Showing papers on "Web resource published in 2022"
TL;DR: In this article , the authors explored the relationship between web accessibility metrics and showed that there are metrics that behave similarly, which, when deciding what metrics to use, assists in picking the metric that is less resource intensive or for which it might be easier to collect the inputs.
Abstract: Abstract Evaluating the accessibility of web resources is usually done by checking the conformance of the resource against a standard or set of guidelines (e.g., the WCAG 2.1). The result of the evaluation will indicate what guidelines are respected (or not) by the resource. While it might hint at the accessibility level of web resources, often it will be complicated to compare the level of accessibility of different resources or of different versions of the same resource from evaluation reports. Web accessibility metrics synthesize the accessibility level of a web resource into a quantifiable value. The fact that there is a wide number of accessibility metrics, makes it challenging to choose which ones to use. In this paper, we explore the relationship between web accessibility metrics. For that purpose, we investigated eleven web accessibility metrics. The metrics were computed from automated accessibility evaluations obtained using QualWeb. A set of around three million web pages were evaluated. By computing the metrics over this sample of nearly three million web pages, it was possible to identify groups of metrics that offer similar results. Our analysis shows that there are metrics that behave similarly, which, when deciding what metrics to use, assists in picking the metric that is less resource intensive or for which it might be easier to collect the inputs.
7 citations
1 Jan 2022
5 citations
TL;DR: The present article recommended a method of physical education online course resources based on collaborative filtering technology that is suitable to the needs and characteristics of a user based on the user’s preferences and educational resources available.
Abstract: Educational resources are available in different repositories and on the web. For example, these resources are in the form of courses, tutorials, simulations, tests, etc., and are available on the web. And these resources are constantly increasing. In this electronic age, it is necessary to develop systems to help people to find what they want and particularly what is more suitable to their personal subject interest. Recommender systems can help professors, researchers, and students to find the best educational resources suitable for his/her profile. The “e”-course characteristics and user profiles have to be considered, while providing the recommendations. A system can be developed where users can express a subject query to satisfy their information needs. During this stage, user characters and preferences are not considered and all users get the same results for that query. When the characteristics like language and preferences like practical and demonstrations are considered, then the information retrieval process should be improved by personalization. The set of characteristics and preferences of each user has to be stored and to be matched with e-course characteristics. There might be some characteristics that may be expressed using fuzzy values. It would be discussed how to find electronic educational resources that are suitable to the needs and characteristics of a user based on the user’s preferences and educational resources available. So, the present article recommended a method of physical education online course resources based on collaborative filtering technology.
3 citations
TL;DR: Wang et al. as discussed by the authors proposed an improved focused web crawler to collect DHIs information from the heterogeneous resources on the Internet, present priority levels of a resource link through anchor text and URLs, and traversing the link with the aid of depth.
Abstract: The types of pharmaceutical products include cosmetics and drugs. Some of the pharmaceutical products comprise a mix of drugs and herbs without considering their interaction effects. Drug-herb interactions (DHIs) refer to the interactions between conventional drugs and herb medicines. However, the available information on DHIs are scattered because it has heterogeneous databases and website resources, apart from some of the paid or subscribed databases. Easy access to information on DHIs would allow researchers to explore more. Therefore, this study proposes improvements in the focus web crawler to collect DHIs information from the heterogeneous resources on the Internet, present priority levels of a resource link through anchor text and URLs, and traversing the link with the aid of depth. The improved focused crawler was tested on two algorithms namely the Breadth-First Search (BFS) and PageRank. Information of DHIs crawled 4,744 herbals from the focus web crawler. The accuracy values for Chinese Med Digital Projects and MedlinePlus were 98% for PageRank and 71% for BFS. Additionally, a focused web crawler may gather more relevant web pages in the same amount of time as a wide crawler. Hence, the proposed crawler may successfully gather DHIs on the web in response to the user queries.
1 citations
6 Jan 2022
TL;DR: This paper proposed an educational resource discovery (ERD) pipeline that automates web resource discovery for novel domains by collecting frequent queries from a set of seed documents and search on the web to obtain candidate resources such as lecture slides and introductory blog posts.
Abstract: Effective human learning depends on a wide selection of educational materials that align with the learner's current understanding of the topic. While the Internet has revolutionized human learning or education, a substantial resource accessibility barrier still exists. Namely, the excess of online information can make it challenging to navigate and discover high-quality learning materials. In this paper, we propose the educational resource discovery (ERD) pipeline that automates web resource discovery for novel domains. The pipeline consists of three main steps: data collection, feature extraction, and resource classification. We start with a known source domain and conduct resource discovery on two unseen target domains via transfer learning. We first collect frequent queries from a set of seed documents and search on the web to obtain candidate resources, such as lecture slides and introductory blog posts. Then we introduce a novel pretrained information retrieval deep neural network model, query-document masked language modeling (QD-MLM), to extract deep features of these candidate resources. We apply a tree-based classifier to decide whether the candidate is a positive learning resource. The pipeline achieves F1 scores of 0.94 and 0.82 when evaluated on two similar but novel target domains. Finally, we demonstrate how this pipeline can benefit an application: leading paragraph generation for surveys. This is the first study that considers various web resources for survey generation, to the best of our knowledge. We also release a corpus of 39,728 manually labeled web resources and 659 queries from NLP, Computer Vision (CV), and Statistics (STATS).
TL;DR: In this article , the statistics of attacks for the last few years are analyzed, as well as possible vulnerabilities that allow these attacks, and the correlation of loss levels in different types of industries is analyzed.
Abstract: This article provides basic information about possible web attacks on the web resources of a regular user or organization. The statistics of attacks for the last few years are analyzed, as well as possible vulnerabilities that allow these attacks. The correlation of loss levels in different types of industries is analyzed. The list of the most common vulnerabilities is given.
1 Jan 2022
TL;DR: The relationship between social media and library web resources is deeper than it may seem at first glance, and effective use of modern tools can improve the search output of a web resource, which in turn will allow the library to effectively perform its information management functions as discussed by the authors .
Abstract: The relationship between social media and library web resources is deeper than it may seem at first glance, and the effective use of modern tools can improve the search output of a web resource, which in turn will allow the library to effectively perform its information management functions in the modern digital economy .
TL;DR: The Chemical Functional Ontology (ChemFOnt) as mentioned in this paper is a hierarchical, OWL-compatible ontology describing the functions and actions of >341 000 biologically important chemicals, including primary metabolites, secondary metabolites, natural products, food chemicals, synthetic food additives, drugs, herbicides, pesticides and environmental chemicals.
Abstract: Abstract The Chemical Functional Ontology (ChemFOnt), located at https://www.chemfont.ca, is a hierarchical, OWL-compatible ontology describing the functions and actions of >341 000 biologically important chemicals. These include primary metabolites, secondary metabolites, natural products, food chemicals, synthetic food additives, drugs, herbicides, pesticides and environmental chemicals. ChemFOnt is a FAIR-compliant resource intended to bring the same rigor, standardization and formal structure to the terms and terminology used in biochemistry, food chemistry and environmental chemistry as the gene ontology (GO) has brought to molecular biology. ChemFOnt is available as both a freely accessible, web-enabled database and a downloadable Web Ontology Language (OWL) file. Users may download and deploy ChemFOnt within their own chemical databases or integrate ChemFOnt into their own analytical software to generate machine readable relationships that can be used to make new inferences, enrich their omics data sets or make new, non-obvious connections between chemicals and their direct or indirect effects. The web version of the ChemFOnt database has been designed to be easy to search, browse and navigate. Currently ChemFOnt contains data on 341 627 chemicals, including 515 332 terms or definitions. The functional hierarchy for ChemFOnt consists of four functional ‘aspects’, 12 functional super-categories and a total of 173 705 functional terms. In addition, each of the chemicals are classified into 4825 structure-based chemical classes. ChemFOnt currently contains 3.9 million protein-chemical relationships and ∼10.3 million chemical-functional relationships. The long-term goal for ChemFOnt is for it to be adopted by databases and software tools used by the general chemistry community as well as the metabolomics, exposomics, metagenomics, genomics and proteomics communities.