Reviewerly/exHarmony huggingface dataset:
An automatic detection method of solar radio burst based on Otsu binarization

With the development of solar radio spectrometer, it is difficult to process a large number of observed data quickly by manual detection method. An automatic detection method of solar radio burst based on Otsu binarization is proposed in this paper. In this method, channel normalization is used to denoise the original solar radio image. Through experimental comparison, Otsu method is selected as a binary method of solar radio spectrum, and Open and Close operations are used to smooth the binary image. Experiments show that the proposed method for automatic detection of solar radio bursts is effective
PENERAPAN MEDIA PEMBELAJARAN POP-UP BOOK ANAK USIA DINI PADA KELOMPOK B UNTUK MENINGKATKAN KEMAMPUAN KEAKSARAAN AWAL DI TK NUSA INDAH

One important aspect developed in early childhood is the ability of early literacy. For this reason, we need a learning media that can improve children's early literacy skills, one of which is pop-up book learning media. The purpose of this study was to determine the effect of using pop-up book learning media on children's early literacy abilities. This study used the Quasi Experiment method with the Nonequevalent Control Group Design research design. The pre-test results showed that there was no significant difference in the experimental group and the control group with a p-value&gt; 0.05 which was 0.065&gt; 0.05 which was tested by the Mann Whitney test results. While the post-test results showed that the initial literacy ability after the application of the pop-up book learning media had a significant difference with the p-value &lt;0.05 which was 0.236. Thus the pop-up book learning media significantly influences early childhood literacy abilities. Based on this study, the study recommends early childhood educators so that the pop-up book learning media can be used as a medium of learning to improve children's early literacy skills.
Cloud Server Oriented FPGA Accelerator for Long Short-Term Memory Recurrent Neural Networks

Abstract Long Short-Term Memory network(LSTM), which is the most widely used and representative recurrent neural network architecture, plays an important role in language modeling, machine translation, image captioning, etc. However, due to its recurrent nature, general-purpose processors like CPUs and GPUs cannot achieve high parallelism, not to mention their high power consumption. FPGA accelerators can outperform them by flexibility, energy-efficiency and more delicate optimization in each phase of the algorithm. In this paper, we present a cloud-oriented FPGA accelerator for LSTM based on OpenCL. Different from previous works which are designed for embedded systems, our FPGA accelerator performs multiple time series predictions in parallel. We provide a general matrix optimization model to optimize the computation of LSTM in the cloud environments. The performance of our implementation beats both the CPU implementation and other previous hardware implementations. We present and analyze the performance results of our work.
MODELING GLOBAL, DIRECT, DIFFUSE SOLAR RADIATION AND THE OPTIMUM TILT ANGLE IN SOME SELECTED LIBYAN CITIES

Solar system devices and photovoltaic panels are widely used all over the world to produce household heating and electricity. In this work two models were developed to estimate global, direct, diffuse solar radiation and the optimum tilt surface in five selected Libyan cities (Tripoli, Benghazi, Ejdabia, Misratah and Alkufra). It is found that the daily mean of sunshine hours in Tripoli is 9.01 hours. As for Benghazi city, the daily mean is 8.84 hours. In Misratah it is 8.57 hours. In Edjabia the daily mean is 9.46 hours, while in Alkufra, it is 10.10 hours of sunshine. The maximum beam radiation in Tripoli occurs in July (6.26 kw/m2), while the minimum is in January (2.54kw/m2). The maximum diffuse radiation occurs in June (1.85kw/m2), while the minimum is in December (0.73kw/m2). The maximum beam radiation in Benghazi occurs in July (6.26 kw/m2), while the minimum is in December (2.02kw/m2), the maximum diffuse radiation occurs in June (1.78 kw/m2), while the minimum is in November (0.97kw/m2). The maximum beam radiation in Misratah occurs in July (6.24kw/m2), while the minimum is in December (2.23kw/m2). The maximum diffuse radiation occurs in June (1.93 kw/m2), while the minimum is in December (0.94kw/m2). The maximum beam radiation in Edjabia occurs in July (6.19kw/m2), while the minimum is in December (2.59kw/m2). The maximum diffuse radiation occurs in May (1.83 kw/m2), while the minimum is in December (0.93kw/m2). The maximum beam radiation in Alkufra occurs in June and July (5.85kw/m2), while the minimum is in December (3.33kw/m2). The maximum diffuse radiation occurs in May (1.83 kw/m2), while the minimum is in December (1.03kw/2). Results also showed that the annual optimum tilt angles to receive maximum solar radiation for Tripoli is 31.21o and decreases gradually to 23.33o in Alkufra city. The higher optimum angle for all cities occur in winter and decreases in spring and summer, while it reincreases in autumn.
Explanatory AI for Pertinent Communication in Autonomic Systems

Autonomic computing systems maintain high-level goals by continuously adapting to a changing environment, yet their internal operations have no comprehensible meaning to humans. This paper proposes a dialogue management system based on a communication interface acting as a bridge between subsymbolic and symbolic knowledge representation levels. The communication interface explains the autonomic system operation in a human comprehensible form by representing the sensed data in conceptual spaces. Based on a knowledge base generated by the communication interface, the dialogue management system produces a pertinent flow of conversation between autonomic systems and humans, activating only when exceptional situations are encountered. Our approach is incremental, with the objective of enabling pertinent communication with any artificial system. We build a proof-of-concept implementation of the proposed solution in a smart home platform managed by an autonomic system.
TraffickStop: Detecting and Measuring Illicit Traffic Monetization Through Large-Scale DNS Analysis

Illicit traffic monetization is a type of Internet fraud that hijacks users' web requests and reroutes them to a traffic network (e.g., advertising network), in order to unethically gain monetary rewards. Despite its popularity among Internet fraudsters, our understanding of the problem is still limited. Since the behavior is highly dynamic (can happen at any place including client-side, transport-layer and server-side) and selective (could target a regional network), prior approaches like active probing can only reveal a small piece of the entire ecosystem. So far, questions including how this fraud works at a global scale and what fraudsters' preferred methods are, still remain unanswered. To fill the missing pieces, we developed TraffickStop the first system that can detect this fraud passively. Our key contribution is a novel algorithm that works on large-scale DNS logs and efficiently discovers abnormal domain correlations. TraffickStop enables the first landscape study of this fraud, and we have some interesting findings. By analyzing over 231 billion DNS logs of two weeks, we discovered 1,457 fraud sites. Regarding its scale, the fraud sites receive more than 53 billion DNS requests within one year, and a company could lose up to 53K dollars per day due to fraud traffic. We also discovered two new strategies that are leveraged by fraudsters to evade inspection. Our work provides new insights into illicit traffic monetization, raises its public awareness, and contributes to a better understanding and ultimate elimination of this threat.
DIGITAL EDUCATION AND ITS TECHNOLOGICAL PART OF TEACHING MATHEMATICS AND PHYSICS

Problem and goal. Research of the publications on the topic of “Digital Education” shows that there is still no clear definition of this concept, what should it be like in the digital economy era and how to train those who are tasked with creating a digital economy, revealing their creative potential in these new circumstances. Article looks at an integrated system of digital education in the form of digital educational platform (DEP), following the example of successful digital business platforms. Special attention is given to its technology in the form of digital educational resources (DER) for teaching mathematics and physics. Examples of DER, allowing for creation and use of animated drawings as part of the new technology of digital learning, are provided. The risks associated with implementing digital technologies in the educational process are discussed as well as means to avoid them. Methodology of the study. Study and analysis of literature on digital business and digital education as well as synthesis of experience of the digital learning elements testing researchers. The results. The guidelines of building digital education in the form of digital educational platform are outlined, which is relevant in identifying the goals and objectives of the digital economy creators training. Its technological part is outlined in the form of digital educational resources and a new educational technology using the animated features of computer environment is briefly presented. The risks associated with the use of computer technology in teaching and ways of overcoming them are discussed. Conclusion. General concept of digital education, provided in the article, as well as its technology with elements of new Didactics in the form of using animated features of computer environments describes a holistic view of the digital education architecture aimed at training those who are tasked with creating a digital economy. Creating the digital educational content within the framework of the digital educational resource using animated features of computer environments will enhance technological equipment of modern teachers of mathematics and physics, which will allow them to achieve better educational outcomes.
KNOWLEDGE SHARING BEHAVIOR GURU DITINJAU DARI TRANSFORMATIONAL LEADERSHIP DAN SELF-EFFICACY [TEACHERS’ KNOWLEDGE SHARING BEHAVIOR FROM TRANSFORMATIONAL LEADERSHIP AND SELF-EFFICACY’S PERSPECTIVE]

&lt;p&gt;&lt;span lang="EN-US"&gt;The changing context of the age and the characteristics of today's generation demand that the role of the teacher be more creative and innovative. A way to answer this need is to cultivate a knowledge sharing behavior in the school environment. There are many factors that can support the establishment of this knowledge sharing behavior, including transformational leadership and self-efficacy. This study aims to determine the effect of transformational leadership and self-efficacy on the knowledge sharing behavior of teachers. The research subjects were 112 teachers who teach in two locations of XYZ Private High School in West Jakarta which have the potential to develop knowledge sharing behavior. The design used in this study was correlational, using surveys to collect the data and then analyze using the PLS-SEM method. The results of this study showed that transformational leadership and self-efficacy affected knowledge sharing behavior positively.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span lang="EN-US"&gt;&lt;strong&gt;BAHASA INDONESIA ABSTRAK: &lt;/strong&gt;Perubahan konteks zaman dan karakteristik generasi zaman sekarang menuntut peran guru yang semakin kreatif dan inovatif. Salah satu upaya untuk menjawab hal ini adalah dengan membudayakan perilaku &lt;em&gt;knowledge sharing behavior&lt;/em&gt; di lingkungan sekolah. Ada banyak faktor yang dapat mendukung terbangunnya perilaku &lt;em&gt;knowledge sharing behavior&lt;/em&gt; ini, di antaranya &lt;em&gt;transformational leadership&lt;/em&gt; dan &lt;em&gt;self-efficacy&lt;/em&gt;. Penelitian ini bertujuan untuk mengetahui pengaruh &lt;em&gt;transformational leadership&lt;/em&gt; dan &lt;em&gt;self-efficacy&lt;/em&gt; terhadap &lt;em&gt;knowledge sharing behavior&lt;/em&gt; guru. Subjek penelitian adalah 112 guru yang mengajar di dua lokasi Sekolah Menengah Atas Swasta XYZ di Jakarta Barat yang memiliki potensi untuk membangun perilaku &lt;em&gt;knowledge sharing behavior&lt;/em&gt;. Desain penelitian bersifat korelational, data diambil berdasarkan survey yang dianalisi dengan metode PLS-SEM. Hasil penelitian yang diperoleh memperlihatkan bahwa &lt;em&gt;transformational leadership&lt;/em&gt; dan &lt;em&gt;self-efficacy&lt;/em&gt; mempengaruhi &lt;em&gt;knowledge sharing behavior&lt;/em&gt; secara positif.&lt;br /&gt;&lt;/span&gt;&lt;/p&gt;
Combined DWT-DISB based image watermarking optimized for decision making problems

Currently, the protection of digital information, especially in the form of multimedia information such as images, video, text, and audio. The digital nature of the multimedia data has made it prone to misuse and attack, such as is of duplication, transformation, modification, and diffusion. In this sense, it is significant to create a system for protecting the intellectual property rights of the multimedia content. The system should guarantee copyright protection, authentication, and protection against duplication of the material. The drastic development in network multimedia system has made the development of these protection systems challenging. Numerous researches have proposed the use of watermarking to address these issues. The watermarking technique obscures vital information in the original multimedia data in which the hidden data is utilized for copyright protection and authentication. The primary need for any watermarking system should be to guarantee robustness against imminent attack while retaining the quality of the watermark images. This research presents a robust image watermarking technique used to hide details of the RGB Color elements. The proposed approach is an integration of the discrete wavelet transform (DWT) and the relatively new dual intermediate significant bit (DISB). The performance evaluation of the proposed approach produced quality watermarked images that are robust. The proposed method has a PSNR of 101.97 and an NCC of 0.9780 which compare considerable well with the individual techniques.
Review of single clustering methods

&lt;div style="’text-align: justify;"&gt;Clustering provides a prime important role as an unsupervised learning method in data analytics to assist many real-world problems such as image segmentation, object recognition or information retrieval. It is often an issue of difficulty for traditional clustering technique due to non-optimal result exist because of the presence of outliers and noise data. This review paper provides a review of single clustering methods that were applied in various domains. The aim is to see the potential suitable applications and aspect of improvement of the methods. Three categories of single clustering methods were suggested, and it would be beneficial to the researcher to see the clustering aspects as well as to determine the requirement for clustering method for an employment based on the state of the art of the previous research findings.&lt;/div&gt;
A Proposal on Phishing URL Classification for Web Security

Data mining and machine learning is one of the most essential tools in new generation technology.That is used in a number of applications i.e. security, banking and decision making.In this paper, data mining application of web data security is described in details.In this context the domain of phishing URL detection and classification is key aim of the proposed work.This paper includes the different aspects of phishing and recently made contributions for accurately classification of phishing URLs.In addition of that a data mining based model is also proposed that is help to classify the phishing URLs more accurately.Finally the paper provides the future extension of the work.
Visualization in data science

This Special Issue of IEEE Computer Graphics and Applications highlights six papers from the Symposium on Visualization in Data Science (VDS) held in Berlin, Germany in October 2018. VDS brought together hundreds of domain scientists and methods researchers (including visualization, usability and HCI, data management, statistics, machine learning, and software engineering) to discuss common interests, talk about practical issues, and identify open research problems in VDS. This Special Issue features extended papers from six submissions to VDS 2018, which we encouraged authors to cross disciplinary boundaries and show how VDS can be a catalyst for discovery. Brief summaries of the selected papers are presented.
Hidden Markov Models for Time Series: An Introduction Using R. Walter Zucchini, Iain L. McDonald, and Roland Langrock, Boca Raton, CRC Press

BiometricsVolume 75, Issue 2 p. 715-716 BOOK REVIEW Hidden Markov Models for Time Series: An Introduction Using R. Walter Zucchini, Iain L. McDonald, and Roland Langrock, Boca Raton, CRC Press Toby Patterson, Corresponding Author Toby Patterson toby.patterson@csiro.au CSIRO Oceans and Atmosphere, Hobart, Tasmania, AustraliaSearch for more papers by this author Toby Patterson, Corresponding Author Toby Patterson toby.patterson@csiro.au CSIRO Oceans and Atmosphere, Hobart, Tasmania, AustraliaSearch for more papers by this author First published: 22 August 2019 https://doi.org/10.1111/biom.12980Citations: 1Read 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.Citing Literature Volume75, Issue2June 2019Pages 715-716 RelatedInformation
Certified lattice reduction

Quadratic form reduction and lattice reduction are fundamental tools in computational number theory and in computer science, especially in cryptography. The celebrated Lenstra–Lenstra–Lovász reduction algorithm (so-called LLL) has been improved in many ways through the past decades and remains one of the central methods used for reducingintegrallattice basis. In particular, its floating-point variants---where the rational arithmetic required by Gram–Schmidt orthogonalization is replaced by floating-point arithmetic---are now the fastest known. However, the systematic study of the reduction theory ofrealquadratic forms or, more generally, of real lattices is not widely represented in the literature. When the problem arises, the lattice is usually replaced by an integral approximation of (a multiple of) the original lattice, which is then reduced. While practically useful and proven in some special cases, this method doesn&#39;t offer any guarantee of success in general. In this work, we present an adaptive-precision version of a generalized LLL algorithm that covers this case in all generality. In particular, we replace floating-point arithmetic by Interval Arithmetic to certify the behavior of the algorithm. We conclude by giving a typical application of the result in algebraic number theory for the reduction of ideal lattices in number fields.
HTSTL: Head-and-Tail Search Network With Scale-Transfer Layer for Traffic Sign Text Detection

Although promising results have been achieved in the area of traffic sign detection, little attention has been paid to text detection on traffic signs.In fact, in today's popular driver-less automobile industry, traffic sign text which brings abundant and valuable traffic information plays an important and indispensable role.In this work, we design an effective detector for traffic sign text, whose pipeline only consists of a preprocessing module to tackle with some complex situations, a Fully Convolutional Network (FCN) in which a Scale-transfer layer is proposed to speed up the network and a simple post-processing step.Extensive experiments on the Chinese traffic sign text dataset (CTST-1600), ICDAR 2013 and MSRA-TD500 show that the proposed method has achieved the state-of-the-art results, which proves the ability of our detector on both particularity and universality applications.We collect the Chinese text-based traffic sign dataset named CTST-1600, and it can be found at https://github.com/pummi823/test/blob/master/CTST-1600.
Mining Negative Associations between Regular and Frequent Patterns hidden in Static Databases

Most of the research in the field of data mining concentrated around finding the positive associations that exist among different kind of frequent patterns.Frequent patterns have no bearing on the time duration.The regularity of the patterns has a time stamping but no bearing on the frequency.The maximal property, in conjunction with regularity and frequency, leads to all important patterns.Negative associations among the mined patterns are quite important as they reveal significant contradictions which happen quite frequently in medical drugs and weather fluctuations.The pattern mining also differs a lot based on the kind of data source used which may include static databases, distributed databases, incremental databases In this paper, a method proposed that mines regular, frequent, and maximal patterns and the negative associations that exist among those patterns considering static databases
The Development of a Model for Decision Support System of Assessment and Selection of University Academic Staff

The paper deals with the development of the assessment, ranking and selection model of the university's academic staff.To develop this model, there was used the multi-criterion analysis method TOPSIS, which is based on the theory of fuzzy sets.Based on this model, we can establish the decision-making support system for the assessment and selection of academic staff on the basis of the expert assessments
NeuronBlocks: Building Your NLP DNN Models Like Playing Lego

Deep Neural Networks (DNN) have been widely employed in industry to address various Natural Language Processing (NLP) tasks.However, many engineers find it a big overhead when they have to choose from multiple frameworks, compare different types of models, and understand various optimization mechanisms.An NLP toolkit for DNN models with both generality and flexibility can greatly improve the productivity of engineers by saving their learning cost and guiding them to find optimal solutions to their tasks.In this paper, we introduce NeuronBlocks 1 2 , a toolkit encapsulating a suite of neural network modules as building blocks to construct various DNN models with complex architecture.This toolkit empowers engineers to build, train, and test various NLP models through simple configuration of JSON files.The experiments on several NLP datasets such as GLUE, WikiQA and CoNLL-2003 demonstrate the effectiveness of NeuronBlocks.
AVATAR 1, MEDIA AND SOCIO-TECHNOLOGICAL IMAGINARIES: IMPLICATIONS FOR HCI RESEARCH

In this paper, we discuss the media coverage of a research product made for social innovation and its implications on our research and commercialization of the product that followed.We were interested in how media coverage shaped socio-technological imaginaries, cultural and social views of people around this new technology.The research product that was in the eye of the media interest was a networked artifact in the shape of a small robot called AV1, developed to reduce isolation among long-term ill children and adolescents.Discourse analysis was used to examine 170 news articles to explore diverse perspectives on AV1.Our work uncovers potential opportunities and challenges for researchers working with responsible social innovation and research products before commercialization.Further, it highlights the opportunities for research products to surface deeper entanglements between socio-cultural factors and technology innovation.