Automated Discovery of Network Cameras in Heterogeneous Web Pages

Reduction in the cost of Network Cameras along with a rise in connectivity enables entities all around the world to deploy vast arrays of camera networks. Network cameras offer real-time visual data that can be used for studying traffic patterns, emergency response, security, and other applications. Although many sources of Network Camera data are available, collecting the data remains difficult due to variations in programming interface and website structures. Previous solutions rely on manually parsing the target website, taking many hours to complete. We create a general and automated solution for aggregating Network Camera data spread across thousands of uniquely structured web pages. We analyze heterogeneous web page structures and identify common characteristics among 73 sample Network Camera websites (each website has multiple web pages). These characteristics are then used to build an automated camera discovery module that crawls and aggregates Network Camera data. Our system successfully extracts 57,364 Network Cameras from 237,257 unique web pages.

Raif-Semantics: A Robust Automated Interlinking Framework for Semantic Web Using MapReduce and Multi-node Data Processing

The era of the web has evolved and the industry strives to work better every day, the constant need for data to be accessible at a random moment is expanding, and with this expansion, the need to create a meaningful query technique in the web is a major concerns. To transmit meaningful data or rich semantics, machines/projects need to have the ability to reach the correct information and make adequate connections, this problem is addressed after the emergence of Web 3.0, the semantic web is developing and being collected an immense. Information to prepare, this passes the giant data management test, to provide an ideal result at any time needed. Accordingly, in this article, we present an ideal system for managing huge information using MapReduce structures that internally help an engine bring information using the strength of fair preparation using smaller map occupations and connection disclosure measures. Calculations for similarity can be challenging, this work performs five similarity detection algorithms and determines the time it takes to address the patterns that has to be a better choice in the calculation decision. The proposed framework is created using the most recent and widespread information design, that is, the JSON design, the HIVE query language to obtain and process the information planned according to the customer’s needs and calculations for the disclosure of the interface. Finally, the results on a web page is made available that helps a user stack json information and make connections somewhere in the range of dataset 1 and dataset 2. The results are examined in 2 different sets, the results show that the proposed approach helps to interconnect significantly faster; Regardless of how large the information is, the time it takes is not radically extended. The results demonstrate the interlinking of the dataset 1 and dataset 2 is most notable using LD and JW, the time required is ideal in both calculations, this paper has mechanized the method involved with interconnecting via a web page, where customers can merge two sets of data that should be associated and used.

Phish-Shelter

Phishing attack is a deceitful attempt to steal the confidential data such as credit card information, and account passwords. In this paper, Phish-Shelter, a novel anti-phishing browser is developed, which analyzes the URL and the content of phishing page. Phish-Shelter is based on combined supervised machine learning model.Phish-Shelter browser uses two novel feature set, which are used to determine the web page identity. The proposed feature sets include eight features to evaluate the obfuscation-based rule, and eight features to identify search engine. Further, we have taken eleven features which are used to discover contents, and blacklist based rule. Phish-Shelter exploited matching identity features, which determines the degree of similarity of a URL with the blacklisted URLs. Proposed features are independent from third-party services such as web browser history or search engines result. The experimental results indicate that, there is a significant improvement in detection accuracy using proposed features over traditional features.

Digital Citizenship and E-Government Integration

In the study, the archive database of the e-government portal web page of the Turkish Republic of Northern Cyprus, which creates a very large archive database in the field of digital citizenship, was examined. Due to the limit of research conducted through the database of the Turkish Republic of Northern Cyprus e-government portal web page in the field of digital citizenship, the study will contribute to the literature. The e-government portal, which can be considered the largest and most inclusive database within the scope of e-government in Northern Cyprus, was selected using the purposive sample technique for official web page purposes. In this study, which is thought to lead to new researches and discussions on digital citizenship and e-government applications, descriptive analyses were revealed as quantitative and qualitative data, and content analysis was applied using document analysis technique from qualitative research methods data collection tools.

Music Recommender System Using ChatBot

In this era of technological advances, text-based music recommendations are much needed as they will help humans relieve stress with soothing music according to their moods. In this project, we have implemented a chatbot that recommends music based on the user's text tone. By analyzing the tone of the text expressed by the user, we can identify the mood. Once the mood is identified, the application will play songs in the form of a web page based on the user's choice as well as his current mood. In our proposed system, themain goal is to reliably determine a user's mood based on their text tone with an application that can be installed on the user's desktop. In today's world, human computer interaction (HCI) plays a crucial role, and the most popular concept in HCI is recognition of emotion from text. As part of this process, the frontal view of the user's text is used to determine the mood. The extraction of text tone from the user's text is anotherimportant aspect. We have used IBM Analyser to check the text tone of the user and to predict the mood based on the text of the user, and Last.FM API to recommend songs based on themood of the user. Keywords: Introduction, Product-Architecture, Tone Analyzer, Music Classification Based on Mood, Acoustic Analysis, Experiment, Future/Current Use, Importance, Background, Literature Survey, Methodology, Equations, Planning, Tools and Technology, Conclusion.

Perancangan Sistem Informasi Pembelajaran Algoritma dan Pemrograman Berbasis Web pada Program Studi Teknik Informatika STMIK ERESHA

This study aims to present web-based algorithm and programming materials in order to increase interest and motivation in studying in the informatics engineering study program, this learning system is made so that all who want to learn algorithms and programming easily get solutions from the material and videos that are available on the web page. And the existence of this web-based learning information system can provide material archive storage and exercises so that there is no more loss of material or exercise files. This application is created using the PHP and MySQL programming languages.

The Problem of Reference Rot in Spatial Metadata Catalogues

The content at the end of any hyperlink is subject to two phenomena: the link may break (Link Rot) or the content at the end of the link may no longer be the same as it was when it was created (Content Drift). Reference Rot denotes the combination of both effects. Spatial metadata records rely on hyperlinks for indicating the location of the resources they describe. Therefore, they are also subject to Reference Rot. This paper evaluates the presence of Reference Rot and its impact on the 22,738 distribution URIs of 18,054 metadata records from 26 European INSPIRE spatial data catalogues. Our Link Rot checking method detects broken links while considering the specific requirements of spatial data services. Our Content Drift checking method uses the data format as an indicator. It compares the data formats declared in the metadata with the actual data types returned by the hyperlinks. Findings show that 10.41% of the distribution URIs suffer from Link Rot and at least 6.21% of records suffer from Content Drift (do not declare its distribution types correctly). Additionally, 14.94% of metadata records only contain intermediate HTML web pages as distribution URIs and 31.37% contain at least one HTML web page; thus, they cannot be accessed or checked directly.

The Cohesion’s Elements of Education Articles in Kompasiana

The purpose of this study is to describe the kind of grammatical and lexical cohesion that used in education articles written by Syarifudin Yunus. The method that used in this study is qualitative descriptive method. Data source that used in this study are 24 education articles written by Syarifudin Yunus at Kompasiana.com web page in 2019 until 2020 year range. Technic that used in this study is basic technique of agih method or also called the BUL technique (For Direct Elements). The results of this study showed that there are four aspects of grammatical cohesions. The finding percentage of grammatical cohesion of this study are 54.4% conjunctions aspect, 35.4% references aspect, 6.1% substitutions aspect, and 4.1% ellipsis aspect. Besides, there are six lexical cohesion that found in this study. The percentage of lexical cohesions in this study are 63.5% repetition aspects, 14% equivalency aspects, 8.8% collocation aspects, 7.2% synonym aspects, 3.8% hyponyim aspects, and 2.7% antonym aspects. This study can be implicated for Indonesian language learning at junior high school and senior high school.Keywords: Grammatical cohesion; lexical cohesion; discourse.

Malaria Dashboard: a digital platform for epidemiological data from India (Preprint)

BACKGROUND A digital dashboard on malaria epidemiological data will be an invaluable resource for the research community and the planning of malaria control. OBJECTIVE To develop a digital Malaria Dashboard (MDB) for malaria epidemiological data METHODS We have developed a digital Malaria Dashboard (MDB) using the R software. A total of thirteen different R packages were used in this process, within which shiny and ggplot2 were used more intensively. The MDB is a web application that can work online as well as offline. Presently it is available in offline mode only. The MS Excel file may be used as an input data source and any personal computer may be used for this application. RESULTS The MDB is a highly versatile interface that allows prompt and interactive analysis of malaria epidemiological data. The primary interface of MDB is like a web page that has 14 tabs (or pages), some more tabs may be added or deleted as per requirement and each tab corresponds to a particular analysis. A user may move from one tab to another via tab icons. Each tab thus allows flexibility in correlating various parameters like SPR, API, AFI, ABER, RT, malaria cases, death due to malaria, BSC, and BSE. The data can be analyzed in required granularity (national, state, district), and its enhanced visualization allows for facile usage. Using the MDB, one can quickly assess national or more granular scenarios in a time series manner and then compare the malaria epidemiology in various states and their constituent districts. CONCLUSIONS This MDB is a highly effective digital tool for studying the malaria situation and strategizing for malaria elimination and researcher may use it as a prototype for developing some other dashboards in their own fields.