Web Directories Research Articles

Web-based Internet of Things on environmental and lighting control and monitoring system using node-RED, MQTT and Modbus communications within embedded Linux platform

This paper presents our innovative approach in the field of Internet of Things (IoT). Our focus was on enhancing the research environment at Cheng Shiu University's laboratory through the creation of a dynamic network monitoring setup and lighting control system. Our aim was to simplify equipment management and promote energy efficiency. The core of our development is the Raspberry Pi 4B embedded Linux platform, which serves as a host for both a Message Queuing Telemetry Transport (MQTT) broker and a Node-RED server. Python programs, developed in PyCharm IDE, utilize multiple threads for efficient data exchange via Modbus TCP and MQTT protocols, facilitating seamless interaction between PLCs and MQTT broker servers. The Node-RED IoT development tool was instrumental in crafting a cross-platform Human-Machine Interface (HMI) web server with MQTT Publish/Subscribe capabilities, ensuring smooth communication with the MQTT broker server. By configuring the Apache HTTP server to direct to the Node-RED web directory, we can effectively monitor laboratory conditions and manage the lighting system. The experimental results validate the cost-effectiveness of our approach in transforming traditional control systems into a web-based supervisory control system, thereby enhancing the overall functionality of existing equipment. This project underscores our commitment to advancing IoT solutions in practical settings.

Analisis Kerentanan Keamanan Website Menggunakan Metode PTES (Penetration Testing Execution And Standart)

With rapid advances in Information Technology (IT), the need for an IT-based learning framework and mechanism has become an unavoidable necessity. This requires solid security in a system, using the Penetration Testing Execution and Standard (PTES) method. This research method is quantitative and descriptive, used to illuminate website security and provide input for improvement. The research involved Tenable Nessus Professional tools that emit vulnerabilities, such as Browseable Web Directory and Potentially Clickjacking Web. The PTES stages covered include Planning, Data Collection, Scanning, Access Rights, Security, and Web Application Firewall (WAF) configuration. The hope is that this method will provide an in-depth understanding of potential threats and vulnerabilities in government information systems, as well as present solutions. The research results are expected to provide education and insight into website security. This allows related parties to take preventive and corrective steps, improve security, and protect the integrity and confidentiality of data entities. As a result, information systems are expected to be more secure and resistant to potential cyber threats.

Fast fuzzy C-means clustering and deep Q network for personalised web directories recommendation

Fast fuzzy C-means clustering and deep Q network for personalised web directories recommendation

Development of a Digital Directory Web for SMEs under the guidance of Telkom Witel South Surabaya

Results in MSME business turnover being hampered by limited capital and limited business diversification.Based on observations and interviews with Partners, there are main problems experienced by Telkom Witel South Surabaya-assisted MSMEs, namely how can they manage MSMEs and then introduce and sell their products with the widest possible reach so that they are not only in the Surabaya area and its surroundings so that Community Service activities are expected to provide contribution in terms of skills and abilities in generating profits and overcoming problems. The solution to solving this problem is to develop a digital directory as well as its features and functions within the organization at Telkom Witel, South Surabaya. The output that will be achieved in this activity is digital marketing efforts through the website which are expected to be able to increase profits and provide a sense of trust to consumers to be interested in buying the products offered. The results of this PKM activity include several calls for mandatory outputs, namely (1) scientific publications in national social service journals with ISSN, (2) activity videos, (3) increasing the application of science and technology in society (online platforms). The stages of PKM implementation consist of (1) developing a digital MSME web directory.

A Large Visual, Qualitative, and Quantitative Dataset for Web Intelligence Applications

The Web is the communication platform and source of information par excellence. The volume and complexity of its content have grown enormously, with organizing, retrieving, and cleaning Web information becoming a challenge for traditional techniques. Web intelligence is a novel research area to improve Web‐based services and applications using artificial intelligence and automatic learning algorithms, for which a large amount of Web‐related data are essential. Current datasets are, however, limited and do not combine visual representation and attributes of Web pages. Our work provides a large dataset of 49,438 Web pages, composed of webshots, along with qualitative and quantitative attributes. This dataset covers all the countries in the world and a wide range of topics, such as art, entertainment, economics, business, education, government, news, media, science, and the environment, addressing different cultural characteristics and varied design preferences. We use this dataset to develop three Web Intelligence applications: knowledge extraction on Web design using statistical analysis, recognition of error Web pages using a customized convolutional neural network (CNN) to eliminate invalid pages, and Web categorization based solely on screenshots using a CNN with transfer learning to assist search engines, indexers, and Web directories.

Homepage2Vec: Language-Agnostic Website Embedding and Classification

Currently, publicly available models for website classification do not offer an embedding method and have limited support for languages beyond English. We release a dataset of more than two million category-labeled websites in 92 languages collected from Curlie, the largest multilingual human-edited Web directory. The dataset contains 14 website categories aligned across languages. Alongside it, we introduce Homepage2Vec, a machine-learned pre-trained model for classifying and embedding websites based on their homepage in a language-agnostic way. Homepage2Vec, thanks to its feature set (textual content, metadata tags, and visual attributes) and recent progress in natural language representation, is language-independent by design and generates embedding-based representations. We show that Homepage2Vec correctly classifies websites with a macro-averaged F1-score of 0.90, with stable performance across low- as well as high-resource languages. Feature analysis shows that a small subset of efficiently computable features suffices to achieve high performance even with limited computational resources. We make publicly available the curated Curlie dataset aligned across languages, the pre-trained Homepage2Vec model, and libraries: https://github.com/epfl-dlab/homepage2vec.

The current state of genetic risk models for the development of kidney cancer: a review and validation.

To review the current state of genetic risk models for predicting the development of kidney cancer, by identifying and comparing the performance of published models. Risk models were identified from a recent systematic review and the Cancer-PRS web directory. A narrative synthesis of the models, previous validation studies and related genome-wide association studies (GWAS) was carried out. The discrimination and calibration of the identified models was then assessed and compared in the UK Biobank (UKB) cohort (cases, 452; controls, 487 925). A total of 39 genetic models predicting the development of kidney cancer were identified and 31 were validated in the UKB. Several of the genetic-only models (seven of 25) and most of the mixed genetic-phenotypic models (five of six) had some discriminatory ability (area under the receiver operating characteristic curve >0.5) in this cohort. In general, models containing a larger number of genetic variants identified in GWAS performed better than models containing a small number of variants associated with known causal pathways. However, the performance of the included models was consistently poorer than genetic risk models for other cancers. Although there is potential for genetic models to identify those at highest risk of developing kidney cancer, their performance is poorer than the best genetic risk models for other cancers. This may be due to the comparatively small number of genetic variants associated with kidney cancer identified in GWAS to date. The development of improved genetic risk models for kidney cancer is dependent on the identification of more variants associated with this disease. Whether these will have utility within future kidney cancer screening pathways is yet to determined.

Classification mechanisms for digitalized information in web application development, taking into account cloud server deployment trends

With the transition from natural to virtual communication, there are new technologies for presenting information, the perception of which depends on its presentation by modern multimedia. Visual perception plays an important role in everyday life, helping in learning and communicating with other people. Behind the most effective perception is a complex process of understanding the information obtained. Visual perception of information includes the processes of seeing and reading. Building assumptions on what we see, we rely on expectations, beliefs, past knowledge and previous experience. Reading is a complex activity that involves both the process of perception and thought. At the very beginning of the formation of reading, understanding is not common, but only after perception, as the development and automation of reading skills, understanding begins to outpace the process of perception, manifested in the emergence of semantic assumptions, guessing meaning within words. Perception of visual image is accompanied by the actualization of its auditory-motor image, without which it is impossible to recognize. Reading is accompanied by inner speech. The same information can be understood by different people with different degrees of depth, which entails the existence of factors that affect the process of perception and understanding of texts. Such factors can be divided into the following groups: characteristics of the individual who acts as a subject of perception and understanding; parameters of the text and the reality that is reflected in this text; features of the situation in which the process of perception and understanding takes place. Text in a virtual environment, ie media information - is more accurately and quickly perceived by a person and it depends on various factors influencing perception, as well as the type and method of presenting information.
 The purpose of the study is to analyze the perception of digitalized information, the formation of recommendations for professionals with UI / UX, which will be further formed into a prototype of a possible web directory. The aim is also to develop a possible tool model that can be used not only by the above specialists, but also by SMM-managers, business analysts and others. The methods of research were chosen analysis - the study of individual parts of the objects of study, comparing different approaches or their individual components to obtain a holistic picture of the subject area.

Business Account Management and Statistical Report Analysis System Based on Front-end and Back-end Separation Framework

By integrating the needs of each team, combining the previous development experience and the current mature development framework mode, this paper proposes to build a business account management and statistical report analysis system based on the front-end and back-end separation framework, which can be used by each team to improve work efficiency and enhance the reusability and practicality of various marketing data. This paper introduces the web directory structure of the whole system and the contents and related operations displayed on the corresponding management pages. For the needs and problems raised by users, the system adds corresponding functions and pages to solve them. The back-end of the marketing data management system is based on J2EE framework and developed by springboot+mybatis. The front-end is developed by react + ant design + node + echarts+ less.

Economic perspectives on the future of academic publishing: Introduction to the special issue

Economic perspectives on the future of academic publishing: Introduction to the special issue

Ensemble approach for web page classification

Over the decades World Wide Web has become abundance source of distributed web content repository hyper-linked with diverse information domains. Performance of search engines in locating the information is exemplary but still there is inadequacy in search engines for focused crawling of web content. Web Page Classification being pivotal for information retrieval and management task plays imperative role for natural language processing in creating classified web document repositories and building indexed web directories. The conventional machine learning approaches extract the desired features from web pages in order to classify them whereas deep leaning algorithms learns the covet features as the network goes deeper and deeper. Transfer learning based Pre-trained models such as BERT attains impressive performance for text classification. In this study, we evaluate the effectiveness of adopting pre-trained model BERT for the task of classifying web pages into different categories. In this paper, we proposed an ensemble approach for web page classification by learning contextual representation using pre-trained bidirectional BERT and then applying deep Inception modelling with Residual connections for fine-tunes the target task by utilizing parallel multi-scale semantics. Experimental evaluation exhibit that proposed ensemble model outperforms benchmark baselines and achieve better performance in contrast to other transfer learning approaches evaluated on the web page classification task for different classification datasets.

Мониторинг берегов рек в рамках государственного мониторинга водных объектов: современное состояние и перспективы развития.

In the last decade, a program of state monitoring of water bodies has been implemented in Russia, aimed at collecting and summarizing relevant information on the state of rivers, lakes, and reservoirs. The data obtained during its implementation is entered into a unified automated information system for state monitoring of water bodies - AIS GMVO (automated information system of state water bodies monitoring), available to any user in the format of a web directory. The main body of information presented in the AIS GMVO is the materials of observations of the hydrological, hydro/chemical and hydro/biological characteristics of water bodies obtained at stations and gauges subordinate to the Federal Service for Hydrometeorology and Environmental Monitoring (Roshydromet) and individual executive bodies of the constituent entities of the Russian Federation. An important block in them is represented by the results of stationary observations of the variability and state of the river banks within the water protection zones. In the case of further development of the monitoring network, methodological improvement and unification of the observation program, the analysis of the obtained data will gain great scientific and practical importance, allowing to significantly expand the understanding of regional features and spatio-temporal variability of channel processes in Russia. The purpose of this work is to analyze the current state of the riverbanks monitoring system based on more than 10 years of data accumulation, as well as to assess the available results of its implementation.

The Age of Invention: Matching Inventor Ages to Patents Based on Web-Scraped Sources

This paper overviews the data collection procedures and resulting data for inventor ages and associated death dates. We use information about inventors from patents (name and location) and search for age and date of death information from publicly available online web directories and build a scoring system to indicate the quality of information that we collect. After applying a variety of heuristics and robustness checks, we are confident of 1,508,676 inventor ages associated with patents granted between 1976 and 2018. We also find the death dates of 206,589 inventors, though we are not as confident of the accuracy of the death information. The datasets and associated replication files are freely available at: https://doi.org/10.7910/DVN/YRLSKU

A scalable parallel algorithm for building web directories

SummaryWeb directories like Wikipedia and Open Directory Mozilla facilitate efficient information retrieval (IR) of web documents from a huge web corpus. Maintenance of these web directories is understandably a difficult task that requires manual curation by human editors or semi‐automated mechanisms. Research on parallel algorithms for the automated curation of these web directories will be beneficial to the IR domain. Hence, in this article, we propose a parallel algorithm for automatically creating web directories from a corpus of web‐documents. We have used centrality‐based techniques to split the corpus into fine‐grained clusters and subsequently an agglomeration based on locality sensitive hashing to identify coarse‐grained clusters in the web‐directory. Experimental results show that the algorithm generates meaningful hierarchies of the input corpus as measured by cluster‐validity indices, like F‐measure, rand index, and cluster purity. The algorithm achieves a significant speedup and scales well both with the number of processors and the size of the input corpus.

Influences of Color Salience and Location of Website Links on User Performance and Affective Experience with a Mobile Web Directory

ABSTRACT We investigated the impact of color salience and location of a website link on users’ performance, affective experiences and approach-avoidance tendencies with 10 mock mobile Web directories. Task completion times were recorded by a computer program, and users’ affective experiences and approach-avoidance responses were reported in questionnaires. Results implied that visual attention in the display area on a mobile Web directory is directed by a combination of bottom-up and top-down processes, which is different from the primarily top-down process implicated in a similar study of PC Web directories. A salient color of the website link helped attract users’ attention and increase users’ sense of control over the process. Also, users are likely to search webpages from top left to bottom right. Our findings suggest that a target website link placed in the top left corner or displayed in a distinct color in the center area not only attracts users’ attention but also results in a more highly rated affective experience. Color of the target website link can be changed to increase users’ sense of control of a Web directory and approach behaviors.

Chatbots for learning: A review of educational chatbots for the Facebook Messenger

With the exponential growth in the mobile device market over the last decade, chatbots are becoming an increasingly popular option to interact with users, and their popularity and adoption are rapidly spreading. These mobile devices change the way we communicate and allow ever-present learning in various environments. This study examined educational chatbots for Facebook Messenger to support learning. The independent web directory was screened to assess chatbots for this study resulting in the identification of 89 unique chatbots. Each chatbot was classified by language, subject matter and developer's platform. Finally, we evaluated 47 educational chatbots using the Facebook Messenger platform based on the analytic hierarchy process against the quality attributes of teaching, humanity, affect, and accessibility. We found that educational chatbots on the Facebook Messenger platform vary from the basic level of sending personalized messages to recommending learning content. Results show that chatbots which are part of the instant messaging application are still in its early stages to become artificial intelligence teaching assistants. The findings provide tips for teachers to integrate chatbots into classroom practice and advice what types of chatbots they can try out.

An explainable data-driven approach to web directory taxonomy mapping

Abstract The spread of e-commerce and web applications has fostered the integration of cross-domain business activities. To efficiently retrieve products and services, web directories allow customers to browse multiple-level taxonomies to find specific products or services according to a predefined categorization. Providers need to periodically update web directory lists by aligning in-house taxonomies to domain-specific hierarchies coming from external sources. However, such taxonomy mapping procedures are often semi-automatic and rely on traditional word disambiguation techniques to capture the semantics behind categories and products descriptions. Hence, the flexibility and explainability of the underlying models are quite limited. This paper proposes an automated, explainable approach to web directory taxonomy mapping based on text categorization. It exploits two complementary word-based text representations: a frequency-based representation, which captures syntactic text similarities, and an embedding one, which highlights the underlying semantic relationships among words. Since the proposed solution is purely data-driven, it can be successfully applied to business domains where there is a lack of semantic models. The frequency-based text representation has shown to be particularly suitable for driving the automated taxonomy mapping procedure, whereas the embedding space has been profitably used to provide local explanations of the category assignments.

Diversity in the Last Decade of the Association of Program Directors in Surgery: A Descriptive Analysis of Leadership and Future Directions

The Association of Program Directors in Surgery Diversity and Inclusion Taskforce (APDS-DIT) was created in 2017 after the Executive Committee recognized low diversity in its membership. The DIT was charged to address gaps in diversity and inclusion at various phases of training and development from medical student to surgical leader. The aim of this study was to examine APDS demographics and determine the status of inclusion of women, racial and ethnic minorities, and nonuniversity surgeons. Eleven years (2008-2018) of APDS annual-meeting programs, web directory, 2018-membership lists, and 2017-AAMC data were analyzed. Leadership positions were examined by officer (program chair/vice chair, executive committee, and board of directors. Internet searches identified gender, race, and institutional affiliation. Representative members to other organizations, resident liaisons, and historian members were excluded. APDS "Member," "Associate," and "Resident" lists and AAMC data were divided by gender. Fifty-one individuals fulfilled 223 leadership positions over 11 years; 13 (25%) were women and 5 (10%) were non-Caucasian. Since 2013, the percentage of nonuniversity surgeons in APDS leadership has declined while, over the last 2 years, the percentage of women and ethnic/racial minority has increased. In 2018, the percentage of women in leadership (38%) was higher than the percentage of women in membership (combined total of program directors and associate program directors [26%]) and nonuniversity-affiliated surgeons comprised 35% of the APDS membership but only 14% of leadership roles. Over the last 11 years, representation of women, non-Caucasians, and nonuniversity surgeons has been at or less than 1/3 of their counterparts. As an organization that is tasked with creating future generations of the surgical workforce, it is imperative to recognize an under-representation of those members with diverse backgrounds that would add to the creative growth of the organization. The creation of the APDS-DIT emphasizes the organization's commitment to diversity and inclusion and an effort to create a pipeline of diverse leaders in the APDS and surgical training in general.

2 Way Crawling

As we know that the deep web grows at very fast pace, there has been increased interest in techniques which help efficiently locate and check deep web interfaces. So, it is important to achieve wide coverage and high efficiency on the large volume of web resources. For this we propose a multistage framework, Smart crawler. Smart crawler is a two-stage crawler used to efficiently harvest deep web interfaces. In the first stage, the crawler performs site-based searching for center pages and avoids visiting non-relevant sites. In the second stage, an adaptive link ranking technique is used which helps to searching relevant site by excavating most relevant links. It is important to eliminate bias on visiting highly relevant links which is hidden in web directories, for this a link tree data structure is designed to achieve wider coverage for a website. The proposed framework gives experimental result on different domains and shows the agility and accuracy of the proposed framework, which retrieves deep-web interfaces from a large volume of sites and achieves higher harvest rates than other crawler.

Design and evaluation of a parallel document clustering algorithm based on hierarchical latent semantic analysis

SummaryWe propose a parallel generalization scheme for Singular Value Decomposition–based clustering algorithms. The scheme enables the clustering algorithm to generate a hierarchy of clusters instead of a flat set of clusters. The generalization scheme infers the number of levels to be formed and the number of clusters per level of the hierarchy automatically without depending on any user‐supplied parameter. The performance of the suggested hierarchical clustering algorithm was evaluated using the web directory taxonomy hosted by the Open Directory DMOZ. Empirical evaluations and statistical tests reveal that the proposed generalization scheme produces a superior cluster hierarchy when compared with two existing generalization techniques in terms of the precision, recall, f‐measure, and the rand index. The generalization scheme is well‐equipped to deal with large datasets and the speed‐up achieved by the parallelized generalization scheme over its sequential variant was measured using a multicore computer.