Reviewerly/exHarmony huggingface dataset:
Analysis of General Middleware Fault Prediction Model under Log Mining Technology

In order to study of general middleware failure prediction model, a fault prediction model is constructed in the general middleware, the big data intelligent analysis method is used, the prediction process and feasibility of the model are analyzed, and the integrated network management system and the application in the field of information management system of electric power are explored under log mining technology. The results show that the Apriori-LTIS fault prediction algorithm is designed by analyzing the log processing module and its mining steps. The algorithm has short analysis time, high accuracy and recall rate, and good feasibility. It is applied to the integrated network management system and the electric power information management field, the fault location is located quickly, the accuracy rate is high, the error rate is low, the work efficiency of the system is improved, and the service life of the system is extended. The results show that the fault prediction model is of great significance to the safe and stable operation of the system. The error problems fed back in the operation process can provide more valuable information for the operation and maintenance personnel and provide reference for the future study of the fault prediction model.
Bronchitis Treatment Market Projected to Gain Significant Value by 2024

Transparency Market Research (TMR) has published a new report titled, “Bronchitis Treatment Market - Global Industry Analysis, Size, Share, Growth, Trends, and Forecast, 2018–2026”. According
Towards an Argument Mining Pipeline Transforming Texts to Argument Graphs

This paper targets the automated extraction of components of argumentative information and their relations from natural language text. Moreover, we address a current lack of systems to provide complete argumentative structure from arbitrary natural language text for general usage. We present an argument mining pipeline as a universally applicable approach for transforming German and English language texts to graph-based argument representations. We also introduce new methods for evaluating the results based on existing benchmark argument structures. Our results show that the generated argument graphs can be beneficial to detect new connections between different statements of an argumentative text. Our pipeline implementation is publicly available on GitHub.
Finite field elements of high order on a base of modified Gao approach.

Elements of high multiplicative order in finite fields are of great interest in several applications (cryptography, error correcting codes) that use finite fields. Obviously, the best possible are primitive elements, but there is no any algorithm to find them. Therefore, they consider a less ambitious question: to find an element with provable high order
Mutual Information and Machine Learning Based Distinguishers for Pseudo Random Bit Sequences

In cryptology, a strong pseudo random bit sequence must be indistinguishable from pure random bit sequence or other strong pseudo random bit sequence. In this paper, distinguishers based on machine learning and mutual information are presented to distinguish various pseudo random bit sequences (generated by AES, 3DES, IDEA, Blowfish, RC4, and ‘rand’ function of C) from the strong pseudo random bit sequence of AES in CBC mode. Significant features are selected by using mutual information from the extracted features. SVM, Resilient Propagation ANN, and Multinomial Naïve Bayes classifiers are used to distinguish. It is observed that pseudo random bit sequences of four block ciphers in ECB mode are distinguishable with more than 99% accuracy as compared to CBC and OFB mode. Bit sequences generated by RC4 stream cipher and ‘rand’ function of C are also significantly distinguishable.
Analysis of Network Security of Port of Port Issues in the Era of Big Data

Lou, J.L., 2020. Analysis of network security of port of port issues in the era of big data. In: Al-Tarawneh, O. and Megahed, A. (eds.), Recent Developments of Port, Marine, and Ocean Engineering. Journal of Coastal Research, Special Issue No. 110, pp. 223–226. Coconut Creek (Florida), ISSN 0749-0208.In the era of big data, the network has provided convenience and also brought potential threats. Viruses, Trojan horses, and hackers' attacks have caused irretrievable losses for people, and they have become a major obstacle for the expansion of internet. This paper first analyzes the related concepts of big data and the current situation of network security of port of port in the era of big data, then uses the five indicators of network security of port of port to establish a network security of port of port evaluation model, and finally puts forward the countermeasures for ensuring information security of computer networks in the era of big data.
A Novel Framework with Information Fusion and Neighborhood Enhancement for User Identity Linkage

User identity linkage across social networks is an essential problem for cross-network data mining. Since network structure, profile and content information describe different aspects of users, it is critical to learn effective user representations that integrate heterogeneous information. This paper proposes a novel framework with INformation FUsion and Neighborhood Enhancement (INFUNE) for user identity linkage. The information fusion component adopts a group of encoders and decoders to fuse heterogeneous information and generate discriminative node embeddings for preliminary matching. Then, these embeddings are fed to the neighborhood enhancement component, a novel graph neural network, to produce adaptive neighborhood embeddings that reflect the overlapping degree of neighborhoods of varying candidate user pairs. The importance of node embeddings and neighborhood embeddings are weighted for final prediction. The proposed method is evaluated on real-world social network data. The experimental results show that INFUNE significantly outperforms existing state-of-the-art methods.
Bayesian Based Data Analysis Method for Reliability Prediction of Electronics

A Bayesian based data analysis method is proposed to fulfill the modification of basic failure rate of electronic component or optimization of reliability prediction for electronics, combining the data from prediction handbook and field. Analysis process and data requirement are given in detail, as well as two brief cases to demonstrate its feasibility, with Exponential and Weibull distribution assumption respectively. The method can be used to reduce the error caused by prediction handbook and improve the reliability design decision for electronics.
A Study on the Development of 3D Product Design Program Based on Maker Education Focused on art education in high school

본 연구는 고등학생을 대상으로 메이커교육을 기반으로 하여 3D 제품디자인 제작 능력을 향상시킬 수 있도록 학습 프로그램을 개발한 것이다. 이를 통해 학습자로 하여금 미술 교과에서 창의 융합 능력과 자기 주도적 학습 능력을 향상시키고, 디자인적 사고와 공간시각화 능력을 향상시키는 데 목적을 두었다. 4차 혁명 시대로 흘러가고 있는 요즘 2015 개정 교육 과정에서추구하고 있는 인재상은 창의·융합형 인재이다. 이로 인해 교육은 기존의 획일적인 교육을 지양하고 개인의 꿈과 끼를 실현시키고 창의력을 발현할 수 있는 융합교육이 두드러지고 있다. 이에 학습자의 창의적 사고력을 길러 주기 위해 기술과 예술을 접목하여 메이커교육 기반의 제품디자인 수업을 구안하였다. 아이디어를 탐색하고 구체화 하는 데 있어서 학생들 간의소통을 중시하였으며, 아이디어를 시각화하기 위해서 Tinker CAD의 3D 프로그램을 사용하였다. 이러한 과정을 통해 학습자들은 공간시각화 능력과 문제해결력, 독창적인 디자인 사고력을 기를 수 있을 것이며, 나아가 실용적인 디자인 학문에대해 이해하며 탐구적 측면의 자기 주도적 미술 교육이 될 수 있을 것이다.This study is to develop a learning program for high school students to improve their ability to produce 3D product designs based on maker education. The purpose of this study was to improve the ability of the learners to integrate creative and self-directed learning in art subjects, and to improve the ability of design thinking and spatial visualization through the newly developed learning program. The right people, who are being pursued in the curriculum revised in 2015, and in the era of the fourth revolution. As a result, education is highlighted by convergence education that can avoid the existing uniform education, realize individual dreams and talents, and express creativity. Thus, in order to develop the creative thinking ability of learners, a product design class based on maker education was proposed by combining technology and art. Communication among students was valued in exploring and embodying ideas, and the 3D program of Tinker CAD was used to visualize ideas. Through these courses, learners will be able to develop spatial visualization ability, problem solving ability, and creative design thinking skills, and furthermore, they will be able to understand practical design studies and become self-directed art education in the exploratory aspect.
Spatial Association Pattern Mining Using In-Memory Computational Framework

Spatial association pattern mining is a useful spatial data mining task for discovering interesting relationship patterns of spatial features based on spatial proximity. Spatial data mining is known as data-intensive computing. The explosive growth of spatial data demands computationally efficient methods for analyzing large complex data. Parallel and distributed computing is effective for large-scale data mining algorithms. This paper presents spatial association pattern mining with Spark which is a specially-designed in-memory parallel computing platform. The performance of Spark-based method proposed is compared with its corresponding MapReduce-based method.
Complexity of low-frequency earthquakes activity in western Shikoku

&amp;lt;p&amp;gt;Short-duration transient seismic events known as low-frequency earthquakes (LFEs) are a component of the slow earthquakes family observed in the transition zone, at the root of seismogenic regions of the subduction zones or active faults. LFEs are the signature of impulse seismic energy radiation associated to and often mixed within complex tectonic tremor signal. Detailed analysis and characterization of LFE space-time activity in relation to other slow earthquake phenomena can provide important information about the state and the processes of fault interface.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We derive a catalog of LFEs in western Shikoku (Japan) by applying a full waveform coherency-based detection and location method to the 4-year continuous data covering the period of 2013-2016 and recorded at Hi-net seismic stations of NIED. The obtained catalog of over 150,000 detected events allows looking into the details of LFE space-time activity during the tectonic tremor sequences and inter-sequence periods.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We use this catalogue of LFEs to perform a systematic statistical analysis of the event occurrence patterns by applying correlation and clustering analysis to infer the large-scale (long temporal ~ 1-2 day duration) space-time characteristics and interaction patterns of activity and its potential relation to the structural complexity of the subducting plate. We also analyze the correlation between the migration of clustered LFE activity during energetic tremor sequences and short-term slow slip events occurring in the area during the analyzed period.&amp;lt;/p&amp;gt;
ARCHITECTURE OF DATABASE MODELS FOR VISУUALIZING RESULTS OF MONITORING EPIZOOTIC ANIMAL STATE

The organizational and technical aspects of creating databases are described in relation to the processes of applied veterinary medicine, in particular, to visualize the results of monitoring the epizootic state of animals. The conceptual architecture of the processes of knowledge integration in the formation of the database of the developed information system based on the template form, prepared on the basis of the results of the analysis of the subject area, is proposed, the algorithm of actions is structured, both for specialists in the subject area and specialists in IT technologies, as well as system analysts. The proposed methodological benchmark allows the formation of the most adequate database prototype in the Excel software environment, as well as logical models of attributes and their relationships. The structure of the output reports is compiled in accordance with the requirements of veterinary reporting and taking into account the business logic of mathematical and statistical data processing in order to establish the laws of the processes under study and build their models, which is necessary for the timely adoption of prognostic veterinary measures. The results can be useful in the development and implementation of modern information and communication technologies in real business processes in the field of applied veterinary medicine.
Interoperable Data Extraction and Analytics Queries over Blockchains

The explosion of interests by diverse organisations for deploying their services on blockchains to exploit decentralize transaction governance and advanced cryptographic protocols, is fostering the emergence of new challenges for distributed ledger technologies (DLTs). The next generation of blockchain services are now extending well beyond cryptocurrencies, accumulating and storing vast amounts of data. Therefore, the need to efficiently extract data over blockchains and subsequently foster data analytics, is more evident than ever. However, despite the wide public interest and the release of several frameworks, efficiently accessing and processing data from blockchains still imposes significant challenges. This article, first, introduces the key limitations faced by organisations in need for efficiently accessing and managing data over DLTs. Afterwards, it introduces Datachain, a lightweight, flexible and interoperable framework deliberately designed to ease the extraction of data hosted on DLTs. Through high-level query abstractions, users connect to underlying blockchains, perform data requests, extract transactions, manage data assets and derive high-level analytic insights. Most importantly, due to the inherent interoperable nature of Datachain, queries and analytic jobs are reusable and can be executed without alterations on different underlying blockchains. To illustrate the wide applicability of Datachain, we present a realistic use-case on top of Hyperledger and BigchainDB.
Empirical Bounds on Linear Regions of Deep Rectifier Networks.

We can compare the expressiveness of neural networks that use rectified linear units (ReLUs) by the number of linear regions, which reflect the number of pieces of the piecewise linear functions modeled by such networks. However, enumerating these regions is prohibitive and the known analytical bounds are identical for networks with same dimensions. In this work, we approximate the number of linear regions through empirical bounds based on features of the trained network and probabilistic inference. Our first contribution is a method to sample the activation patterns defined by ReLUs using universal hash functions. This method is based on a Mixed-Integer Linear Programming (MILP) formulation of the network and an algorithm for probabilistic lower bounds of MILP solution sets that we call MIPBound, which is considerably faster than exact counting and reaches values in similar orders of magnitude. Our second contribution is a tighter activation-based bound for the maximum number of linear regions, which is particularly stronger in networks with narrow layers. Combined, these bounds yield a fast proxy for the number of linear regions of a deep neural network.
Efficient Architectures for Generalized Integrated Interleaved Decoder

Generalized integrated interleaved (GII) codes allow localized decoding of short sub-codewords. They are essential to hyper-speed data storage, communications, and continued scaling of distributed storage. Sub-codewords, which are usually Reed-Solomon (RS) or BCH codewords, are nested to generate codewords of higher correction capabilities. If the decoding of individual sub-codewords fails, the higher-order syndromes from the nested codewords are utilized to correct more errors. GII decoder design faces many challenges. The major ones include: 1) high-speed nested decoding utilizing the higher-order syndromes; 2) efficient updating of higher-order syndromes after sub-codewords are corrected; 3) computation of nested syndromes and matrix inversion for converting them to higher-order sub-codeword syndromes; and 4) efficient architectures capable of addressing the variable correction capabilities of the nested codewords. This paper proposes novel algorithmic reformulations and architectural transformations to address each bottleneck. For an example, GII code that has the same rate and length as eight un-nested (255, 223) RS codes, the proposed GII decoder achieves more than seven orders of magnitude improvement in error-correcting performance with less than 30% area overhead compared to the RS decoder. With a critical path of seven XOR gates, the proposed decoder can easily achieve more than 40 GByte/s throughput.
Comparative Analysis of Approaches to Depth Map Generation for Robot Navigation

This paper considers approaches, which implement depth map reconstruction using stereo pair, as well deep neural network (DNN) – based approaches. Comparative evaluation of these approaches for different classes of images is performed, exampled by specific solutions; whereby the images in these classes differ in their features, distance from the camera viewport, as well scene luminosity level. Averaged frame processing time in video sequence was, in terms of NN-approach, 0.094 s, and in the stereo pair-based approach – 0.105 s. Upon the results of the performed experimental evaluation, suggestions are proposed concerning the criteria, which define preference of the approaches in question in depth map composition, depending on the conditions in the scene.
The Chair's Field of Operations: Creating a Cognitive Map

The Department ChairVolume 31, Issue 2 p. 18-20 Articles The Chair's Field of Operations: Creating a Cognitive Map Don Chu, Don ChuSearch for more papers by this author Don Chu, Don ChuSearch for more papers by this author First published: 29 September 2020 https://doi.org/10.1002/dch.30350Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume31, Issue2Fall 2020Pages 18-20 RelatedInformation
An Exploratory Study about the Impact of Administrative Burden in Evaluation on the Quality of Evaluation System

신공공관리론이 우리나라 정부의 공공관리원칙으로 소개된 후 다양한 형태의 성과관리 및 성과평가제도가 도입되어 운영되어 왔다. 문제는 다양한 평가제도가 운영됨에도 불구하고 국민이 체감하는 공공부문의 성과는 높지 않다는 점이다. 성과가 낮은 것은 다양한 성과관리 및 평가제도를 운영해왔지만 동시에 평가에 관한 부담이 증가해 제도가 형식적으로 운영되고 있기 때문이다. 이런 의미에서 본 연구는 평가부담을 개념화하고 평가부담이 평가효과성에 미치는 인과관계를 구조방정식 모형을 통해 실증적으로 분석하였다. 분석결과 평가제도의 중복과 평가자원의 부족은 평가부담을 높이는 것으로 나타났다. 또한, 평가부담은 평가역량과 평가의 효과성을 낮추는 것으로 분석되었다. 분석결과를 토대로 본 연구는 우리나라 성과평가제도의 평가부담을 완화하고 평가의 효과성을 높이기 위한 시사점을 제시하였다.Since the New Public Management was introduced in Korea, an amount of performance management and evaluation practices have been adopted. Despite numerous performance practices, the public does not feel that government performance has been enhanced a lot. Such poor government performance may be the side effects of high level of evaluation burden from a number of evaluation systems which are prevalent in public organizations in Korea. In this sense, this study analyzes what factors affect evaluation burden and how evaluation burden impacts the effectiveness of performance evaluation systems. The result shows that evaluation duplication and the lack of resources have negative impacts on evaluation burden, which worsens the capacity and effectiveness of evaluation systems. Based on the results, this study suggests policy implications on how burdensome evaluation systems can be streamlined.
Adaptive evaluation model of web spam based on link relation

Abstract The credibility evaluation of web pages is an important part of current network information mining. Generally, content‐based evaluation and link relationship‐based evaluation are used to detect web spam. This paper proposes an algorithm based on link relationship which adjusts the weight of the link object according to the credibility of the link object to calculate the credibility of the web page. Considering the different linking conditions, (i) low‐scoring web pages actively linking to high‐scoring web pages, resulting in the reduction of high‐scoring web pages score; (ii) in order to endorse the low‐score web page, high‐score web pages actively linking to the low‐score web pages, adjust the score transmission. Obviously, the proposed algorithm is a recursive model. To avoid the amplification of the error caused by the loop and different evaluation results caused by different initial nodes, this paper presents a novel method to adaptively adjust the weights of the links according to the link conditions, then proves the convergence of the algorithm. Finally, we validate the proposed algorithm on the public datasets, which is compared with other up‐to‐date algorithms under different experimental indexes. The results show that the proposed algorithm can effectively reduce the ranks of spam pages in all web pages and improve the detection efficiency of spam pages.
Forecasting sub-resolution temporal variability of irradiance

&amp;lt;p&amp;gt;Variability of solar irradiance is an important factor concerning large-scale integration of solar photovoltaics (PV) systems onto the electricity grid. Calculations of irradiance are computationally expensive, leaving operational meso-scale forecasting models struggling to achieve accurate results. Moreover, such models deliver outputs at a temporal resolution in the order of hours, whereas from a grid-integration point of view, minute-to-minute variability is a major concern. In previous work, we found that absolute power peaks in the order of seconds are up to 18% higher compared to 15-minute resolution for irradiance and even upwards of 22% higher for household PV systems. Moreover, these maximum peaks in output power are solely observed under mixed-cloud conditions, for which alse the greatest variability is found. In this work we present a machine-learning model which can forecast sub-resolution variability of irradiance, based on standard meso-scale outputs of the HARMONIE model of the The Royal Netherlands Meteorological Institute (KNMI). For training and validation, irradiance measurements obtained at a 1-second interval are used of the Baseline Surface Radiation Network (BSRN) site of Cabauw. A tree-based model was employed, for which the optimum members were constructed using extreme gradient boosting. In this work, we explore the dominant features of the model and link the machine-learned-relations to meteorological processes and dynamics. This research was executed in collaboration with the Distribution Grid Operator Alliander.&amp;lt;/p&amp;gt;
DAFEE: A Scalable Distributed Automatic Feature Engineering Algorithm for Relational Datasets

Automatic feature engineering aims to construct informative features automatically and reduce manual labor for machine learning applications. The majority of existing approaches are designed to handle tasks with only one data source, which are less applicable to real scenarios. In this paper, we present a distributed automatic feature engineering algorithm, DAFEE, to generate features among multiple large-scale relational datasets. Starting from the target table, the algorithm uses a Breadth-First-Search type algorithm to find its related tables and constructs advanced high-order features that are remarkably effective in practical applications. Moreover, DAFEE implements a feature selection method to reduce the computational cost and improve predictive performance. Furthermore, it is highly optimized to process a massive volume of data. Experimental results demonstrate that it can significantly improve the predictive performance by 7% compared to SOTA algorithms.