Reviewerly/exHarmony huggingface dataset:
FUZZY BASE ARTIFICIAL NEURAL NETWORK MODEL FOR TEXT EXTRACTION FROM IMAGES

Content Extraction assumes a significant job in discovering essential and important data. Content extraction includes discovery, restriction, following, binarization, extraction, improvement and acknowledgment of the content from the given picture. This paper proposes a bi-leveled picture characterization framework to group printed and transcribed reports into totally unrelated predefined classes. In the present article, we propose a Fuzzy guideline guided novel strategy that is utilitarian without any outer intercession during execution. The Fuzzy validation processor utilizes subtleties of divider speed and force, just as change, to decide if the deliberate reverberation signal part to be separated genuinely speaks to the divider speed as it were. Test results propose that this methodology is a proficient one in contrast with various different strategies widely tended to in writing. At long last, the exhibition of the proposed framework is contrasted and the current frameworks and it is seen that proposed framework performs superior to numerous different frameworks.
A Sports Teaching Mode Based on Social Networking Service Teaching Resources

As today's ideal in regard to health globally goes deep, sports courses receive more and more attention. However, high-quality teaching resource scarcity exists in current PE teaching, thus affecting the teaching effect. In this study, a teaching mode based on Social Networking Service (SNS) teaching resources was designed. Firstly, SNS-based PE teaching network learning community was designed, and self-organized construction of teaching resources was achieved in the learning community, including network courseware, teaching knowledge points, common problems, cases and media materials, etc. Thus, the network teaching mode oriented to self-organization of teaching resources. To test the teaching effect of this new teaching mode, Tai Ji course was used for teaching practice. A kind of virtual software for Tai Ji teaching was used, and virtual 3D technology and multi-functional operating interface were applied to create a brand-new Tai Ji learning mode. The results show that the average score of the students who took the new Tai Ji course teaching mode is much higher than that of students with regular teaching, and the proportion of high score students is also higher, indicating that the teaching mode proposed in this study can significantly improve the teaching effect of Tai Ji course, and also provide a beneficial thought for constructing a new teaching mode for other courses.
Dehaze Enhancement Algorithm Based on Retinex Theory for Aerial Images Combined with Dark Channel

In order to improve the visual effect of aerial images in foggy weather, a Retinex defogging algorithm combining dark channel priors is proposed to solve the phenomenon of insufficient defogging and over-enhancement of aerial defogging in Reinex defogging.First, the foggy original image is decomposed into foggy incident light components and foggy reflected light components by Retinex theory.Then, the principle of foggy image degradation is analyzed by an atmospheric scattering model, and the dark channel prior defogging algorithm is used to obtain the incident light component and reflected light component after defogging.Finally, a fog-free image was restored through the Reitnex model.The effectiveness of this algorithm is verified through experiments.By comparing and analyzing this algorithm with other defogging algorithms, this algorithm has higher contrast and color fidelity.
A New Local Knowledge-Based Collaborative Representation for Image Recognition

Recently, collaborative representation based classifiers (CRC) have shown outstanding performances in recognition tasks. The key to success of most CRC algorithms states that the testing samples can be coded well by a suitable dictionary globally, while the local knowledge between samples has not been fully considered. We observe that the representations of similar samples have a high degree of similarity. In order to take advantage of this important similarity information, this paper proposes a new local knowledge-based collaborative representation model for image classification. Specifically, certain adjacent training samples of the testing image should be determined firstly, and then the representations of these neighborhoods can be applied to guide the coefficients of the testing samples to be more discriminative. Further, we derive a robust version of the proposed method to treat the face recognition with occlusions or corruptions. Extensive experiments are carried out to show the superiority of the proposed method over other state-of-the-art classifiers on various image recognition tasks.
Machine learning: technologies and potential application at mining companies

Implementation of machine learning systems is currently one of the most sought-after spheres of human activities at the interface of information technologies, mathematical analysis and statistics. Machine learning technologies are penetrating into our life through applied software created with the help of artificial intelligence algorithms. It is obvious that machine learning technologies will be developing fast and becoming part of the human information space both in our everyday life and in professional activities. However, building of machine learning systems requires great labour contribution of specialists in the sphere of artificial intelligence and the subject area where this technology is to be applied. The article considers technologies and potential application of machine learning at mining companies. The article describes basic methods of machine learning: unsupervised learning, action learning, semi-supervised machine learning. The criteria are singled out to assess machine learning: operation speed; assessment time; implemented model accuracy; ease of integration; flexible deployment within the subject area; ease of practical application; result visualization. The article describes practical application of machine learning technologies and considers the dispatch system at a mining enterprise (as exemplified by the dispatch system of the mining and transportation complex “Quarry” used to increase efficiency of operating management of enterprise performance; to increase reliability and agility of mining and transportation complex performance records and monitoring. There is also a list of equipment performance data that can be stored in the database and used as a basis for processing by machine learning algorithms and obtaining new knowledge. Application of machine learning technologies in the mining industry is a promising and necessary condition for increasing mining efficiency and ensuring environmental security. Selection of the optimal process flow sheet of mining operations, selection of the optimal complex of stripping and mining equipment, optimal planning of mining operations and mining equipment performance control are some of the tasks where machine learning technologies can be used. However, despite prospectivity of machine learning technologies, this trend still remains understudied and requires further research.
Stability and Steady-State Performance of Hammerstein Spline Adaptive Filter Based on Stochastic Gradient Algorithm

This paper proposes an adaptive step-size approach based on the stochastic gradient-based algorithm for Hammerstein spline adaptive filtering (HSAF).With the good convergence and low complexity computation, a normalized least mean square (NLMS) algorithm is developed with HSAF.A nonlinear Hammerstein adaptive filter consists of memoryless function modified during learning and the spline control point is automatically controlled by gradient-based method.An adaptive step-size approach based on NLMS algorithm with the HSAF is introduced how to derive using the direction of steepest descent method for tracking performance.Hence, the stability and steady-state performance are detailed.The simulation results of trajectories of both update step-size parameters are shown that the initial step-size are reduced, all convergence rates are correspondingly reduced to each own optimum over 100 independent trials of the experiment.Experimental plots of learning curves of mean-squared error of the proposed algorithm for varying the initial step-size parameters are obtained that the proposed algorithm conducts more robust performance and fast convergence compared with the HSAF based on least mean square algorithm, even the large variations of 100-fold initial step-size parameters are used.
Robust M-Estimation Based Bayesian Cluster Enumeration for Real Elliptically Symmetric Distributions

Robustly determining the optimal number of clusters in a data set is an essential factor in a wide range of applications. Cluster enumeration becomes challenging when the true underlying structure in the observed data is corrupted by heavy-tailed noise and outliers. Recently, Bayesian cluster enumeration criteria have been derived by formulating cluster enumeration as maximization of the posterior probability of candidate models. This article generalizes robust Bayesian cluster enumeration so that it can be used with any arbitrary Real Elliptically Symmetric (RES) distributed mixture model. Our framework also covers the case of M-estimators that allow for mixture models, which are decoupled from a specific probability distribution. Examples of Huber's and Tukey's M-estimators are discussed. We derive a robust criterion for data sets with finite sample size, and also provide an asymptotic approximation to reduce the computational cost at large sample sizes. The algorithms are applied to simulated and real-world data sets, including radar-based person identification, and show a significant robustness improvement in comparison to existing methods.
Nepali Text Document Classification Using Deep Neural Network

An automated text classification is a well-studied problem in text mining which generally demands the automatic assignment of a label or class to a particular text documents on the basis of its content. To design a computer program that learns the model form training data to assign the specific label to unseen text document, many researchers has applied deep learning technologies. For Nepali language, this is first attempt to use deep learning especially Recurrent Neural Network (RNN) and compare its performance to traditional Multilayer Neural Network (MNN). In this study, the Nepali texts were collected from online News portals and their pre-processing and vectorization was done. Finally deep learning classification framework was designed and experimented for ten experiments: five for Recurrent Neural Network and five for Multilayer Neural Network. On comparing the result of the MNN and RNN, it can be concluded that RNN outperformed the MNN as the highest accuracy achieved by MNN is 48 % and highest accuracy achieved by RNN is 63%.
Chinese Sentence Semantic Matching Based on Multi-Granularity Fusion Model

Sentence semantic matching is the cornerstone of many natural language processing tasks, including Chinese language processing. It is well known that Chinese sentences with different polysemous words or word order may have totally different semantic meanings. Thus, to represent and match the sentence semantic meaning accurately, one challenge that must be solved is how to capture the semantic features from the multi-granularity perspective, e.g., characters and words. To address the above challenge, we propose a novel sentence semantic matching model which is based on the fusion of semantic features from character-granularity and word-granularity, respectively. Particularly, the multi-granularity fusion intends to extract more semantic features to better optimize the downstream sentence semantic matching. In addition, we propose the equilibrium cross-entropy, a novel loss function, by setting mean square error (MSE) as an equilibrium factor of cross-entropy. The experimental results conducted on Chinese open data set demonstrate that our proposed model combined with binary equilibrium cross-entropy loss function is superior to the existing state-of-the-art sentence semantic matching models.
Improving Fault-Localization Accuracy by Referencing Debugging History to Alleviate Structure Bias in Code Suspiciousness

Spectrum-based fault localization (SBFL) techniques can automatically localize software faults. They employ the program spectrum, such as code coverage profile with test verdicts, to rank the program entities based on their code suspiciousness. In the past decades, researchers have proposed many approaches to optimize these techniques; however, the program structure, which can influence their performance, is not taken into consideration in developing and improving these techniques. In this article, we identify and analyze the effect of the program structure on the application of SBFL techniques. We observe that some specific program structures may introduce structure bias to code suspiciousness and negatively influence the output of SBFL techniques. To mitigate these effects and improve the performance of fault localization, we propose Delta4Ts, a structure-aware technique. Delta4Ts references debugging history to alleviate the impact of structure bias in the calculation of code suspiciousness. It reasons from the observable suspicious value towards the desired suspicious value and the impact of structure bias. To evaluate Delta4Ts under practical constraints, we conduct a controlled experiment using nine widely-studied SBFL formulae on 12 C programs and 6 Java programs. The experiment results show that Delta4Ts can significantly improve the accuracy of the studied SBFL formulae by an average of 34.8% on 12 C programs and 30.6% on 6 Java programs, and improve more on subject programs associated with more history versions or having larger code sizes.
Reaction of Stock Volatility to Data Breach: an Event Study

The paper assesses how stock market volatility reacts to data breach disclosure. The paper applies Volatility Event Analysis and Kolmogorov-Smirnov Test to analyse how equity risk (stock volatility) of 96 firms listed on the S&amp;P 500 index reacted to the announcement of a data breach using records from Breach Level Index (BLI ) over the period between January 2013 and December 2018. Two levels of empirical analysis were performed: cross-section level and industry level. We employ statistical tests that adjust for the effects of cross-section firm-specific mean and volatility. The analysis delivers the following results: Firstly, cross-sectional analysis shows that there is evidence of significant abnormal across the firms and significant difference between before and after cyberattacks announcements. Secondly, the industry level analysis reveals that the firms in the financial industry exhibit more abnormal volatility and returns than firms in other sectors. Additionally, there is significant evidence of the difference in pre and post cyberattacks or data breach announcements, however, this effect tends to be more pronounced after the announcement of a data breach. Implying that data breach announcements can significantly influence equity volatility. In conclusion, the paper posits, equity investors and other stakeholders should reconsider their approach to cybersecurity events when updating the risk measures of their stocks and portfolios.
A Real-Time Query Log Protection Method for Web Search Engines

Web search engines (e.g., Google, Bing, Qwant, and DuckDuckGo) may process a myriad of search queries per second. According to Internet Live Stats, Google handles more than two hundred million queries per hour, i.e., about two trillion queries per year. For monetization purposes, the queries can be stored and complemented with additional data, referred to as query logs. Together, they can correlate valuable information to build accurate user profiles. Before releasing the query logs to third parties (e.g., for profit purposes), the personal information contained in the query logs must be properly protected by the web search engines. Current regulations establish strict control, and require from provable anonymization processing (e.g., in terms of statistical disclosure) of any personally identifiable information. In this paper, we tackle this challenge. We propose a real-time anonymization solution to protect streams of unstructured data at the server side. Our approach is based on the use of a probabilistic k-anonymity technique. It allows probabilistic processing of personally identifiable attributes contained in the query logs, with provable privacy properties. Our solution handles limitations of traditional k-anonymity approaches with respect to unstructured data and real-time processing. We present the implementation of our solution and report experimental evaluation results. The evaluation is conducted in terms of privacy, utility, and scalability achievement. Results validate the feasibility of our proposal.
Classification of rare traffic signs

The paper studies the possibility of using neural networks for the classification of objects that are few or absent at all in the training set. The task is illustrated by the example of classification of rare traffic signs. We consider neural networks trained using a contrastive loss function and its modifications, also we use different methods for generating synthetic samples for classification problems. As a basic method, the indexing of classes using neural network features is used. A comparison is made of classifiers trained with three different types of synthetic samples and their mixtures with real data. We propose a method of classification of rare traffic signs using a neural network discriminator of rare and frequent signs. The experimental evaluation shows that the proposed method allows rare traffic signs to be classified without significant loss of frequent sign classification quality.
Improving IoT Security Using Blockchain

Internet of Things (IoT) is the new technology that will change the Internet era in the coming years. IoT can be used in many fields, such as digitally connected factories, facility management, production flow management inventory management, safety, security, health care, logistics, supply chain optimization, and so on. However, due to limited capabilities in IoT, it is impractical to implement extensive security solutions. Also, security standards for IoT are not mature enough. As a result, many security incidents affect IoT reliability. In this paper, we propose an effective security mechanisms that can be implemented to ensure data accuracy and integrity of IoT devices along with considering its limited hardware resources. We implement Blockchain in IoT to enhance IoT security. We apply our approach using the Ethereum protocol by having multiple Raspberry Pi devices connected as peers to the Blockchain. Also, we discuss the implementation and the expected outcome of this project, which is to implement the Access Control List mechanism using Blockchain to improve the overall IoT security and to connect IoT devices to Ethereum as light nodes.
A fast occluded passenger detector based on MetroNet and Tiny MetroNet

Metro passenger detection is always a significant task and a bottleneck in metro video surveillance system. Much recent research has demonstrated that Convolutional Neural Network (CNN) is more powerful than other machine learning algorithms in numerous computer vision tasks. Motivated by the research, this paper proposes MetroNet and Tiny MetroNet for detecting occluded metro passengers in metro embedded system with limited hardware resources. MetroNet consists of smaller CNN-SqueezeNet, Region Proposal Network (RPN) and Detection Head subnet. Besides, the repulsion loss is adopted to effectively prevent detection results from worsening caused by severe passengers’ occlusion during training phase. On the other hand, considering that some platforms have more limited hardware resources, a simple version of the MetroNet named Tiny MetroNet is designed and a novel, tiny passenger feature network is proposed as backbone. Based on three datasets, two MetroNets are tested and compared to existing state-of-the-art detection networks on CPU and GPU mode. The experiment results demonstrate that MetroNet has real-time performance and better detection accuracy. Tiny MetroNet achieves fast detection speed and smaller model size with acceptable performance degradation. Even for the ARM embedded system, their performance is competitive and can meet the application requirements of high-speed metros.
Joint Character-Level Convolutional and Generative Adversarial Networks for Text Classification

With the continuous renewal of text classification rules, text classifiers need more powerful generalization ability to process the datasets with new text categories or small training samples. In this paper, we propose a text classification framework under insufficient training sample conditions. In the framework, we first quantify the texts by a character-level convolutional neural network and input the textual features into an adversarial network and a classifier, respectively. Then, we use the real textual features to train a generator and a discriminator so as to make the distribution of generated data consistent with that of real data. Finally, the classifier is cooperatively trained by real data and generated data. Extensive experimental validation on four public datasets demonstrates that our method significantly performs better than the comparative methods.
A Multilayer Pyramid Network Based on Learning for Vehicle Logo Recognition

In this paper, we present a novel learning-based scheme for vehicle logo recognition (VLR).This scheme is termed Multilayer Pyramid Network Based on Learning (MLPNL) and is based on the principle that considering multiple resolutions is helpful for extracting valuable features that benefit the final recognition performance.The innovations of this scheme include (1) a multilayer pyramid network, with pixel difference matrices (PDMs) as its input and output and feature parameters mapping one PDM to another; (2) an objective function and a corresponding optimization method designed to facilitate the learning of the feature parameters of the proposed multilayer pyramid network; and (3) a multi-codebook-based encoding method that makes best use of the features extracted from PDMs corresponding to different resolutions.Extensive experiments conducted with an open dataset, HFUT-VL, demonstrate that the proposed MLPNL scheme outperforms state-of-the-art handcrafted descriptors and non-deep-learning-based learning methods when fewer training samples exist.Experiments conducted with a benchmark dataset, XMU, demonstrate that MLPNL outperforms existing state-ofthe-art VLR methods.Experiments conducted both on HFUT-VL and XMU demonstrate that MLPNL is faster than most deep-learning-based learning methods while maintaining nearly the same recognition rate.Code has been made available at: https://github.com/HFUT-CV/MLPNL.
Evaluating the Influence of the HMD, Usability, and Fatigue in 360VR Video Quality Assessments

VR communications present great challenges that should be addressed in subjective assessment methodologies. Thus, the HMD, the usability of controllers and touchpad, or even the fatigue produced by the test session are factors that highly influence the QoE. In addition, in a 360VR scenario, most of the experiments are focused on the evaluation of video quality or the evaluation of socioemotional concepts such as the sense of presence. However, no standardized methodology to evaluate both kinds of concepts is available nowadays. In this paper, we present an experiment where video quality and sense of presence are jointly assessed in two of the most popular HMDs. As a result, we have observed that while presence is affected by the evaluation mechanism or duration of the test, quality is mainly affected by the HMD. This statement implies that methodologies that take into account technical concepts, such as encoding and transmission, and socioemotional concepts are necessary to obtain reliable and robust results of QoE in a VR environment.