixed number of pixels implies an inherent trade-off between angular resolution and spatial resolution- one apparently fixed at the time of capture. To enable flexible trade-offs in spatial and angular resolution after the fact, in this paper we apply techniques from super resolution in an integrated fashion. Our approach explores the similarity between light field super resolution (LFSR) and single image super resolution (SISR) and proposes a neural network framework that can carry out flexible super resolution tasks. We present concrete instances of the framework for center-view spatial LFSR, full-view spatial LFSR, and combined spatial and angular LFSR. Experiments with synthetic and real-world data sets show the center-view and full-views approaches outperform state-of-the-art spatial LFSR by over 1dB in PSNR and that the combined approach achieves comparable performance to state-of-the-art spatial LFSR algorithms. Visual results for images rendered from the combined approach show improved resolution of detail, without rendering artifacts.
Source Parameters of Moderate-To-Large Chinese Earthquakes From the Time Evolution of P-Wave Peak Displacement on Strong Motion Recordings

In this work we propose and apply a straightforward methodology for the automatic characterization of the extended earthquake source, based on the progressive measurement of the P-wave displacement amplitude at the available stations deployed around the source. Specifically, we averaged the P-wave peak displacement measurements among all the available stations and corrected the observed amplitude for distance attenuation effect to build the logarithm of amplitude vs. time function, named LPDT curve. The curves have an exponential growth shape, with an initial increase and a final plateau level. By analyzing and modelling the LPDT curves, the information about earthquake rupture process and earthquake magnitude can be obtained. We applied this method to the Chinese strong motion data from 2007 to 2015 with Ms ranging between 4 and 8. We used a refined model to reproduce the shape of the curves and different source models based on magnitude to infer the source-related parameters for the study dataset. Our study shows that the plateau level of LPDT curves has a clear scaling with magnitude, with no saturation effect for large events. By assuming a rupture velocity of 0.9 Vs, we found a consistent self-similar, constant stress drop scaling law for earthquakes in China with stress drop mainly distributed at a lower level (0.2 MPa) and a higher level (3.7 MPa). The derived relation between the magnitude and rupture length may be feasible for real-time applications of Earthquake Early Warning systems.
High Capacity Neural Block Classifiers with Logistic Neurons and Random Coding

We show that neural networks with logistic output neurons and random codewords can store and classify far more patterns than those that use softmax neurons and 1-in-K encoding. Logistic neurons can choose binary codewords from an exponentially large set of codewords. Random coding picks the binary or bipolar codewords for training such deep classifier models. This method searched for the bipolar codewords that minimized the mean of an inter-codeword similarity measure. The method used blocks of networks with logistic input and output layers and with few hidden layers. Adding such blocks gave deeper networks and reduced the problem of vanishing gradients. It also improved learning because the input and output neurons of an interior block must equal the input pattern's code word. Deep-sweep training of the neural blocks further improved the classification accuracy. The networks trained on the CIFAR-100 and the Caltech-256 image datasets. Networks with 40 output logistic neurons and random coding achieved much of the accuracy of 100 softmax neurons on the CIFAR- 100 patterns. Sufficiently deep random-coded networks with just 80 or more logistic output neurons had better accuracy on the Caltech-256 dataset than did deep networks with 256 softmax output neurons.
A Data Mining Based Fraud Detection Hybrid Algorithm in E-bank

Many banks now provide the online bank service, allowing customer to do transaction online. However, it also boosts the telecom fraud especially for the credit card fraud. In past several years, some studies have worked on some data mining-based method to detect online transaction fraud. However, most of studies fitted with little data, small amount of features and just single model classifier. Moreover, the model classifiers used by them are usually too weak to fit with large set of data. To handle this problem, in this paper, we proposed a hybrid data mining-based algorithm to detect fraud. The dataset is IEEE-CIS Fraud dataset provided by E-banks and it is big enough to train a good classifier. In addition, proper feature engineering is done to achieve a better accuracy. In the experiment section, we compare our hybrid method with the other classical method with single model to show the power of our method.
The Acceptability of Tools for the Data Marketplace among Firms Using Market Research Online Communities

In recent years, data have come to be not only targets of analysis, but also economic goods traded and exchanged among different fields in the digital economy. Under such circumstances, the data marketplace and related businesses/technologies are receiving global attention. However, unlike other well-known markets, the data marketplace is an emerging one, and decision makers in firms are cautious because there is little information regarding the choice to introduce new technologies into the data marketplace. In this study, we discuss the usefulness of two samples (Data Jacket Store and Variable Quest) in order to understand the acceptability of tools for the data marketplace among firms. We conducted a survey using market research online communities (MROCs). The evaluations differed depending on the number of employees in the firms and the positions of the subjects. Moreover, even in firms with the same number of employees, decision makers who are engineers and non-engineers assessed the same tool using different criteria and had different viewpoints.
Stochastic Filtering in Electromagnetics

This article presents the estimation of electric and magnetic fields using the Kalman filter (KF). The electric and magnetic fields in the entire space have been estimated using the scalar and vector potential. For this estimation, the measurements at a sparse discrete set of spatial pixels have been used. To implement the KF, the state space model has been obtained using the wave equations with sources satisfied by the scalar and vector potential. The proposed method has been implemented on a Hertzian dipole antenna. The fields estimated using KF have been compared with the recursive least squares (RLS) method. The KF presents better estimation than RLS, as it is an optimal estimator. This work uses the Kronecker product for compact representation of discretized fields in the form of vectors and partial differential operators in the form of matrices.
Statistics in Corpus Linguistics: A Practical Guide

Corpus linguistics is a powerful quantitative methodology, which heavily relies on frequency data and statistical procedures. It is difficult to talk about corpus linguistics without mentioning sta...
Empirical Analysis of Current Cryptocurrencies in Different Aspects

This research paper examines different types of cryptocurrencies, their properties and answers various questions around their history or value in market. We begin by explaining what is cryptocurrency? We have discussed What are different properties while differentiating the cryptocurrencies. These properties include Short form, current value in market, currency's market capitalization, programming language of implementation, its year of release, founder of cryptocurrency, block time, all time high, all time low, market rank of the currency, its type of proof and algorithm used in currency. These properties are really helpful in determining whether to use the cryptocurrency or invest in it for future. We currently have hundreds of cryptocurrencies in market, which makes it really hard for one to choose a currency. In this research we have chosen some cryptocurrencies which have a very high success probability in future. We have also defined each term used in comparison, to make a clear picture in the mind of reader about what he/she is reading about.
Efficient Temporal-Spatial Feature Grouping For Video Action Recognition

Temporal information plays an important role in action recognition. Recently, 3D CNN is widely used in extracting temporal features from videos. Compared to 2D CNN, 3D CNN has more parameters and brings heavy computation burden. It is necessary to improve the efficiency of action recognition. In this paper, inspired by group convolution and convolution kernel decomposition, we propose a novel module called grouped decomposed module (GDM) which separates channels into three groups and applies 3D, 2D and 1D convolution in parallel respectively. This module extracts spatial and temporal features efficiently. Based on GDM, we design a new network named grouped decomposed network (GDN). The grouped decomposed network achieves state-of-the-art performance on two temporal-related datasets (Something-Something Vl & V2) but requires few parameters and FLOPs.
A Novel Centroid Update Approach For Clustering-Based Superpixel Methods And Superpixel-Based Edge Detection

Superpixel is widely used in image processing. And among the methods for superpixel generation, clustering-based methods have a high speed and a good performance at the same time. However, most clustering-based superpixel methods are sensitive to noise. To solve these problems, in this paper, we first analyze the features of noise. Then according to the statistical features of noise, we propose a novel centroid update approach to enhance the robustness of clustering-based superpixel methods. Besides, we propose a novel superpixel-based edge detection method. The experiments on BSD500 dataset show that our approach can significantly enhance the performance of clustering-based superpixel methods in noisy environment. Moreover, we also show that our proposed edge detection method outperforms other classical methods.
Relative Vehicle Velocity Estimation Using Monocular Video Stream

In the past few years, the intelligent driving systems have witnessed rapid development, either it is self-driving cars or driver assistant systems. All these systems are built around perceiving the environment of the vehicle and taking appropriate steps in the given context. Computer vision has been playing a significant role in reducing the number of costly sensors used to perceiving the environment. In the past, the velocity of the vehicle was major estimated using sensors. In this paper, we propose a data-based methodology to estimate the relative velocity of vehicles using monocular cameras hence omitting the need for costly sensors such as lidars. Our proposed methods achieve a low mean velocity square error of 1.806, for estimating the velocity of the vehicle in a real-time environment.
Towards efficient federated learning‐based scheme in medical <scp>cyber‐physical</scp> systems for distributed data

Summary In recent years, Cyber‐Physical Systems (CPS) and Artificial Intelligence (AI) have made good progress in the medical field. The medical CPS (MCPS) based on AI can realize the efficient and reasonable utilization of medical resources and improve the quality of medical process. However, current MCPS are still facing several challenges, and the privacy protection of medical data is one of the most critical challenges. Since medical data is stored in different hospitals, most studies collect data from decentralized hospitals to train a disease diagnosis model, which is not conducive to the privacy protection of patients. And in some existing solutions, it is also difficult for doctors to select the optimal model from multiple models in clinical diagnosis. In this paper, we propose a novel scheme based on federated learning in MCPS for training disease diagnosis models from distributed medical image data. Our scheme is divided into three parts: the model provider, the server, and the consumer, and a detailed working process is designed for each part. This scheme can not only effectively solve the problem of privacy protection, but also solve the problem of model selection for doctors and save storage space. It can ensure that consumers automatically get a steadily improved disease diagnosis model. This scheme is performed on simulated distributed medical image datasets. The experimental results show the effectiveness and superiority of our scheme.
Feature Selection and Extraction for Dogri Text Summarization

Text summarization is defined as the process of condensing information from the source text into a shorter form without affecting the context of the information. Based on the summary generated by the summarization system, it is classified into abstractive and extractive summarization. Extractive summarization is the technique of extracting important sentences from the document that delivers the logical summary of the document. The candidate sentences for summary generation are decided by using statistical and linguistic features of the given source text. The proposed approach for Extractive Dogri Text summarization is presented in this paper. Various statistical and linguistic features that can contribute to the selection of appropriate sentences for Dogri text summarization are also illustrated in the paper. Statistical features like term frequency, length of a sentence, position of a sentence and term frequency-inverse sentence frequency (TF-ISF) are taken into consideration. And the linguistic features like presence of proper noun, numerical information, English-Dogri words are also considered for determining the candidature of the sentences for inclusion in the final summary generation.
Exploration of Outliers in If-Then Rule-Based Knowledge Bases

The article presents both methods of clustering and outlier detection in complex data, such as rule-based knowledge bases. What distinguishes this work from others is, first, the application of clustering algorithms to rules in domain knowledge bases, and secondly, the use of outlier detection algorithms to detect unusual rules in knowledge bases. The aim of the paper is the analysis of using four algorithms for outlier detection in rule-based knowledge bases: Local Outlier Factor (LOF), Connectivity-based Outlier Factor (COF), K-MEANS, and SMALLCLUSTERS. The subject of outlier mining is very important nowadays. Outliers in rules If-Then mean unusual rules, which are rare in comparing to others and should be explored by the domain expert as soon as possible. In the research, the authors use the outlier detection methods to find a given number of outliers in rules (1%, 5%, 10%), while in small groups, the number of outliers covers no more than 5% of the rule cluster. Subsequently, the authors analyze which of seven various quality indices, which they use for all rules and after removing selected outliers, improve the quality of rule clusters. In the experimental stage, the authors use six different knowledge bases. The best results (the most often the clusters quality was improved) are achieved for two outlier detection algorithms LOF and COF.
MAMS-A: Multi-Agent Multi-Scale A*

We present a multi-scale forward search algorithm for distributed agents to solve single-query shortest path planning problems. Each agent first builds a representation of its own search space of the common environment as a multi-resolution graph, it communicates with the other agents the result of its local search, and it uses received information from other agents to refine its own graph and update the local inconsistency conditions. As a result, all agents attain a common subgraph that includes a provably optimal path in the most informative graph available among all agents, if one exists, without necessarily communicating the entire graph. We prove the completeness and optimality of the proposed algorithm, and present numerical results supporting the advantages of the proposed approach.
Split-Merge Evolutionary Clustering for Multi-View Streaming Data

In this study, we propose a new multi-view stream clustering approach, called MV Split-Merge Clustering. The proposed approach is an extension of an existing split-merge evolutionary clustering algorithm (entitled Split-Merge Clustering) to multi-view data applications. The extended version can be used to integrate data from multiple views in a streaming manner and discover cluster structure for each data chunk. The MV Split-Merge Clustering can be applied for grouping distinct chunks of multi-view streaming data so that a global integrated clustering model is built on each data chunk. At each time window, an updated clustering solution (local model) is initially produced on each view of the current data chunk by applying the Split-Merge Clustering algorithm. Formal Concept Analysis is then used in order to integrate information from the multiple views (local clustering models) and generate a global model (formal concept lattice) that reveals the correlations among the clusters of the local models. The proposed MV Split-Merge Clustering has been initially evaluated on a publicly available data set. Our results show that the approach is able to identify a clustering structure and relationships among the different views comparable to those produced in a batch scenario.
New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data

This work focuses on classification over time series data. When a time series is generated by non-stationary phenomena, the pattern relating the series with the class to be predicted may evolve over time (concept drift). Consequently, predictive models aimed to learn this pattern may become eventually obsolete, hence failing to sustain performance levels of practical use. To overcome this model degradation, online learning methods incrementally learn from new data samples arriving over time, and accommodate eventual changes along the data stream by implementing assorted concept drift strategies. In this manuscript we elaborate on the suitability of online learning methods to predict the road congestion level based on traffic speed time series data. We draw interesting insights on the performance degradation when the forecasting horizon is increased. As opposed to what is done in most literature, we provide evidence of the importance of assessing the distribution of classes over time before designing and tuning the learning model. This previous exercise may give a hint of the predictability of the different congestion levels under target. Experimental results are discussed over real traffic speed data captured by inductive loops deployed over Seattle (USA). Several online learning methods are analyzed, from traditional incremental learning algorithms to more elaborated deep learning models. As shown by the reported results, when increasing the prediction horizon, the performance of all models degrade severely due to the distribution of classes along time, which supports our claim about the importance of analyzing this distribution prior to the design of the model.
CQ-VQA: Visual Question Answering on Categorized Questions

This paper proposes CQ-VQA, a novel two-level hierarchical but end-to-end model to solve the task of visual question answering (VQA). The first level of CQ-VQA, referred to as Question Categorizer (QC), classifies questions to reduce the potential answer search space. The QC uses attended and fused features of the input question and image. The second level, referred to as Answer Predictor (AP), comprises of a set of distinct classifiers corresponding to each question category. Depending on the question category predicted by QC, only one of the classifiers of AP remains active. The loss functions of QC and AP are aggregated together to make it an end-to-end model. The proposed model (CQ-VQA) is evaluated on the TDIUC dataset and is benchmarked against state-of-the-art approaches. Results indicate a competitive or better performance of CQ-VQA.
Efficient and Correct Compilation of Quantum Circuits

High-level descriptions of quantum algorithms do not take the restrictions of physical hardware into account. Therefore actually executing an algorithm in the form of a quantum circuit on a quantum computer requires compiling it for the desired target architecture first. The compilation of quantum circuits depends on efficient methods to be feasible for all but the trivial instances. To this end, different compiling methods have been introduced in the past, but room for improvement still exists. Moreover, just an efficient compilation process itself is not sufficient-the resulting circuits must be correct as well. In this summary paper, we review how existing compilation approaches can be optimized by utilizing heuristic search algorithms or exact reasoning engines. Furthermore, we review how the correctness of the obtained results can be verified afterwards by clever data structures such as decision diagrams. This illustrates core steps of a compilation flow which can generate minimal or close-to-minimal results for many instances and, additionally, guarantees correctness throughout the process.
Adversarial Perturbation Attacks on GLRT-Based Detectors

Existing work on adversarial attacks on classification tasks has focused on classifiers that make use of simple hypothesis testing models. In this work, we study the vulnerability of composite classifiers employing generalized likelihood ratio tests to adversarial perturbation attacks. We derive imperceptible adversarial attacks for a multiple composite hypothesis testing setting using gradient methods. The work considers scenarios where the attacker has access to the ground truth class. The classification performance, with and without perturbation, is characterized based on the notions of posterior sensitivity and specificity.
A Study Of Prediction Methods Based On Machine Learning Techniques For Lossless Image Coding

In recent years, a new research strategy for coding has emerged by exploring the advances brought by modern machine learning techniques. Novel hybrid coding solutions were proposed by replacing specific modules in conventional coding frameworks with more efficient modules based on innovative deep-learning (DL) methods. The paper studies first our recently proposed DL-based prediction methods for lossless image coding by analyzing their designs and employed ML concepts. A novel neural network architecture is proposed, based on a new structure of layers which proves to deliver an improved prediction compared to the reference designs. Context-tree based bit-plane coding is employed to encode the resulting prediction error. The experimental results reveal that the proposed codec reduces the lossless coding rate with 1.9% compared to state-of-the-art DL-based methods while having 4.95% less parameters. The performance gap of almost 50% compared to traditional codecs recommends the use of ML-based tools in the design of future standards for lossless image compression.